tf.layers.Dropout

Applies Dropout to the input.

Inherits From: Dropout, Layer

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
scope_name

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/layers/Dropout