tf.compat.v1.layers.Dropout
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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
)
Caution: This API was designed for TensorFlow v1.
Continue reading for details on how to migrate from this API to a native
TensorFlow v2 equivalent. See the
TensorFlow v1 to TensorFlow v2 migration guide
for instructions on how to migrate the rest of your code.
This API is not compatible with eager execution or tf.function
.
Please refer to tf.layers section of the migration guide
to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2
layer is tf.keras.layers.Dropout
.
Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
dropout = tf . compat . v1 . layers . Dropout ()
After:
dropout = tf . keras . layers . Dropout ()
Description
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.
Args
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
scope_name
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license .
Last updated 2021-08-16 UTC.
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