6.MNIST数据集分类简单版本

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

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# 批次大小
batch_size = 64
# 计算一个周期一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

# 定义两个placeholder
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])

# 创建一个简单的神经网络:784-10
W = tf.Variable(tf.truncated_normal([784,10], stddev=0.1))
b = tf.Variable(tf.zeros([10]) + 0.1)
prediction = tf.nn.softmax(tf.matmul(x,W)+b)

# 二次代价函数
loss = tf.losses.mean_squared_error(y, prediction)
# 使用梯度下降法
train = tf.train.GradientDescentOptimizer(0.3).minimize(loss)

# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
    # 变量初始化
    sess.run(tf.global_variables_initializer())
    # 周期epoch：所有数据训练一次，就是一个周期
    for epoch in range(21):
        for batch in range(n_batch):
            # 获取一个批次的数据和标签
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train,feed_dict={x:batch_xs,y:batch_ys})
        # 每训练一个周期做一次测试
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))