TensorFlow 1 version |
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 given 3-D input and filter tensors. (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 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. (deprecated arguments)
dynamic_rnn(...)
: Creates a recurrent neural network specified by RNNCell cell
. (deprecated)
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. (deprecated)
fractional_max_pool(...)
: Performs fractional max pooling on the input. (deprecated)
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 axis
using an L2 norm. (deprecated arguments)
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. (deprecated arguments)
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_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_v2(...)
: 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(...)
: 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).
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
.
softmax(...)
: Computes softmax activations. (deprecated arguments)
softmax_cross_entropy_with_logits(...)
: Computes softmax cross entropy between logits
and labels
. (deprecated)
softmax_cross_entropy_with_logits_v2(...)
: Computes softmax cross entropy between logits
and labels
. (deprecated arguments)
softplus(...)
: Computes softplus: log(exp(features) + 1)
.
softsign(...)
: Computes softsign: features / (abs(features) + 1)
.
space_to_batch(...)
: SpaceToBatch for 4-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
.
static_bidirectional_rnn(...)
: Creates a bidirectional recurrent neural network. (deprecated)
static_rnn(...)
: Creates a recurrent neural network specified by RNNCell cell
. (deprecated)
static_state_saving_rnn(...)
: RNN that accepts a state saver for time-truncated RNN calculation. (deprecated)
sufficient_statistics(...)
: Calculate the sufficient statistics for the mean and variance of x
.
swish(...)
: Computes the 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.
uniform_candidate_sampler(...)
: Samples a set of classes using a uniform base distribution.
weighted_cross_entropy_with_logits(...)
: Computes a weighted cross entropy. (deprecated arguments)
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
.