用自己实现的全连接神经网络分类MNIST

# coding:utf-8import numpy as npimport mnist_loader# 用full-connected神经网络搞的LeCun的手写数字识别# 784*30*10speed = 1def 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) * (1.0 - sigmoid(z))W = []W1 = np.random.randn(30, 784)W2 = np.random.randn(10, 30)W.append(W1)W.append(W2)b = []b1 = np.random.randn(30,1)b2 = np.random.randn(10,1)b.append(b1)b.append(b2)training_data, validation_data, test_data = mnist_loader.load_data_wrapper()for i in range(0, 50000):    zs = []    activations = []    activations.append(training_data[i][0])    z0 = np.dot(W[0], training_data[i][0]) + b[0]    zs.append(z0)    a1 = sigmoid(z0)    activations.append(a1)    z1 = np.dot(W[1], a1) + b[1]    zs.append(z1)    a2 = sigmoid(z1)    activations.append(a2)    error = []    error0 = activations[2] - training_data[i][1]    error0 = error0 * sigmoid_prime(zs[1])    error.append(error0)    error1 = np.dot(W[1].transpose(),error[0])    error1 = error1 * sigmoid_prime(zs[0])    error.append(error1)    print i    dw0 = np.dot(error[1], activations[0].transpose())    dw1 = np.dot(error[0], activations[1].transpose())    W[0] = W[0] - dw0        b[0] = error[1] - b[0]    W[1] = W[1] - dw1    b[1] = error[0] - b[1]count = 0for i in range(100):    a = test_data[i][0]    for bb, ww in zip(b, W):        a = sigmoid(np.dot(ww, a) + bb)        print a    print test_data[i][1]    if np.argmax(a) == test_data[i][1]:        count = count + 1print count