LostTech.TensorFlow : API Documentation

Type slim

Namespace tensorflow.contrib.slim

Methods

Properties

Fields

Public static methods

object adaptive_clipping_fn(double std_factor, double decay, object static_max_norm, IGraphNodeBase global_step, bool report_summary, double epsilon, string name)

object adaptive_clipping_fn_dyn(ImplicitContainer<T> std_factor, ImplicitContainer<T> decay, object static_max_norm, object global_step, ImplicitContainer<T> report_summary, ImplicitContainer<T> epsilon, object name)

object add_arg_scope(PythonFunctionContainer func)

object add_arg_scope_dyn(object func)

void add_model_variable(object var)

object add_model_variable_dyn(object var)

Tensor apply_regularization(object regularizer, IEnumerable<IGraphNodeBase> weights_list)

object apply_regularization_dyn(object regularizer, object weights_list)

IContextManager<T> arg_scope_(IEnumerable<object> list_ops_or_scope, IDictionary<string, object> kwargs)

IContextManager<T> arg_scope_(IDictionary<object, object> list_ops_or_scope, IDictionary<string, object> kwargs)

object arg_scope__dyn(object list_ops_or_scope, IDictionary<string, object> kwargs)

string arg_scope_func_key(PythonFunctionContainer op)

object arg_scope_func_key_dyn(object op)

object arg_scoped_arguments(object func)

object arg_scoped_arguments_dyn(object func)

void assert_global_step(IGraphNodeBase global_step_tensor)

Asserts `global_step_tensor` is a scalar int `Variable` or `Tensor`.
Parameters
IGraphNodeBase global_step_tensor
`Tensor` to test.

object assert_global_step_dyn(object global_step_tensor)

Asserts `global_step_tensor` is a scalar int `Variable` or `Tensor`.
Parameters
object global_step_tensor
`Tensor` to test.

object assert_or_get_global_step(object graph, object global_step_tensor)

object assert_or_get_global_step_dyn(object graph, object global_step_tensor)

ValueTuple<object, IDictionary<Tensor, object>> assign_from_checkpoint(Byte[] model_path, IDictionary<string, object> var_list, bool ignore_missing_vars)

ValueTuple<object, IDictionary<Tensor, object>> assign_from_checkpoint(string model_path, IDictionary<string, object> var_list, bool ignore_missing_vars)

ValueTuple<object, IDictionary<Tensor, object>> assign_from_checkpoint(string model_path, ValueTuple<object, IEnumerable<object>> var_list, bool ignore_missing_vars)

ValueTuple<object, IDictionary<Tensor, object>> assign_from_checkpoint(Byte[] model_path, ValueTuple<object, IEnumerable<object>> var_list, bool ignore_missing_vars)

object assign_from_checkpoint_dyn(object model_path, object var_list, ImplicitContainer<T> ignore_missing_vars)

object assign_from_checkpoint_fn(string model_path, IEnumerable<PartitionedVariable> var_list, bool ignore_missing_vars, bool reshape_variables)

object assign_from_checkpoint_fn(Byte[] model_path, IDictionary<object, object> var_list, bool ignore_missing_vars, bool reshape_variables)

object assign_from_checkpoint_fn(string model_path, IDictionary<object, object> var_list, bool ignore_missing_vars, bool reshape_variables)

object assign_from_checkpoint_fn(Byte[] model_path, IEnumerable<PartitionedVariable> var_list, bool ignore_missing_vars, bool reshape_variables)

object assign_from_checkpoint_fn_dyn(object model_path, object var_list, ImplicitContainer<T> ignore_missing_vars, ImplicitContainer<T> reshape_variables)

ValueTuple<object, IDictionary<Tensor, object>> assign_from_values(IDictionary<string, object> var_names_to_values)

object assign_from_values_dyn(object var_names_to_values)

object assign_from_values_fn(IDictionary<string, object> var_names_to_values)

object assign_from_values_fn_dyn(object var_names_to_values)

Tensor avg_pool2d(IGraphNodeBase inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool2d(IGraphNodeBase inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<int> scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<object> scope)

Tensor avg_pool2d(IGraphNodeBase inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool2d(IGraphNodeBase inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<int> scope)

Tensor avg_pool2d(IGraphNodeBase inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool2d(IGraphNodeBase inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<int> scope)

Tensor avg_pool2d(IGraphNodeBase inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool2d(IGraphNodeBase inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<int> scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<int> scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<int> scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, IEnumerable<int> scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool2d(IEnumerable<IGraphNodeBase> inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

object avg_pool2d_dyn(object inputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, object outputs_collections, object scope)

Tensor avg_pool3d(IGraphNodeBase inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool3d(IGraphNodeBase inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool3d(IGraphNodeBase inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor avg_pool3d(IGraphNodeBase inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

object avg_pool3d_dyn(object inputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, object outputs_collections, object scope)

Tensor batch_norm(IGraphNodeBase inputs, IEnumerable<int> decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, Variable is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(IGraphNodeBase inputs, double decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, bool is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, IEnumerable<int> decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, Variable is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, Variable is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(IEnumerable<object> inputs, double decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, Variable is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, IEnumerable<int> decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, bool is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(IGraphNodeBase inputs, double decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, Variable is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, double decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, bool is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, double decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, Variable is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(IGraphNodeBase inputs, IEnumerable<int> decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, bool is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(IEnumerable<object> inputs, double decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, bool is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

Tensor batch_norm(IEnumerable<object> inputs, IEnumerable<int> decay, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, IDictionary<object, object> param_initializers, IDictionary<object, object> param_regularizers, ImplicitContainer<T> updates_collections, bool is_training, Nullable<bool> reuse, IDictionary<string, IEnumerable<object>> variables_collections, object outputs_collections, bool trainable, IGraphNodeBase batch_weights, Nullable<bool> fused, ImplicitContainer<T> data_format, bool zero_debias_moving_mean, string scope, bool renorm, object renorm_clipping, double renorm_decay, object adjustment)

object batch_norm_dyn(object inputs, ImplicitContainer<T> decay, ImplicitContainer<T> center, ImplicitContainer<T> scale, ImplicitContainer<T> epsilon, object activation_fn, object param_initializers, object param_regularizers, ImplicitContainer<T> updates_collections, ImplicitContainer<T> is_training, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object batch_weights, object fused, ImplicitContainer<T> data_format, ImplicitContainer<T> zero_debias_moving_mean, object scope, ImplicitContainer<T> renorm, object renorm_clipping, ImplicitContainer<T> renorm_decay, object adjustment)

Tensor bias_add(IGraphNodeBase inputs, PythonFunctionContainer activation_fn, ImplicitContainer<T> initializer, object regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, ImplicitContainer<T> data_format, object scope)

object bias_add_dyn(object inputs, object activation_fn, ImplicitContainer<T> initializer, object regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, ImplicitContainer<T> data_format, object scope)

_BinarySvmTargetColumn binary_svm_target(object label_name, string weight_column_name)

object binary_svm_target_dyn(object label_name, object weight_column_name)

Tensor bow_encoder(IEnumerable<object> ids, int vocab_size, int embed_dim, bool sparse_lookup, object initializer, object regularizer, bool trainable, string scope, Nullable<bool> reuse)

Tensor bow_encoder(SparseTensor ids, int vocab_size, int embed_dim, bool sparse_lookup, object initializer, object regularizer, bool trainable, string scope, Nullable<bool> reuse)

object bow_encoder_dyn(object ids, object vocab_size, object embed_dim, ImplicitContainer<T> sparse_lookup, object initializer, object regularizer, ImplicitContainer<T> trainable, object scope, object reuse)

Tensor bucketize(IGraphNodeBase input_tensor, IEnumerable<object> boundaries, string name)

object bucketize_dyn(object input_tensor, object boundaries, object name)

_BucketizedColumn bucketized_column(_FeatureColumn source_column, IEnumerable<double> boundaries)

_BucketizedColumn bucketized_column(string source_column, IEnumerable<double> boundaries)

object bucketized_column_dyn(object source_column, object boundaries)

Represents discretized dense input.

Buckets include the left boundary, and exclude the right boundary. Namely, `boundaries=[0., 1., 2.]` generates buckets `(-inf, 0.)`, `[0., 1.)`, `[1., 2.)`, and `[2., +inf)`.

