# LostTech.TensorFlow : API Documentation

Type tf.nn

Namespace tensorflow

### Public static methods

#### objectall_candidate_sampler(IGraphNodeBase true_classes, int num_true, int num_sampled, bool unique, object seed, string name)

Generate the set of all classes.

Deterministically generates and returns the set of all possible classes. For testing purposes. There is no need to use this, since you might as well use full softmax or full logistic regression.
##### Parameters
IGraphNodeBase true_classes
A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
int num_true
An int. The number of target classes per training example.
int num_sampled
An int. The number of possible classes.
bool unique
A bool. Ignored. unique.
object seed
An int. An operation-specific seed. Default is 0.
string name
A name for the operation (optional).
##### Returns
object

#### objectall_candidate_sampler_dyn(object true_classes, object num_true, object num_sampled, object unique, object seed, object name)

Generate the set of all classes.

Deterministically generates and returns the set of all possible classes. For testing purposes. There is no need to use this, since you might as well use full softmax or full logistic regression.
##### Parameters
object true_classes
A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
object num_true
An int. The number of target classes per training example.
object num_sampled
An int. The number of possible classes.
object unique
A bool. Ignored. unique.
object seed
An int. An operation-specific seed. Default is 0.
object name
A name for the operation (optional).
##### Returns
object

#### Tensoratrous_conv2d(IGraphNodeBase value, IGraphNodeBase filters, int rate, string padding, string name)

Atrous convolution (a.k.a. convolution with holes or dilated convolution).

This function is a simpler wrapper around the more general tf.nn.convolution, and exists only for backwards compatibility. You can use tf.nn.convolution to perform 1-D, 2-D, or 3-D atrous convolution.

Computes a 2-D atrous convolution, also known as convolution with holes or dilated convolution, given 4-D value and filters tensors. If the rate parameter is equal to one, it performs regular 2-D convolution. If the rate parameter is greater than one, it performs convolution with holes, sampling the input values every rate pixels in the height and width dimensions. This is equivalent to convolving the input with a set of upsampled filters, produced by inserting rate - 1 zeros between two consecutive values of the filters along the height and width dimensions, hence the name atrous convolution or convolution with holes (the French word trous means holes in English).

More specifically:

 output[batch, height, width, out_channel] = sum_{dheight, dwidth, in_channel} ( filters[dheight, dwidth, in_channel, out_channel] * value[batch, height + rate*dheight, width + rate*dwidth, in_channel] ) 

Atrous convolution allows us to explicitly control how densely to compute feature responses in fully convolutional networks. Used in conjunction with bilinear interpolation, it offers an alternative to conv2d_transpose in dense prediction tasks such as semantic image segmentation, optical flow computation, or depth estimation. It also allows us to effectively enlarge the field of view of filters without increasing the number of parameters or the amount of computation.

For a description of atrous convolution and how it can be used for dense feature extraction, please see: [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](http://arxiv.org/abs/1412.7062). The same operation is investigated further in [Multi-Scale Context Aggregation by Dilated Convolutions](http://arxiv.org/abs/1511.07122). Previous works that effectively use atrous convolution in different ways are, among others, [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](http://arxiv.org/abs/1312.6229) and [Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks](http://arxiv.org/abs/1302.1700). Atrous convolution is also closely related to the so-called noble identities in multi-rate signal processing.

There are many different ways to implement atrous convolution (see the refs above). The implementation here reduces to the following three operations: Advanced usage. Note the following optimization: A sequence of atrous_conv2d operations with identical rate parameters, 'SAME' padding, and filters with odd heights/ widths: can be equivalently performed cheaper in terms of computation and memory as: because a pair of consecutive space_to_batch and batch_to_space ops with the same block_size cancel out when their respective paddings and crops inputs are identical.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default "NHWC" format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, in_channels, out_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value. Output shape with 'VALID' padding is:

[batch, height - 2 * (filter_width - 1), width - 2 * (filter_height - 1), out_channels].

Output shape with 'SAME' padding is:

[batch, height, width, out_channels].
Show Example
atrous_conv2d(value, filters, rate, padding=padding)

#### objectatrous_conv2d_dyn(object value, object filters, object rate, object padding, object name)

Atrous convolution (a.k.a. convolution with holes or dilated convolution).

This function is a simpler wrapper around the more general tf.nn.convolution, and exists only for backwards compatibility. You can use tf.nn.convolution to perform 1-D, 2-D, or 3-D atrous convolution.

Computes a 2-D atrous convolution, also known as convolution with holes or dilated convolution, given 4-D value and filters tensors. If the rate parameter is equal to one, it performs regular 2-D convolution. If the rate parameter is greater than one, it performs convolution with holes, sampling the input values every rate pixels in the height and width dimensions. This is equivalent to convolving the input with a set of upsampled filters, produced by inserting rate - 1 zeros between two consecutive values of the filters along the height and width dimensions, hence the name atrous convolution or convolution with holes (the French word trous means holes in English).

More specifically:

 output[batch, height, width, out_channel] = sum_{dheight, dwidth, in_channel} ( filters[dheight, dwidth, in_channel, out_channel] * value[batch, height + rate*dheight, width + rate*dwidth, in_channel] ) 

Atrous convolution allows us to explicitly control how densely to compute feature responses in fully convolutional networks. Used in conjunction with bilinear interpolation, it offers an alternative to conv2d_transpose in dense prediction tasks such as semantic image segmentation, optical flow computation, or depth estimation. It also allows us to effectively enlarge the field of view of filters without increasing the number of parameters or the amount of computation.

For a description of atrous convolution and how it can be used for dense feature extraction, please see: [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](http://arxiv.org/abs/1412.7062). The same operation is investigated further in [Multi-Scale Context Aggregation by Dilated Convolutions](http://arxiv.org/abs/1511.07122). Previous works that effectively use atrous convolution in different ways are, among others, [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](http://arxiv.org/abs/1312.6229) and [Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks](http://arxiv.org/abs/1302.1700). Atrous convolution is also closely related to the so-called noble identities in multi-rate signal processing.

There are many different ways to implement atrous convolution (see the refs above). The implementation here reduces to the following three operations: Advanced usage. Note the following optimization: A sequence of atrous_conv2d operations with identical rate parameters, 'SAME' padding, and filters with odd heights/ widths: can be equivalently performed cheaper in terms of computation and memory as: because a pair of consecutive space_to_batch and batch_to_space ops with the same block_size cancel out when their respective paddings and crops inputs are identical.
##### Parameters
object value
A 4-D Tensor of type float. It needs to be in the default "NHWC" format. Its shape is [batch, in_height, in_width, in_channels].
object filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, in_channels, out_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
object rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
object name
Optional name for the returned tensor.
##### Returns
object
A Tensor with the same type as value. Output shape with 'VALID' padding is:

[batch, height - 2 * (filter_width - 1), width - 2 * (filter_height - 1), out_channels].

