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‎docs/_sidebar.md‎

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* API
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* [tf](api/tf.md)
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* [tf.nn](api/tf.nn.md)
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* Contribute

‎docs/api/tf.nn.md‎

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# API of `tf.nn`
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## Classes
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| Name | Description | Has Test Case | Has Completed |
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| ----------- | ----------------------------------------- | ------------- | ------------- |
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| [`class RNNCellDeviceWrapper`](https://www.tensorflow.org/api_docs/python/tf/nn/RNNCellDeviceWrapper) | : Operator that ensures an RNNCell runs on a particular device. | | |
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| [`class RNNCellDropoutWrapper`](https://www.tensorflow.org/api_docs/python/tf/nn/RNNCellDropoutWrapper) | : Operator adding dropout to inputs and outputs of the given cell. | | |
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| [`class RNNCellResidualWrapper`](https://www.tensorflow.org/api_docs/python/tf/nn/RNNCellResidualWrapper) | : RNNCell wrapper that ensures cell inputs are added to the outputs. | | |
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## Functions
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| Name | Description | Has Test Case | Has Completed |
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| ----------- | ----------------------------------------- | ------------- | ------------- |
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| [`all_candidate_sampler(...)`](https://www.tensorflow.org/api_docs/python/tf/random/all_candidate_sampler) | : Generate the set of all classes. | | |
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| [`atrous_conv2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d) | : Atrous convolution (a.k.a. convolution with holes or dilated convolution). | | |
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| [`atrous_conv2d_transpose(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d_transpose) | : The transpose of `atrous_conv2d`. | | |
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| [`avg_pool(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool) | : Performs the avg pooling on the input. | | |
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| [`avg_pool1d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool1d) | : Performs the average pooling on the input. | | |
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| [`avg_pool2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool2d) | : Performs the average pooling on the input. | | |
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| [`avg_pool3d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool3d) | : Performs the average pooling on the input. | | |
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| [`batch_norm_with_global_normalization(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/batch_norm_with_global_normalization) | : Batch normalization. | | |
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| [`batch_normalization(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization) | : Batch normalization. | | |
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| [`bias_add(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/bias_add) | : Adds `bias` to `value`. | | |
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| [`collapse_repeated(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/collapse_repeated) | : Merge repeated labels into single labels. | | |
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| [`compute_accidental_hits(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/compute_accidental_hits) | : Compute the position ids in `sampled_candidates` matching `true_classes`. | | |
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| [`compute_average_loss(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/compute_average_loss) | : Scales per-example losses with sample_weights and computes their average. | | |
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| [`conv1d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/conv1d) | : Computes a 1-D convolution given 3-D input and filter tensors. | | |
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| [`conv1d_transpose(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/conv1d_transpose) | : The transpose of `conv1d`. | | |
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| [`conv2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d) | : Computes a 2-D convolution given `input` and 4-D `filters` tensors. | | |
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| [`conv2d_transpose(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose) | : The transpose of `conv2d`. | | |
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| [`conv3d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/conv3d) | : Computes a 3-D convolution given 5-D `input` and `filters` tensors. | | |
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| [`conv3d_transpose(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/conv3d_transpose) | : The transpose of `conv3d`. | | |
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| [`conv_transpose(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/conv_transpose) | : The transpose of `convolution`. | | |
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| [`convolution(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/convolution) | : Computes sums of N-D convolutions (actually cross-correlation). | | |
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| [`crelu(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/crelu) | : Computes Concatenated ReLU. | | |
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| [`ctc_beam_search_decoder(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_beam_search_decoder) | : Performs beam search decoding on the logits given in input. | | |
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| [`ctc_greedy_decoder(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_greedy_decoder) | : Performs greedy decoding on the logits given in input (best path). | | |
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| [`ctc_loss(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss) | : Computes CTC (Connectionist Temporal Classification) loss. | | |
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[`ctc_unique_labels(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_unique_labels) | : Get unique labels and indices for batched labels for [`tf.nn.ctc_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss). | | | |
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| [`depth_to_space(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/depth_to_space) | : DepthToSpace for tensors of type T. | | |
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| [`depthwise_conv2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d) | : Depthwise 2-D convolution. | | |
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| [`depthwise_conv2d_backprop_filter(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d_backprop_filter) | : Computes the gradients of depthwise convolution with respect to the filter. | | |
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| [`depthwise_conv2d_backprop_input(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d_backprop_input) | : Computes the gradients of depthwise convolution with respect to the input. | | |
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| [`dilation2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/dilation2d) | : Computes the grayscale dilation of 4-D `input` and 3-D `filters` tensors. | | |
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| [`dropout(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) | : Computes dropout: randomly sets elements to zero to prevent overfitting. | | |
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| [`elu(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/elu) | : Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise. | | |
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| [`embedding_lookup(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup) | : Looks up embeddings for the given `ids` from a list of tensors. | | |
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| [`embedding_lookup_sparse(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup_sparse) | : Looks up embeddings for the given ids and weights from a list of tensors. | | |
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| [`erosion2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/erosion2d) | : Computes the grayscale erosion of 4-D `value` and 3-D `filters` tensors. | | |
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| [`fixed_unigram_candidate_sampler(...)`](https://www.tensorflow.org/api_docs/python/tf/random/fixed_unigram_candidate_sampler) | : Samples a set of classes using the provided (fixed) base distribution. | | |
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| [`fractional_avg_pool(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/fractional_avg_pool) | : Performs fractional average pooling on the input. | | |
