记第一次跑神经网络程序--在mac下的MNIST手写体识别

在http://www.tensorfly.cn/tfdoc/tutorials/mnist_download.html下载数据
以https://blog.youkuaiyun.com/lusing/article/details/79965160为教程

完整程序

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

def init_weights(shape):
    return tf.Variable(tf.random_normal(shape, stddev=0.01))

def model(X, w):
    return tf.matmul(X, w) # 模型还是矩阵乘法

#将数据下载到本地后加载，在上面列出的第一个网址下载源数据
mnist = input_data.read_data_sets("Users⁩/caoxionggang/下载/MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])

w = init_weights([784, 10]) 
py_x = model(X, w)

# 计算误差
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) 

# construct optimizer
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), 128), 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})))