【原】TF之LiR：基于tensorflow实现手写数字图片识别准确率

TF之LiR：基于tensorflow实现手写数字图片识别准确率

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输出结果

Extracting MNIST_data\train-images-idx3-ubyte.gz
Please use tf.data to implement this functionality.
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Please use tf.one_hot on tensors.
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
Datasets(train=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x00000207535F9EB8>, validation=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x00000207611319E8>, test=<tensorflow.contrib.learn.python.learn.datasets.mnist.DataSet object at 0x0000020761131A20>)
迭代次数Epoch: 0001 下降值cost= 0.000000000
迭代次数Epoch: 0002 下降值cost= 0.000000000
迭代次数Epoch: 0003 下降值cost= 0.000000000
迭代次数Epoch: 0004 下降值cost= 0.000000000
迭代次数Epoch: 0005 下降值cost= 0.000000000
迭代次数Epoch: 0006 下降值cost= 0.000000000
迭代次数Epoch: 0007 下降值cost= 0.000000000
迭代次数Epoch: 0008 下降值cost= 0.000000000
迭代次数Epoch: 0009 下降值cost= 0.000000000
迭代次数Epoch: 0010 下降值cost= 0.000000000
迭代次数Epoch: 0011 下降值cost= 0.000000000
迭代次数Epoch: 0012 下降值cost= 0.000000000
迭代次数Epoch: 0013 下降值cost= 0.000000000
迭代次数Epoch: 0014 下降值cost= 0.000000000
迭代次数Epoch: 0015 下降值cost= 0.000000000
迭代次数Epoch: 0016 下降值cost= 0.000000000
……
迭代次数Epoch: 0099 下降值cost= 0.000000000
迭代次数Epoch: 0100 下降值cost= 0.000000000
Optimizer Finished!

代码设计

# -*- coding: utf-8 -*-

#TF之LiR：基于tensorflow实现手写数字图片识别准确率

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
print(mnist)
 
#设置超参数
lr=0.001                      #学习率
training_iters=100         #训练次数
batch_size=128                #每轮训练数据的大小，如果一次训练5000张图片，电脑会卡死，分批次训练会更好
display_step=1

#tf Graph的输入
x=tf.placeholder(tf.float32, [None,784])
y=tf.placeholder(tf.float32, [None, 10])
 
#设置权重和偏置
w =tf.Variable(tf.zeros([784,10]))
b =tf.Variable(tf.zeros([10]))

#设定运行模式
pred =tf.nn.softmax(tf.matmul(x,w)+b)  #
#设置cost function为cross entropy
cost =tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1))
#GD算法
optimizer=tf.train.GradientDescentOptimizer(lr).minimize(cost) 

#初始化权重
init=tf.global_variables_initializer() 
#开始训练
with tf.Session() as sess: 
    sess.run(init)
    for epoch in range(training_iters):  #输入所有训练数据
        avg_cost=0.
        total_batch=int(mnist.train.num_examples/batch_size)
        for i in range(total_batch): #遍历每个batch
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            _, c=sess.run([optimizer,cost],feed_dict={x:batch_xs,y:batch_ys}) #把每个batch数据放进去训练
            avg_cost==c/total_batch
        if (epoch+1) % display_step ==0:  #显示每次迭代日志
            print("迭代次数Epoch:","%04d" % (epoch+1),"下降值cost=","{:.9f}".format(avg_cost))
    print("Optimizer Finished!")
    
    #测试模型
    correct_prediction=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
    accuracy=tf.equal_mean(tf.cast(correct_prediction),tf.float32)
    print("Accuracy:",accuracy_eval({x:mnist.test.image[:3000],y:mnist}))