mnist 神经网络

r囧r小猫 2022-05-19 05:09 226阅读 0赞

# -*- coding: utf-8 -*-
    
    
    # 神经网络
    import tensorflow as tf
    import numpy as np
    from tensorflow.examples.tutorials.mnist import input_data  # 加载数据
    
    
    def init_weight(shape):  # 初始化
        return tf.Variable(tf.random_normal(shape, stddev=0.01))  # (stddev是标准差，random_normal是正态分布的随机输出值)张量的可变随机值
    
    
    def model(X, w_h, w_o):  #
        h = tf.nn.sigmoid(tf.matmul(X, w_h))
        return tf.matmul(h, w_o)
    
    
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  # 把数据转换为一位数组的格式
    trX = mnist.train.images  # 训练的图片
    trY = mnist.train.labels  # 训练的标签
    teX = mnist.test.images  # 测试的图片
    teY = mnist.test.labels  # 测试的标签
    
    
    # 占位符
    X = tf.placeholder("float", [None, 784])  # 第一维长度不定，第二维长度为784的二维矩阵
    Y = tf.placeholder("float", [None, 10])
    
    
    w_h = init_weight([784, 625])
    w_o = init_weight([625, 10])
    
    
    py_x = model(X, w_h, w_o)
    
    
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
    train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost)
    predict_op = tf.argmax(py_x, 1)
    
    
    with tf.Session() as sess:
        #
        tf.global_variables_initializer().run()
    
        for i in range(100):
            for start, end in zip(range(0, len(trX)), range(128, len(trX)+1, 128)):
                sess.run(train_op, feed_dict={X: trX[start: end], Y: trY[start:end]})
            print(i, np.mean(np.argmax(teY, axis=1) ==
                             sess.run(predict_op, feed_dict={X: teX})))
    
        # for i in range(100):
        # for start, end in zip(range(0, 1000), range(128, 1000+1, 128)):
        # sess.run(train_op, feed_dict={X: trX[start: end], Y: trY[start:end]})
        # print(i, np.mean(np.argmax(teY, axis=1) ==
        # sess.run(predict_op, feed_dict={X: teX})))