lesson22-24 MNIST数据集，模块化搭建神经网络八股，手写数字识别准确率输出

https://www.bilibili.com/video/av25541757/?p=11 

import tensorflow as tf


#MNIST数据集输出识别准确率
#MNIST数据集：
#提供6w张28*28像素点的0-9手写数字图片和标签，用于训练
#提供1w张28*28像素点的0-9手写数字图片和标签，用于测试


#每张图片的784个像素点（28*28=784）组成长度为784的一维数组。作为输入特征
#图片的标签以一维数组形式给出，每个元素表示对应分类出现的 概率。


#tf.get_clooection("") #从集合中取全部变量，生成一个列表
#tf.add_n([]) #列表内对应元素相加
#tf.cast(x,dtype) #把x转为dtype类型
#tf.argmax(x,axis) #返回最大值所在索引号 如：tf.argmax([1,0,0],1)返回0
#os.path,join("home","name")
#字符串.split()#按指定拆分符对字符串切片，返回分割后的列表
#如：‘./model/mnist_model-10001’.split('/')[-1].split('-')[-1] 返回1001

#with tf.Graph().as_default() as g:#其内定义的节点在计算图g中

#mnist_app.py

def pre_pic(picName):
    img = Image.open

    
#前向传播：mnist_forward.py
INPUT_MODE = 784
OUTPUT_MODE = 10
LAYER1_MODE = 500

def get_weight(shape, regularizer):
    w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
    if regularizer != None: 
        tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

def get_bias(shape):
    b = tf.Variable(tf.zeros(shape))
    return b

def forward(x, regularizer):
    w1 = get_weight([INPUT_MODE, LAYER1_MODE], regularizer)
    b1 = get_bias([LAYER1_MODE])
    y1 = tf.nn.relu(tf.matmul(x, w1) + b1)
    
    w2 = get_weight([LAYER1_MODE,OUTPUT_MODE], regularizer)
    b2 = get_bias([OUTPUT_MODE])
    y = tf.matmul(y1, w2) + b2
    return y

    



#反向传播： mnist_backward.py
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os

BATCH_SIZE = 200
LEARNING_RATE_BASE = 0.1
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "./model/"
MODEL_NAME="mnist_model"
STEPS = 50000

def backward(mnist):
    
    x = tf.placeholder(tf.float32, [None, INPUT_MODE])
    y_ = tf.placeholder(tf.float32, [None,OUTPUT_MODE])
    y = forward(x, REGULARIZER)
    global_step = tf.Variable(0,trainable=False)
    #损失函数loss含正则化regularization
    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_,1))
    cem = tf.reduce_mean(ce)
    loss = cem + tf.add_n(tf.get_collection('losses'))
    #学习率
    learning_rate = tf.train.exponential_decay(
    LEARNING_RATE_BASE,
    global_step,
    mnist.train.num_examples / BATCH_SIZE,
    LEARNING_RATE_DECAY,
    staircase=True)
    
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
    #滑动平均
    ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    ema_op = ema.apply(tf.trainable_variables())
    #with tf.control_dependencies(train_step, ema_op):
    with tf.control_dependencies([train_step, ema_op]):
        train_op = tf.no_op(name='train')
    
    #实例化saver对象   保存模型
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()
        sess.run(init_op)
        
        for i in range(STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            _,loss_value,step= sess.run([train_op, loss, global_step], feed_dict={x:xs,y_:ys})
            if i % 1000 == 0:
                print(step,loss_value,saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step))

#测试输出准确率 ： mnist_test.py

#def main():
#    mnist = input_data.read_data_sets("./date", one_hot=True)
#    backward(mnist)
    
#if __name__ == '__main__':
#    main()




#mnist_test.py
import time
TEST_INTERVAL_SECS=5


def test(mnist):
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32,[None,INPUT_MODE])
        y_=tf.placeholder(tf.float32,[None,OUTPUT_MODE])
        y = forward(x, None)
        
