tf.layers.dropout

Applies Dropout to the input. (deprecated)

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Compat aliases for migration

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tf.compat.v1.layers.dropout

tf.layers.dropout(
    inputs, rate=0.5, noise_shape=None, seed=None, training=False, name=None
)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use keras.layers.dropout instead.

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
inputs	Tensor input.
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.
training	Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode (apply dropout) or in inference mode (return the input untouched).
name	The name of the layer (string).

Returns
Output tensor.

Raises
ValueError	if eager execution is enabled.