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Keras layers API

Layers are the basic building blocks of neural networks in Keras. A layer consists of a tensor-in tensor-out computation function (the layer's call method) and some state, held in TensorFlow variables (the layer's weights).

A Layer instance is callable, much like a function:

from tensorflow.keras import layers

layer = layers.Dense(32, activation='relu')
inputs = tf.random.uniform(shape=(10, 20))
outputs = layer(inputs)

Unlike a function, though, layers maintain a state, updated when the layer receives data during training, and stored in layer.weights:

>>> layer.weights
[<tf.Variable 'dense/kernel:0' shape=(20, 32) dtype=float32>,
 <tf.Variable 'dense/bias:0' shape=(32,) dtype=float32>]

Creating custom layers

While Keras offers a wide range of built-in layers, they don't cover ever possible use case. Creating custom layers is very common, and very easy.

See the guide Writing your own layers for an extensive overview, and refer to the documentation for the base Layer class.


Layers API overview

The base Layer class

Layer activations

Layer weight initializers

Layer weight regularizers

Layer weight constraints

Core layers

Convolution layers

Pooling layers

Recurrent layers

Preprocessing layers

Normalization layers

Regularization layers

Attention layers

Reshaping layers

Merging layers

Locally-connected layers

Activation layers