Mnits手写体识别

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

mnist = input_data.read_data_sets("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)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y)) # 计算误差
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct optimizer
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})))