Output shape with 'SAME' padding is:

[batch, height, width, out_channels].
Show Example
atrous_conv2d(value, filters, rate, padding=padding)

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, IEnumerable<int> output_shape, int rate, ValueTuple<IEnumerable<object>, object> padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
IEnumerable<int> output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, IEnumerable<int> output_shape, int rate, string padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
IEnumerable<int> output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, int rate, ValueTuple<IEnumerable<object>, object> padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, IGraphNodeBase output_shape, int rate, ValueTuple<IEnumerable<object>, object> padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
IGraphNodeBase output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, int rate, string padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, object output_shape, int rate, ValueTuple<IEnumerable<object>, object> padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
object output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, object output_shape, int rate, string padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
object output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensoratrous_conv2d_transpose(IGraphNodeBase value, IGraphNodeBase filters, IGraphNodeBase output_shape, int rate, string padding, string name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
IGraphNodeBase filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
IGraphNodeBase output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
int rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### objectatrous_conv2d_transpose_dyn(object value, object filters, object output_shape, object rate, object padding, object name)

The transpose of atrous_conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of atrous_conv2d rather than an actual deconvolution.
##### Parameters
object value
A 4-D Tensor of type float. It needs to be in the default NHWC format. Its shape is [batch, in_height, in_width, in_channels].
object filters
A 4-D Tensor with the same type as value and shape [filter_height, filter_width, out_channels, in_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
object output_shape
A 1-D Tensor of shape representing the output shape of the deconvolution op.
object rate
A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm.
object name
Optional name for the returned tensor.
##### Returns
object
A Tensor with the same type as value.

#### Tensoravg_pool(IEnumerable<object> value, int ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IEnumerable<object> value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(object value, int ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(object value, int ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(object value, ValueTuple<int, object> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(ndarray value, IEnumerable<int> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(object value, ValueTuple<int, object> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IEnumerable<object> value, int ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IEnumerable<object> value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IGraphNodeBase value, IEnumerable<int> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IGraphNodeBase value, int ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IGraphNodeBase value, ValueTuple<int, object> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IGraphNodeBase value, ValueTuple<int, object> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IGraphNodeBase value, int ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(PythonClassContainer value, IEnumerable<int> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
PythonClassContainer value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(PythonClassContainer value, IEnumerable<int> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
PythonClassContainer value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(PythonClassContainer value, ValueTuple<int, object> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
PythonClassContainer value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(PythonClassContainer value, ValueTuple<int, object> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
PythonClassContainer value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(PythonClassContainer value, int ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
PythonClassContainer value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(PythonClassContainer value, int ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
PythonClassContainer value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IGraphNodeBase value, IEnumerable<int> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IEnumerable<object> value, ValueTuple<int, object> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IEnumerable<object> value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(object value, IEnumerable<int> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(object value, IEnumerable<int> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IEnumerable<object> value, IEnumerable<int> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IEnumerable<object> value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IEnumerable<object> value, IEnumerable<int> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IEnumerable<object> value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(ndarray value, int ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(IEnumerable<object> value, ValueTuple<int, object> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IEnumerable<object> value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(ndarray value, ValueTuple<int, object> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(ndarray value, ValueTuple<int, object> ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(ndarray value, IEnumerable<int> ksize, object strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool(ndarray value, int ksize, PythonClassContainer strides, object padding, string data_format, string name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
PythonClassContainer strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the operation.
object input
Alias for value.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### objectavg_pool_dyn(object value, object ksize, object strides, object padding, ImplicitContainer<T> data_format, object name, object input)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object value
A 4-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
object ksize
An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
ImplicitContainer<T> data_format
A string. 'NHWC' and 'NCHW' are supported.
object name
Optional name for the operation.
object input
Alias for value.
##### Returns
object
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool_v2(ndarray input, int ksize, int strides, string padding, object data_format, string name)

Performs the avg pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels] if data_format does not start with "NC" (default), or [batch_size, num_channels] + input_spatial_shape if data_format starts with "NC". Pooling happens over the spatial dimensions only.
int ksize
An int or list of ints that has length 1, N or N+2. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string. Specifies the channel dimension. For N=1 it can be either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default) or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW".
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor of format specified by data_format. The average pooled output tensor.

#### Tensoravg_pool_v2(IGraphNodeBase input, int ksize, int strides, string padding, object data_format, string name)

Performs the avg pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels] if data_format does not start with "NC" (default), or [batch_size, num_channels] + input_spatial_shape if data_format starts with "NC". Pooling happens over the spatial dimensions only.
int ksize
An int or list of ints that has length 1, N or N+2. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string. Specifies the channel dimension. For N=1 it can be either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default) or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW".
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor of format specified by data_format. The average pooled output tensor.

#### objectavg_pool_v2_dyn(object input, object ksize, object strides, object padding, object data_format, object name)

Performs the avg pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
object input
Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels] if data_format does not start with "NC" (default), or [batch_size, num_channels] + input_spatial_shape if data_format starts with "NC". Pooling happens over the spatial dimensions only.
object ksize
An int or list of ints that has length 1, N or N+2. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string. Specifies the channel dimension. For N=1 it can be either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default) or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW".
object name
Optional name for the operation.
##### Returns
object
A Tensor of format specified by data_format. The average pooled output tensor.

#### Tensoravg_pool1d(IGraphNodeBase input, int ksize, int strides, string padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.

Note internally this op reshapes and uses the underlying 2d operation.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of the format specified by data_format.
int ksize
An int or list of ints that has length 1 or 3. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1 or 3. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
An optional string from: "NWC", "NCW". Defaults to "NWC".
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor of format specified by data_format. The max pooled output tensor.

#### Tensoravg_pool1d(ndarray input, int ksize, int strides, string padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.

Note internally this op reshapes and uses the underlying 2d operation.
##### Parameters
ndarray input
A 3-D Tensor of the format specified by data_format.
int ksize
An int or list of ints that has length 1 or 3. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1 or 3. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
An optional string from: "NWC", "NCW". Defaults to "NWC".
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor of format specified by data_format. The max pooled output tensor.

#### objectavg_pool1d_dyn(object input, object ksize, object strides, object padding, ImplicitContainer<T> data_format, object name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.

Note internally this op reshapes and uses the underlying 2d operation.
##### Parameters
object input
A 3-D Tensor of the format specified by data_format.
object ksize
An int or list of ints that has length 1 or 3. The size of the window for each dimension of the input tensor.
object strides
An int or list of ints that has length 1 or 3. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
ImplicitContainer<T> data_format
An optional string from: "NWC", "NCW". Defaults to "NWC".
object name
A name for the operation (optional).
##### Returns
object
A Tensor of format specified by data_format. The max pooled output tensor.

#### Tensoravg_pool3d(ndarray input, int ksize, IEnumerable<int> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
IEnumerable<int> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, int ksize, ValueTuple<int, object, object> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
ValueTuple<int, object, object> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, int ksize, IEnumerable<int> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
IEnumerable<int> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, int ksize, int strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, ValueTuple<int, object, object> ksize, int strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object, object> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, ValueTuple<int, object, object> ksize, ValueTuple<int, object, object> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object, object> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
ValueTuple<int, object, object> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, IEnumerable<int> ksize, int strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, IEnumerable<int> ksize, ValueTuple<int, object, object> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
ValueTuple<int, object, object> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, IEnumerable<int> ksize, IEnumerable<int> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
IEnumerable<int> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, int ksize, int strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, IEnumerable<int> ksize, IEnumerable<int> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
IEnumerable<int> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, IEnumerable<int> ksize, ValueTuple<int, object, object> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
ValueTuple<int, object, object> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, IEnumerable<int> ksize, int strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
IEnumerable<int> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, ValueTuple<int, object, object> ksize, IEnumerable<int> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object, object> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
IEnumerable<int> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, int ksize, ValueTuple<int, object, object> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
int ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
ValueTuple<int, object, object> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, ValueTuple<int, object, object> ksize, ValueTuple<int, object, object> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object, object> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
ValueTuple<int, object, object> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(IGraphNodeBase input, ValueTuple<int, object, object> ksize, IEnumerable<int> strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
IGraphNodeBase input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object, object> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
IEnumerable<int> strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### Tensoravg_pool3d(ndarray input, ValueTuple<int, object, object> ksize, int strides, object padding, string data_format, string name)

Performs the average pooling on the input.

