Module: tf.nn Wrappers for primitive Neural Net (NN) Operations. Classes class RNNCellDeviceWrapper: Operator that ensures an RNNCell runs on a particular device. class RNNCellDropoutWrapper: Operator adding dropout to inputs and outputs of the given cell. class RNNCellResidualWrapper: RNNCell wrapper that ensures cell inputs are added to the outputs. 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 avg 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. batch_norm_with_global_normalization(...): Batch normalization. batch_normalization(...): Batch normalization. bias_add(...): Adds bias to value. 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 given 3-D input and filter tensors. conv1d_transpose(...): The transpose of conv1d. conv2d(...): Computes a 2-D convolution given input and 4-D filters tensors. conv2d_transpose(...): The transpose of conv2d. conv3d(...): Computes a 3-D convolution given 5-D input and filters tensors. 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_greedy_decoder(...): Performs greedy decoding on the logits given in input (best path). ctc_loss(...): 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. dilation2d(...): Computes the grayscale dilation of 4-D input and 3-D filters tensors. dropout(...): Computes dropout: randomly sets elements to zero to prevent overfitting. elu(...): Computes exponential linear: exp(features) - 1 if < 0, features otherwise. 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 filters tensors. fixed_unigram_candidate_sampler(...): Samples a set of classes using the provided (fixed) base distribution. fractional_avg_pool(...): Performs fractional average pooling on the input. fractional_max_pool(...): Performs fractional max pooling on the input. gelu(...): Compute the Gaussian Error Linear Unit (GELU) activation function. in_top_k(...): Says whether the targets are in the top K predictions. isotonic_regression(...): Solves isotonic regression problems along the given axis. l2_loss(...): L2 Loss. l2_normalize(...): Normalizes along dimension axis using an L2 norm. leaky_relu(...): Compute the Leaky ReLU activation function. learned_unigram_candidate_sampler(...): Samples a set of classes from a distribution learned during training. local_response_normalization(...): Local Response Normalization. log_poisson_loss(...): Computes log Poisson loss given log_input. log_softmax(...): Computes log softmax activations. lrn(...): Local Response Normalization. max_pool(...): Performs the max pooling on the input. max_pool1d(...): Performs the max pooling on the input. max_pool2d(...): Performs the max pooling on the input. max_pool3d(...): Performs the max pooling on the input. max_pool_with_argmax(...): Performs max pooling on the input and outputs both max values and indices. moments(...): Calculates the mean and variance of x. nce_loss(...): Computes and returns the noise-contrastive estimation training loss. normalize_moments(...): Calculate the mean and variance of based on the sufficient statistics. pool(...): Performs an N-D pooling operation. relu(...): Computes rectified linear: max(features, 0). relu6(...): Computes Rectified Linear 6: min(max(features, 0), 6). safe_embedding_lookup_sparse(...): Lookup embedding results, accounting for invalid IDs and empty features. sampled_softmax_loss(...): Computes and returns the sampled softmax training loss. scale_regularization_loss(...): Scales the sum of the given regularization losses by number of replicas. selu(...): Computes scaled exponential linear: scale * alpha * (exp(features) - 1) separable_conv2d(...): 2-D convolution with separable filters. sigmoid(...): Computes sigmoid of x element-wise. sigmoid_cross_entropy_with_logits(...): Computes sigmoid cross entropy given logits. silu(...): Computes the SiLU or Swish activation function: x * sigmoid(x). softmax(...): Computes softmax activations. softmax_cross_entropy_with_logits(...): Computes softmax cross entropy between logits and labels. softplus(...): Computes softplus: log(exp(features) + 1). softsign(...): Computes softsign: features / (abs(features) + 1). space_to_batch(...): SpaceToBatch for N-D tensors of type T. space_to_depth(...): SpaceToDepth for tensors of type T. sparse_softmax_cross_entropy_with_logits(...): Computes sparse softmax cross entropy between logits and labels. sufficient_statistics(...): Calculate the sufficient statistics for the mean and variance of x. swish(...): Computes the SiLU or Swish activation function: x * sigmoid(x). tanh(...): Computes hyperbolic tangent of x element-wise. top_k(...): Finds values and indices of the k largest entries for the last dimension. weighted_cross_entropy_with_logits(...): Computes a weighted cross entropy. weighted_moments(...): Returns the frequency-weighted mean and variance of x. with_space_to_batch(...): Performs op on the space-to-batch representation of input. zero_fraction(...): Returns the fraction of zeros in value.