卷积神经网络处理图像识别（三）

import  tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import os
import numpy as np
import CNN_MNIST_inference

MODEL_SAVE_PATH ="E:/Python36/my tensorflow/CNN/model_path/"
MODEL_NAME = "MNIST_CNNmodel.ckpt"
print(os.path.join(MODEL_SAVE_PATH, MODEL_NAME))
BATCH_SIZE  =100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
MOVING_AVERAGE_DECAY = 0.99
TRAINING_STEPS = 20000

def train(mnist):
    '''training'''
    x = tf.placeholder(tf.float32,
                       [None,
                        CNN_MNIST_inference.IMAGE_HEIGHT,
                        CNN_MNIST_inference.IMAGE_WIDTH,
                        CNN_MNIST_inference.NUM_CHANNELS], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, CNN_MNIST_inference.OUTPUT_NODE], name = 'y-input')
    #I2 正则
    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
    y = CNN_MNIST_inference.inference(x, True, regularizer, None, reuse = False)
    global_step = tf.Variable(0, trainable = False)
    #平均移动
    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables()) # moving average applied
    average_y = CNN_MNIST_inference.inference(x, True, regularizer,variable_averages, reuse = True)
    
    # loss
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = y, labels = tf.argmax(y_, 1))
    cross_entropy_mean = tf.reduce_mean(cross_entropy)
    tf.add_to_collection('losses', cross_entropy_mean)
    loss = tf.add_n(tf.get_collection('losses'))
    #loss = cross_entropy_mean
    
    #learning rate with decay
    learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step,mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY, staircase = True)
    #learning_rate = 0.01
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step = global_step)
    train_op = tf.group(train_step, variables_averages_op)
    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
    saver = tf.train.Saver() #初始化持久类
    
    with tf.Session() as sess:
        tf.global_variables_initializer().run() # 真正变量初始化
        
        validation_set  = np.reshape(mnist.validation.images,
                                     [-1,
                                      CNN_MNIST_inference.IMAGE_HEIGHT,
                                      CNN_MNIST_inference.IMAGE_WIDTH,
                                      CNN_MNIST_inference.NUM_CHANNELS])
        validate_feed  = {x: validation_set, y_ : mnist.validation.labels} #验证集

        test_set  = np.reshape(mnist.test.images,
                                     [-1,
                                      CNN_MNIST_inference.IMAGE_HEIGHT,
                                      CNN_MNIST_inference.IMAGE_WIDTH,
                                      CNN_MNIST_inference.NUM_CHANNELS])
        test_feed        = {x: test_set, y_ : mnist.test.labels} #测试集（训练集）
        
        steps = [] # only for plot
        accs = [] # only for plot
        losses = [] # only for plot
        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            xs = np.reshape(xs,
                            [BATCH_SIZE,
                             CNN_MNIST_inference.IMAGE_HEIGHT,
                             CNN_MNIST_inference.IMAGE_WIDTH,
                             CNN_MNIST_inference.NUM_CHANNELS])
                                
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict = {x : xs, y_: ys})

            #print(i,loss_value)
            if i % 25  == 0:
                validate_acc = sess.run(accuracy, feed_dict = validate_feed) #验证集 准确度
                steps.append(step); accs.append(validate_acc*100); losses.append(loss_value) # only for plot
                print("After %d training steps, validation dataset accuracy after this batch is %g%%, test dataset loss on this batch is %g"%(step, validate_acc*100,loss_value))
                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step =global_step)
                
        saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step =global_step)    
        test_acc = sess.run(accuracy, feed_dict = test_feed)
        print("After %d training steps, test accuracy using average model is %g%%"%
              (TRAINING_STEPS, test_acc*100))
        writer = tf.summary.FileWriter("E://TensorBoard//test",sess.graph)
        
        saver.save(sess, r"E:\Python36\my tensorflow\ckpt files\mode_mnist.ckpt")
    #only for plot
    from matplotlib import pyplot as plt
    import matplotlib.ticker as mtick
    plt.subplot(211)
    plt.plot(steps, losses,color="red")
    plt.scatter(steps, losses,s=20,color="red")
    plt.xlabel("训练的步数（Batch数）"); plt.ylabel("训练batch上的Loss(含L2正则Loss)")
    plt.subplot(212)
    plt.plot(steps, accs,color="green")
    plt.scatter(steps, accs,s=20,color="green")
    yticks = mtick.FormatStrFormatter("%.3f%%")
    plt.gca().yaxis.set_major_formatter(yticks)
    plt.xlabel("step"); plt.ylabel("验证集上的预测准确率")
    plt.show()
 
def main(argv = None):
    mnist = input_data.read_data_sets(r"E:\Python36\my tensorflow\MNIST_data",one_hot =True)
    train(mnist)

