tf.keras.layers

TensorFlow 1 version

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Applies an activation function to an output.

Inherits From: Layer, Module

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.Activation

tf.keras.layers.Activation(
    activation, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Mixed precision Save and load Keras models Ragged tensors	Load text Basic text classification Classifying CIFAR-10 with XLA

Arguments
`activation`	Activation function, such as `tf.nn.relu`, or string name of built-in activation function, such as "relu".

Usage:

layer = tf.keras.layers.Activation('relu')
output = layer([-3.0, -1.0, 0.0, 2.0])
list(output.numpy())
[0.0, 0.0, 0.0, 2.0]
layer = tf.keras.layers.Activation(tf.nn.relu)
output = layer([-3.0, -1.0, 0.0, 2.0])
list(output.numpy())
[0.0, 0.0, 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 input.

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.Overview

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Used in the notebooks

Arguments

Usage:

Input shape:

Output shape: