人工神经网络

import tensorflow as tf

import numpy as np
import input_data//如果出现找不到input_data module 错误，可以将python -tensorflow-mnist路径下的input_data.py复制到当前的.py文件路径下
mnist = input_data.read_data_sets("home/mnist", one_hot=True)//home/mnist是存放下载数据集的路径，根据个人情况加以更改

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.initialize_all_variables()

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_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})cs