【深度学习图像识别课程】keras实现CNN系列：（8）keras迁移学习实战-狗类品种分类

1、实战分析

背景：数据集8351张图，每张都是狗狗照片，共133种。

目标：用CNN实现狗类品种分类

方法：使用ImageNet上预先训练好的VGG16

分析场景：狗类数据集较小，与ImageNet相似度较高



将最后的全连接层删除，换成新的连接层。冻结前面模型的权重，只训练最后一个连接层的权重。



 

我们将所有的图片，都直接穿过之前的网络一直到红色的最大池化层，做为新的输入。则我们需要训练的网络只剩下2层。





 

二、实战

2.1 模型1

1、加载数据库
from sklearn.datasets import load_files from keras.utils import np_utils
import numpy as np from glob import glob # define function to load train, test,
and validation datasets def load_dataset(path): data = load_files(path)
dog_files = np.array(data['filenames']) dog_targets =
np_utils.to_categorical(np.array(data['target']), 133) return dog_files,
dog_targets # load train, test, and validation datasets train_files,
train_targets = load_dataset('dogImages/train') valid_files, valid_targets =
load_dataset('dogImages/valid') test_files, test_targets =
load_dataset('dogImages/test') # load ordered list of dog names dog_names =
[item[25:-1] for item in glob('dogImages/train/*/')] # print statistics about
the dataset print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % str(len(train_files) +
len(valid_files) + len(test_files))) print('There are %d training dog images.'
% len(train_files)) print('There are %d validation dog images.' %
len(valid_files)) print('There are %d test dog images.'% len(test_files))


2、可视化部分图片
import cv2 import matplotlib.pyplot as plt %matplotlib inline def
visualize_img(img_path, ax): img = cv2.imread(img_path)
ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) fig = plt.figure(figsize=(20,
10)) for i in range(12): ax = fig.add_subplot(3, 4, i + 1, xticks=[],
yticks=[]) visualize_img(train_files[i], ax)


3、读入VGG16瓶颈特征
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_vgg16 = bottleneck_features['train'] valid_vgg16 =
bottleneck_features['valid'] test_vgg16 = bottleneck_features['test']
4、模型1
from keras.layers import Dense, Flatten from keras.models import Sequential
model = Sequential() model.add(Flatten(input_shape=(7, 7, 512)))
model.add(Dense(133, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop',
metrics=['accuracy']) model.summary()


5、模型编译
model.compile(loss='categorical_crossentropy', optimizer='rmsprop',
metrics=['accuracy'])
6、模型训练
from keras.callbacks import ModelCheckpoint # train the model checkpointer =
ModelCheckpoint(filepath='dogvgg16.weights.best.hdf5', verbose=1,
save_best_only=True) model.fit(train_vgg16, train_targets, epochs=20,
validation_data=(valid_vgg16, valid_targets), callbacks=[checkpointer],
verbose=1, shuffle=True)




7、加载最优模型
# load the weights that yielded the best validation accuracy
model.load_weights('dogvgg16.weights.best.hdf5')
8、测试
# get index of predicted dog breed for each image in test set
vgg16_predictions = [np.argmax(model.predict(np.expand_dims(feature, axis=0)))
for feature in test_vgg16] # report test accuracy test_accuracy =
100*np.sum(np.array(vgg16_predictions)== np.argmax(test_targets,
axis=1))/len(vgg16_predictions) print('\nTest accuracy: %.4f%%' % test_accuracy)


 

2.2 模型2

跟模型1的区别就是步骤4的模型不同，引入全局平均池化层 GAP减少训练参数。

1、加载数据库
from sklearn.datasets import load_files from keras.utils import np_utils
import numpy as np from glob import glob # define function to load train, test,
and validation datasets def load_dataset(path): data = load_files(path)
dog_files = np.array(data['filenames']) dog_targets =
np_utils.to_categorical(np.array(data['target']), 133) return dog_files,
dog_targets # load train, test, and validation datasets train_files,
train_targets = load_dataset('dogImages/train') valid_files, valid_targets =
load_dataset('dogImages/valid') test_files, test_targets =
load_dataset('dogImages/test') # load ordered list of dog names dog_names =
[item[25:-1] for item in glob('dogImages/train/*/')] # print statistics about
the dataset print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % str(len(train_files) +
len(valid_files) + len(test_files))) print('There are %d training dog images.'
% len(train_files)) print('There are %d validation dog images.' %
len(valid_files)) print('There are %d test dog images.'% len(test_files))


2、可视化部分图片
import cv2 import matplotlib.pyplot as plt %matplotlib inline def
visualize_img(img_path, ax): img = cv2.imread(img_path)
ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) fig = plt.figure(figsize=(20,
10)) for i in range(12): ax = fig.add_subplot(3, 4, i + 1, xticks=[],
yticks=[]) visualize_img(train_files[i], ax)


3、读入VGG16瓶颈特征
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_vgg16 = bottleneck_features['train'] valid_vgg16 =
bottleneck_features['valid'] test_vgg16 = bottleneck_features['test']
4、模型2
from keras.layers import Dense, GlobalAveragePooling2D from keras.models
import Sequential model = Sequential() model.add(GlobalAveragePooling2D
(input_shape=(7, 7, 512))) model.add(Dense(133, activation='softmax'))
model.summary()GlobalAveragePooling2D(input_shape=(7, 7, 512)))
model.add(Dense(133, activation='softmax')) model.summary()


5、模型编译
model.compile(loss='categorical_crossentropy', optimizer='rmsprop',
metrics=['accuracy'])
6、模型训练
from keras.callbacks import ModelCheckpoint # train the model checkpointer =
ModelCheckpoint(filepath='dogvgg16.weights.best.hdf5', verbose=1,
save_best_only=True) model.fit(train_vgg16, train_targets, epochs=20,
validation_data=(valid_vgg16, valid_targets), callbacks=[checkpointer],
verbose=1, shuffle=True)




7、加载最优模型
# load the weights that yielded the best validation accuracy
model.load_weights('dogvgg16.weights.best.hdf5')
8、测试
# get index of predicted dog breed for each image in test set
vgg16_predictions = [np.argmax(model.predict(np.expand_dims(feature, axis=0)))
for feature in test_vgg16] # report test accuracy test_accuracy =
100*np.sum(np.array(vgg16_predictions)== np.argmax(test_targets,
axis=1))/len(vgg16_predictions) print('\nTest accuracy: %.4f%%' % test_accuracy)


 

 

 

三、优化

引入全局平均池化层GAP进行优化。最近MIT发现，包含GAP的CNN还具有目标定位的功能。



 

Gap简单说明：



全连接层：上面程序中，将（7, 7, 512）展开，得到7*7*512个元素，再softmax成133类

GAP全局平均池化：上面程序中，（7,7,512）中的7*7变成1个点，512个元素直接softmax成133类。

 

论文：
http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf

<http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf>

         提出用GAP层进行目标定位的第一篇论文

代码：https://github.com/alexisbcook/ResNetCAM-keras
<https://github.com/alexisbcook/ResNetCAM-keras>

         使用CNN进行目标定位

视频：https://www.youtube.com/watch?v=fZvOy0VXWAI
<https://www.youtube.com/watch?v=fZvOy0VXWAI> 

         使用CNN进行目标定位

代码：https://github.com/alexisbcook/keras_transfer_cifar10
<https://github.com/alexisbcook/keras_transfer_cifar10>

         用可视化技术更好的理解瓶颈特征

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