代码如下:
#!/usr/bin/python #-*- coding:utf-8 -*- ############################ #File Name: Softmax_Regression.py #Author: yang #Mail: milkyang2008@126.com #Created Time: 2017-08-22 20:51:58 ############################ import os os.environ['TF_CPP_MIN_LOG_LEVEL']='2' #load training data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/",one_hot=True) import tensorflow as tf x = tf.placeholder("float",[None,784]) W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x,W)+b) y_ = tf.placeholder("float", [None,10]) cross_entropy = -tf.reduce_sum(y_*tf.log(y)) train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) correct_predition = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_predition, "float")) result =sess.run(accuracy, feed_dict={x: mnist.test.images,y_: mnist.test.labels}) print("the accuracy is: %g " % result)
训练结果如下:
cting MNIST_data/train-images-idx3-ubyte.gz Extracting MNIST_data/train-labels-idx1-ubyte.gz Extracting MNIST_data/t10k-images-idx3-ubyte.gz Extracting MNIST_data/t10k-labels-idx1-ubyte.gz the accuracy is: 0.9154