For example, if the inputs are then the output will be Example: `bucketized_column` can also be crossed with another categorical column using `crossed_column`:
Parameters
object source_column
A one-dimensional dense column which is generated with `numeric_column`.
object boundaries
A sorted list or tuple of floats specifying the boundaries.
Returns
object
A `BucketizedColumn`.
Show Example 
boundaries = [0, 10, 100]
            input tensor = [[-5, 10000]
                            [150,   10]
                            [5,    100]]

void check_feature_columns(IEnumerable<_RealValuedColumn> feature_columns)

void check_feature_columns(IDictionary<object, object> feature_columns)

void check_feature_columns(object feature_columns)

object check_feature_columns_dyn(object feature_columns)

object convolution(IGraphNodeBase inputs, int num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<object, object> normalizer_params, Initializer weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope, Nullable<int> conv_dims)

object convolution(IGraphNodeBase inputs, int num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<object, object> normalizer_params, Initializer weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope, Nullable<int> conv_dims)

object convolution(IGraphNodeBase inputs, int num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<object, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope, Nullable<int> conv_dims)

object convolution(IGraphNodeBase inputs, int num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<object, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope, Nullable<int> conv_dims)

object convolution_dyn(object inputs, object num_outputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, object data_format, ImplicitContainer<T> rate, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope, object conv_dims)

object convolution1d(object inputs, object num_outputs, object kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution1d_dyn(object inputs, object num_outputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, object data_format, ImplicitContainer<T> rate, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

object convolution2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, IEnumerable<int> num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IGraphNodeBase inputs, int num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IEnumerable<IGraphNodeBase> inputs, int num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IGraphNodeBase inputs, IEnumerable<int> num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IGraphNodeBase inputs, IEnumerable<int> num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IndexedSlices inputs, int num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IndexedSlices inputs, IEnumerable<int> num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IndexedSlices inputs, IEnumerable<int> num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, int num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, int num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, IEnumerable<int> num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IEnumerable<IGraphNodeBase> inputs, int num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IndexedSlices inputs, int num_outputs, IEnumerable<int> kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d(IGraphNodeBase inputs, int num_outputs, int kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d_dyn(object inputs, object num_outputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, object data_format, ImplicitContainer<T> rate, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

Tensor convolution2d_in_plane(object inputs, object kernel_size, int stride, string padding, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d_in_plane_dyn(object inputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

object convolution2d_transpose(IGraphNodeBase inputs, int num_outputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution2d_transpose_dyn(object inputs, object num_outputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

object convolution3d(object inputs, object num_outputs, object kernel_size, int stride, string padding, object data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution3d_dyn(object inputs, object num_outputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, object data_format, ImplicitContainer<T> rate, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

object convolution3d_transpose(object inputs, object num_outputs, object kernel_size, int stride, string padding, ImplicitContainer<T> data_format, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, object scope)

object convolution3d_transpose_dyn(object inputs, object num_outputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

IDictionary<object, object> create_feature_spec_for_parsing(IDictionary<object, object> feature_columns)

IDictionary<object, object> create_feature_spec_for_parsing(IEnumerable<_RealValuedColumn> feature_columns)

IDictionary<object, object> create_feature_spec_for_parsing(object feature_columns)

object create_feature_spec_for_parsing_dyn(object feature_columns)

object create_global_step(Graph graph)

Create global step tensor in graph.
Parameters
Graph graph
The graph in which to create the global step tensor. If missing, use default graph.
Returns
object
Global step tensor.

object create_global_step_dyn(object graph)

Create global step tensor in graph.
Parameters
object graph
The graph in which to create the global step tensor. If missing, use default graph.
Returns
object
Global step tensor.

_CrossedColumn crossed_column(IEnumerable<object> columns, int hash_bucket_size, string combiner, object ckpt_to_load_from, string tensor_name_in_ckpt, object hash_key)

object crossed_column_dyn(object columns, object hash_bucket_size, ImplicitContainer<T> combiner, object ckpt_to_load_from, object tensor_name_in_ckpt, object hash_key)

IDictionary<object, object> current_arg_scope()

object current_arg_scope_dyn()

SparseTensor dense_to_sparse(IGraphNodeBase tensor, int eos_token, object outputs_collections, object scope)

object dense_to_sparse_dyn(object tensor, ImplicitContainer<T> eos_token, object outputs_collections, object scope)

object dropout(IGraphNodeBase inputs, double keep_prob, object noise_shape, IGraphNodeBase is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout(IGraphNodeBase inputs, IEnumerable<int> keep_prob, object noise_shape, bool is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout(IGraphNodeBase inputs, IEnumerable<int> keep_prob, object noise_shape, IGraphNodeBase is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout(IGraphNodeBase inputs, double keep_prob, object noise_shape, bool is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> keep_prob, object noise_shape, IGraphNodeBase is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> keep_prob, object noise_shape, bool is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout(IEnumerable<IGraphNodeBase> inputs, double keep_prob, object noise_shape, IGraphNodeBase is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout(IEnumerable<IGraphNodeBase> inputs, double keep_prob, object noise_shape, bool is_training, string outputs_collections, string scope, Nullable<int> seed)

object dropout_dyn(object inputs, ImplicitContainer<T> keep_prob, object noise_shape, ImplicitContainer<T> is_training, object outputs_collections, object scope, object seed)

Tensor embed_sequence(IEnumerable<object> ids, Nullable<int> vocab_size, Nullable<int> embed_dim, bool unique, object initializer, object regularizer, bool trainable, string scope, Nullable<bool> reuse)

object embed_sequence_dyn(object ids, object vocab_size, object embed_dim, ImplicitContainer<T> unique, object initializer, object regularizer, ImplicitContainer<T> trainable, object scope, object reuse)

_EmbeddingColumn embedding_column(_CategoricalColumn sparse_id_column, int dimension, string combiner, Initializer initializer, object ckpt_to_load_from, string tensor_name_in_ckpt, Nullable<double> max_norm, bool trainable)

object embedding_column_dyn(object sparse_id_column, object dimension, ImplicitContainer<T> combiner, object initializer, object ckpt_to_load_from, object tensor_name_in_ckpt, object max_norm, ImplicitContainer<T> trainable)

`DenseColumn` that converts from sparse, categorical input.

Use this when your inputs are sparse, but you want to convert them to a dense representation (e.g., to feed to a DNN).

Inputs must be a `CategoricalColumn` created by any of the `categorical_column_*` function. Here is an example of using `embedding_column` with `DNNClassifier`: Here is an example using `embedding_column` with model_fn:
Parameters
object sparse_id_column
object dimension
An integer specifying dimension of the embedding, must be > 0.
ImplicitContainer<T> combiner
A string specifying how to reduce if there are multiple entries in a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with 'mean' the default. 'sqrtn' often achieves good accuracy, in particular with bag-of-words columns. Each of this can be thought as example level normalizations on the column. For more information, see `tf.embedding_lookup_sparse`.
object initializer
A variable initializer function to be used in embedding variable initialization. If not specified, defaults to `truncated_normal_initializer` with mean `0.0` and standard deviation `1/sqrt(dimension)`.
object ckpt_to_load_from
String representing checkpoint name/pattern from which to restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
object tensor_name_in_ckpt
Name of the `Tensor` in `ckpt_to_load_from` from which to restore the column weights. Required if `ckpt_to_load_from` is not `None`.
object max_norm
If not `None`, embedding values are l2-normalized to this value.
ImplicitContainer<T> trainable
Whether or not the embedding is trainable. Default is True.
Returns
object
`DenseColumn` that converts from sparse input.
Show Example 
video_id = categorical_column_with_identity(
                key='video_id', num_buckets=1000000, default_value=0)
            columns = [embedding_column(video_id, 9),...] 
 estimator = tf.estimator.DNNClassifier(feature_columns=columns,...) 
 label_column =...
def input_fn():
  features = tf.io.parse_example(
     ..., features=make_parse_example_spec(columns + [label_column]))
  labels = features.pop(label_column.name)
  return features, labels 
 estimator.train(input_fn=input_fn, steps=100)

Tensor embedding_lookup_sparse_with_distributed_aggregation(IEnumerable<IGraphNodeBase> params, SparseTensor sp_ids, SparseTensor sp_weights, string partition_strategy, string name, string combiner, object max_norm)

Tensor embedding_lookup_sparse_with_distributed_aggregation(PartitionedVariable params, SparseTensor sp_ids, SparseTensor sp_weights, string partition_strategy, string name, string combiner, object max_norm)

object embedding_lookup_sparse_with_distributed_aggregation_dyn(object params, object sp_ids, object sp_weights, ImplicitContainer<T> partition_strategy, object name, object combiner, object max_norm)

Tensor embedding_lookup_unique(PartitionedVariable params, IEnumerable<object> ids, string partition_strategy, string name)

Tensor embedding_lookup_unique(Variable params, IEnumerable<object> ids, string partition_strategy, string name)

Tensor embedding_lookup_unique(IEnumerable<object> params, IEnumerable<object> ids, string partition_strategy, string name)

object embedding_lookup_unique_dyn(object params, object ids, ImplicitContainer<T> partition_strategy, object name)

IList<object> filter_variables(IEnumerable<Variable> var_list, IEnumerable<string> include_patterns, IEnumerable<string> exclude_patterns, bool reg_search)

object filter_variables_dyn(object var_list, object include_patterns, object exclude_patterns, ImplicitContainer<T> reg_search)

object flatten(IGraphNodeBase inputs, string outputs_collections, string scope)

Flattens an input tensor while preserving the batch axis (axis 0). (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use keras.layers.flatten instead.
Parameters
IGraphNodeBase inputs
Tensor input.
string outputs_collections
string scope
Returns
object
Reshaped tensor.

