TF之NN：利用DNN算法(SGD+softmax+cross_entropy)对mnist手写数字图片识别训练集(TF自带函数下载)实现87.4%识别

TF之NN：利用DNN算法(SGD+softmax+cross_entropy)对mnist手写数字图片识别训练集(TF自带函数下载)实现87.4%识别

 

 

目录

输出结果

代码设计

 

 

 

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 
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))

 

 

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