Each entry in output is the mean of the corresponding size ksize window in value.
##### Parameters
ndarray input
A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
ValueTuple<int, object, object> ksize
An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
int strides
An int or list of ints that has length 1, 3 or 5. The stride of the sliding window for each dimension of the input tensor.
object padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NDHWC' and 'NCDHW' are supported.
string name
Optional name for the operation.
##### Returns
Tensor
A Tensor with the same type as value. The average pooled output tensor.

#### objectbatch_norm_with_global_normalization(object t, object m, object v, object beta, object gamma, Nullable<double> variance_epsilon, Nullable<bool> scale_after_normalization, string name, object input, object mean, object variance)

Batch normalization.

This op is deprecated. See tf.nn.batch_normalization.
##### Parameters
object t
A 4D input Tensor.
object m
A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.
object v
A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.
object beta
A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor.
object gamma
A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
Nullable<bool> scale_after_normalization
A bool indicating whether the resulted tensor needs to be multiplied with gamma.
string name
A name for this operation (optional).
object input
Alias for t.
object mean
Alias for m.
object variance
Alias for v.
##### Returns
object
A batch-normalized t.

#### objectbatch_norm_with_global_normalization_dyn(object t, object m, object v, object beta, object gamma, object variance_epsilon, object scale_after_normalization, object name, object input, object mean, object variance)

Batch normalization.

This op is deprecated. See tf.nn.batch_normalization.
##### Parameters
object t
A 4D input Tensor.
object m
A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.
object v
A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.
object beta
A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor.
object gamma
A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor.
object variance_epsilon
A small float number to avoid dividing by 0.
object scale_after_normalization
A bool indicating whether the resulted tensor needs to be multiplied with gamma.
object name
A name for this operation (optional).
object input
Alias for t.
object mean
Alias for m.
object variance
Alias for v.
##### Returns
object
A batch-normalized t.

#### objectbatch_normalization(IEnumerable<IGraphNodeBase> x, object mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IEnumerable<IGraphNodeBase> x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(ValueTuple<PythonClassContainer, PythonClassContainer> x, object mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IndexedSlices x, IEnumerable<object> mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IndexedSlices x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IndexedSlices x, IEnumerable<object> mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IndexedSlices x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IndexedSlices x, object mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IndexedSlices x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IndexedSlices x, object mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IndexedSlices x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IGraphNodeBase x, IEnumerable<object> mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IGraphNodeBase x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IGraphNodeBase x, IEnumerable<object> mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IGraphNodeBase x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IGraphNodeBase x, object mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IGraphNodeBase x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IGraphNodeBase x, object mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IGraphNodeBase x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IEnumerable<IGraphNodeBase> x, IEnumerable<object> mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IEnumerable<IGraphNodeBase> x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IEnumerable<IGraphNodeBase> x, IEnumerable<object> mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IEnumerable<IGraphNodeBase> x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(IEnumerable<IGraphNodeBase> x, object mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
IEnumerable<IGraphNodeBase> x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(ValueTuple<PythonClassContainer, PythonClassContainer> x, object mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(ValueTuple<PythonClassContainer, PythonClassContainer> x, IEnumerable<object> mean, IEnumerable<object> variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
IEnumerable<object> variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization(ValueTuple<PythonClassContainer, PythonClassContainer> x, IEnumerable<object> mean, object variance, object offset, object scale, Nullable<double> variance_epsilon, string name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
ValueTuple<PythonClassContainer, PythonClassContainer> x
Input Tensor of arbitrary dimensionality.
IEnumerable<object> mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
Nullable<double> variance_epsilon
A small float number to avoid dividing by 0.
string name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### objectbatch_normalization_dyn(object x, object mean, object variance, object offset, object scale, object variance_epsilon, object name)

Batch normalization.

Normalizes a tensor by mean and variance, and applies (optionally) a scale \$$\gamma\$$ to it, as well as an offset \$$\beta\$$:

\$$\frac{\gamma(x-\mu)}{\sigma}+\beta\$$

mean, variance, offset and scale are all expected to be of one of two shapes:

* In all generality, they can have the same number of dimensions as the input x, with identical sizes as x for the dimensions that are not normalized over (the 'depth' dimension(s)), and dimension 1 for the others which are being normalized over. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=True) during training, or running averages thereof during inference. * In the common case where the 'depth' dimension is the last dimension in the input tensor x, they may be one dimensional tensors of the same size as the 'depth' dimension. This is the case for example for the common [batch, depth] layout of fully-connected layers, and [batch, height, width, depth] for convolutions. mean and variance in this case would typically be the outputs of tf.nn.moments(..., keep_dims=False) during training, or running averages thereof during inference.

See Source: [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy] (http://arxiv.org/abs/1502.03167).
##### Parameters
object x
Input Tensor of arbitrary dimensionality.
object mean
A mean Tensor.
object variance
A variance Tensor.
object offset
An offset Tensor, often denoted \$$\beta\$$ in equations, or None. If present, will be added to the normalized tensor.
object scale
A scale Tensor, often denoted \$$\gamma\$$ in equations, or None. If present, the scale is applied to the normalized tensor.
object variance_epsilon
A small float number to avoid dividing by 0.
object name
A name for this operation (optional).
##### Returns
object
the normalized, scaled, offset tensor.

#### Tensorbias_add(IEnumerable<int> value, IGraphNodeBase bias, string data_format, string name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IGraphNodeBase bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IGraphNodeBase value, IndexedSlices bias, string data_format, PythonFunctionContainer name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IGraphNodeBase value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IndexedSlices bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
PythonFunctionContainer name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IGraphNodeBase value, IndexedSlices bias, string data_format, string name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IGraphNodeBase value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IndexedSlices bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IGraphNodeBase value, ValueTuple<PythonClassContainer, PythonClassContainer> bias, string data_format, string name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IGraphNodeBase value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
ValueTuple<PythonClassContainer, PythonClassContainer> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IGraphNodeBase value, ValueTuple<PythonClassContainer, PythonClassContainer> bias, string data_format, PythonFunctionContainer name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IGraphNodeBase value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
ValueTuple<PythonClassContainer, PythonClassContainer> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
PythonFunctionContainer name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IGraphNodeBase value, IEnumerable<int> bias, string data_format, string name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IGraphNodeBase value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IEnumerable<int> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IGraphNodeBase value, IEnumerable<int> bias, string data_format, PythonFunctionContainer name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IGraphNodeBase value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IEnumerable<int> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
PythonFunctionContainer name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IEnumerable<int> value, IEnumerable<int> bias, string data_format, string name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IEnumerable<int> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IEnumerable<int> value, IEnumerable<int> bias, string data_format, PythonFunctionContainer name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IEnumerable<int> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
PythonFunctionContainer name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IEnumerable<int> value, ValueTuple<PythonClassContainer, PythonClassContainer> bias, string data_format, PythonFunctionContainer name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
ValueTuple<PythonClassContainer, PythonClassContainer> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
PythonFunctionContainer name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IEnumerable<int> value, ValueTuple<PythonClassContainer, PythonClassContainer> bias, string data_format, string name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
ValueTuple<PythonClassContainer, PythonClassContainer> bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IEnumerable<int> value, IndexedSlices bias, string data_format, PythonFunctionContainer name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IndexedSlices bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
PythonFunctionContainer name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IEnumerable<int> value, IndexedSlices bias, string data_format, string name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IndexedSlices bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorbias_add(IEnumerable<int> value, IGraphNodeBase bias, string data_format, PythonFunctionContainer name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
IEnumerable<int> value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
IGraphNodeBase bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
string data_format
A string. 'N...C' and 'NC...' are supported.
PythonFunctionContainer name
A name for the operation (optional).
##### Returns
Tensor
A Tensor with the same type as value.

