|TensorFlow 1 version||View source on GitHub|
Applies Dropout to the input.
Compat aliases for migration
See Migration guide for more details.
tf.keras.layers.Dropout( rate, noise_shape=None, seed=None, **kwargs )
Used in the notebooks
|Used in the guide||Used in the tutorials|
The Dropout layer randomly sets input units to 0 with a frequency of
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
such that no values are dropped during inference. When using
training will be appropriately set to True automatically, and in other
contexts, you can set the kwarg 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.)
layer = tf.keras.layers.Dropout(.2, input_shape=(2,))
data = np.arange(10).reshape(5, 2).astype(np.float32)
outputs = layer(data, training=True)
[[ 0. 1.25]
[ 2.5 3.75]
[ 5. 6.25]
[ 7.5 8.75]
[10. 0. ]], shape=(5, 2), dtype=float32)
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.
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).