tf.layers.dropout( inputs, rate=0.5, noise_shape=None, seed=None, training=False, name=None )
See the guide: Reading data > Multiple input pipelines
Applies Dropout to the input.
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.
inputs: Tensor input.
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
int32representing 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
training: 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).
name: The name of the layer (string).
ValueError: if eager execution is enabled.