#### objectbias_add_dyn(object value, object bias, object data_format, object name)

Adds bias to value.

This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions. Unlike tf.add, the type of bias is allowed to differ from value in the case where both types are quantized.
##### Parameters
object value
A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
object bias
A 1-D Tensor with size matching the channel dimension of value. Must be the same type as value unless value is a quantized type, in which case a different quantized type may be used.
object data_format
A string. 'N...C' and 'NC...' are supported.
object name
A name for the operation (optional).
##### Returns
object
A Tensor with the same type as value.

#### ValueTuple<object, object>bidirectional_dynamic_rnn(LSTMCell cell_fw, LSTMCell cell_bw, IGraphNodeBase inputs, IGraphNodeBase sequence_length, IGraphNodeBase initial_state_fw, IGraphNodeBase initial_state_bw, DType dtype, object parallel_iterations, bool swap_memory, Nullable<bool> time_major, object scope)

Creates a dynamic version of bidirectional recurrent neural network. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use keras.layers.Bidirectional(keras.layers.RNN(cell)), which is equivalent to this API

Takes input and builds independent forward and backward RNNs. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate states are ever returned -- the network is fully unrolled for the given (passed in) length(s) of the sequence(s) or completely unrolled if length(s) is not given.
##### Parameters
LSTMCell cell_fw
An instance of RNNCell, to be used for forward direction.
LSTMCell cell_bw
An instance of RNNCell, to be used for backward direction.
IGraphNodeBase inputs
The RNN inputs. If time_major == False (default), this must be a tensor of shape: [batch_size, max_time,...], or a nested tuple of such elements. If time_major == True, this must be a tensor of shape: [max_time, batch_size,...], or a nested tuple of such elements.
IGraphNodeBase sequence_length
(optional) An int32/int64 vector, size [batch_size], containing the actual lengths for each of the sequences in the batch. If not provided, all batch entries are assumed to be full sequences; and time reversal is applied from time 0 to max_time for each sequence.
IGraphNodeBase initial_state_fw
(optional) An initial state for the forward RNN. This must be a tensor of appropriate type and shape [batch_size, cell_fw.state_size]. If cell_fw.state_size is a tuple, this should be a tuple of tensors having shapes [batch_size, s] for s in cell_fw.state_size.
IGraphNodeBase initial_state_bw
(optional) Same as for initial_state_fw, but using the corresponding properties of cell_bw.
DType dtype
(optional) The data type for the initial states and expected output. Required if initial_states are not provided or RNN states have a heterogeneous dtype.
object parallel_iterations
(Default: 32). The number of iterations to run in parallel. Those operations which do not have any temporal dependency and can be run in parallel, will be. This parameter trades off time for space. Values >> 1 use more memory but take less time, while smaller values use less memory but computations take longer.
bool swap_memory
Transparently swap the tensors produced in forward inference but needed for back prop from GPU to CPU. This allows training RNNs which would typically not fit on a single GPU, with very minimal (or no) performance penalty.
Nullable<bool> time_major
The shape format of the inputs and outputs Tensors. If true, these Tensors must be shaped [max_time, batch_size, depth]. If false, these Tensors must be shaped [batch_size, max_time, depth]. Using time_major = True is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.
object scope
VariableScope for the created subgraph; defaults to "bidirectional_rnn"
##### Returns
ValueTuple<object, object>
A tuple (outputs, output_states) where:

#### ValueTuple<Tensor, object>collapse_repeated(IGraphNodeBase labels, IEnumerable<int> seq_length, string name)

Merge repeated labels into single labels.
##### Parameters
IGraphNodeBase labels
Tensor of shape [batch, max value in seq_length]
IEnumerable<int> seq_length
Tensor of shape [batch], sequence length of each batch element.
string name
A name for this Op. Defaults to "collapse_repeated_labels".
##### Returns
ValueTuple<Tensor, object>
A tuple (collapsed_labels, new_seq_length) where

#### ValueTuple<Tensor, object>collapse_repeated(IEnumerable<object> labels, IEnumerable<int> seq_length, string name)

Merge repeated labels into single labels.
##### Parameters
IEnumerable<object> labels
Tensor of shape [batch, max value in seq_length]
IEnumerable<int> seq_length
Tensor of shape [batch], sequence length of each batch element.
string name
A name for this Op. Defaults to "collapse_repeated_labels".
##### Returns
ValueTuple<Tensor, object>
A tuple (collapsed_labels, new_seq_length) where

#### ValueTuple<Tensor, object>collapse_repeated(IGraphNodeBase labels, IGraphNodeBase seq_length, string name)

Merge repeated labels into single labels.
##### Parameters
IGraphNodeBase labels
Tensor of shape [batch, max value in seq_length]
IGraphNodeBase seq_length
Tensor of shape [batch], sequence length of each batch element.
string name
A name for this Op. Defaults to "collapse_repeated_labels".
##### Returns
ValueTuple<Tensor, object>
A tuple (collapsed_labels, new_seq_length) where

#### ValueTuple<Tensor, object>collapse_repeated(IEnumerable<object> labels, IGraphNodeBase seq_length, string name)

Merge repeated labels into single labels.
##### Parameters
IEnumerable<object> labels
Tensor of shape [batch, max value in seq_length]
IGraphNodeBase seq_length
Tensor of shape [batch], sequence length of each batch element.
string name
A name for this Op. Defaults to "collapse_repeated_labels".
##### Returns
ValueTuple<Tensor, object>
A tuple (collapsed_labels, new_seq_length) where

#### objectcollapse_repeated_dyn(object labels, object seq_length, object name)

Merge repeated labels into single labels.
##### Parameters
object labels
Tensor of shape [batch, max value in seq_length]
object seq_length
Tensor of shape [batch], sequence length of each batch element.
object name
A name for this Op. Defaults to "collapse_repeated_labels".
##### Returns
object
A tuple (collapsed_labels, new_seq_length) where

#### objectcompute_accidental_hits(IGraphNodeBase true_classes, IndexedSlices sampled_candidates, int num_true, object seed, string name)

Compute the position ids in sampled_candidates matching true_classes.

In Candidate Sampling, this operation facilitates virtually removing sampled classes which happen to match target classes. This is done in Sampled Softmax and Sampled Logistic.

See our [Candidate Sampling Algorithms Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).

We presuppose that the sampled_candidates are unique.

We call it an 'accidental hit' when one of the target classes matches one of the sampled classes. This operation reports accidental hits as triples (index, id, weight), where index represents the row number in true_classes, id represents the position in sampled_candidates, and weight is -FLOAT_MAX.

The result of this op should be passed through a sparse_to_dense operation, then added to the logits of the sampled classes. This removes the contradictory effect of accidentally sampling the true target classes as noise classes for the same example.
##### Parameters
IGraphNodeBase true_classes
A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
IndexedSlices sampled_candidates
A tensor of type int64 and shape [num_sampled]. The sampled_candidates output of CandidateSampler.
int num_true
An int. The number of target classes per training example.
object seed
An int. An operation-specific seed. Default is 0.
string name
A name for the operation (optional).
##### Returns
object

#### objectcompute_accidental_hits(IGraphNodeBase true_classes, IGraphNodeBase sampled_candidates, int num_true, object seed, string name)

Compute the position ids in sampled_candidates matching true_classes.

In Candidate Sampling, this operation facilitates virtually removing sampled classes which happen to match target classes. This is done in Sampled Softmax and Sampled Logistic.

See our [Candidate Sampling Algorithms Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).

We presuppose that the sampled_candidates are unique.

We call it an 'accidental hit' when one of the target classes matches one of the sampled classes. This operation reports accidental hits as triples (index, id, weight), where index represents the row number in true_classes, id represents the position in sampled_candidates, and weight is -FLOAT_MAX.