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| [`fractional_max_pool(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/fractional_max_pool) | : Performs fractional max pooling on the input. | | |
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| [`gelu(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/gelu) | : Compute the Gaussian Error Linear Unit (GELU) activation function. | | |
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| [`in_top_k(...)`](https://www.tensorflow.org/api_docs/python/tf/math/in_top_k) | : Says whether the targets are in the top `K` predictions. | | |
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| [`isotonic_regression(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/isotonic_regression) | : Solves isotonic regression problems along the given axis. | | |
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| [`l2_loss(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss) | : L2 Loss. | | |
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| [`l2_normalize(...)`](https://www.tensorflow.org/api_docs/python/tf/math/l2_normalize) | : Normalizes along dimension `axis` using an L2 norm. | | |
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| [`leaky_relu(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/leaky_relu) | : Compute the Leaky ReLU activation function. | | |
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| [`learned_unigram_candidate_sampler(...)`](https://www.tensorflow.org/api_docs/python/tf/random/learned_unigram_candidate_sampler) | : Samples a set of classes from a distribution learned during training. | | |
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| [`local_response_normalization(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization) | : Local Response Normalization. | | |
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| [`log_poisson_loss(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/log_poisson_loss) | : Computes log Poisson loss given `log_input`. | | |
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| [`log_softmax(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/log_softmax) | : Computes log softmax activations. | | |
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| [`lrn(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization) | : Local Response Normalization. | | |
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| [`max_pool(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool) | : Performs the max pooling on the input. | | |
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| [`max_pool1d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool1d) | : Performs the max pooling on the input. | | |
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| [`max_pool2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool2d) | : Performs the max pooling on the input. | | |
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| [`max_pool3d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool3d) | : Performs the max pooling on the input. | | |
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| [`max_pool_with_argmax(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool_with_argmax) | : Performs max pooling on the input and outputs both max values and indices. | | |
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| [`moments(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/moments) | : Calculates the mean and variance of `x`. | | |
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| [`nce_loss(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/nce_loss) | : Computes and returns the noise-contrastive estimation training loss. | | |
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| [`normalize_moments(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/normalize_moments) | : Calculate the mean and variance of based on the sufficient statistics. | | |
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| [`pool(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/pool) | : Performs an N-D pooling operation. | | |
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| [`relu(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/relu) | : Computes rectified linear: `max(features, 0)`. | | |
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| [`relu6(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/relu6) | : Computes Rectified Linear 6: `min(max(features, 0), 6)`. | | |
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| [`safe_embedding_lookup_sparse(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/safe_embedding_lookup_sparse) | : Lookup embedding results, accounting for invalid IDs and empty features. | | |
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| [`sampled_softmax_loss(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss) | : Computes and returns the sampled softmax training loss. | | |
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| [`scale_regularization_loss(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/scale_regularization_loss) | : Scales the sum of the given regularization losses by number of replicas. | | |
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| [`selu(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/selu) | : Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` | | |
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| [`separable_conv2d(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d) | : 2-D convolution with separable filters. | | |
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| [`sigmoid(...)`](https://www.tensorflow.org/api_docs/python/tf/math/sigmoid) | : Computes sigmoid of `x` element-wise. | | |
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| [`sigmoid_cross_entropy_with_logits(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits) | : Computes sigmoid cross entropy given `logits`. | | |
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| [`silu(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/silu) | : Computes the SiLU or Swish activation function: `x * sigmoid(x)`. | | |
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| [`softmax(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) | : Computes softmax activations. | | |
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| [`softmax_cross_entropy_with_logits(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits) | : Computes softmax cross entropy between `logits` and `labels`. | | |
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| [`softplus(...)`](https://www.tensorflow.org/api_docs/python/tf/math/softplus) | : Computes softplus: `log(exp(features) + 1)`. | | |
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| [`softsign(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/softsign) | : Computes softsign: `features / (abs(features) + 1)`. | | |
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| [`space_to_batch(...)`](https://www.tensorflow.org/api_docs/python/tf/space_to_batch) | : SpaceToBatch for N-D tensors of type T. | | |
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| [`space_to_depth(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/space_to_depth) | : SpaceToDepth for tensors of type T. | | |
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| [`sparse_softmax_cross_entropy_with_logits(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits) | : Computes sparse softmax cross entropy between `logits` and `labels`. | | |
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| [`sufficient_statistics(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/sufficient_statistics) | : Calculate the sufficient statistics for the mean and variance of `x`. | | |
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| [`swish(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/silu) | : Computes the SiLU or Swish activation function: `x * sigmoid(x)`. | | |
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| [`tanh(...)`](https://www.tensorflow.org/api_docs/python/tf/math/tanh) | : Computes hyperbolic tangent of `x` element-wise. | | |
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| [`top_k(...)`](https://www.tensorflow.org/api_docs/python/tf/math/top_k) | : Finds values and indices of the `k` largest entries for the last dimension. | | |
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| [`weighted_cross_entropy_with_logits(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/weighted_cross_entropy_with_logits) | : Computes a weighted cross entropy. | | |
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| [`weighted_moments(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/weighted_moments) | : Returns the frequency-weighted mean and variance of `x`. | | |
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| [`with_space_to_batch(...)`](https://www.tensorflow.org/api_docs/python/tf/nn/with_space_to_batch) | : Performs `op` on the space-to-batch representation of `input`. | | |
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| [`zero_fraction(...)`](https://www.tensorflow.org/api_docs/python/tf/math/zero_fraction) | : Returns the fraction of zeros in `value`. | | |

‎docs/zh-cn/_sidebar.md‎

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* API
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* [tf](api/tf.md)
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* [tf.nn](api/tf.nn.md)
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* 加入专业团队

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