图像识别问题：卷积神经网络与应用

import tarfile
import urllib.request
import os

#下载
url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
filepath = 'data\cifar-10-python.tar.gz'
if not os.path.isfile(filepath):
    result=urllib.request.urlretrieve(url,filepath)
    print('downloaded:' ,result)
else:
    print('Data file already exists.')
#解压
if not os.path.exists('data\cifar-10-batches-py'):
    tfile = tarfile.open(filepath, 'r:gz')
    result=tfile.extractall('data/')
    print('Extracted to ./data/cifar-10-batches-py/')
else:
    print('Directory already exists.')

import os
import numpy as np
import pickle as p

def load_CIFAR_batch(filename):
    """load single batch of cifar"""
    with open(filename, 'rb')as f:
        #—个样本由标签和图像数据组成
        # <i x label> <3072 x pixel> (3072=32x32x3)
        #...
        #<1 x label> <3072 x pixel>
        data_dict = p.load(f, encoding='bytes')
        images= data_dict[b'data']
        labels = data_dict[b'labels']
        #把原始数据结构调整为:BCWH
        images = images.reshape(10000,3,32,32)
        # tensorflow处理图像数据的结构:BWHC
        #把通道数据C移动到最后—个维度
        images = images.transpose (0,2,3,1)
                       
        labels = np.array(labels)
                       
        return images, labels

def load_CIFAR_data(data_dir):
    """load CIFAR data"""
    
    images_train=[]
    labels_train=[]
    for i in range(5):
        f=os.path.join(data_dir,'data_batch_%d'% (i+1))
        print('loading',f)
        #调用load_CIFAR_batch()获得批量的图像及其对应的标签
        image_batch,label_batch=load_CIFAR_batch(f)
        images_train.append(image_batch)
        labels_train.append(label_batch)
        Xtrain=np.concatenate(images_train)
        Ytrain=np.concatenate(labels_train)
        del image_batch ,label_batch
        
    Xtest,Ytest=load_CIFAR_batch(os.path.join(data_dir, 'test_batch'))
    print('finished loadding CIFAR-10 data')
    
    #返回训练集的图像和标签，测试集的图像和标签
    return Xtrain, Ytrain,Xtest,Ytest

data_dir = 'data\cifar-10-batches-py'
Xtrain,Ytrain,Xtest,Ytest = load_CIFAR_data(data_dir)

print('training data shape:',Xtrain.shape)
print('training labels shape: ',Ytrain.shape)
print('test data shape:',Xtest.shape)
print('test labels shape:',Ytest.shape)

import matplotlib.pyplot as plt

#定义标签字典，每—个数字所代表的图像类别的名称
label_dict={0:" airplane",1:"automobile",2:"bird",3:"cat",4:"deer",
        5:"dog",6:"frog",7:"horse" ,8:"ship" ,9:"truck"}
#定义显示图像数据及其对应标签的函数
def plot_images_labels_prediction(images,labels,prediction,idx,num=10):
    fig = plt.gcf()
    fig.set_size_inches(12,6)
    if num>10:
        num=10
    for i in range(0, num):
        ax=plt.subplot(2,5,1+i)
        ax.imshow(images[idx],cmap='binary')
    
        title=str(i)+','+label_dict[labels[idx]]
        if len(prediction)>0:
            title+='= >'+label_dict[prediction[idx]]
    
        ax.set_title(title,fontsize=10)
    
        idx+=1
    plt.show()
#显示图像数据及其对应标签
plot_images_labels_prediction(Xtest,Ytest,[],11,20)

Xtrain[0][0][0]

Xtrain_normalize = Xtrain.astype('float32')/255.0
Xtest_normalize = Xtest.astype('float32')/255.0
Xtrain_normalize[0][0][0]

from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
yy =[[0],[1],[2],[3],[4],[5],[6],[7],[8],[9]]
encoder.fit(yy)
Ytrain_reshape = Ytrain.reshape(-1,1)
Ytrain_onehot = encoder.transform(Ytrain_reshape)
Ytest_reshape = Ytest.reshape(-1,1)
Ytest_onehot = encoder.transform(Ytest_reshape)

#定义权值
def weight(shape):
    #在构建模型时，需要使用tf.Variable来创建—个变量
    #在训练时，这个变量不断更新
    #使用函数tf.truncated_normal(截断的正态分布)生成标准差为0.1的随机数来初始化权值
    return tf.Variable(tf.truncated_normal(shape, stddev=0.1), name ='W')
#定义偏置
#初始化为0.1
def bias(shape):
    return tf.Variable(tf.constant(0.1, shape=shape), name = 'b')
#定义卷积操作
#步长为1,padding为'SAME'
def conv2d(x, W):
    # tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
#定义池化操作
#步长为2，即原尺寸的长和宽各除以2
def max_pool_2x2(x):
# tf.nn.max_pool(value, ksize, strides, padding, name=None)
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

import tensorflow as tf
tf.reset_default_graph()
#输入层
#32x32图像，通道为3( RGB )
with tf.name_scope('input_layer'):
    x = tf.placeholder('float',shape=[None,32,32,3],name="x")
#第1个卷积层
#输入通道:3，输出通道:32，卷积后图像尺寸不变，依然是32x32
with tf.name_scope('conv_1'):
    W1 = weight([3,3,3,32])#[lk_width, k_height, input_chn, output chn]
    b1 = bias([32])#与output_chn—致
    conv_1=conv2d(x,W1)+ b1
    conv_1 = tf.nn.relu(conv_1 )
#第1个池化层
#将32x32图像缩小为16x16，池化不改变通道数量，因此依然是32个
with tf.name_scope('pool_1'):
    pool_1 = max_pool_2x2(conv_1)
#第2个卷积层
#输入通道:32，输出通道∶64，卷积后图像尺寸不变，依然是16x16
with tf.name_scope('conv_2'):
    W2 = weight([3,3,32,64])
    b2 = bias([64])
    conv_2=conv2d(pool_1,W2)+ b2
    conv_2 = tf.nn.relu(conv_2)

