Activation Functions
- relu
- relu6
- elu
- softplus
- softsign
- dropout
- bias_add
- sigmoid
- tanh
Convolution
- conv2d
- depthwise_conv2d
- separable_conv2d
- atrous_conv2d
- conv2d_transpose
- conv3d
Pooling
- avg_pool
- max_pool
- max_pool_with_argmax
- avg_pool3d
- max_pool3d
Morphological filtering
Morphological operators are non-linear filters used in image processing.
- dilation2d
- erosion2d
Normalization
- l2_normalize
- local_response_normalization
- sufficient_statistics
- normalize_moments
- moments
Losses
- l2_loss
- log_poisson_loss
Classification
TensorFlow provides several operations that help you perform classification.
- sigmoid_cross_entropy_with_logits
- softmax
- log_softmax
- softmax_cross_entropy_with_logits
- sparse_softmax_cross_entropy_with_logits
- weighted_cross_entropy_with_logits
Embeddings
TensorFlow provides library support for looking up values in embedding tensors.
- embedding_lookup
- embedding_lookup_sparse
Recurrent Neural Networks
TensorFlow provides a number of methods for constructing Recurrent Neural Networks. Most accept an RNNCell-subclassed object (see the documentation for tf.nn.rnn_cell).
- dynamic_rnn
- rnn
- state_saving_rnn
- bidirectional_rnn
Conectionist Temporal Classification (CTC)
- ctc_loss
- ctc_greedy_decoder
- ctc_beam_search_decoder
Evaluation
The evaluation ops are useful for measuring the performance of a network. Since they are nondifferentiable, they are typically used at evaluation time.
- top_k
- in_top_k
Candidate Sampling
Do you want to train a multiclass or multilabel model with thousands or millions of output classes (for example, a language model with a large vocabulary)? Training with a full Softmax is slow in this case, since all of the classes are evaluated for every training example. Candidate Sampling training algorithms can speed up your step times by only considering a small randomly-chosen subset of contrastive classes (called candidates) for each batch of training examples.
Sampled Loss Functions
TensorFlow provides the following sampled loss functions for faster training.
- nce_loss
- sampled_softmax_loss
Candidate Samplers
TensorFlow provides the following samplers for randomly sampling candidate classes when using one of the sampled loss functions above.
- uniform_candidate_sampler
- log_uniform_candidate_sampler
- learned_unigram_candidate_sampler
- fixed_unigram_candidate_sampler
Miscellaneous candidate sampling utilities
- Miscellaneous candidate sampling utilities
Other Functions and Classes
- batch_normalization
- depthwise_conv2d_native
