tensorflow入门实践（一）：mnist实例--用简单的神经网络来训练和测试

使用非常简单的两层全连接网络来完成MNIST数据的分类问题。

环境：ubuntu16.04+tensorflow+cpu

文件路径：/home/qf/tensorflow/tf/tf1

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]))        #
b = tf.Variable(tf.zeros([10]))            #
y_predict = tf.nn.softmax(tf.matmul(x,W) + b)     #
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_actual*tf.log(y_predict),reduction_indices=1))   #
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)   #

correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1))   #
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))                #

init = tf.initialize_all_variables()
with tf.Session() as sess:
    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_actual: batch_ys})   #
        if(i%100==0):                  #
            print "accuracy:",sess.run(accuracy, feed_dict={x: mnist.test.images, y_actual: mnist.test.labels})

## Session() 和 InteractiveSession() 的用法。后者用 Tensor.eval() 和 Operation.run() 来替代了前者的 Session.run().