View source on GitHub |
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
)
Migrate to TF2
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
|