tf.keras.layers.Dropout

Applies dropout to the input.

Inherits From: Layer, Operation

tf.keras.layers.Dropout(
    rate, noise_shape=None, seed=None, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Effective Tensorflow 2 Migrate checkpoint saving Migrate evaluation Migrate the fault tolerance mechanism Migrate TensorBoard: TensorFlow's visualization toolkit	Overfit and underfit Using DTensors with Keras Simple audio recognition: Recognizing keywords Deep Convolutional Generative Adversarial Network Basic text classification

The Dropout layer randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by 1 / (1 - rate) such that the sum over all inputs is unchanged.

Note that the Dropout layer only applies when training is set to True in call(), such that no values are dropped during inference. When using model.fit, training will be appropriately set to True automatically. In other contexts, you can set the argument explicitly to True when calling the layer.

(This is in contrast to setting trainable=False for a Dropout layer. trainable does not affect the layer's behavior, as Dropout does not have any variables/weights that can be frozen during training.)

Args
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).

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)