Examples:

``` x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32') y = flatten(x) # now `y` has shape `(None, 16)`

x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32') y = flatten(x) # now `y` has shape `(None, None)` ```

object flatten(IGraphNodeBase inputs, IEnumerable<int> outputs_collections, string scope)

object flatten_dyn(object inputs, object outputs_collections, object scope)

object fully_connected(IEnumerable<IGraphNodeBase> inputs, TensorShape num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(IGraphNodeBase inputs, IEnumerable<int> num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(IEnumerable<IGraphNodeBase> inputs, Dimension num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(IGraphNodeBase inputs, Dimension num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(IEnumerable<IGraphNodeBase> inputs, int num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(PythonClassContainer inputs, IEnumerable<int> num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(IGraphNodeBase inputs, TensorShape num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(PythonClassContainer inputs, Dimension num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(PythonClassContainer inputs, TensorShape num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(PythonClassContainer inputs, int num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected(IGraphNodeBase inputs, int num_outputs, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, Nullable<bool> reuse, IEnumerable<string> variables_collections, string outputs_collections, bool trainable, object scope)

object fully_connected_dyn(object inputs, object num_outputs, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

object gdn(IGraphNodeBase inputs, bool inverse, double beta_min, double gamma_init, ImplicitContainer<T> reparam_offset, string data_format, object activity_regularizer, bool trainable, string name, object reuse)

object gdn_dyn(object inputs, ImplicitContainer<T> inverse, ImplicitContainer<T> beta_min, ImplicitContainer<T> gamma_init, ImplicitContainer<T> reparam_offset, ImplicitContainer<T> data_format, object activity_regularizer, ImplicitContainer<T> trainable, object name, object reuse)

IDictionary<string, object> get_default_binary_metrics_for_eval(IEnumerable<double> thresholds)

object get_default_binary_metrics_for_eval_dyn(object thresholds)

object get_global_step(object graph)

object get_global_step_dyn(object graph)

Get the global step tensor.

The global step tensor must be an integer variable. We first try to find it in the collection `GLOBAL_STEP`, or by name `global_step:0`.
Parameters
object graph
The graph to find the global step in. If missing, use default graph.
Returns
object
The global step variable, or `None` if none was found.

IList<object> get_local_variables(string scope, object suffix)

object get_local_variables_dyn(object scope, object suffix)

IList<object> get_model_variables(string scope, object suffix)

object get_model_variables_dyn(object scope, object suffix)

object get_or_create_global_step(object graph)

Returns and create (if necessary) the global step tensor.
Parameters
object graph
The graph in which to create the global step tensor. If missing, use default graph.
Returns
object
The global step tensor.

object get_or_create_global_step_dyn(object graph)

Returns and create (if necessary) the global step tensor.
Parameters
object graph
The graph in which to create the global step tensor. If missing, use default graph.
Returns
object
The global step tensor.

IList<object> get_trainable_variables(string scope, object suffix)

object get_trainable_variables_dyn(object scope, object suffix)

object get_unique_variable(string var_op_name)

object get_unique_variable_dyn(object var_op_name)

string get_variable_full_name(PartitionedVariable var)

string get_variable_full_name(object var)

string get_variable_full_name(ValueTuple<object, IEnumerable<object>> var)

string get_variable_full_name(IEnumerable<object> var)

object get_variable_full_name_dyn(object var)

IList<object> get_variables(string scope, string suffix, ImplicitContainer<T> collection)

IList<object> get_variables(VariableScope scope, string suffix, ImplicitContainer<T> collection)

string get_variables_by_name(string given_name, string scope)

object get_variables_by_name_dyn(object given_name, object scope)

object get_variables_by_suffix(string suffix, string scope)

object get_variables_by_suffix_dyn(object suffix, object scope)

object get_variables_dyn(object scope, object suffix, ImplicitContainer<T> collection)

IList<object> get_variables_to_restore(IEnumerable<string> include, IEnumerable<string> exclude)

object get_variables_to_restore_dyn(object include, object exclude)

Variable global_variable(int initial_value, bool validate_shape, string name, Nullable<bool> use_resource)

Variable global_variable(IEnumerable<int> initial_value, bool validate_shape, string name, Nullable<bool> use_resource)

object global_variable_dyn(object initial_value, ImplicitContainer<T> validate_shape, object name, object use_resource)

Tensor group_norm(IGraphNodeBase inputs, int groups, Nullable<int> channels_axis, IEnumerable<int> reduction_axes, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, object param_initializers, Nullable<bool> reuse, object variables_collections, object outputs_collections, bool trainable, string scope, bool mean_close_to_zero)

object group_norm_dyn(object inputs, ImplicitContainer<T> groups, ImplicitContainer<T> channels_axis, ImplicitContainer<T> reduction_axes, ImplicitContainer<T> center, ImplicitContainer<T> scale, ImplicitContainer<T> epsilon, object activation_fn, object param_initializers, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope, ImplicitContainer<T> mean_close_to_zero)

bool has_arg_scope(object func)

object has_arg_scope_dyn(object func)

Tensor images_to_sequence(object inputs, ImplicitContainer<T> data_format, object outputs_collections, object scope)

object images_to_sequence_dyn(object inputs, ImplicitContainer<T> data_format, object outputs_collections, object scope)

IList<_RealValuedColumn> infer_real_valued_columns(IGraphNodeBase features)

IList<_RealValuedColumn> infer_real_valued_columns(IDictionary<string, object> features)

object infer_real_valued_columns_dyn(object features)

Tensor input_from_feature_columns(IDictionary<object, object> columns_to_tensors, IDictionary<string, object> feature_columns, IEnumerable<string> weight_collections, bool trainable, VariableScope scope, IDictionary<object, object> cols_to_outs)

Tensor input_from_feature_columns(IDictionary<object, object> columns_to_tensors, IEnumerable<_RealValuedVarLenColumn> feature_columns, IEnumerable<string> weight_collections, bool trainable, VariableScope scope, IDictionary<object, object> cols_to_outs)

object input_from_feature_columns_dyn(object columns_to_tensors, object feature_columns, object weight_collections, ImplicitContainer<T> trainable, object scope, object cols_to_outs)

Tensor instance_norm(IGraphNodeBase inputs, bool center, bool scale, double epsilon, PythonFunctionContainer activation_fn, object param_initializers, Nullable<bool> reuse, object variables_collections, object outputs_collections, bool trainable, ImplicitContainer<T> data_format, string scope)

object instance_norm_dyn(object inputs, ImplicitContainer<T> center, ImplicitContainer<T> scale, ImplicitContainer<T> epsilon, object activation_fn, object param_initializers, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, ImplicitContainer<T> data_format, object scope)

object joint_weighted_sum_from_feature_columns(IDictionary<object, object> columns_to_tensors, IDictionary<object, object> feature_columns, int num_outputs, IEnumerable<string> weight_collections, bool trainable, VariableScope scope)

object joint_weighted_sum_from_feature_columns(IDictionary<object, object> columns_to_tensors, string feature_columns, int num_outputs, IEnumerable<string> weight_collections, bool trainable, VariableScope scope)

object joint_weighted_sum_from_feature_columns(IDictionary<object, object> columns_to_tensors, IEnumerable<object> feature_columns, int num_outputs, IEnumerable<string> weight_collections, bool trainable, VariableScope scope)

object joint_weighted_sum_from_feature_columns_dyn(object columns_to_tensors, object feature_columns, object num_outputs, object weight_collections, ImplicitContainer<T> trainable, object scope)

object l1_l2_regularizer(double scale_l1, double scale_l2, string scope)

object l1_l2_regularizer(int scale_l1, int scale_l2, string scope)

object l1_l2_regularizer(int scale_l1, PythonClassContainer scale_l2, string scope)

object l1_l2_regularizer(PythonClassContainer scale_l1, PythonClassContainer scale_l2, string scope)

object l1_l2_regularizer(PythonClassContainer scale_l1, int scale_l2, string scope)

object l1_l2_regularizer(PythonClassContainer scale_l1, double scale_l2, string scope)

object l1_l2_regularizer(int scale_l1, double scale_l2, string scope)

object l1_l2_regularizer(double scale_l1, int scale_l2, string scope)

object l1_l2_regularizer(double scale_l1, PythonClassContainer scale_l2, string scope)

object l1_l2_regularizer_dyn(ImplicitContainer<T> scale_l1, ImplicitContainer<T> scale_l2, object scope)

object l1_regularizer(int scale, string scope)

object l1_regularizer(double scale, string scope)

object l1_regularizer(PythonClassContainer scale, string scope)

object l1_regularizer_dyn(object scale, object scope)

object l2_regularizer(PythonClassContainer scale, string scope)

object l2_regularizer(double scale, string scope)

object l2_regularizer(int scale, string scope)

object l2_regularizer_dyn(object scale, object scope)

