tf.compat.v1.layers.Dropout
Applies Dropout to the input.
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 | |
|---|---|
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 |
|
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/layers/Dropout