The result of this op should be passed through a sparse_to_dense operation, then added to the logits of the sampled classes. This removes the contradictory effect of accidentally sampling the true target classes as noise classes for the same example.
##### Parameters
IGraphNodeBase true_classes
A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
IGraphNodeBase sampled_candidates
A tensor of type int64 and shape [num_sampled]. The sampled_candidates output of CandidateSampler.
int num_true
An int. The number of target classes per training example.
object seed
An int. An operation-specific seed. Default is 0.
string name
A name for the operation (optional).
##### Returns
object

#### objectcompute_accidental_hits(IGraphNodeBase true_classes, ValueTuple<PythonClassContainer, PythonClassContainer> sampled_candidates, int num_true, object seed, string name)

Compute the position ids in sampled_candidates matching true_classes.

In Candidate Sampling, this operation facilitates virtually removing sampled classes which happen to match target classes. This is done in Sampled Softmax and Sampled Logistic.

See our [Candidate Sampling Algorithms Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).

We presuppose that the sampled_candidates are unique.

We call it an 'accidental hit' when one of the target classes matches one of the sampled classes. This operation reports accidental hits as triples (index, id, weight), where index represents the row number in true_classes, id represents the position in sampled_candidates, and weight is -FLOAT_MAX.

The result of this op should be passed through a sparse_to_dense operation, then added to the logits of the sampled classes. This removes the contradictory effect of accidentally sampling the true target classes as noise classes for the same example.
##### Parameters
IGraphNodeBase true_classes
A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
ValueTuple<PythonClassContainer, PythonClassContainer> sampled_candidates
A tensor of type int64 and shape [num_sampled]. The sampled_candidates output of CandidateSampler.
int num_true
An int. The number of target classes per training example.
object seed
An int. An operation-specific seed. Default is 0.
string name
A name for the operation (optional).
##### Returns
object

#### objectcompute_accidental_hits_dyn(object true_classes, object sampled_candidates, object num_true, object seed, object name)

Compute the position ids in sampled_candidates matching true_classes.

In Candidate Sampling, this operation facilitates virtually removing sampled classes which happen to match target classes. This is done in Sampled Softmax and Sampled Logistic.

See our [Candidate Sampling Algorithms Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).

We presuppose that the sampled_candidates are unique.

We call it an 'accidental hit' when one of the target classes matches one of the sampled classes. This operation reports accidental hits as triples (index, id, weight), where index represents the row number in true_classes, id represents the position in sampled_candidates, and weight is -FLOAT_MAX.

The result of this op should be passed through a sparse_to_dense operation, then added to the logits of the sampled classes. This removes the contradictory effect of accidentally sampling the true target classes as noise classes for the same example.
##### Parameters
object true_classes
A Tensor of type int64 and shape [batch_size, num_true]. The target classes.
object sampled_candidates
A tensor of type int64 and shape [num_sampled]. The sampled_candidates output of CandidateSampler.
object num_true
An int. The number of target classes per training example.
object seed
An int. An operation-specific seed. Default is 0.
object name
A name for the operation (optional).
##### Returns
object

#### objectcompute_average_loss(IGraphNodeBase per_example_loss, IEnumerable<double> sample_weight, Nullable<int> global_batch_size)

Scales per-example losses with sample_weights and computes their average.

Usage with distribution strategy and custom training loop:
##### Parameters
IGraphNodeBase per_example_loss
Per-example loss.
IEnumerable<double> sample_weight
Optional weighting for each example.
Nullable<int> global_batch_size
Optional global batch size value. Defaults to (size of first dimension of losses) * (number of replicas).
##### Returns
object
Scalar loss value.
Show Example
with strategy.scope():
def compute_loss(labels, predictions, sample_weight=None):  # If you are using a Loss class instead, set reduction to NONE so that
# we can do the reduction afterwards and divide by global batch size.
per_example_loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, predictions)  # Compute loss that is scaled by sample_weight and by global batch size.
return tf.compute_average_loss(
per_example_loss,
sample_weight=sample_weight,
global_batch_size=GLOBAL_BATCH_SIZE) 

#### objectcompute_average_loss(IEnumerable<int> per_example_loss, IEnumerable<double> sample_weight, Nullable<int> global_batch_size)

Scales per-example losses with sample_weights and computes their average.

Usage with distribution strategy and custom training loop:
##### Parameters
IEnumerable<int> per_example_loss
Per-example loss.
IEnumerable<double> sample_weight
Optional weighting for each example.
Nullable<int> global_batch_size
Optional global batch size value. Defaults to (size of first dimension of losses) * (number of replicas).
##### Returns
object
Scalar loss value.
Show Example
with strategy.scope():
def compute_loss(labels, predictions, sample_weight=None):  # If you are using a Loss class instead, set reduction to NONE so that
# we can do the reduction afterwards and divide by global batch size.
per_example_loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, predictions)  # Compute loss that is scaled by sample_weight and by global batch size.
return tf.compute_average_loss(
per_example_loss,
sample_weight=sample_weight,
global_batch_size=GLOBAL_BATCH_SIZE) 

#### objectcompute_average_loss_dyn(object per_example_loss, object sample_weight, object global_batch_size)

Scales per-example losses with sample_weights and computes their average.

Usage with distribution strategy and custom training loop:
##### Parameters
object per_example_loss
Per-example loss.
object sample_weight
Optional weighting for each example.
object global_batch_size
Optional global batch size value. Defaults to (size of first dimension of losses) * (number of replicas).
##### Returns
object
Scalar loss value.
Show Example
with strategy.scope():
def compute_loss(labels, predictions, sample_weight=None):  # If you are using a Loss class instead, set reduction to NONE so that
# we can do the reduction afterwards and divide by global batch size.
per_example_loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, predictions)  # Compute loss that is scaled by sample_weight and by global batch size.
return tf.compute_average_loss(
per_example_loss,
sample_weight=sample_weight,
global_batch_size=GLOBAL_BATCH_SIZE) 

#### Tensorconv_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, string strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
IGraphNodeBase filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, int strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
int filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, string strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
int filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, int strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
int filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, int strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
IGraphNodeBase filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, string strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
int filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, int strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
IGraphNodeBase filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, string strides, string padding, object data_format, object dilations, string name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
IGraphNodeBase filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
string name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
Tensor
A Tensor with the same type as value.

#### objectconv_transpose_dyn(object input, object filters, object output_shape, object strides, ImplicitContainer<T> padding, object data_format, object dilations, object name)

The transpose of convolution.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of convolution rather than an actual deconvolution.
##### Parameters
object input
An N+2 dimensional Tensor of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC". It must be one of the following types: half, bfloat16, float32, float64.
object filters
An N+2 dimensional Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ImplicitContainer<T> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
object data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object dilations
An int or list of ints that has length 1, N or N+2, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the spatial dimensions. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details.
object name
A name for the operation (optional). If not specified "conv_transpose" is used.
##### Returns
object
A Tensor with the same type as value.

#### Tensorconv1d(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filters, IEnumerable<int> stride, string padding, Nullable<bool> use_cudnn_on_gpu, string data_format, string name, IGraphNodeBase input, object dilations)

Computes a 1-D convolution given 3-D input and filter tensors. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NCHW'). They will be removed in a future version. Instructions for updating: NCHW for data_format is deprecated, use NCW instead

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NHWC'). They will be removed in a future version. Instructions for updating: NHWC for data_format is deprecated, use NWC instead

Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation.

