tf.layers.Dropout

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Class Dropout

Applies Dropout to the input.

Inherits From: Dropout, Layer

Aliases:

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).

__init__

View source

__init__(
    rate=0.5,
    noise_shape=None,
    seed=None,
    name=None,
    **kwargs
)

Properties

graph

DEPRECATED FUNCTION

scope_name