Applies Dropout to the input.
Dropout consists in randomly setting
rate of input units to 0 at each update during training time,
which helps prevent overfitting.
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).
__init__( rate, noise_shape=None, seed=None, **kwargs )