if __name__ == "__main__":
    tf.app.run() #调用main()

import  tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import os
import numpy as np
import CNN_MNIST_inference
import CNN_MNIST_train
import matplotlib.pyplot as plt

def evaluate(mnist):   #评估验证集的预测准确度
    with tf.Graph().as_default() as g:
        x = tf.placeholder(tf.float32,
                           [None,
                            CNN_MNIST_inference.IMAGE_HEIGHT,
                            CNN_MNIST_inference.IMAGE_WIDTH,
                            CNN_MNIST_inference.NUM_CHANNELS], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, CNN_MNIST_inference.OUTPUT_NODE], name = 'y-input')
        validation_set  = np.reshape(mnist.validation.images,
                                     [-1,
                                      CNN_MNIST_inference.IMAGE_HEIGHT,
                                      CNN_MNIST_inference.IMAGE_WIDTH,
                                      CNN_MNIST_inference.NUM_CHANNELS])
        validate_feed  = {x: validation_set, y_ : mnist.validation.labels} #验证集
        
        y = CNN_MNIST_inference.inference(x, False, None, None, reuse = False)
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
            #平均移动
        variable_averages = tf.train.ExponentialMovingAverage(CNN_MNIST_train.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)
        with tf.Session() as sess:
            #print(CNN_MNIST_train.MODEL_SAVE_PATH)
            #找到目录中最新的模型文件
            ckpt = tf.train.get_checkpoint_state(CNN_MNIST_train.MODEL_SAVE_PATH)
            #print(ckpt)
            if ckpt and ckpt.model_checkpoint_path:
                #加载模型
                saver.restore(sess, ckpt.model_checkpoint_path)
                #模型的迭代轮数
                global_step = ckpt.model_checkpoint_path.split('/')[-1].split("-")[-1]
                accuary_score = sess.run(accuracy, feed_dict =validate_feed)
                print("After %s training steps, validation accuary = %g" %(global_step, accuary_score)) #global_step是str
            else:
                print('No checkpoint file found')
                return
                
 #把所有输入数据input_data、声明的常量放进with tf.Graph().as_default(): 里面就行了，就可以统一到同一个graph了，
#不然input_data是放到系统默认创建的Graph，跟你又重新with tf.Graph().as_default():不是同一个Graph()就会报错           
def recognize(input_x):
    g = tf.get_default_graph() # 因为 input_x 默认的图中，所以可把下面的计算也默认的图中
    with g.as_default():
        y = CNN_MNIST_inference.inference(input_x, False, None, None, reuse = False)
        saver = tf.train.Saver()
        with tf.Session() as sess:
            #找到目录中最新的模型文件
            ckpt = tf.train.get_checkpoint_state(CNN_MNIST_train.MODEL_SAVE_PATH)
            if ckpt and ckpt.model_checkpoint_path:
                #加载模型
                saver.restore(sess, ckpt.model_checkpoint_path)
                predicted_label = tf.argmax(y, 1)
                print("predicted_label: ", sess.run(predicted_label)[0])
            else:
                print('No checkpoint file found')
                return
                
def plotImage(path):#仅用于绘制待识别的图片
    image_rawdata = tf.gfile.FastGFile(path,"rb").read()
    img_data = tf.image.decode_jpeg(image_rawdata)
    if img_data.dtype != tf.float32:
        img_data = tf.image.convert_image_dtype(img_data, dtype = tf.float32)
    with tf.Session() as sess:
        image_data = img_data.eval() # return a numpy array#需要运行在会话中
    image_data_shaped1 = image_data.reshape(image_data.shape[0],image_data.shape[1])#numpy array
    #print(image_data_shaped1)
    plt.imshow(image_data_shaped1,cmap='gray')
    plt.show()
    
def main(argv=None): 
    mnist = input_data.read_data_sets(r"E:\Python36\my tensorflow\MNIST_data",one_hot =True)
    evaluate(mnist) #评估在验证集上的预测准确度
    #输入
    image_path = r"E:\Python36\MNIST picture\test\50.jpg"
    image_rawdata = tf.gfile.FastGFile(image_path,"rb").read()
    img_data0 = tf.image.decode_jpeg(image_rawdata)
    if img_data0.dtype != tf.float32:
        img_data = tf.image.convert_image_dtype(img_data0, dtype = tf.float32)
    
    #根据神经网络的要求转换图片数据的shape！    
    input_x =  tf.reshape(img_data, [1,
                                    CNN_MNIST_inference.IMAGE_HEIGHT,
                                    CNN_MNIST_inference.IMAGE_WIDTH,
                                    CNN_MNIST_inference.NUM_CHANNELS])
    plotImage(image_path)
    recognize(input_x)

if __name__ =="__main__":
    #tf.app.run() #调用main()
    main()#