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Applies Dropout to the input.
Inherits From: Dropout, Layer, Layer, Module
tf.compat.v1.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 | |
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rate
|
The dropout rate, between 0 and 1. E.g. rate=0.1 would drop out
10% of input units.
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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].
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seed
|
A Python integer. Used to create random seeds. See
tf.compat.v1.set_random_seed.
for behavior.
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name
|
The name of the layer (string). |
Attributes | |
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graph
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scope_name
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