        #实列化可还原滑动平均值的saver
        ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
        ema_restore = ema.variables_to_restore()
        saver = tf.train.Saver(ema_restore)
        
        #准确率计算方法
        correct_prediction = tf.equal(tf.argmax(y,1), tf.arg_max(y_,1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        
        while True:
            with tf.Session() as sess:
                #价值ckpt模型
                ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
                #如果已有ckpt模型则恢复
                if ckpt and ckpt.model_checkpoint_path:
                    #恢复会话
                    saver.restore(sess, ckpt.model_checkpoint_path)
                    #恢复轮数
                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    #计算准确率
                    accuracy_score = sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels})
                    #给出提示
                    print(global_step,accuracy_score)
                else:
                    print("No checkpoint file found")
                    return
            
            time.sleep(TEST_INTERVAL_SECS)
            
def main():
    mnist = input_data.read_data_sets("./data/",one_hot=True)
    test(mnist)
    
if __name__=='__main__':
    main()

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(1, 4.678938, './model/mnist_model-1')
(1001, 1.8297149, './model/mnist_model-1001')
(2001, 1.7304844, './model/mnist_model-2001')
(3001, 1.709178, './model/mnist_model-3001')
(4001, 1.6456411, './model/mnist_model-4001')
(5001, 1.6040242, './model/mnist_model-5001')
(6001, 1.5808734, './model/mnist_model-6001')
(7001, 1.5344688, './model/mnist_model-7001')
(8001, 1.5165496, './model/mnist_model-8001')
(9001, 1.5073484, './model/mnist_model-9001')
(10001, 1.4617984, './model/mnist_model-10001')
(11001, 1.4598992, './model/mnist_model-11001')
(12001, 1.4594458, './model/mnist_model-12001')
(13001, 1.4198472, './model/mnist_model-13001')
(14001, 1.4244304, './model/mnist_model-14001')
(15001, 1.4247683, './model/mnist_model-15001')
(16001, 1.3590205, './model/mnist_model-16001')
(17001, 1.354147, './model/mnist_model-17001')
(18001, 1.3466628, './model/mnist_model-18001')
(19001, 1.3093699, './model/mnist_model-19001')
(20001, 1.2971283, './model/mnist_model-20001')
(21001, 1.289797, './model/mnist_model-21001')
(22001, 1.272865, './model/mnist_model-22001')
(23001, 1.2688172, './model/mnist_model-23001')
(24001, 1.2655104, './model/mnist_model-24001')
(25001, 1.2440013, './model/mnist_model-25001')
(26001, 1.2412161, './model/mnist_model-26001')
(27001, 1.2221898, './model/mnist_model-27001')
(28001, 1.2146492, './model/mnist_model-28001')
(29001, 1.2083429, './model/mnist_model-29001')
(30001, 1.2041415, './model/mnist_model-30001')
(31001, 1.1921252, './model/mnist_model-31001')
(32001, 1.1892465, './model/mnist_model-32001')
(33001, 1.1844958, './model/mnist_model-33001')
(34001, 1.172609, './model/mnist_model-34001')
(35001, 1.1687734, './model/mnist_model-35001')
(36001, 1.1579167, './model/mnist_model-36001')
(37001, 1.1569072, './model/mnist_model-37001')
(38001, 1.1456205, './model/mnist_model-38001')
(39001, 1.1516279, './model/mnist_model-39001')
(40001, 1.1405009, './model/mnist_model-40001')
(41001, 1.1383176, './model/mnist_model-41001')
(42001, 1.128678, './model/mnist_model-42001')
(43001, 1.1366055, './model/mnist_model-43001')
(44001, 1.1176779, './model/mnist_model-44001')
(45001, 1.1101508, './model/mnist_model-45001')
(46001, 1.109341, './model/mnist_model-46001')
(47001, 1.1146913, './model/mnist_model-47001')
(48001, 1.103515, './model/mnist_model-48001')
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