tensorflow学习笔记之简单的神经网络训练和测试

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
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),reduction_indies=1))  #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.initialize_all_variables()
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})