Programming Machine Learning

The Final Test

It’s been a while since we reviewed the code of the neural network. Here it is—all of it, for the last time:

15_development/neural_network.py

import numpy as np


def sigmoid(z):
    return 1 / (1 + np.exp(-z))


def softmax(logits):
    exponentials = np.exp(logits)
    return exponentials / np.sum(exponentials, axis=1).reshape(-1, 1)


def sigmoid_gradient(sigmoid):
    return np.multiply(sigmoid, (1 - sigmoid))


def loss(Y, y_hat):
    return -np.sum(Y * np.log(y_hat)) / Y.shape[0]


def prepend_bias(X):
    return np.insert(X, 0, 1, axis=1)

def forward(X, w1, w2):
    h = sigmoid(np.matmul(prepend_bias(X), w1))
    y_hat = softmax(np.matmul(prepend_bias(h), w2))
    return (y_hat, ...