使用神经网络识别手写数字 - Python实现

使用MNIST数据集训练神经网络模型。训练数据由28*28的手写数字的图像组成，输入层包含784=28*28个神经元。输入像素是灰度级的，值为0.0表示白色，值为1.0表示黑色，中间数值表示逐渐暗淡的灰色。

Algorithm

神经网络快速入门 PPT

Codes

mnist_loader.py: 加载数据

import numpy as np
import pickle
import gzip

def load_data():
    f = gzip.open('data/mnist.pkl.gz', 'rb')
    training_data, validation_data, test_data = pickle.load(f, encoding="latin1")
    f.close()
    return (training_data, validation_data, test_data)

def load_data_wrapper():
    tr_d, va_d, te_d = load_data()
    # tr_d[0]: x; 1*784
    # tr_d[1]: y; 0-9
    training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]
    training_results = [vectorized_result(y) for y in tr_d[1]]
    training_data = zip(training_inputs, training_results)
    validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]
    validation_data = zip(validation_inputs, va_d[1])
    test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]
    test_data = zip(test_inputs, te_d[1])
    return (training_data, validation_data, test_data