调试了很多次才成功!
tensorflow版本换为1.4 不要用高版本的,懂得不多,高版本不知道怎么调啊
# -*- coding:utf-8 -*- import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #载入数据 mnist = input_data.read_data_sets("MNIST-data/", one_hot=True) # 每个批次的大小 batch_size = 100 # 计算一共有多少个批次 n_batch = mnist.train.num_examples // batch_size # 定义占位符 x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) # 创建神经网路 W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) prediction = tf.nn.softmax(tf.matmul(x, W)+b) #二次代价函数 loss = tf.reduce_mean(tf.square(y-prediction)) train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) init = tf.global_variables_initializer() correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # 返回一维张量中最大的值所在位置 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 准确率 with tf.Session() as sess: sess.run(init) for epoch in range(21): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys}) acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Iter " + str(epoch) + ",Testing Accuracy" + str(acc))
运行结果: