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- import tensorflow as tf
- import tensorflow.examples.tutorials.mnist.input_data as input_data
- import os
- os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
- mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
- x = tf.placeholder(tf.float32, [None, 784])
- y_actual = tf.placeholder(tf.float32, shape=[None, 10])
- W = tf.Variable(tf.zeros([784, 10])) # 初始化权值W
- b = tf.Variable(tf.zeros([10])) # 初始化偏置项b
- y_predict = tf.nn.softmax(tf.matmul(x, W) + b) # 加权变换并进行softmax回归，得到预测概率
- cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_actual * tf.log(y_predict))) # 求交叉熵
- train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) # 用梯度下降法使得残差最小
- correct_prediction = tf.equal(tf.argmax(y_predict, 1), tf.argmax(y_actual, 1)) # 在测试阶段，测试准确度计算
- accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # 多个批次的准确度均值
- init = tf.global_variables_initializer()
- with tf.Session() as sess:
- sess.run(init)
- for i in range(1000): # 训练阶段，迭代1000次
- batch_xs, batch_ys = mnist.train.next_batch(100) # 按批次训练，每批100行数据
- sess.run(train_step, feed_dict={x: batch_xs, y_actual: batch_ys}) # 执行训练
- if i % 100 == 0: # 每训练100次，测试一次
- print("accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y_actual: mnist.test.labels}))
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