Internally, this op reshapes the input tensors and invokes tf.nn.conv2d. For example, if data_format does not start with "NC", a tensor of shape [batch, in_width, in_channels] is reshaped to [batch, 1, in_width, in_channels], and the filter is reshaped to [1, filter_width, in_channels, out_channels]. The result is then reshaped back to [batch, out_width, out_channels] $$where out_width is a function of the stride and padding as in conv2d$$ and returned to the caller.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 3D Tensor. Must be of type float16, float32, or float64.
IGraphNodeBase filters
A 3D Tensor. Must have the same type as value.
IEnumerable<int> stride
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
'SAME' or 'VALID'
Nullable<bool> use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of [batch, in_width, in_channels]. The "NCW" format stores data as [batch, in_channels, in_width].
string name
A name for the operation (optional).
IGraphNodeBase input
Alias for value.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv1d(IGraphNodeBase value, IGraphNodeBase filters, IEnumerable<int> stride, string padding, Nullable<bool> use_cudnn_on_gpu, string data_format, string name, IGraphNodeBase input, object dilations)

Computes a 1-D convolution given 3-D input and filter tensors. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NCHW'). They will be removed in a future version. Instructions for updating: NCHW for data_format is deprecated, use NCW instead

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NHWC'). They will be removed in a future version. Instructions for updating: NHWC for data_format is deprecated, use NWC instead

Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation.

Internally, this op reshapes the input tensors and invokes tf.nn.conv2d. For example, if data_format does not start with "NC", a tensor of shape [batch, in_width, in_channels] is reshaped to [batch, 1, in_width, in_channels], and the filter is reshaped to [1, filter_width, in_channels, out_channels]. The result is then reshaped back to [batch, out_width, out_channels] $$where out_width is a function of the stride and padding as in conv2d$$ and returned to the caller.
##### Parameters
IGraphNodeBase value
A 3D Tensor. Must be of type float16, float32, or float64.
IGraphNodeBase filters
A 3D Tensor. Must have the same type as value.
IEnumerable<int> stride
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
'SAME' or 'VALID'
Nullable<bool> use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of [batch, in_width, in_channels]. The "NCW" format stores data as [batch, in_channels, in_width].
string name
A name for the operation (optional).
IGraphNodeBase input
Alias for value.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv1d(IGraphNodeBase value, IGraphNodeBase filters, int stride, string padding, Nullable<bool> use_cudnn_on_gpu, string data_format, string name, IGraphNodeBase input, object dilations)

Computes a 1-D convolution given 3-D input and filter tensors. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NCHW'). They will be removed in a future version. Instructions for updating: NCHW for data_format is deprecated, use NCW instead

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NHWC'). They will be removed in a future version. Instructions for updating: NHWC for data_format is deprecated, use NWC instead

Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation.

Internally, this op reshapes the input tensors and invokes tf.nn.conv2d. For example, if data_format does not start with "NC", a tensor of shape [batch, in_width, in_channels] is reshaped to [batch, 1, in_width, in_channels], and the filter is reshaped to [1, filter_width, in_channels, out_channels]. The result is then reshaped back to [batch, out_width, out_channels] $$where out_width is a function of the stride and padding as in conv2d$$ and returned to the caller.
##### Parameters
IGraphNodeBase value
A 3D Tensor. Must be of type float16, float32, or float64.
IGraphNodeBase filters
A 3D Tensor. Must have the same type as value.
int stride
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
'SAME' or 'VALID'
Nullable<bool> use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of [batch, in_width, in_channels]. The "NCW" format stores data as [batch, in_channels, in_width].
string name
A name for the operation (optional).
IGraphNodeBase input
Alias for value.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv1d(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filters, int stride, string padding, Nullable<bool> use_cudnn_on_gpu, string data_format, string name, IGraphNodeBase input, object dilations)

Computes a 1-D convolution given 3-D input and filter tensors. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NCHW'). They will be removed in a future version. Instructions for updating: NCHW for data_format is deprecated, use NCW instead

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NHWC'). They will be removed in a future version. Instructions for updating: NHWC for data_format is deprecated, use NWC instead

Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation.

Internally, this op reshapes the input tensors and invokes tf.nn.conv2d. For example, if data_format does not start with "NC", a tensor of shape [batch, in_width, in_channels] is reshaped to [batch, 1, in_width, in_channels], and the filter is reshaped to [1, filter_width, in_channels, out_channels]. The result is then reshaped back to [batch, out_width, out_channels] $$where out_width is a function of the stride and padding as in conv2d$$ and returned to the caller.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 3D Tensor. Must be of type float16, float32, or float64.
IGraphNodeBase filters
A 3D Tensor. Must have the same type as value.
int stride
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
'SAME' or 'VALID'
Nullable<bool> use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of [batch, in_width, in_channels]. The "NCW" format stores data as [batch, in_channels, in_width].
string name
A name for the operation (optional).
IGraphNodeBase input
Alias for value.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### objectconv1d_dyn(object value, object filters, object stride, object padding, object use_cudnn_on_gpu, object data_format, object name, object input, object dilations)

Computes a 1-D convolution given 3-D input and filter tensors. (deprecated argument values) (deprecated argument values)

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NCHW'). They will be removed in a future version. Instructions for updating: NCHW for data_format is deprecated, use NCW instead

Warning: SOME ARGUMENT VALUES ARE DEPRECATED: (data_format='NHWC'). They will be removed in a future version. Instructions for updating: NHWC for data_format is deprecated, use NWC instead

Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation.

Internally, this op reshapes the input tensors and invokes tf.nn.conv2d. For example, if data_format does not start with "NC", a tensor of shape [batch, in_width, in_channels] is reshaped to [batch, 1, in_width, in_channels], and the filter is reshaped to [1, filter_width, in_channels, out_channels]. The result is then reshaped back to [batch, out_width, out_channels] $$where out_width is a function of the stride and padding as in conv2d$$ and returned to the caller.
##### Parameters
object value
A 3D Tensor. Must be of type float16, float32, or float64.
object filters
A 3D Tensor. Must have the same type as value.
object stride
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
object padding
'SAME' or 'VALID'
object use_cudnn_on_gpu
An optional bool. Defaults to True.
object data_format
An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of [batch, in_width, in_channels]. The "NCW" format stores data as [batch, in_channels, in_width].
object name
A name for the operation (optional).
object input
Alias for value.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
##### Returns
object
A Tensor. Has the same type as input.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, string strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, string strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, int strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, string strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, string strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, int strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, int strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, int strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, int strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, string strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, string strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IEnumerable<int> output_shape, int strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, int strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, IGraphNodeBase filters, IGraphNodeBase output_shape, string strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
IGraphNodeBase filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, int strides, string padding, string data_format, object dilations, string name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
int strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
string name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IEnumerable<int> output_shape, IEnumerable<int> strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv1d_transpose(IGraphNodeBase input, int filters, IGraphNodeBase output_shape, string strides, string padding, string data_format, object dilations, PythonFunctionContainer name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
int filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
string strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
PythonFunctionContainer name
Optional name for the returned tensor.
##### Returns
Tensor
A Tensor with the same type as value.