#第2个池化层
#将16x16图像缩小为8x8，池化不改变通道数量，因此依然是64个
with tf.name_scope('pool_2'):
    pool_2 = max_pool_2x2(conv_2)
#全连接层
#将池第2个池化层的64个8x8的图像转换为一维的向量，长度是64*8*8=4096#128个神经元
with tf.name_scope('fc'):
    W3= weight([4096,128])#有128个神经元
    b3= bias([128])
    flat = tf.reshape(pool_2,[-1,4096])
    h = tf.nn.relu(tf.matmul(flat, W3)+ b3)
    h_dropout= tf.nn.dropout(h, keep_prob=0.8)
#输出层
#输出层共有10个神经元，对应到0-9这10个类别
with tf.name_scope('output_layer'):
    W4 = weight([128,10])
    b4 = bias([10])
    pred= tf.nn.softmax(tf.matmul(h_dropout, W4)+b4)

with tf.name_scope("optimizer"):
    #定义占位符
    y = tf.placeholder("float", shape=[None,10],
    name="label")
    #定义损失函数
    loss_function = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits_v2
                (logits=pred,
                labels=y))
    #选择优化器
    optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)\
            .minimize(loss_function)

with tf.name_scope("evaluation"):
    correct_prediction = tf.equal(tf.argmax(pred,1),
                        tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

import os
from time import time
train_epochs =25
batch_size = 50
total_batch = int(len(Xtrain)/batch_size)
epoch_list=[];accuracy_list=[];loss_list=[];

epoch = tf.Variable(0,name='epoch',trainable=False)
                        
startTime=time()

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

#设置检查点存储目录
ckpt_dir = "CIFAR10_log"
if not os.path.exists(ckpt_dir):
    os.makedirs(ckpt_dir)
#生成saver
saver = tf.train.Saver(max_to_keep=1)
#如果有检查点文件，读取最新的检查点文件，恢复各种变量值
ckpt = tf.train.latest_checkpoint(ckpt_dir )
if ckpt != None:
    saver.restore(sess, ckpt)#力加载所有的参数
    #从这里开始就可以直接使用模型进行预测，或者接着继续训练了
else:
    print("Training from scratch.")
#获取续训参数
start = sess.run(epoch)
print("Training starts form {} epoch.".format(start+1))

def get_train_batch(number, batch_size):
    return Xtrain_normalize[number*batch_size:(number+1)*batch_size],\
                    Ytrain_onehot[number*batch_size:(number+1)*batch_size]
for ep in range(start, train_epochs):
    for i in range(total_batch):
        batch_x, batch_y = get_train_batch(i,batch_size)
        sess.run(optimizer,feed_dict={x: batch_x, y: batch_y})
        if i %100 == 0:
            print("Step {}".format(i), "finished")
    loss,acc = sess.run([loss_function,accuracy],feed_dict={x: batch_x, y: batch_y})
    epoch_list.append(ep+1)
    loss_list.append(loss);
    accuracy_list.append(acc)
    print("Train epoch:",'%02d'% (sess.run(epoch)+1),\
            "Loss=","{:.6f}".format(loss),"Accuracy=",acc)
    #保存检查点
    saver.save(sess,ckpt_dir+".\CIFAR10_cnn_model.cpkt",global_step=ep+1)
    sess.run(epoch.assign(ep+1))
                                                            
duration =time()-startTime
print("Train finished takes: ",duration)

%matplotlib inline
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.set_size_inches(4,2)
plt.plot(epoch_list, loss_list, label = 'loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['loss'], loc='upper right')

plt.plot(epoch_list, accuracy_list,label="accuracy" )
fig = plt.gcf()
fig.set_size_inches(4,2)
plt.ylim(0.1,1)
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend()
plt.show()

test_total_batch = int(len(Xtest_normalize)/batch_size)
test_acc_sum = 0.0
for i in range(test_total_batch):
    test_image_batch = Xtest_normalize[i*batch_size:(i+1)*batch_size]
    test_label_batch = Ytest_onehot[i*batch_size:(i+1)*batch_size]
    test_batch_acc = sess.run(accuracy, feed_dict = {x:test_image_batch,y:test_label_batch})
    test_acc_sum += test_batch_acc
test_acc = float(test_acc_sum/test_total_batch)
print("Test accuracy:{:.6f}".format(test_acc))


test_pred=sess.run(pred, feed_dict={x: Xtest_normalize[:10]})
prediction_result = sess.run(tf.argmax(test_pred,1))

plot_images_labels_prediction(Xtest, Ytest,prediction_result,0,10)