Support for Custom Layers and Functions

Keras is flexible enough to allow developers to define custom layers, activation functions, and loss functions. This allows for innovative architectures and methods to be explored.

• Example of a custom layer:pythonCopy codefrom keras.layers import Layer class CustomLayer(Layer): def __init__(self, units=32): super(CustomLayer, self).__init__() self.units = units def build(self, input_shape): self.w = self.add_weight(shape=(input_shape[-1], self.units), initializer='random_normal', trainable=True) def call(self, inputs): return tf.matmul(inputs, self.w)