Tensor layer_norm(IGraphNodeBase inputs, bool center, bool scale, PythonFunctionContainer activation_fn, Nullable<bool> reuse, object variables_collections, object outputs_collections, bool trainable, int begin_norm_axis, int begin_params_axis, string scope)

object layer_norm_dyn(object inputs, ImplicitContainer<T> center, ImplicitContainer<T> scale, object activation_fn, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, ImplicitContainer<T> begin_norm_axis, ImplicitContainer<T> begin_params_axis, object scope)

Tensor legacy_fully_connected(IGraphNodeBase x, int num_output_units, PythonFunctionContainer activation_fn, ImplicitContainer<T> weight_init, ImplicitContainer<T> bias_init, string name, ImplicitContainer<T> weight_collections, ImplicitContainer<T> bias_collections, ImplicitContainer<T> output_collections, bool trainable, object weight_regularizer, object bias_regularizer)

object legacy_fully_connected_dyn(object x, object num_output_units, object activation_fn, ImplicitContainer<T> weight_init, ImplicitContainer<T> bias_init, object name, ImplicitContainer<T> weight_collections, ImplicitContainer<T> bias_collections, ImplicitContainer<T> output_collections, ImplicitContainer<T> trainable, object weight_regularizer, object bias_regularizer)

Variable local_variable(IEnumerable<int> initial_value, bool validate_shape, string name, Nullable<bool> use_resource)

Variable local_variable(IGraphNodeBase initial_value, bool validate_shape, string name, Nullable<bool> use_resource)

Variable local_variable(int initial_value, bool validate_shape, string name, Nullable<bool> use_resource)

Variable local_variable(double initial_value, bool validate_shape, string name, Nullable<bool> use_resource)

object local_variable_dyn(object initial_value, ImplicitContainer<T> validate_shape, object name, object use_resource)

IDictionary<object, object> make_place_holder_tensors_for_base_features(IEnumerable<object> feature_columns)

object make_place_holder_tensors_for_base_features_dyn(object feature_columns)

Tensor max_pool2d(IGraphNodeBase inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(IEnumerable<IGraphNodeBase> inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(IEnumerable<IGraphNodeBase> inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(IEnumerable<IGraphNodeBase> inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(IGraphNodeBase inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(IGraphNodeBase inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(ValueTuple<PythonClassContainer, PythonClassContainer> inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(IEnumerable<IGraphNodeBase> inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool2d(IGraphNodeBase inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

object max_pool2d_dyn(object inputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, object outputs_collections, object scope)

Tensor max_pool3d(IGraphNodeBase inputs, int kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool3d(IGraphNodeBase inputs, TensorShape kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool3d(IGraphNodeBase inputs, IEnumerable<int> kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

Tensor max_pool3d(IGraphNodeBase inputs, Dimension kernel_size, int stride, string padding, ImplicitContainer<T> data_format, string outputs_collections, string scope)

object max_pool3d_dyn(object inputs, object kernel_size, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, object outputs_collections, object scope)

Tensor maxout(IGraphNodeBase inputs, int num_units, int axis, object scope)

object maxout_dyn(object inputs, object num_units, ImplicitContainer<T> axis, object scope)

object model_variable(string name, TensorShape shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, TensorShape shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, TensorShape shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, Dimension shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, TensorShape shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, Dimension shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, TensorShape shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, int shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, TensorShape shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, Dimension shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, ValueTuple shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, Dimension shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, int shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, int shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, int shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, IEnumerable<int> shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, IEnumerable<int> shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, IEnumerable<int> shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, Dimension shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, IEnumerable<int> shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, IEnumerable<int> shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, ValueTuple shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, ValueTuple shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, ValueTuple shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, int shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, ValueTuple shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, ValueTuple shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, Dimension shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IDictionary<object, object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, IEnumerable<int> shape, ImplicitContainer<T> dtype, object initializer, object regularizer, Nullable<bool> trainable, PythonClassContainer collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable(string name, int shape, ImplicitContainer<T> dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, IEnumerable<object> collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object model_variable_dyn(object name, object shape, ImplicitContainer<T> dtype, object initializer, object regularizer, ImplicitContainer<T> trainable, object collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation)

_MultiClassTargetColumn multi_class_target(Nullable<int> n_classes, object label_name, string weight_column_name)

object multi_class_target_dyn(object n_classes, object label_name, object weight_column_name)

_OneHotColumn one_hot_column(_CategoricalColumn sparse_id_column)

object one_hot_column_dyn(object sparse_id_column)

Tensor one_hot_encoding(ndarray labels, int num_classes, double on_value, double off_value, string outputs_collections, object scope)

Tensor one_hot_encoding(IGraphNodeBase labels, int num_classes, double on_value, double off_value, string outputs_collections, object scope)

object one_hot_encoding_dyn(object labels, object num_classes, ImplicitContainer<T> on_value, ImplicitContainer<T> off_value, object outputs_collections, object scope)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IDictionary<object, object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, object clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IEnumerable<object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, double clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, object clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, double clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IEnumerable<object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IDictionary<object, object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IEnumerable<object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, object clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, string clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, double clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IEnumerable<object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IDictionary<object, object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, object clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IEnumerable<object> loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, string clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, PythonFunctionContainer optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, string clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, IDictionary<object, object> clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, string clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss(IGraphNodeBase loss, IGraphNodeBase global_step, Nullable<double> learning_rate, object optimizer, Nullable<double> gradient_noise_scale, IDictionary<object, object> gradient_multipliers, double clip_gradients, PythonFunctionContainer learning_rate_decay_fn, IEnumerable<IGraphNodeBase> update_ops, IEnumerable<object> variables, string name, IEnumerable<string> summaries, bool colocate_gradients_with_ops, bool increment_global_step)

object optimize_loss_dyn(object loss, object global_step, object learning_rate, object optimizer, object gradient_noise_scale, object gradient_multipliers, object clip_gradients, object learning_rate_decay_fn, object update_ops, object variables, object name, object summaries, ImplicitContainer<T> colocate_gradients_with_ops, ImplicitContainer<T> increment_global_step)

IDictionary<object, object> parse_feature_columns_from_examples(IEnumerable<object> serialized, IEnumerable<_RealValuedColumn> feature_columns, string name, object example_names)

IDictionary<object, object> parse_feature_columns_from_examples(IEnumerable<object> serialized, object feature_columns, string name, object example_names)

object parse_feature_columns_from_examples_dyn(object serialized, object feature_columns, object name, object example_names)

