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Module: tf.compat.v1.nn

Wrappers for primitive Neural Net (NN) Operations.

Modules

rnn_cell module: Module for constructing RNN Cells.

Functions

all_candidate_sampler(...): Generate the set of all classes.

atrous_conv2d(...): Atrous convolution (a.k.a. convolution with holes or dilated convolution).

atrous_conv2d_transpose(...): The transpose of atrous_conv2d.

avg_pool(...): Performs the average pooling on the input.

avg_pool1d(...): Performs the average pooling on the input.

avg_pool2d(...): Performs the average pooling on the input.

avg_pool3d(...): Performs the average pooling on the input.

avg_pool_v2(...): Performs the avg pooling on the input.

batch_norm_with_global_normalization(...): Batch normalization.

batch_normalization(...): Batch normalization.

bias_add(...): Adds bias to value.

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

collapse_repeated(...): Merge repeated labels into single labels.

compute_accidental_hits(...): Compute the position ids in sampled_candidates matching true_classes.

compute_average_loss(...): Scales per-example losses with sample_weights and computes their average.

conv1d(...): Computes a 1-D convolution of input with rank >=3 and a 3-D filter. (deprecated argument values) (deprecated argument values)

conv1d_transpose(...): The transpose of conv1d.

conv2d(...): Computes a 2-D convolution given 4-D input and filter tensors.

conv2d_backprop_filter(...): Computes the gradients of convolution with respect to the filter.

conv2d_backprop_input(...): Computes the gradients of convolution with respect to the input.

conv2d_transpose(...): The transpose of conv2d.

conv3d(...): Computes a 3-D convolution given 5-D input and filter tensors.

conv3d_backprop_filter(...): Computes the gradients of 3-D convolution with respect to the filter.

conv3d_backprop_filter_v2(...): Computes the gradients of 3-D convolution with respect to the filter.

conv3d_transpose(...): The transpose of conv3d.

conv_transpose(...): The transpose of convolution.

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

crelu(...): Computes Concatenated ReLU.

ctc_beam_search_decoder(...): Performs beam search decoding on the logits given in input.

ctc_beam_search_decoder_v2(...): Performs beam search decoding on the logits given in input.

ctc_greedy_decoder(...): Performs greedy decoding on the logits given in input (best path).

ctc_loss(...): Computes the CTC (Connectionist Temporal Classification) Loss.

ctc_loss_v2(...): Computes CTC (Connectionist Temporal Classification) loss.

ctc_unique_labels(...): Get unique labels and indices for batched labels for tf.nn.ctc_loss.

depth_to_space(...): DepthToSpace for tensors of type T.

depthwise_conv2d(...): Depthwise 2-D convolution.

depthwise_conv2d_backprop_filter(...): Computes the gradients of depthwise convolution with respect to the filter.

depthwise_conv2d_backprop_input(...): Computes the gradients of depthwise convolution with respect to the input.

depthwise_conv2d_native(...): Computes a 2-D depthwise convolution.

depthwise_conv2d_native_backprop_filter(...): Computes the gradients of depthwise convolution with respect to the filter.

depthwise_conv2d_native_backprop_input(...): Computes the gradients of depthwise convolution with respect to the input.

dilation2d(...): Computes the grayscale dilation of 4-D input and 3-D filter tensors.

dropout(...): Computes dropout. (deprecated arguments)

dynamic_rnn(...): Creates a recurrent neural network specified by RNNCell cell. (deprecated)

elu(...): Computes the exponential linear function.

embedding_lookup(...): Looks up embeddings for the given ids from a list of tensors.

embedding_lookup_sparse(...): Looks up embeddings for the given ids and weights from a list of tensors.

erosion2d(...): Computes the grayscale erosion of 4-D value and 3-D kernel tensors.

fixed_unigram_candidate_sampler(...): Samples a set of classes using the provided (fixed) base distribution.