MLP.py

from layer import Layer

class MLP:
    def __init__(self,nets,outs):
        sizes = [nets] + outs
        self.layers = []
        for i in range(len(outs)):
            self.layers.append(Layer(sizes[i],sizes[i + 1]))

    def __call__(self, x):
        for i, layer in enumerate(self.layers):
            x = layer(x, activation=(i != len(self.layers)-1))
        return x

    def parameters(self):
        parameters = []
        for layer in self.layers:
            parameters.extend(layer.parameters())
        return parameters

    def __repr__(self):
        desc = 'Model Summary:\n'
        total_params = 0
        for idx, layer in enumerate(self.layers):
            num_neurons = len(layer.neurons)
            num_weights = sum(len(n.w) for n in layer.neurons)
            num_biases = len(layer.neurons)
            layer_params = num_weights + num_biases
            total_params += layer_params
            desc += (f'Layer {idx}: {num_neurons} neurons, '
                     f'Weights: {num_weights}, Biases: {num_biases}, '
                     f'Total: {layer_params}\n')
        desc += f'Total Parameters: {total_params}'
        return desc