How do you implement a custom layer in Keras?

To implement a custom layer in Keras, you need to create a class that extends the tf.keras.layers.Layer class. This class should contain the logic for the layer’s forward pass, as well as any trainable weights.

The class should also contain the following methods:

– __init__: This method is used to define the layer’s properties, such as the number of inputs and outputs, the type of data it accepts, and any other parameters.

– build: This method is used to create the layer’s weights.

– call: This method is used to define the layer’s forward pass.

– compute_output_shape: This method is used to define the shape of the output tensor.

– get_config: This method is used to define the layer’s configuration, which can be used to recreate the layer.

Once the class is defined, it can be used to create a layer instance, which can then be added to a model.