输出结果

代码设计

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data

print ("packs loaded")

print ("Download and Extract MNIST dataset")

mnist = input_data.read_data_sets('/tmp/data/', one_hot=True)

print

print (" tpye of 'mnist' is %s" % (type(mnist)))

print (" number of trian data is %d" % (mnist.train.num_examples))

print (" number of test data is %d" % (mnist.test.num_examples))

packs loaded

Download and Extract MNIST dataset

tpye of 'mnist' is <class 'tensorflow.contrib.learn.python.learn.datasets.base.Datasets'>

number of trian data is 55000

number of test data is 10000

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data ＃这是TensorFlow 为了教学Mnist而提前设计好的程序

# number 1 to 10 data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True) #TensorFlow 会检测数据是否存在。当数据不存在时，系统会自动将数据下载到MNIST_data／文件夹中。当执行完语句后，读者可以自行前往MNIST_data／文件夹下查看上述4 个文件是否已经被正确地下载

def add_layer(inputs, in_size, out_size, activation_function=None,):

   # add one more layer and return the output of this layer

   Weights = tf.Variable(tf.random_normal([in_size, out_size]))

   biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)

   Wx_plus_b = tf.matmul(inputs, Weights) + biases

   if activation_function is None:

       outputs = Wx_plus_b

   else:

       outputs = activation_function(Wx_plus_b,)

   return outputs

def compute_accuracy(v_xs, v_ys):      global prediction              

   y_pre = sess.run(prediction, feed_dict={xs: v_xs})

   correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))

   accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  

   result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})        

   return result

# define placeholder for inputs to network

xs = tf.placeholder(tf.float32, [None, 784])

ys = tf.placeholder(tf.float32, [None, 10])

# add output layer

prediction = add_layer(xs, 784, 10,  activation_function=tf.nn.softmax)

# the error between prediction and real data

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),

                                             reduction_indices=[1]))      

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()

# important step

sess.run(tf.global_variables_initializer())

for i in range(1000):

   batch_xs, batch_ys = mnist.train.next_batch(100)  

   sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})

   if i % 50 == 0:

       print(compute_accuracy(

           mnist.test.images, mnist.test.labels))