tf.keras.layers.Dropout
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Applies Dropout to the input.
Inherits From: Layer
tf.keras.layers.Dropout(
rate, noise_shape=None, seed=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.
Arguments |
rate
|
Float between 0 and 1. Fraction of the input units to drop.
|
noise_shape
|
1D integer tensor 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 to use as random seed.
|
Call arguments:
inputs
: Input tensor (of any rank).
training
: Python boolean indicating whether the layer should behave in
training mode (adding dropout) or in inference mode (doing nothing).
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Last updated 2020-10-01 UTC.
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