tf.contrib.layers.l2_regularizer()

tf.contrib.layers.l2_regularizer(scale, scope=None) Returns a function that can be used to apply L2 regularization to weights

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tf.contrib.layers.variance_scaling_initializer()

tf.contrib.layers.variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False, seed=None, dtype=tf.float32) Returns

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tf.contrib.layers.xavier_initializer_conv2d()

tf.contrib.layers.xavier_initializer_conv2d(uniform=True, seed=None, dtype=tf.float32) Returns an initializer performing "Xavier"

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tf.contrib.layers.stack()

tf.contrib.layers.stack(inputs, layer, stack_args, **kwargs) Builds a stack of layers by applying layer repeatedly using stack_args

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tf.contrib.layers.summarize_collection()

tf.contrib.layers.summarize_collection(collection, name_filter=None, summarizer=summarize_tensor) Summarize a graph collection

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tf.contrib.layers.summarize_activations()

tf.contrib.layers.summarize_activations(name_filter=None, summarizer=summarize_activation) Summarize activations, using

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tf.contrib.layers.fully_connected()

tf.contrib.layers.fully_connected(*args, **kwargs) Adds a fully connected layer. fully_connected

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tf.contrib.layers.layer_norm()

tf.contrib.layers.layer_norm(*args, **kwargs) Adds a Layer Normalization layer from

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