ValueTuple<IDictionary<object, object>, object> parse_feature_columns_from_sequence_examples(object serialized, IEnumerable<_OneHotColumn> context_feature_columns, IEnumerable<object> sequence_feature_columns, string name, object example_name)

object parse_feature_columns_from_sequence_examples_dyn(object serialized, object context_feature_columns, object sequence_feature_columns, object name, object example_name)

Tensor pool(IGraphNodeBase inputs, Dimension kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, TensorShape kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, IEnumerable<int> kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, int kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, TensorShape kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, TensorShape kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, int kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, IEnumerable<int> kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, int kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, IEnumerable<int> kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, TensorShape kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, IEnumerable<int> kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, Dimension kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, Dimension kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, Dimension kernel_size, string pooling_type, string padding, string data_format, int dilation_rate, int stride, string outputs_collections, string scope)

Tensor pool(IGraphNodeBase inputs, int kernel_size, string pooling_type, string padding, string data_format, IEnumerable<int> dilation_rate, IEnumerable<int> stride, string outputs_collections, string scope)

object pool_dyn(object inputs, object kernel_size, object pooling_type, ImplicitContainer<T> padding, object data_format, ImplicitContainer<T> dilation_rate, ImplicitContainer<T> stride, object outputs_collections, object scope)

_RealValuedColumn real_valued_column(string column_name, int dimension, double default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, IEnumerator<object> dimension, int default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, IEnumerator<object> dimension, IEnumerable<object> default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, IEnumerator<object> dimension, double default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, double dimension, string default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, double dimension, int default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, double dimension, IEnumerable<object> default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, int dimension, IEnumerable<object> default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, int dimension, int default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, int dimension, string default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, double dimension, double default_value, ImplicitContainer<T> dtype, object normalizer)

_RealValuedColumn real_valued_column(string column_name, IEnumerator<object> dimension, string default_value, ImplicitContainer<T> dtype, object normalizer)

object real_valued_column_dyn(object column_name, ImplicitContainer<T> dimension, object default_value, ImplicitContainer<T> dtype, object normalizer)

object recompute_grad(object fn, ImplicitContainer<T> use_data_dep, bool tupleize_grads)

object recompute_grad_dyn(object fn, ImplicitContainer<T> use_data_dep, ImplicitContainer<T> tupleize_grads)

_RegressionTargetColumn regression_target(object label_name, string weight_column_name, int label_dimension)

object regression_target_dyn(object label_name, object weight_column_name, ImplicitContainer<T> label_dimension)

object repeat(IGraphNodeBase inputs, int repetitions, object layer, Object[] args)

object repeat(IGraphNodeBase inputs, int repetitions, object layer, IDictionary<string, object> kwargs, Object[] args)

object repeat_dyn(object inputs, object repetitions, object layer, Object[] args)

object repeat_dyn(object inputs, object repetitions, object layer, IDictionary<string, object> kwargs, Object[] args)

string rev_block(object x1, object x2, IEnumerable<object> f, object g, int num_layers, IEnumerable<object> f_side_input, IEnumerable<object> g_side_input, bool is_training)

string rev_block(object x1, object x2, object f, object g, int num_layers, IEnumerable<object> f_side_input, IEnumerable<object> g_side_input, bool is_training)

object rev_block_dyn(object x1, object x2, object f, object g, ImplicitContainer<T> num_layers, object f_side_input, object g_side_input, ImplicitContainer<T> is_training)

Tensor safe_embedding_lookup_sparse(PartitionedVariable embedding_weights, IGraphNodeBase sparse_ids, IGraphNodeBase sparse_weights, string combiner, Nullable<int> default_id, string name, string partition_strategy, object max_norm)

Lookup embedding results, accounting for invalid IDs and empty features.

The partitioned embedding in `embedding_weights` must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of `P`. `embedding_weights` may be a `PartitionedVariable` as returned by using `tf.compat.v1.get_variable()` with a partitioner.

Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs with non-positive weight. For an entry with no features, the embedding vector for `default_id` is returned, or the 0-vector if `default_id` is not supplied.

The ids and weights may be multi-dimensional. Embeddings are always aggregated along the last dimension.
Parameters
PartitionedVariable embedding_weights
A list of `P` float `Tensor`s or values representing partitioned embedding `Tensor`s. Alternatively, a `PartitionedVariable` created by partitioning along dimension 0. The total unpartitioned shape should be `[e_0, e_1,..., e_m]`, where `e_0` represents the vocab size and `e_1,..., e_m` are the embedding dimensions.
IGraphNodeBase sparse_ids
`SparseTensor` of shape `[d_0, d_1,..., d_n]` containing the ids. `d_0` is typically batch size.
IGraphNodeBase sparse_weights
`SparseTensor` of same shape as `sparse_ids`, containing float weights corresponding to `sparse_ids`, or `None` if all weights are be assumed to be 1.0.
string combiner
A string specifying how to combine embedding results for each entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the default.
Nullable<int> default_id
The id to use for an entry with no features.
string name
A name for this operation (optional).
string partition_strategy
A string specifying the partitioning strategy. Currently `"div"` and `"mod"` are supported. Default is `"div"`.
object max_norm
If not `None`, all embeddings are l2-normalized to max_norm before combining.
Returns
Tensor
Dense `Tensor` of shape `[d_0, d_1,..., d_{n-1}, e_1,..., e_m]`.

Tensor safe_embedding_lookup_sparse(Variable embedding_weights, IGraphNodeBase sparse_ids, IGraphNodeBase sparse_weights, string combiner, Nullable<int> default_id, string name, string partition_strategy, object max_norm)

Lookup embedding results, accounting for invalid IDs and empty features.

The partitioned embedding in `embedding_weights` must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of `P`. `embedding_weights` may be a `PartitionedVariable` as returned by using `tf.compat.v1.get_variable()` with a partitioner.

Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs with non-positive weight. For an entry with no features, the embedding vector for `default_id` is returned, or the 0-vector if `default_id` is not supplied.

The ids and weights may be multi-dimensional. Embeddings are always aggregated along the last dimension.
Parameters
Variable embedding_weights
A list of `P` float `Tensor`s or values representing partitioned embedding `Tensor`s. Alternatively, a `PartitionedVariable` created by partitioning along dimension 0. The total unpartitioned shape should be `[e_0, e_1,..., e_m]`, where `e_0` represents the vocab size and `e_1,..., e_m` are the embedding dimensions.
IGraphNodeBase sparse_ids
`SparseTensor` of shape `[d_0, d_1,..., d_n]` containing the ids. `d_0` is typically batch size.
IGraphNodeBase sparse_weights
`SparseTensor` of same shape as `sparse_ids`, containing float weights corresponding to `sparse_ids`, or `None` if all weights are be assumed to be 1.0.
string combiner
A string specifying how to combine embedding results for each entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the default.
Nullable<int> default_id
The id to use for an entry with no features.
string name
A name for this operation (optional).
string partition_strategy
A string specifying the partitioning strategy. Currently `"div"` and `"mod"` are supported. Default is `"div"`.
object max_norm
If not `None`, all embeddings are l2-normalized to max_norm before combining.
Returns
Tensor
Dense `Tensor` of shape `[d_0, d_1,..., d_{n-1}, e_1,..., e_m]`.

Tensor safe_embedding_lookup_sparse(IEnumerable<object> embedding_weights, IGraphNodeBase sparse_ids, IGraphNodeBase sparse_weights, string combiner, Nullable<int> default_id, string name, string partition_strategy, object max_norm)

Lookup embedding results, accounting for invalid IDs and empty features.

The partitioned embedding in `embedding_weights` must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of `P`. `embedding_weights` may be a `PartitionedVariable` as returned by using `tf.compat.v1.get_variable()` with a partitioner.

Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs with non-positive weight. For an entry with no features, the embedding vector for `default_id` is returned, or the 0-vector if `default_id` is not supplied.

The ids and weights may be multi-dimensional. Embeddings are always aggregated along the last dimension.
Parameters
