tf.layers.Dropout

Applies Dropout to the input.

Inherits From: Dropout, Layer

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.layers.Dropout

tf.layers.Dropout(
    rate=0.5, noise_shape=None, seed=None, name=None, **kwargs
)

Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting. The units that are kept are scaled by 1 / (1 - rate), so that their sum is unchanged at training time and inference time.

Arguments
rate	The dropout rate, between 0 and 1. E.g. rate=0.1 would drop out 10% of input units.
noise_shape	1D tensor of type int32 representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape (batch_size, timesteps, features), and you want the dropout mask to be the same for all timesteps, you can use noise_shape=[batch_size, 1, features].
seed	A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed. for behavior.
name	The name of the layer (string).

Attributes
graph	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph.
scope_name