tf.contrib.layers.one_hot_encoding(*args, **kwargs) Transform numeric labels into onehot_labels using tf.one_hot.

tf.contrib.layers.avg_pool2d(*args, **kwargs) Adds a 2D average pooling op. It is assumed that

tf.contrib.layers.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner=None, default_id=None, name=None, partition_strategy='div')

tf.contrib.layers.repeat(inputs, repetitions, layer, *args, **kwargs) Applies the same layer with the same arguments repeatedly

tf.contrib.layers.summarize_tensor(tensor, tag=None) Summarize a tensor using a suitable summary type. This

tf.contrib.layers.unit_norm(*args, **kwargs) Normalizes the given input across the specified dimension to unit length.

tf.contrib.layers.separable_convolution2d(*args, **kwargs) Adds a depth-separable 2D convolution with optional batch_norm layer

tf.contrib.layers.apply_regularization(regularizer, weights_list=None) Returns the summed penalty by applying regularizer

tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32) Returns an initializer performing "Xavier" initialization

tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, learning_rate_decay_fn=None