network.py

import random# Third-party librariesimport numpy as npclass Network(object):    def __init__(self, sizes):        self.num_layers = len(sizes)        self.sizes = sizes        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]        self.weights = [np.random.randn(y, x)                        for x, y in zip(sizes[:-1], sizes[1:])]    def feedforward(self, a):        """Return the output of the network if ``a`` is input."""        for b, w in zip(self.biases, self.weights):            a = sigmoid(np.dot(w, a)+b)        return a    def SGD(self, training_data, epochs, mini_batch_size, eta,            test_data=None):        if test_data:            test_data = list(test_data)            n_test = len(test_data)        training_data = list(training_data)        n = len(training_data)        for j in range(epochs):            random.shuffle(training_data)            mini_batches = [                training_data[k:k+mini_batch_size]                for k in range(0, n, mini_batch_size)]            for mini_batch in mini_batches:                self.update_mini_batch(mini_batch, eta)            if test_data:                print ("Epoch {0}: {1} / {2}".format(                    j, self.evaluate(test_data), n_test))            else:                print ("Epoch {0} complete".format(j))    def update_mini_batch(self, mini_batch, eta):        """Update the network's weights and biases by applying        gradient descent using backpropagation to a single mini batch.        The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``        is the learning rate."""        nabla_b = [np.zeros(b.shape) for b in self.biases]        nabla_w = [np.zeros(w.shape) for w in self.weights]        for x, y in mini_batch:            delta_nabla_b, delta_nabla_w = self.backprop(x, y)            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]        self.weights = [w-(eta/len(mini_batch))*nw                        for w, nw in zip(self.weights, nabla_w)]        self.biases = [b-(eta/len(mini_batch))*nb                       for b, nb in zip(self.biases, nabla_b)]    def backprop(self, x, y):        """Return a tuple ``(nabla_b, nabla_w)`` representing the        gradient for the cost function C_x.  ``nabla_b`` and        ``nabla_w`` are layer-by-layer lists of numpy arrays, similar        to ``self.biases`` and ``self.weights``."""        nabla_b = [np.zeros(b.shape) for b in self.biases]        nabla_w = [np.zeros(w.shape) for w in self.weights]        # feedforward        activation = x        activations = [x] # list to store all the activations, layer by layer        zs = [] # list to store all the z vectors, layer by layer        for b, w in zip(self.biases, self.weights):            z = np.dot(w, activation)+b            zs.append(z)            activation = sigmoid(z)            activations.append(activation)        # backward pass        delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])        nabla_b[-1] = delta        nabla_w[-1] = np.dot(delta, activations[-2].transpose())        # Note that the variable l in the loop below is used a little        # differently to the notation in Chapter 2 of the book.  Here,        # l = 1 means the last layer of neurons, l = 2 is the        # second-last layer, and so on.  It's a renumbering of the        # scheme in the book, used here to take advantage of the fact        # that Python can use negative indices in lists.        for l in range(2, self.num_layers):            z = zs[-l]            sp = sigmoid_prime(z)            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp            nabla_b[-l] = delta            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())        return nabla_b, nabla_w    def evaluate(self, test_data):        """Return the number of test inputs for which the neural        network outputs the correct result. Note that the neural        network's output is assumed to be the index of whichever        neuron in the final layer has the highest activation."""        test_results = [(np.argmax(self.feedforward(x)), y)                        for (x, y) in test_data]        return sum(int(x == y) for (x, y) in test_results)    def cost_derivative(self, output_activations, y):        """Return the vector of partial derivatives \partial C_x /        \partial a for the output activations."""        return (output_activations-y)#### Miscellaneous functionsdef sigmoid(z):    """The sigmoid function."""    return 1.0/(1.0+np.exp(-z))def sigmoid_prime(z):    """Derivative of the sigmoid function."""    return sigmoid(z)*(1-sigmoid(z))