• 1.keras实现-->自己训练卷积模型实现猫狗二分类(CNN)

    原数据集：包含 25000张猫狗图像，两个类别各有12500

    新数据集：猫、狗 （照片大小不一样）

    • 训练集：各1000个样本
    • 验证集：各500个样本
    • 测试集：各500个样本

    1= 狗，0= 猫

    # 将图像复制到训练、验证和测试的目录

import os,shutil

orginal_dataset_dir = 'kaggle_original_data/train'
base_dir = 'cats_and_dogs_small'
os.mkdir(base_dir)#保存新数据集的目录

train_dir = os.path.join(base_dir,'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir,'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir,'test')
os.mkdir(test_dir)

#猫、狗的训练图像目录
train_cats_dir = os.path.join(train_dir,'cats')
os.mkdir(train_cats_dir)
train_dogs_dir = os.path.join(train_dir,'dogs')
os.mkdir(train_dogs_dir)

#猫、狗的验证图像目录
validation_cats_dir = os.path.join(validation_dir,'cats')
os.mkdir(validation_cats_dir)
validation_dogs_dir = os.path.join(validation_dir,'dogs')
os.mkdir(validation_dogs_dir)

#猫、狗的测试图像目录
test_cats_dir = os.path.join(test_dir,'cats')
os.mkdir(test_cats_dir)
test_dogs_dir = os.path.join(test_dir,'dogs')
os.mkdir(test_dogs_dir)

#将前1000张猫的图像复制到train_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(orginal_dataset_dir,fname)
    dst = os.path.join(train_cats_dir,fname)
    shutil.copyfile(src,dst)

#将接下来500张猫的图像复制到validation_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000,1500)]
for fname in fnames:
    src = os.path.join(orginal_dataset_dir,fname)
    dst = os.path.join(validation_cats_dir,fname)
    shutil.copyfile(src,dst)

#将接下来的500张猫的图像复制到test_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1500,2000)]
for fname in fnames:
    src = os.path.join(orginal_dataset_dir,fname)
    dst = os.path.join(test_cats_dir,fname)
    shutil.copyfile(src,dst)

#将前1000张狗的图像复制到train_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
    src = os.path.join(orginal_dataset_dir,fname)
    dst = os.path.join(train_dogs_dir,fname)
    shutil.copyfile(src,dst)

#将接下来500张狗的图像复制到validation_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000,1500)]
for fname in fnames:
    src = os.path.join(orginal_dataset_dir,fname)
    dst = os.path.join(validation_dogs_dir,fname)
    shutil.copyfile(src,dst)

#将接下来的500张狗的图像复制到test_cats_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1500,2000)]
for fname in fnames:
    src = os.path.join(orginal_dataset_dir,fname)
    dst = os.path.join(test_dogs_dir,fname)
    shutil.copyfile(src,dst)
    		   
    #将猫狗分类的小型卷积神经网络实例化
from keras import layers
from keras import models

model = models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Flatten())
model.add(layers.Dense(512,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))

    该问题为二分类问题，所以网咯最后一层是使用sigmoid激活的

    单一单元，大小为1的Dense层。 

     
      
    from keras import optimizers

model.compile(loss='binary_crossentropy',
             optimizer = optimizers.RMSprop(lr=1e-4),
             metrics = ['acc'])
    		  

    loss: binary_crossentropy

    优化器: RMSprop

    度量:acc精度
      
    #使用ImageDataGenerator从目录中读取图像
#ImageDataGenerator可以快速创建Python生成器，能够将硬盘上的图像文件自动转换为预处理好的张量批量
from keras.preprocessing.image import ImageDataGenerator

#将所有图像乘以1/255缩放
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size = (150,150),
    batch_size = 20,
    class_mode = 'binary'  #因为使用了binary_crossentropy损失，所以需要用二进制标签
)

validation_generator = test_datagen.flow_from_directory(
    validation_dir,
    target_size = (150,150),
    batch_size = 20,
    class_mode = 'binary'
)
    		  

     

     用flow_from_directory最值得注意的是directory这个参数：
    它的目录格式一定要注意是包含一个子目录下的所有图片这种格式，
    driectoty路径只要写到标签路径上面的那个路径即可。 
      
    for data_batch,labels_batch in train_generator:
    print('data batch shape:',data_batch.shape)
    print('labels batch shape:',labels_batch.shape)
    break
    		 
    data batch shape: (20, 150, 150, 3)
labels batch shape: (20,)
    		 
    #利用批量生成器拟合模型
history = model.fit_generator(
    train_generator,
    steps_per_epoch = 50,
    epochs = 30,
    validation_data = validation_generator,
    validation_steps = 50#需要从验证生成器中抽取50个批次用于评估
)

      #保存模型
      model.save('cats_and_dogs_small_1.h5')

    		   
    

      from keras.models import load_model
      model = load_model('cats_and_dogs_small_1.h5') 
    

