tf.keras.layers.Layer

TensorFlow 1 version

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Leaky version of a Rectified Linear Unit.

Inherits From: Layer, Module

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.LeakyReLU

tf.keras.layers.LeakyReLU(
    alpha=0.3, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Customize what happens in Model.fit Writing a training loop from scratch	Deep Convolutional Generative Adversarial Network Pix2Pix

It allows a small gradient when the unit is not active:

  f(x) = alpha * x if x < 0
  f(x) = x if x >= 0

Usage:

layer = tf.keras.layers.LeakyReLU()
output = layer([-3.0, -1.0, 0.0, 2.0])
list(output.numpy())
[-0.9, -0.3, 0.0, 2.0]
layer = tf.keras.layers.LeakyReLU(alpha=0.1)
output = layer([-3.0, -1.0, 0.0, 2.0])
list(output.numpy())
[-0.3, -0.1, 0.0, 2.0]

Input shape:

Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the batch axis) when using this layer as the first layer in a model.

Output shape:

Same shape as the input.

Arguments
`alpha`	Float >= 0. Negative slope coefficient. Default to 0.3.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.keras / layers / layers.Layer

View aliases

Used in the notebooks

Usage:

Input shape:

Output shape:

Arguments