tensor.py

#coding=utf-8
import argparse
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

def main(_):
	mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
	x = tf.placeholder(tf.float32, [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(tf.float32,[None,10])
	#联合熵
	cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))
	train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

	session = tf.InteractiveSession()
	tf.initialize_all_variables().run()
	for _ in range(1000):
		batch_xs, batch_ys = mnist.train.next_batch(100)
		session.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
	correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
	accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
	print '使用tensorflow 的准确度为:',session.run(accuracy, feed_dict={x: mnist.test.images,y_: mnist.test.labels})

if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--data_dir', type=str, default='/tmp/data',help='Directory for storing data')
	FLAGS = parser.parse_args()
	tf.app.run()