IEnumerable<object> embedding_weights
A list of `P` float `Tensor`s or values representing partitioned embedding `Tensor`s. Alternatively, a `PartitionedVariable` created by partitioning along dimension 0. The total unpartitioned shape should be `[e_0, e_1,..., e_m]`, where `e_0` represents the vocab size and `e_1,..., e_m` are the embedding dimensions.
IGraphNodeBase sparse_ids
`SparseTensor` of shape `[d_0, d_1,..., d_n]` containing the ids. `d_0` is typically batch size.
IGraphNodeBase sparse_weights
`SparseTensor` of same shape as `sparse_ids`, containing float weights corresponding to `sparse_ids`, or `None` if all weights are be assumed to be 1.0.
string combiner
A string specifying how to combine embedding results for each entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the default.
Nullable<int> default_id
The id to use for an entry with no features.
string name
A name for this operation (optional).
string partition_strategy
A string specifying the partitioning strategy. Currently `"div"` and `"mod"` are supported. Default is `"div"`.
object max_norm
If not `None`, all embeddings are l2-normalized to max_norm before combining.
Returns
Tensor
Dense `Tensor` of shape `[d_0, d_1,..., d_{n-1}, e_1,..., e_m]`.

Tensor safe_embedding_lookup_sparse(object embedding_weights, IGraphNodeBase sparse_ids, IGraphNodeBase sparse_weights, string combiner, Nullable<int> default_id, string name, string partition_strategy, object max_norm)

Lookup embedding results, accounting for invalid IDs and empty features.

The partitioned embedding in `embedding_weights` must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of `P`. `embedding_weights` may be a `PartitionedVariable` as returned by using `tf.compat.v1.get_variable()` with a partitioner.

Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs with non-positive weight. For an entry with no features, the embedding vector for `default_id` is returned, or the 0-vector if `default_id` is not supplied.

The ids and weights may be multi-dimensional. Embeddings are always aggregated along the last dimension.
Parameters
object embedding_weights
A list of `P` float `Tensor`s or values representing partitioned embedding `Tensor`s. Alternatively, a `PartitionedVariable` created by partitioning along dimension 0. The total unpartitioned shape should be `[e_0, e_1,..., e_m]`, where `e_0` represents the vocab size and `e_1,..., e_m` are the embedding dimensions.
IGraphNodeBase sparse_ids
`SparseTensor` of shape `[d_0, d_1,..., d_n]` containing the ids. `d_0` is typically batch size.
IGraphNodeBase sparse_weights
`SparseTensor` of same shape as `sparse_ids`, containing float weights corresponding to `sparse_ids`, or `None` if all weights are be assumed to be 1.0.
string combiner
A string specifying how to combine embedding results for each entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the default.
Nullable<int> default_id
The id to use for an entry with no features.
string name
A name for this operation (optional).
string partition_strategy
A string specifying the partitioning strategy. Currently `"div"` and `"mod"` are supported. Default is `"div"`.
object max_norm
If not `None`, all embeddings are l2-normalized to max_norm before combining.
Returns
Tensor
Dense `Tensor` of shape `[d_0, d_1,..., d_{n-1}, e_1,..., e_m]`.

object safe_embedding_lookup_sparse_dyn(object embedding_weights, object sparse_ids, object sparse_weights, object combiner, object default_id, object name, ImplicitContainer<T> partition_strategy, object max_norm)

object scale_gradient(object inputs, object gradient_multiplier)

object scale_gradient_dyn(object inputs, object gradient_multiplier)

_ScatteredEmbeddingColumn scattered_embedding_column(string column_name, int size, int dimension, int hash_key, string combiner, object initializer)

object scattered_embedding_column_dyn(object column_name, object size, object dimension, object hash_key, ImplicitContainer<T> combiner, object initializer)

Tensor scattered_embedding_lookup(IEnumerable<object> params, ndarray values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(IEnumerable<object> params, RaggedTensor values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(PartitionedVariable params, RaggedTensor values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(IEnumerable<object> params, IDictionary<object, object> values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(PartitionedVariable params, ValueTuple<double, object> values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(PartitionedVariable params, IEnumerable<double> values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(PartitionedVariable params, IDictionary<object, object> values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(IEnumerable<object> params, IEnumerable<double> values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(IEnumerable<object> params, ValueTuple<double, object> values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(PartitionedVariable params, ndarray values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(IEnumerable<object> params, IGraphNodeBase values, int dimension, string name, object hash_key)

Tensor scattered_embedding_lookup(PartitionedVariable params, IGraphNodeBase values, int dimension, string name, object hash_key)

object scattered_embedding_lookup_dyn(object params, object values, object dimension, object name, object hash_key)

Tensor scattered_embedding_lookup_sparse(PartitionedVariable params, IGraphNodeBase sparse_values, int dimension, string combiner, object default_value, string name, object hash_key)

Tensor scattered_embedding_lookup_sparse(IEnumerable<object> params, IGraphNodeBase sparse_values, int dimension, string combiner, object default_value, string name, object hash_key)

Tensor scattered_embedding_lookup_sparse(Variable params, IGraphNodeBase sparse_values, int dimension, string combiner, object default_value, string name, object hash_key)

object scattered_embedding_lookup_sparse_dyn(object params, object sparse_values, object dimension, object combiner, object default_value, object name, object hash_key)

Tensor separable_convolution2d(IGraphNodeBase inputs, Nullable<int> num_outputs, IEnumerable<int> kernel_size, double depth_multiplier, int stride, string padding, ImplicitContainer<T> data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, Initializer weights_initializer, object pointwise_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope)

Tensor separable_convolution2d(IGraphNodeBase inputs, Nullable<int> num_outputs, IEnumerable<int> kernel_size, int depth_multiplier, int stride, string padding, ImplicitContainer<T> data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object pointwise_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope)

Tensor separable_convolution2d(IGraphNodeBase inputs, Nullable<int> num_outputs, IEnumerable<int> kernel_size, double depth_multiplier, int stride, string padding, ImplicitContainer<T> data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, ImplicitContainer<T> weights_initializer, object pointwise_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope)

Tensor separable_convolution2d(IGraphNodeBase inputs, Nullable<int> num_outputs, IEnumerable<int> kernel_size, int depth_multiplier, int stride, string padding, ImplicitContainer<T> data_format, int rate, ImplicitContainer<T> activation_fn, PythonFunctionContainer normalizer_fn, IDictionary<string, object> normalizer_params, Initializer weights_initializer, object pointwise_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, bool trainable, string scope)

object separable_convolution2d_dyn(object inputs, object num_outputs, object kernel_size, ImplicitContainer<T> depth_multiplier, ImplicitContainer<T> stride, ImplicitContainer<T> padding, ImplicitContainer<T> data_format, ImplicitContainer<T> rate, ImplicitContainer<T> activation_fn, object normalizer_fn, object normalizer_params, ImplicitContainer<T> weights_initializer, object pointwise_initializer, object weights_regularizer, ImplicitContainer<T> biases_initializer, object biases_regularizer, object reuse, object variables_collections, object outputs_collections, ImplicitContainer<T> trainable, object scope)

Tensor sequence_input_from_feature_columns(IDictionary<object, object> columns_to_tensors, IEnumerable<_RealValuedColumn> feature_columns, IEnumerable<string> weight_collections, bool trainable, object scope)

object sequence_input_from_feature_columns_dyn(object columns_to_tensors, object feature_columns, object weight_collections, ImplicitContainer<T> trainable, object scope)

Tensor sequence_to_images(object inputs, int height, string output_data_format, object outputs_collections, object scope)

object sequence_to_images_dyn(object inputs, object height, ImplicitContainer<T> output_data_format, object outputs_collections, object scope)

object shared_embedding_columns(ValueTuple<_SparseColumn> sparse_id_columns, int dimension, string combiner, string shared_embedding_name, object initializer, object ckpt_to_load_from, object tensor_name_in_ckpt, object max_norm, bool trainable)

object shared_embedding_columns(string sparse_id_columns, int dimension, string combiner, string shared_embedding_name, object initializer, object ckpt_to_load_from, object tensor_name_in_ckpt, object max_norm, bool trainable)

object shared_embedding_columns(IEnumerable<object> sparse_id_columns, int dimension, string combiner, string shared_embedding_name, object initializer, object ckpt_to_load_from, object tensor_name_in_ckpt, object max_norm, bool trainable)

object shared_embedding_columns_dyn(object sparse_id_columns, object dimension, ImplicitContainer<T> combiner, object shared_embedding_name, object initializer, object ckpt_to_load_from, object tensor_name_in_ckpt, object max_norm, ImplicitContainer<T> trainable)

