Implementation

Like the single neuron, this model can be implemented in Python. Actually, we do not even have to make too many edits compared to our Neuron class:

import numpy as np

class FullyConnectedLayer(object):
    """A simple fully-connected NN layer.
    Args:
        num_inputs (int): The input vector size/number of input values.
        layer_size (int): The output vector size/number of neurons.
        activation_fn (callable): The activation function for this layer.
    Attributes:
        W (ndarray): The weight values for each input.
        b (ndarray): The bias value, added to the weighted sum.
        size (int): The layer size/number of neurons.
        activation_fn (callable): The neurons' activation function.
    """
    def __init__(self, num_inputs, layer_size, activation_fn):
        super().__init__()
        # Randomly initializing the parameters (using a normal distribution this time):
        self.W = np.random.standard_normal((num_inputs...