TensorFlow学习6：MNIST数据集输出手写数字识别准确

mnist_forward

#在前向传播过程中，需要定义网络模型输入层个数、隐藏层节点数、输出层个数
#定义网络参数w、偏置b，定义由输入到输出的神经网络架构

import tensorflow as tf

#网络输入节点数，代表每张输入图片的像素个数
INPUT_NODE=784
#隐藏层节点数
OUTPUT_NODE=10
#输出节点数
LAYER1_NODE=500

#对参数w的设置，包括参数w的形状和是否正则化的标志
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


#对偏置b的设置
def get_bias(shape):
    b=tf.Variable(tf.zeros(shape))
    return b


#向前传播过程
def forward(x,regularizer):
    w1=get_weight([INPUT_NODE,LAYER1_NODE],regularizer)
    b1=get_bias([LAYER1_NODE])
    y1=tf.nn.relu(tf.matmul(x,w1)+b1)

    w2=get_weight([LAYER1_NODE,OUTPUT_NODE],regularizer)
    b2=get_bias([OUTPUT_NODE])
    y=tf.matmul(y1,w2)+b2

    return y

mnist_backward

#coding:utf-8
#反向传播过程实现利用训练数据集对神经网络模型训练，通过降低损失函数值，
#实现网络模型参数的优化，从而得到准确率高且泛化能力强的神经网络模型
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import os

#每轮喂入神经网络的图片数
BATCH_SIZE=200
#初始学习率
LEARNING_RATE_BASE = 0.1
#学习率衰减率
LEARNING_RATE_DECAY=0.99
#正则化系数
REGULARIZER=0.0001
#训练轮数
STEPS=50000
#滑动平均衰减率
MOVING_AVERAGE_DECAY=0.99
#模型保存路径
MODEL_SAVE_PATH="./model/"
#模型保存名称
MODEL_NAME="mnist_model"

def backward(mnist):
    #占位
    x=tf.placeholder(tf.float32,[None,mnist_forward.INPUT_NODE])
    y_=tf.placeholder(tf.float32,[None,mnist_forward.OUTPUT_NODE])
    #前向传播，计算训练数据集上的预测结果y
    y=mnist_forward.forward(x,REGULARIZER)
    #赋值计算轮数，设置为不可训练类型
    global_step=tf.Variable(0,trainable=False)

    #设置损失函数（所有函数正则化损失）
    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'))

    #指定指数衰减学习率
    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)

    #定义参数的滑动平均
    ema=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
    ema_op=ema.apply(tf.trainable_variables())
    with tf.control_dependencies([train_step,ema_op]):
        train_op=tf.no_op(name='train')

    saver=tf.train.Saver()

    with tf.Session() as sess:
        init_op=tf.initialize_all_variables()
        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("After %d training step(s),loss on training batch is %g."%(step,loss_value))
                saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step)

def main():
    mnist=input_data.read_data_sets("./data/",one_hot=True)
    backward(mnist)


if __name__=='__main__':
    main()

mnist_test

#coding:utf-8
#当训练完模型后，给神经网络模型输入测试集验证网络的正确性和泛化性
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_forward
import mnist_backward
TEST_INTERVAL_SECS=5

def test(mnist):
    with tf.Graph().as_default() as g:
        x=tf.placeholder(tf.float32,[None,mnist_forward.INPUT_NODE])
        y_=tf.placeholder(tf.float32,[None,mnist_forward.OUTPUT_NODE])
        y=mnist_forward.forward(x,None)

        ema=tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY)
        ema_restore=ema.variables_to_restore()
        saver=tf.train.Saver(ema_restore)

        corrent_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
        accuracy=tf.reduce_mean(tf.cast(corrent_prediction,tf.float32))

        while True:
            with tf.Session() as sess:
                ckpt=tf.train.get_checkpoint_state(mnist_backward.MODEL_SAVE_PATH)
                if ckpt and ckpt.model_checkpoint_path:
                    saver.restore(sess,ckpt.model_checkpoint_path)
                    global_step=ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                    accuracy_score=sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels})
                    print("After %s training step(s),test accuracy=%g"%(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()

断点续训

在mnist_backward中增加

ckpt=tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
    saver.restore(sess,ckpt.model_checkpoint_path)

问题

在执行mnist_backward的时候会报错，这边是创建了model文件夹后成功的。

参考：人工智能实践：Tensorflow笔记