#### objectconv1d_transpose_dyn(object input, object filters, object output_shape, object strides, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, object dilations, object name)

The transpose of conv1d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv1d rather than an actual deconvolution.
##### Parameters
object input
A 3-D Tensor of type float and shape [batch, in_width, in_channels] for NWC data format or [batch, in_channels, in_width] for NCW data format.
object filters
A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
ImplicitContainer<T> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
ImplicitContainer<T> data_format
A string. 'NWC' and 'NCW' are supported.
object dilations
An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
object name
Optional name for the returned tensor.
##### Returns
object
A Tensor with the same type as value.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, string padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, int padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
int padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, double padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
double padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, string padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, string padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, double padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
double padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, double padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
double padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, int padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
int padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, string padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, ValueTuple<int, object, object, object> strides, double padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
ValueTuple<int, object, object, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
double padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, int padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
int padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, int padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
int padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, int strides, int padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
int strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
int padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, double padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
double padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, string padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, double padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
double padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, string padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d(IGraphNodeBase input, IGraphNodeBase filter, IEnumerable<int> strides, int padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
IGraphNodeBase filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
int padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ValueTuple<int, object> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d_backprop_filter(IGraphNodeBase input, IGraphNodeBase filter_sizes, IGraphNodeBase out_backprop, IEnumerable<int> strides, string padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name)

Computes the gradients of convolution with respect to the filter.
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
IGraphNodeBase filter_sizes
A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, out_channels] tensor.
IGraphNodeBase out_backprop
A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d_backprop_filter(IGraphNodeBase input, IGraphNodeBase filter_sizes, IGraphNodeBase out_backprop, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name)

Computes the gradients of convolution with respect to the filter.
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
IGraphNodeBase filter_sizes
A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, out_channels] tensor.
IGraphNodeBase out_backprop
A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d_backprop_filter(IGraphNodeBase input, IGraphNodeBase filter_sizes, IGraphNodeBase out_backprop, IEnumerable<int> strides, object padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name)

Computes the gradients of convolution with respect to the filter.
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
IGraphNodeBase filter_sizes
A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, out_channels] tensor.
IGraphNodeBase out_backprop
A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
object padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv2d_backprop_filter(IGraphNodeBase input, IGraphNodeBase filter_sizes, IGraphNodeBase out_backprop, IEnumerable<int> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name)

Computes the gradients of convolution with respect to the filter.
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
IGraphNodeBase filter_sizes
A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, out_channels] tensor.
IGraphNodeBase out_backprop
A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor. Has the same type as input.

#### objectconv2d_backprop_filter_dyn(object input, object filter_sizes, object out_backprop, object strides, object padding, ImplicitContainer<T> use_cudnn_on_gpu, ImplicitContainer<T> data_format, ImplicitContainer<T> dilations, object name)

Computes the gradients of convolution with respect to the filter.
##### Parameters
object input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].
object filter_sizes
A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, out_channels] tensor.
object out_backprop
A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
object strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
object padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
ImplicitContainer<T> use_cudnn_on_gpu
An optional bool. Defaults to True.
ImplicitContainer<T> data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
object name
A name for the operation (optional).
##### Returns
object
A Tensor. Has the same type as input.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ValueTuple<int, object> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, string padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ValueTuple<int, object> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, string padding, bool use_cudnn_on_gpu, string data_format, IEnumerable<object> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
IEnumerable<object> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, IEnumerable<object> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
IEnumerable<object> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ValueTuple<int, object> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ValueTuple<int, object> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, IEnumerable<object> padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
IEnumerable<object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, bool use_cudnn_on_gpu, string data_format, IEnumerable<object> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
ValueTuple<IEnumerable<object>, object> padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
IEnumerable<object> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### Tensorconv2d_backprop_input(IGraphNodeBase input_sizes, IGraphNodeBase filter, IGraphNodeBase out_backprop, IEnumerable<int> strides, string padding, bool use_cudnn_on_gpu, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
IGraphNodeBase input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
IGraphNodeBase filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
IGraphNodeBase out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
string padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
bool use_cudnn_on_gpu
An optional bool. Defaults to True.
string data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
Tensor
A Tensor. Has the same type as filter.

#### objectconv2d_backprop_input_dyn(object input_sizes, object filter, object out_backprop, object strides, object padding, ImplicitContainer<T> use_cudnn_on_gpu, ImplicitContainer<T> data_format, ImplicitContainer<T> dilations, object name, object filters)

Computes the gradients of convolution with respect to the input.
##### Parameters
object input_sizes
A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
object filter
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
object out_backprop
A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.
object strides
A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
object padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
ImplicitContainer<T> use_cudnn_on_gpu
An optional bool. Defaults to True.
ImplicitContainer<T> data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
object name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
object
A Tensor. Has the same type as filter.

#### objectconv2d_dyn(object input, object filter, object strides, object padding, ImplicitContainer<T> use_cudnn_on_gpu, ImplicitContainer<T> data_format, ImplicitContainer<T> dilations, object name, object filters)

Computes a 2-D convolution given 4-D input and filter tensors.

Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. 3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
##### Parameters
object input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.
object filter
A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
object padding
Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
ImplicitContainer<T> use_cudnn_on_gpu
An optional bool. Defaults to True.
ImplicitContainer<T> data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
ImplicitContainer<T> dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
object name
A name for the operation (optional).
object filters
Alias for filter.
##### Returns
object
A Tensor. Has the same type as input.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, object output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, ValueTuple<int, object> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, ValueTuple<int, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, object output_shape, ValueTuple<int, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, object output_shape, object strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, object output_shape, object strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, object strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, object strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, object output_shape, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, object output_shape, ValueTuple<int, object> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, ValueTuple<int, object> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, ValueTuple<int, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, object strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, object strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, IEnumerable<int> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, ValueTuple<int, object> strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, object strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, ValueTuple<int, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object> strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv2d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape, object strides, ValueTuple<IEnumerable<object>, object> padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
IGraphNodeBase filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<IEnumerable<object>, PythonClassContainer> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ValueTuple<IEnumerable<object>, object> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string. 'NHWC' and 'NCHW' are supported.
string name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### objectconv2d_transpose_dyn(object value, object filter, object output_shape, object strides, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, object name, object input, object filters, object dilations)

The transpose of conv2d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv2d rather than an actual deconvolution.
##### Parameters
object value
A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
object filter
A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 0. The dimension order is determined by the value of data_format, see below for details.
ImplicitContainer<T> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
ImplicitContainer<T> data_format
A string. 'NHWC' and 'NCHW' are supported.
object name
Optional name for the returned tensor.
object input
Alias for value.
object filters
Alias for filter.
object dilations
An int or list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.
##### Returns
object
A Tensor with the same type as value.

#### Tensorconv3d(object input, object filter, object strides, object padding, string data_format, ImplicitContainer<T> dilations, string name, object filters)

Computes a 3-D convolution given 5-D input and filter tensors.

In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product.

Our Conv3D implements a form of cross-correlation.
##### Parameters
object input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, in_depth, in_height, in_width, in_channels].
object filter
A Tensor. Must have the same type as input. Shape [filter_depth, filter_height, filter_width, in_channels, out_channels]. in_channels must match between input and filter.
object strides
A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
object padding
A string from: "SAME", "VALID". The type of padding algorithm to use.
string data_format
An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
object filters
##### Returns
Tensor
A Tensor. Has the same type as input.

#### Tensorconv3d_backprop_filter(IGraphNodeBase input, IGraphNodeBase filter_sizes, IGraphNodeBase out_backprop, IEnumerable<int> strides, string padding, string data_format, ImplicitContainer<T> dilations, string name)

Computes the gradients of 3-D convolution with respect to the filter.
##### Parameters
IGraphNodeBase input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, depth, rows, cols, in_channels].
IGraphNodeBase filter_sizes
A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 5-D [filter_depth, filter_height, filter_width, in_channels, out_channels] tensor.
IGraphNodeBase out_backprop
A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].
IEnumerable<int> strides
A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
string padding
A string from: "SAME", "VALID". The type of padding algorithm to use.
string data_format
An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
string name
A name for the operation (optional).
##### Returns
Tensor
A Tensor. Has the same type as input.

