tf.keras.layers.Dropout

TensorFlow 1 version View source on GitHub

Applies Dropout to the input.

Inherits From: Layer

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

Used in the notebooks

Used in the guide Used in the tutorials

Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting.

Arguments:

  • 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).