Conv2DTranspose - LostTech.TensorFlow Documentation

Type Conv2DTranspose

Namespace tensorflow.keras.layers

Parent Conv2D

Transposed convolution layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

When using this layer as the first layer in a model, provide the keyword argument `input_shape` (tuple of integers, does not include the sample axis), e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures in `data_format="channels_last"`.

Methods

NewDyn

Properties

Public static methods

Conv2DTranspose NewDyn(object filters, object kernel_size, ImplicitContainer<T> strides, ImplicitContainer<T> padding, object output_padding, object data_format, ImplicitContainer<T> dilation_rate, object activation, ImplicitContainer<T> use_bias, ImplicitContainer<T> kernel_initializer, ImplicitContainer<T> bias_initializer, object kernel_regularizer, object bias_regularizer, object activity_regularizer, object kernel_constraint, object bias_constraint, IDictionary<string, object> kwargs)

Public properties

object activation get; set;

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

object bias get; set;

object bias_constraint get; set;

object bias_initializer get; set;

object bias_regularizer get; set;

bool built get; set;

string data_format get; set;

object dilation_rate get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

Nullable<int> filters get; set;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

object input_spec_dyn get; set;

object kernel get; set;

object kernel_constraint get; set;

object kernel_initializer get; set;

object kernel_regularizer get; set;

object kernel_size get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_padding get; set;

object output_shape get;

object output_shape_dyn get;

object padding get; set;

object PythonObject get;

int rank get; set;

bool stateful get; set;

object strides get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

bool trainable get; set;

object trainable_dyn get; set;

object trainable_variables get;

object trainable_variables_dyn get;

IList<object> trainable_weights get;

object trainable_weights_dyn get;

IList<object> updates get;

object updates_dyn get;

bool use_bias get; set;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;