#### objectconv3d_backprop_filter_dyn(object input, object filter_sizes, object out_backprop, object strides, object padding, ImplicitContainer<T> data_format, ImplicitContainer<T> dilations, object name)

Computes the gradients of 3-D convolution with respect to the filter.
##### Parameters
object input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, depth, rows, cols, in_channels].
object filter_sizes
A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 5-D [filter_depth, filter_height, filter_width, in_channels, out_channels] tensor.
object out_backprop
A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].
object strides
A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
object padding
A string from: "SAME", "VALID". The type of padding algorithm to use.
ImplicitContainer<T> data_format
An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
object name
A name for the operation (optional).
##### Returns
object
A Tensor. Has the same type as input.

#### objectconv3d_dyn(object input, object filter, object strides, object padding, ImplicitContainer<T> data_format, ImplicitContainer<T> dilations, object name, object filters)

Computes a 3-D convolution given 5-D input and filter tensors.

In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product.

Our Conv3D implements a form of cross-correlation.
##### Parameters
object input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, in_depth, in_height, in_width, in_channels].
object filter
A Tensor. Must have the same type as input. Shape [filter_depth, filter_height, filter_width, in_channels, out_channels]. in_channels must match between input and filter.
object strides
A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
object padding
A string from: "SAME", "VALID". The type of padding algorithm to use.
ImplicitContainer<T> data_format
An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
ImplicitContainer<T> dilations
An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
object name
A name for the operation (optional).
object filters
##### Returns
object
A Tensor. Has the same type as input.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, IEnumerable<int> output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, IEnumerable<int> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, ValueTuple<object, IEnumerable<object>> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object, IEnumerable<object>> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, ValueTuple<object, IEnumerable<object>> output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object, IEnumerable<object>> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, ValueTuple<object> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IGraphNodeBase output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<object> output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<object> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<object, IEnumerable<object>> output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object, IEnumerable<object>> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, ValueTuple<object, IEnumerable<object>> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object, IEnumerable<object>> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, IGraphNodeBase output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, IGraphNodeBase output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IGraphNodeBase output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IEnumerable<IGraphNodeBase> value, IGraphNodeBase filter, ValueTuple<object> output_shape, ValueTuple<int, object, object> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IEnumerable<IGraphNodeBase> value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
ValueTuple<object> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
ValueTuple<int, object, object> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### Tensorconv3d_transpose(IGraphNodeBase value, IGraphNodeBase filter, IEnumerable<int> output_shape, IEnumerable<int> strides, string padding, string data_format, string name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
IGraphNodeBase value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
IGraphNodeBase filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
IEnumerable<int> output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
IEnumerable<int> strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
string padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
string data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
string name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
Tensor
A Tensor with the same type as value.

#### objectconv3d_transpose_dyn(object value, object filter, object output_shape, object strides, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, object name, object input, object filters, object dilations)

The transpose of conv3d.

This operation is sometimes called "deconvolution" after [Deconvolutional Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf), but is really the transpose (gradient) of conv3d rather than an actual deconvolution.
##### Parameters
object value
A 5-D Tensor of type float and shape [batch, depth, height, width, in_channels].
object filter
A 5-D Tensor with the same type as value and shape [depth, height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
object output_shape
A 1-D Tensor representing the output shape of the deconvolution op.
object strides
A list of ints. The stride of the sliding window for each dimension of the input tensor.
ImplicitContainer<T> padding
A string, either 'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.nn.convolution for details.
ImplicitContainer<T> data_format
A string, either 'NDHWC' or 'NCDHW' specifying the layout of the input and output tensors. Defaults to 'NDHWC'.
object name
Optional name for the returned tensor.
object input
Alias of value.
object filters
Alias of filter.
object dilations
An int or list of ints that has length 1, 3 or 5, defaults to 1. The dilation factor for each dimension ofinput. If a single value is given it is replicated in the D, H and W dimension. By default the N and C dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions if a 5-d tensor must be 1.
##### Returns
object
A Tensor with the same type as value.

#### objectconvolution(IEnumerable<IGraphNodeBase> input, IGraphNodeBase filter, string padding, IEnumerable<int> strides, IEnumerable<int> dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IEnumerable<IGraphNodeBase> input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
IGraphNodeBase filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
IEnumerable<int> strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
IEnumerable<int> dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IEnumerable<IGraphNodeBase> input, IGraphNodeBase filter, string padding, IEnumerable<int> strides, int dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IEnumerable<IGraphNodeBase> input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
IGraphNodeBase filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
IEnumerable<int> strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
int dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IEnumerable<IGraphNodeBase> input, IGraphNodeBase filter, string padding, int strides, IEnumerable<int> dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IEnumerable<IGraphNodeBase> input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
IGraphNodeBase filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
int strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
IEnumerable<int> dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IEnumerable<IGraphNodeBase> input, IGraphNodeBase filter, string padding, IEnumerable<int> strides, ValueTuple<int, object> dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IEnumerable<IGraphNodeBase> input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
IGraphNodeBase filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
IEnumerable<int> strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
ValueTuple<int, object> dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IEnumerable<IGraphNodeBase> input, IGraphNodeBase filter, string padding, IEnumerable<int> strides, ValueTuple<object> dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IEnumerable<IGraphNodeBase> input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
IGraphNodeBase filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
IEnumerable<int> strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
ValueTuple<object> dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IEnumerable<IGraphNodeBase> input, ndarray filter, string padding, int strides, ValueTuple<int, object> dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IEnumerable<IGraphNodeBase> input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
ndarray filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
int strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
ValueTuple<int, object> dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IEnumerable<IGraphNodeBase> input, ndarray filter, string padding, int strides, int dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IEnumerable<IGraphNodeBase> input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
ndarray filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
int strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
int dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IGraphNodeBase input, IGraphNodeBase filter, string padding, int strides, int dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q]  where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, padded_input is obtained by zero padding the input using an effective spatial filter shape of (spatial_filter_shape-1) * dilation_rate + 1 and output striding strides as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution).

In the case that data_format does start with "NC", the input and output (but not the filter) are simply transposed as follows:

convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1))

It is required that 1 <= N <= 3.
##### Parameters
IGraphNodeBase input
An (N+2)-D Tensor of type T, of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
IGraphNodeBase filter
An (N+2)-D Tensor with the same type as input and shape spatial_filter_shape + [in_channels, out_channels].
string padding
A string, either "VALID" or "SAME". The padding algorithm.
int strides
Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
int dilation_rate
Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called input stride or dilation. The effective filter size used for the convolution will be spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1), obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1.
string name
Optional name for the returned tensor.
string data_format
A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW".
object filters
Alias of filter.
object dilations
Alias of dilation_rate.
##### Returns
object
A Tensor with the same type as input of shape

[batch_size] + output_spatial_shape + [out_channels]

[batch_size, out_channels] + output_spatial_shape

if data_format starts with "NC", where output_spatial_shape depends on the value of padding.

If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])

If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]).

#### objectconvolution(IGraphNodeBase input, IGraphNodeBase filter, string padding, int strides, ValueTuple<object> dilation_rate, string name, string data_format, object filters, object dilations)

Computes sums of N-D convolutions (actually cross-correlation).

This also supports either output striding via the optional strides parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional dilation_rate parameter. Currently, however, output striding is not supported for atrous convolutions.

Specifically, in the case that data_format does not start with "NC", given a rank (N+2) input Tensor of shape

[num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels],

a rank (N+2) filter Tensor of shape

[spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels],

an optional dilation_rate tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N strides (defaulting [1]*N), this computes for each N-D spatial output position (x[0],..., x[N-1]):

 output[b, x[0],..., x[N-1], k] = sum_{z[0],..., z[N-1], q} filter[z[0],..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[