keras例程-简单CNN猫狗分类

from keras.models import Sequential
from keras.layers import Conv2D,MaxPool2D,Activation,Dropout,Flatten,Dense
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator,img_to_array,load_img

# 定义模型
model = Sequential()
model.add(Conv2D(input_shape=(150,150,3),filters=32,kernel_size=3,padding='same',activation='relu'))
model.add(Conv2D(filters=32,kernel_size=3,padding='same',activation='relu'))
model.add(MaxPool2D(pool_size=2, strides=2))

model.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))
model.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))
model.add(MaxPool2D(pool_size=2, strides=2))

model.add(Conv2D(filters=128,kernel_size=3,padding='same',activation='relu'))
model.add(Conv2D(filters=128,kernel_size=3,padding='same',activation='relu'))
model.add(MaxPool2D(pool_size=2, strides=2))

model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2,activation='softmax'))

# 定义优化器
adam = Adam(lr=1e-4)

# 定义优化器，代价函数，训练过程中计算准确率
model.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])


#训练集图像增强
train_datagen = ImageDataGenerator(
    rotation_range = 40,     # 随机旋转度数
    width_shift_range = 0.2, # 随机水平平移
    height_shift_range = 0.2,# 随机竖直平移
    rescale = 1/255,         # 数据归一化
    shear_range = 20,       # 随机错切变换
    zoom_range = 0.2,        # 随机放大
    horizontal_flip = True,  # 水平翻转
    fill_mode = 'nearest',   # 填充方式
) 
#测试集数据增强
test_datagen = ImageDataGenerator(
    rescale = 1/255,         # 数据归一化
) 

batch_size = 32

# 生成训练数据
train_generator = train_datagen.flow_from_directory(
    'image/train',
    target_size=(150,150),
    batch_size=batch_size,
    )

# 测试数据
test_generator = test_datagen.flow_from_directory(
    'image/test',
    target_size=(150,150),
    batch_size=batch_size,
    )
#打印训练集分类   
train_generator.class_indices#{'cat': 0, 'dog': 1}
#分类每个文件夹表示一个类别，可以用flow_from_directory()+fit_generator()
#如果是回归问题，要先准备好样本和标签，同时放进fit里面
model.fit_generator(train_generator,
                    steps_per_epoch=len(train_generator),
                             epochs=30,
                             validation_data=test_generator,
                             validation_steps=len(test_generator))