List of dense columns that convert from sparse, categorical input.

This is similar to `embedding_column`, except that it produces a list of embedding columns that share the same embedding weights.

Use this when your inputs are sparse and of the same type (e.g. watched and impression video IDs that share the same vocabulary), and you want to convert them to a dense representation (e.g., to feed to a DNN).

Inputs must be a list of categorical columns created by any of the `categorical_column_*` function. They must all be of the same type and have the same arguments except `key`. E.g. they can be categorical_column_with_vocabulary_file with the same vocabulary_file. Some or all columns could also be weighted_categorical_column.

Here is an example embedding of two features for a DNNClassifier model: Here is an example using `shared_embedding_columns` with model_fn:
Parameters
object sparse_id_columns
object dimension
An integer specifying dimension of the embedding, must be > 0.
ImplicitContainer<T> combiner
A string specifying how to reduce if there are multiple entries in a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with 'mean' the default. 'sqrtn' often achieves good accuracy, in particular with bag-of-words columns. Each of this can be thought as example level normalizations on the column. For more information, see `tf.embedding_lookup_sparse`.
object shared_embedding_name
object initializer
A variable initializer function to be used in embedding variable initialization. If not specified, defaults to `truncated_normal_initializer` with mean `0.0` and standard deviation `1/sqrt(dimension)`.
object ckpt_to_load_from
String representing checkpoint name/pattern from which to restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
object tensor_name_in_ckpt
Name of the `Tensor` in `ckpt_to_load_from` from which to restore the column weights. Required if `ckpt_to_load_from` is not `None`.
object max_norm
If not `None`, each embedding is clipped if its l2-norm is larger than this value, before combining.
ImplicitContainer<T> trainable
Whether or not the embedding is trainable. Default is True.
Returns
object
A list of dense columns that converts from sparse input. The order of results follows the ordering of `categorical_columns`.
Show Example 
watched_video_id = categorical_column_with_vocabulary_file(
                'watched_video_id', video_vocabulary_file, video_vocabulary_size)
            impression_video_id = categorical_column_with_vocabulary_file(
                'impression_video_id', video_vocabulary_file, video_vocabulary_size)
            columns = shared_embedding_columns(
                [watched_video_id, impression_video_id], dimension=10) 
 estimator = tf.estimator.DNNClassifier(feature_columns=columns,...) 
 label_column =...
def input_fn():
  features = tf.io.parse_example(
     ..., features=make_parse_example_spec(columns + [label_column]))
  labels = features.pop(label_column.name)
  return features, labels 
 estimator.train(input_fn=input_fn, steps=100)

Tensor softmax(ValueTuple<PythonClassContainer, PythonClassContainer> logits, string scope)

Tensor softmax(IEnumerable<IGraphNodeBase> logits, string scope)

Tensor softmax(ndarray logits, string scope)

Tensor softmax(IGraphNodeBase logits, string scope)

object softmax_dyn(object logits, object scope)

object sparse_column_with_hash_bucket(string column_name, int hash_bucket_size, string combiner, ImplicitContainer<T> dtype, int hash_keys)

object sparse_column_with_hash_bucket(string column_name, double hash_bucket_size, string combiner, ImplicitContainer<T> dtype, IEnumerable<object> hash_keys)

object sparse_column_with_hash_bucket(string column_name, int hash_bucket_size, string combiner, ImplicitContainer<T> dtype, IEnumerable<object> hash_keys)

object sparse_column_with_hash_bucket(string column_name, double hash_bucket_size, string combiner, ImplicitContainer<T> dtype, int hash_keys)

object sparse_column_with_hash_bucket_dyn(object column_name, object hash_bucket_size, ImplicitContainer<T> combiner, ImplicitContainer<T> dtype, object hash_keys)

object sparse_column_with_integerized_feature(string column_name, int bucket_size, string combiner, ImplicitContainer<T> dtype)

object sparse_column_with_integerized_feature_dyn(object column_name, object bucket_size, ImplicitContainer<T> combiner, ImplicitContainer<T> dtype)

object sparse_column_with_keys(string column_name, IEnumerable<string> keys, int default_value, string combiner, ImplicitContainer<T> dtype)

object sparse_column_with_keys_dyn(object column_name, object keys, ImplicitContainer<T> default_value, ImplicitContainer<T> combiner, ImplicitContainer<T> dtype)

object sparse_column_with_vocabulary_file(string column_name, string vocabulary_file, int num_oov_buckets, Nullable<int> vocab_size, int default_value, string combiner, ImplicitContainer<T> dtype)

object sparse_column_with_vocabulary_file_dyn(object column_name, object vocabulary_file, ImplicitContainer<T> num_oov_buckets, object vocab_size, ImplicitContainer<T> default_value, ImplicitContainer<T> combiner, ImplicitContainer<T> dtype)

SparseTensor sparse_feature_cross(IEnumerable<SparseTensor> inputs, bool hashed_output, int num_buckets, string name, Nullable<int> hash_key)

SparseTensor sparse_feature_cross(IEnumerable<SparseTensor> inputs, bool hashed_output, IEnumerable<object> num_buckets, string name, Nullable<int> hash_key)

object sparse_feature_cross_dyn(object inputs, ImplicitContainer<T> hashed_output, ImplicitContainer<T> num_buckets, object name, object hash_key)

Tensor spatial_softmax(IGraphNodeBase features, object temperature, string name, object variables_collections, bool trainable, string data_format)

object spatial_softmax_dyn(object features, object temperature, object name, object variables_collections, ImplicitContainer<T> trainable, ImplicitContainer<T> data_format)

Tensor stack(IGraphNodeBase inputs, object layer, IEnumerable<int> stack_args, IDictionary<string, object> kwargs)

object stack_dyn(object inputs, object layer, object stack_args, IDictionary<string, object> kwargs)

object sum_regularizer(IEnumerable<object> regularizer_list, string scope)

object sum_regularizer_dyn(object regularizer_list, object scope)

Tensor summarize_activation(IGraphNodeBase op)

object summarize_activation_dyn(object op)

IList<object> summarize_activations(object name_filter, ImplicitContainer<T> summarizer)

object summarize_activations_dyn(object name_filter, ImplicitContainer<T> summarizer)

IList<object> summarize_collection(string collection, string name_filter, ImplicitContainer<T> summarizer)

object summarize_collection_dyn(object collection, object name_filter, ImplicitContainer<T> summarizer)

Tensor summarize_tensor(Variable tensor, object tag)

object summarize_tensor_dyn(object tensor, object tag)

IList<object> summarize_tensors(IEnumerable<object> tensors, ImplicitContainer<T> summarizer)

object summarize_tensors_dyn(object tensors, ImplicitContainer<T> summarizer)

IDictionary<object, object> transform_features(IDictionary<object, object> features, IEnumerable<_RealValuedVarLenColumn> feature_columns)

IDictionary<object, object> transform_features(PythonClassContainer features, IEnumerable<_RealValuedVarLenColumn> feature_columns)

object transform_features_dyn(object features, object feature_columns)

Tensor unit_norm(IGraphNodeBase inputs, int dim, double epsilon, object scope)

Tensor unit_norm(IGraphNodeBase inputs, IEnumerable<int> dim, double epsilon, object scope)

object unit_norm_dyn(object inputs, object dim, ImplicitContainer<T> epsilon, object scope)

object variable(string name, IEnumerable<object> shape, DType dtype, IGraphNodeBase initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, Dimension shape, DType dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, int shape, DType dtype, IGraphNodeBase initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, TensorShape shape, DType dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, TensorShape shape, DType dtype, IGraphNodeBase initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, int shape, DType dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, ValueTuple shape, DType dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, IEnumerable<object> shape, DType dtype, Initializer initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, ValueTuple shape, DType dtype, IGraphNodeBase initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable(string name, Dimension shape, DType dtype, IGraphNodeBase initializer, object regularizer, Nullable<bool> trainable, object collections, object caching_device, string device, object partitioner, object custom_getter, object use_resource, VariableSynchronization synchronization, VariableAggregation aggregation)

object variable_dyn(object name, object shape, object dtype, object initializer, object regularizer, ImplicitContainer<T> trainable, object collections, object caching_device, object device, object partitioner, object custom_getter, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation)

object variance_scaling_initializer(double factor, string mode, bool uniform, Nullable<int> seed, ImplicitContainer<T> dtype)

object variance_scaling_initializer_dyn(ImplicitContainer<T> factor, ImplicitContainer<T> mode, ImplicitContainer<T> uniform, object seed, ImplicitContainer<T> dtype)

_WeightedSparseColumn weighted_sparse_column(_SparseColumn sparse_id_column, string weight_column_name, ImplicitContainer<T> dtype)

object weighted_sparse_column_dyn(object sparse_id_column, object weight_column_name, ImplicitContainer<T> dtype)

