tf.compat.v1.layers.dropout

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 a legacy api that is only compatible with eager execution and tf.function if you combine it with tf.compat.v1.keras.utils.track_tf1_style_variables

Please refer to tf.layers model mapping section of the migration guide to learn how to use your TensorFlow v1 model in TF2 with Keras.

The corresponding TensorFlow v2 layer is tf.keras.layers.Dropout.

Structural Mapping to Native TF2

None of the supported arguments have changed name.

Before:

 y = tf.compat.v1.layers.dropout(x)

After:

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)

Description

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 int32 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. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.
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).

Output tensor.

ValueError if eager execution is enabled.