    

    
    		
 手残，误操作，还好我已经保存了模型，用这句话就可以载入模型

    

    


    

    

    #绘制损失曲线和精度曲线
import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1,len(acc)+1)

plt.plot(epochs,acc,'bo',label='Training_acc')
plt.plot(epochs,val_acc,'b',label='Validation_acc')
plt.title('Traing and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs,loss,'bo',label='Training loss')
plt.plot(epochs,val_loss,'b',label='Validation_loss')
plt.title('Traing and validation loss')
plt.legend()

plt.show()
    

    

    
    		
 


          
    

     过拟合太严重了，原因可能是训练样本较少
    
    		

    
    

    

    


    


    

    #因为数据样本较少，容易过拟合，因此我们使用数据增强来减少过拟合

#利用ImageDataGenerator来设置数据增强
datagen = ImageDataGenerator(
    rotation_range = 40,
    width_shift_range = 0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range = 0.2,
    horizontal_flip = True,
    fill_mode = 'nearest'
)
    

    
    		


    数据增强是从现有的训练样本中生成更多的训练数据，其方法是
    

    利用多种能够生成可信图像的随机变换来增加样本。其目标是,
    

    模型在训练时不会两次查看完全相同的图像。这让模型能够观察
    

    到数据的更多内容，从而具有更好的泛化能力。
    
    		

    

    


    

    #显示几个随机增强后的训练图像
from keras.preprocessing import image

fnames = [os.path.join(train_cats_dir,fname) for fname in os.listdir(train_cats_dir)]
# ['cats_and_dogs_small\train\cats\cat.0.jpg','cats_and_dogs_small\train\cats\cat.1.jpg',...]

img_path = fnames[3]#选择一张图像进行增强 
# 'cats_and_dogs_small\train\cats\cat.3.jpg'

img = image.load_img(img_path,target_size=(150,150))#读取图像并调整大小

x = image.img_to_array(img) # ==> array(150,150,3)

x = x.reshape((1,)+x.shape) # ==> array(1,150,150,3)
    #x的秩必须为4，不够需要加一维     

i = 0
for batch in datagen.flow(x,batch_size=1): 
    plt.figure(i)
    implot = plt.imshow(image.array_to_img(batch[0]))
    i += 1
    if i % 4 == 0: #生成随机变换后的图像批量。循环是无限的，因此你需要在某个时刻终止循环
        break #生成4张图之后就终止
        
plt.show()
    

    
    		

    











     		












    

    


    

    #向模型中添加一个Dropout层，添加到密集连接分类器之前
model = models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Conv2D(128,(3,3),activation='relu'))
model.add(layers.MaxPool2D((2,2)))

model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512,activation='relu'))
model.add(layers.Dense(1,activation='sigmoid'))

model.compile(loss='binary_crossentropy',
             optimizer = optimizers.RMSprop(lr=1e-4),
             metrics = ['acc'])
    

    
    		
 

    

    


    

    #利用数据增强生成器训练卷积神经网络
train_datagen = ImageDataGenerator(
    rescale = 1./255,
    rotation_range = 40,
    width_shift_range = 0.2,
    height_shift_range = 0.2,
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True,
)

test_datagen = ImageDataGenerator(rescale = 1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size = (150,150),
    batch_size = 20,
    class_mode = 'binary'  #因为使用了binary_crossentropy损失，所以需要用二进制标签
)

validation_generator = test_datagen.flow_from_directory(
    validation_dir,
    target_size = (150,150),
    batch_size = 20,
    class_mode = 'binary'
)

history = model.fit_generator(
    train_generator,
    steps_per_epoch = 50,
    epochs = 30,
    validation_data = validation_generator,
    validation_steps = 50#需要从验证生成器中抽取50个批次用于评估
)

model.save('cats_and_dogs_small_2.h5')
    

    
    		
 

    

    

    

    

    #绘制损失曲线和精度曲线
import matplotlib.pyplot as plt

acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1,len(acc)+1)

plt.plot(epochs,acc,'bo',label='Training_acc')
plt.plot(epochs,val_acc,'b',label='Validation_acc')
plt.title('Traing and validation accuracy')
plt.legend()

plt.figure()

plt.plot(epochs,loss,'bo',label='Training loss')
plt.plot(epochs,val_loss,'b',label='Validation_loss')
plt.title('Traing and validation loss')
plt.legend()

plt.show()
    

    

    		
 

    

     使用了数据增强和dropout之后，模型不再过拟合，训练曲线紧紧跟着验证曲线
    
    		

    
    

    

    但只靠从头开始训练自己的卷积神经网络，再想提高精度就十分困难，因为可用的数据太少。想要在这个问题上进一步提高精度，下一步需要使用预训练的模型。
    

    
    
          
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            原文地址：https://www.cnblogs.com/nxf-rabbit75/p/9963208.html
          
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