Layer-weight-creation-in-build-input_shape-

In the code you provided, a class called Linear is defined as a subclass of keras.layers.Layer. This class represents a linear layer in a neural network, with the equation y = w.x + b. Here's a breakdown of the code:

1. The __init__ method is the constructor of the class. It initializes the object and sets the number of units (neurons) for the linear layer.

2. The build method is called when the layer is connected to an input for the first time. It is used to define the weights of the layer. Within this method, self.w and self.b are created as trainable weights using self.add_weight. The shape of self.w is (input_shape-1, self.units) where input_shape represents the number of input features. The shape of self.b is (self.units,), matching the number of units specified.

3. The call method defines the forward pass of the layer. It takes the input inputs, performs matrix multiplication between the input and the weight matrix self.w, and adds the bias self.b. The result is returned as the output of the layer.

4. After the class definition, an instance of the Linear layer called linear_layer is created with units=4.

5. Finally, the code applies the linear_layer to a tensor of ones with shape (2, 2). This is done by calling linear_layer(tf.ones((2, 2))). The result is stored in y.

In summary, the code defines a custom linear layer that can be used as a building block in a neural network. It sets up the layer's weights in the build method and performs the forward pass computation in the call method. The instance of the Linear layer is then used to apply the layer to a tensor of ones, generating the output y.