|View source on GitHub|
Applies Dropout to the input.
tf.compat.v1.layers.dropout( inputs, rate=0.5, noise_shape=None, seed=None, training=False, name=None )
Migrate to TF2
This API is not compatible with eager execution or
Structural Mapping to Native TF2
None of the supported arguments have changed name.
y = tf.compat.v1.layers.dropout(x)
To migrate code using TF1 functional layers use the Keras Functional API:
x = tf.keras.Input((28, 28, 1)) y = tf.keras.layers.Dropout()(x) model = tf.keras.Model(x, y)
Used in the notebooks
|Used in the guide|
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.
||The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out 10% of input units.|
1D tensor of type
A Python integer. Used to create random seeds. See
||Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode (apply dropout) or in inference mode (return the input untouched).|
||The name of the layer (string).|
||if eager execution is enabled.|