转 keras分类猫狗数据（中）使用CNN分类模型

转zihttps://blog.youkuaiyun.com/nima1994/article/details/79951445

 

keras分类猫狗数据（上）数据预处理 
keras分类猫狗数据（中）使用CNN分类模型 
keras分类猫狗数据（下）迁移学习 
keras分类猫狗数据（番外篇）深度学习CNN连接SVM分类

1 . 如下代码，数据增强，并根据文件夹创建数据流(我的保存为了catvsdogs/morph.py)。

from keras.preprocessing.image import ImageDataGenerator

train_dir="E:/MLdata/kaggle_Dogsvs.Cats/min_trainfordata/train"

test_dir="E:/MLdata/kaggle_Dogsvs.Cats/min_trainfordata/test"

train_pic_gen=ImageDataGenerator(rescale=1./255,rotation_range=20,width_shift_range=0.2,height_shift_range=0.2,
                                 shear_range=0.2,zoom_range=0.5,horizontal_flip=True,fill_mode='nearest')

test_pic_gen=ImageDataGenerator(rescale=1./255)


train_flow=train_pic_gen.flow_from_directory(train_dir,(128,128),batch_size=32,class_mode='binary')

test_flow=test_pic_gen.flow_from_directory(test_dir,(128,128),batch_size=32,class_mode='binary')

# print(train_flow.class_indices)

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2 .构建模型并训练

from keras.models import Sequential
from keras.layers import Convolution2D,MaxPool2D,Flatten,Dense,Dropout
from keras.callbacks import TensorBoard

model=Sequential([
    Convolution2D(32,3,3,input_shape=(128,128,3),activation='relu'),
    MaxPool2D(pool_size=(2,2)),
    Convolution2D(64,3,3,input_shape=(128,128,3),activation='relu'),
    MaxPool2D(pool_size=(2,2)),
    Flatten(),
    Dense(64,activation='relu'),
    Dropout(0.5),
    Dense(1,activation='sigmoid')
])

model.summary()

model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])

import catvsdogs.morph as morph#引用上文1的数据增加代码

model.fit_generator(
    morph.train_flow,steps_per_epoch=100,epochs=50,verbose=1,validation_data=morph.test_flow,validation_steps=100,
    callbacks=[TensorBoard(log_dir='./logs/1')]
)

model.save('outputs/catdogs_model.h5')