手写数字识别代码加mnist数据集

#encoding=utf-8 import random import numpy as np class 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): 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: n_test = len(test_data) n = len(training_data) for j in xrange(epochs): random.shuffle(training_data) mini_batches = [ training_data[k:k + mini_batch_size] for k in xrange(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): 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): 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()) for l in xrange(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): 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 (output_activations - y) def 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))