手写数字识别改进之全连接网络

1.前向传播  mnist_forward.py

import tensorflow as tf

INPUT_NODE = 784   #28*28
OUTPUT_NODE = 10  #输出0~9
LAYER1_NODE = 500  #隐藏层节点个数

#权值函数

def get_weight(shape,regularizer):
    w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))

#正则化权重，采用l2方法
    if regularizer!=None:
        tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
    return w

#偏执值

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

#前向传播网络，输入x 和正则参数
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

X 为 N行784列

2.反向传播  mnist_back.py

import tensorflow as tf
import mnist_forward
import os
from tensorflow.examples.tutorials.mnist import input_data
BATCH_SIZE = 200
LEARNING_RATE_BASE = 0.1
LEARNING_RATE_DECAY=0.99
REGULARIZER = 0.0001
STEPS =50000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = './model/'
MODEL_NAME = 'mnist_model'

def backward(mnist):
    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,REGULARIZER)
    global_step = tf.Variable(0,trainable=False)

    ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.arg_max(y_,1))
    cem = tf.reduce_mean(ce)
    loss = cem + tf.add_n(tf.get_collection('losses'))

    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)

    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:
        sess.run(tf.global_variables_initializer())

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

        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 steps,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('./datas/MNIST_data',one_hot=True)
    print('a')
    backward(mnist)
if __name__  =='__main__':
       main()

3.测试  test.py

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)

        correct_prediction = tf.equal(tf.arg_max(y,1),tf.arg_max(y_,1))
        accuracy = tf.reduce_mean(tf.cast(correct_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,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('./datas/MNIST_data',one_hot=True)
    test(mnist)
if __name__  =='__main__':
       main()

4.API  mnist_app.py

import tensorflow as tf
from PIL import Image
import numpy as np
import mnist_backward
import mnist_forward

def restore_model(testPictArr):
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32, [None, mnist_forward.INPUT_NODE])
        y = mnist_forward.forward(x,None)
        preValue = tf.argmax(y,1)

        variable_averages = tf.train.ExponentialMovingAverage(mnist_backward.MOVING_AVERAGE_DECAY)
        varibale_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(varibale_to_restore)

        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)

                preValue = sess.run(preValue,feed_dict={x:testPictArr})
                return  preValue
            else:
                print('No checkpoint file found')
                return -1


def pre_pic(picName):
    img = Image.open(picName)
    reIm = img.resize((28,28),Image.ANTIALIAS)#消除锯齿的方法
    im_arr = np.array(reIm.convert('L'))#变成灰度图
    threshold = 50
    for i in range(28):
        for j in range(28):
            im_arr[i][j] = 288-im_arr[i][j]
            if(im_arr[i][j]

5000轮，准率0.972