Module: tf.nn

Module tf.nn

Neural network support. See the Neural Network guide.

Members

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_pool3d(...): Performs 3D average 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.

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

conv1d(...): Computes a 1-D convolution given 3-D input and filter tensors.

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_v2(...): Computes the gradients of 3-D convolution with respect to the filter.

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

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 the CTC (Connectionist Temporal Classification) Loss.

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

depthwise_conv2d_native(...): Computes a 2-D depthwise convolution given 4-D input and filter tensors.

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.

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

elu(...): Computes exponential linear: exp(features) - 1 if < 0, features otherwise.

embedding_lookup(...): Looks up ids in a list of embedding tensors.

embedding_lookup_sparse(...): Computes embeddings for the given ids and weights.

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.

fractional_avg_pool(...): Performs fractional average pooling on the input.

fractional_max_pool(...): Performs fractional max pooling on the input.

fused_batch_norm(...): Batch normalization.

in_top_k(...): Says whether the targets are in the top K predictions.

l2_loss(...): L2 Loss.

l2_normalize(...): Normalizes along dimension dim using an L2 norm.

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.

log_uniform_candidate_sampler(...): Samples a set of classes using a log-uniform (Zipfian) base distribution.

lrn(...): Local Response Normalization.

max_pool(...): Performs the max pooling on the input.

max_pool3d(...): Performs 3D max pooling on the input.

max_pool_with_argmax(...): Performs max pooling on the input and outputs both max values and indices.

moments(...): Calculate 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.

quantized_avg_pool(...): Produces the average pool of the input tensor for quantized types.

quantized_conv2d(...): Computes a 2D convolution given quantized 4D input and filter tensors.

quantized_max_pool(...): Produces the max pool of the input tensor for quantized types.

quantized_relu_x(...): Computes Quantized Rectified Linear X: min(max(features, 0), max_value)

raw_rnn(...): Creates an RNN specified by RNNCell cell and loop function loop_fn.

relu(...): Computes rectified linear: max(features, 0).

relu6(...): Computes Rectified Linear 6: min(max(features, 0), 6).

relu_layer(...): Computes Relu(x * weight + biases).

sampled_softmax_loss(...): Computes and returns the sampled softmax training loss.

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.

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).

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.

tanh(...): Computes hyperbolic tangent of x element-wise.

top_k(...): Finds values and indices of the k largest entries for the last dimension.

uniform_candidate_sampler(...): Samples a set of classes using a uniform base distribution.

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.

xw_plus_b(...): Computes matmul(x, weights) + biases.

zero_fraction(...): Returns the fraction of zeros in value.

Defined in tensorflow/python/ops/nn.py.