object weighted_sum_from_feature_columns(IDictionary<object, object> columns_to_tensors, IEnumerable<object> feature_columns, int num_outputs, IEnumerable<string> weight_collections, bool trainable, VariableScope scope)

object weighted_sum_from_feature_columns(IDictionary<object, object> columns_to_tensors, string feature_columns, int num_outputs, IEnumerable<string> weight_collections, bool trainable, VariableScope scope)

object weighted_sum_from_feature_columns(IDictionary<object, object> columns_to_tensors, IDictionary<string, object> feature_columns, int num_outputs, IEnumerable<string> weight_collections, bool trainable, VariableScope scope)

object weighted_sum_from_feature_columns_dyn(object columns_to_tensors, object feature_columns, object num_outputs, object weight_collections, ImplicitContainer<T> trainable, object scope)

object xavier_initializer(bool uniform, Nullable<int> seed, ImplicitContainer<T> dtype)

object xavier_initializer_dyn(ImplicitContainer<T> uniform, object seed, ImplicitContainer<T> dtype)

object zero_initializer(Variable ref, bool use_locking, string name)

object zero_initializer_dyn(object ref, ImplicitContainer<T> use_locking, ImplicitContainer<T> name)

Public properties

PythonFunctionContainer _BinarySvmTargetColumn_fn get;

PythonFunctionContainer _BucketizedColumn_fn get;

PythonFunctionContainer _CrossedColumn_fn get;

PythonFunctionContainer _DeepEmbeddingLookupArguments_fn get;

PythonFunctionContainer _EmbeddingColumn_fn get;

PythonFunctionContainer _FeatureColumn_fn get;

PythonFunctionContainer _LazyBuilderByColumnsToTensor_fn get;

PythonFunctionContainer _LinearEmbeddingLookupArguments_fn get;

PythonFunctionContainer _MetricKeys_fn get;

PythonFunctionContainer _MultiClassTargetColumn_fn get;

PythonFunctionContainer _OneHotColumn_fn get;

PythonFunctionContainer _RealValuedColumn_fn get;

PythonFunctionContainer _RealValuedVarLenColumn_fn get;

PythonFunctionContainer _RegressionTargetColumn_fn get;

PythonFunctionContainer _ScatteredEmbeddingColumn_fn get;

PythonFunctionContainer _SparseColumn_fn get;

PythonFunctionContainer _SparseColumnHashed_fn get;

PythonFunctionContainer _SparseColumnIntegerized_fn get;

PythonFunctionContainer _SparseColumnKeys_fn get;

PythonFunctionContainer _SparseColumnVocabulary_fn get;

PythonFunctionContainer _SparseIdLookupConfig_fn get;

PythonFunctionContainer _TargetColumn_fn get;

PythonFunctionContainer _Transformer_fn get;

PythonFunctionContainer _WeightedSparseColumn_fn get;

PythonFunctionContainer adaptive_clipping_fn_fn get;

PythonFunctionContainer add_arg_scope_fn get;

PythonFunctionContainer add_model_variable_fn get;

PythonFunctionContainer apply_regularization_fn get;

PythonFunctionContainer arg_scope__fn get;

PythonFunctionContainer arg_scope_func_key_fn get;

PythonFunctionContainer arg_scoped_arguments_fn get;

PythonFunctionContainer assert_global_step_fn get;

PythonFunctionContainer assert_or_get_global_step_fn get;

PythonFunctionContainer assign_from_checkpoint_fn_ get;

PythonFunctionContainer assign_from_checkpoint_fn_fn get;

PythonFunctionContainer assign_from_values_fn_ get;

PythonFunctionContainer assign_from_values_fn_fn get;

PythonFunctionContainer avg_pool2d_fn get;

PythonFunctionContainer avg_pool3d_fn get;

PythonFunctionContainer batch_norm_fn get;

PythonFunctionContainer bias_add_fn get;

PythonFunctionContainer binary_svm_target_fn get;

PythonFunctionContainer bow_encoder_fn get;

PythonFunctionContainer bucketize_fn get;

PythonFunctionContainer bucketized_column_fn get;

PythonFunctionContainer check_feature_columns_fn get;

PythonFunctionContainer convolution_fn get;

PythonFunctionContainer convolution1d_fn get;

PythonFunctionContainer convolution2d_fn get;

PythonFunctionContainer convolution2d_in_plane_fn get;

PythonFunctionContainer convolution2d_transpose_fn get;

PythonFunctionContainer convolution3d_fn get;

PythonFunctionContainer convolution3d_transpose_fn get;

PythonFunctionContainer create_feature_spec_for_parsing_fn get;

PythonFunctionContainer create_global_step_fn get;

PythonFunctionContainer crossed_column_fn get;

PythonFunctionContainer current_arg_scope_fn get;

PythonFunctionContainer DataFrameColumn_fn get;

PythonFunctionContainer dense_to_sparse_fn get;

PythonFunctionContainer dropout_fn get;

Delegate elu get; set;

object elu_dyn get; set;

PythonFunctionContainer embed_sequence_fn get;

PythonFunctionContainer embedding_column_fn get;

PythonFunctionContainer embedding_lookup_sparse_with_distributed_aggregation_fn get;

PythonFunctionContainer embedding_lookup_unique_fn get;

PythonFunctionContainer filter_variables_fn get;

PythonFunctionContainer flatten_fn get;

PythonFunctionContainer fully_connected_fn get;

PythonFunctionContainer get_default_binary_metrics_for_eval_fn get;

PythonFunctionContainer get_global_step_fn get;

PythonFunctionContainer get_local_variables_fn get;

PythonFunctionContainer get_model_variables_fn get;

PythonFunctionContainer get_or_create_global_step_fn get;

PythonFunctionContainer get_trainable_variables_fn get;

PythonFunctionContainer get_unique_variable_fn get;

PythonFunctionContainer get_variable_full_name_fn get;

PythonFunctionContainer get_variables_by_name_fn get;

PythonFunctionContainer get_variables_by_suffix_fn get;

PythonFunctionContainer get_variables_fn get;

PythonFunctionContainer get_variables_to_restore_fn get;

PythonFunctionContainer global_variable_fn get;

PythonFunctionContainer group_norm_fn get;

PythonFunctionContainer has_arg_scope_fn get;

PythonFunctionContainer images_to_sequence_fn get;

PythonFunctionContainer infer_real_valued_columns_fn get;

PythonFunctionContainer input_from_feature_columns_fn get;

PythonFunctionContainer instance_norm_fn get;

PythonFunctionContainer joint_weighted_sum_from_feature_columns_fn get;

PythonFunctionContainer l1_l2_regularizer_fn get;

PythonFunctionContainer l1_regularizer_fn get;

PythonFunctionContainer l2_regularizer_fn get;

PythonFunctionContainer layer_norm_fn get;

PythonClassContainer LAYER_RE get; set;

object LAYER_RE_dyn get; set;

PythonFunctionContainer legacy_fully_connected_fn get;

Delegate legacy_linear get; set;

object legacy_linear_dyn get; set;

Delegate legacy_relu get; set;

object legacy_relu_dyn get; set;

Delegate linear get; set;

object linear_dyn get; set;

PythonFunctionContainer local_variable_fn get;

PythonFunctionContainer make_place_holder_tensors_for_base_features_fn get;

PythonFunctionContainer max_pool2d_fn get;

PythonFunctionContainer max_pool3d_fn get;

PythonFunctionContainer maxout_fn get;

PythonFunctionContainer model_variable_fn get;

PythonFunctionContainer multi_class_target_fn get;

PythonFunctionContainer one_hot_column_fn get;

PythonFunctionContainer one_hot_encoding_fn get;

PythonFunctionContainer optimize_loss_fn get;

IDictionary<string, object> OPTIMIZER_CLS_NAMES get; set;

object OPTIMIZER_CLS_NAMES_dyn get; set;

IList<string> OPTIMIZER_SUMMARIES get; set;

object OPTIMIZER_SUMMARIES_dyn get; set;

PythonFunctionContainer parse_feature_columns_from_examples_fn get;

PythonFunctionContainer parse_feature_columns_from_sequence_examples_fn get;

PythonFunctionContainer ProblemType_fn get;

PythonFunctionContainer real_valued_column_fn get;

PythonFunctionContainer recompute_grad_fn get;

PythonFunctionContainer regression_target_fn get;

Delegate relu get; set;

object relu_dyn get; set;

Delegate relu6 get; set;

object relu6_dyn get; set;

PythonFunctionContainer repeat_fn get;

PythonFunctionContainer rev_block_fn get;

PythonFunctionContainer RevBlock_fn get;

PythonFunctionContainer safe_embedding_lookup_sparse_fn get;

PythonFunctionContainer scale_gradient_fn get;

PythonFunctionContainer scattered_embedding_column_fn get;

PythonFunctionContainer scattered_embedding_lookup_fn get;

PythonFunctionContainer scattered_embedding_lookup_sparse_fn get;

PythonFunctionContainer separable_convolution2d_fn get;

PythonFunctionContainer sequence_input_from_feature_columns_fn get;

PythonFunctionContainer sequence_to_images_fn get;

PythonFunctionContainer shared_embedding_columns_fn get;

PythonFunctionContainer softmax_fn get;

PythonFunctionContainer sparse_column_with_hash_bucket_fn get;

PythonFunctionContainer sparse_column_with_integerized_feature_fn get;

PythonFunctionContainer sparse_column_with_keys_fn get;

PythonFunctionContainer sparse_column_with_vocabulary_file_fn get;

PythonFunctionContainer sparse_feature_cross_fn get;

PythonFunctionContainer spatial_softmax_fn get;

PythonFunctionContainer stack_fn get;

PythonFunctionContainer sum_regularizer_fn get;

PythonFunctionContainer summarize_activation_fn get;

PythonFunctionContainer summarize_activations_fn get;

Delegate summarize_biases get; set;

object summarize_biases_dyn get; set;

PythonFunctionContainer summarize_collection_fn get;

PythonFunctionContainer summarize_tensor_fn get;

PythonFunctionContainer summarize_tensors_fn get;

Delegate summarize_variables get; set;

object summarize_variables_dyn get; set;

Delegate summarize_weights get; set;

object summarize_weights_dyn get; set;

PythonFunctionContainer transform_features_fn get;

PythonFunctionContainer unit_norm_fn get;

PythonFunctionContainer variable_fn get;

PythonFunctionContainer VariableDeviceChooser_fn get;

PythonFunctionContainer variance_scaling_initializer_fn get;

PythonFunctionContainer weighted_sparse_column_fn get;

PythonFunctionContainer weighted_sum_from_feature_columns_fn get;

PythonFunctionContainer xavier_initializer_fn get;

PythonFunctionContainer zero_initializer_fn get;

Public fields

BigInteger SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY

return BigInteger

string DATA_FORMAT_NCHW

return string

string DATA_FORMAT_NHWC

return string

string DATA_FORMAT_NCDHW

return string

string DATA_FORMAT_NDHWC

return string