Keras：自建数据集图像分类的模型训练、保存与恢复
Keras：使用预训练网络的bottleneck特征
准备
fine-tune的三个步骤:
- 搭建vgg-16并载入权重;
- 将之前定义的全连接网络加载到模型顶部,并载入权重;
- 冻结vgg16网络的一部分参数.
在之前的Keras：自建数据集图像分类的模型训练、保存与恢复里制作了实验用的数据集并初步进行了训练.然后在Keras：使用预训练网络的bottleneck特征中定义并训练了要使用全连接网络,并将网络权重保存到了bottleneck_fc_model.h5文件中.
fine-tune过程
根据keras中…/keras/applications/vgg16.py的VGG16模型形式,构造VGG16模型的卷积部分,并载入权重(vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5).然后添加预训练好的模型.训练时冻结最后一个卷积块前的卷基层参数.
示例:
#!/usr/bin/python
# coding:utf8
from keras.models import Sequential
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Flatten, Dense, Dropout, Conv2D, MaxPooling2D
from keras import backend as K
K.set_image_dim_ordering('th')
# 构造VGG16模型
model = Sequential()
# Block 1
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', input_shape=(3, 150, 150)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'))
# Block 2
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1'))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'))
# Block 3
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'))
# Block 4
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'))
# Block 5
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'))
model.load_weights('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',by_name=True)
model.summary()
# 在初始化好的VGG网络上添加预训练好的模型
top_model = Sequential()
top_model.add(Flatten(input_shape=model.output_shape[1:])) # (4,4,512)
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))
top_model.load_weights('bottleneck_fc_model.h5',by_name=True)
model.add(top_model)
# 将最后一个卷积块前的卷基层参数冻结,把随后卷积块前的权重设置为不可训练（权重不会更新）
for layer in model.layers[:25]:
layer.trainable = False
model.compile(loss='binary_crossentropy',
optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
# 以低学习率进行训练
train_datagen = ImageDataGenerator(rescale=1./255,
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',
target_size=(150,150),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory('validation',
target_size=(150,150),
batch_size=32,
class_mode='binary')
model.fit_generator(train_generator,
steps_per_epoch=10,
epochs=50,
validation_data=validation_generator,
validation_steps=10)
输出:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
block1_conv1 (Conv2D) (None, 64, 150, 150) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 64, 150, 150) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 64, 75, 75) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 128, 75, 75) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 128, 75, 75) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 128, 37, 37) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 256, 37, 37) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 256, 37, 37) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 256, 37, 37) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 256, 18, 18) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 512, 18, 18) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 512, 18, 18) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 512, 18, 18) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 512, 9, 9) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 512, 9, 9) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 512, 9, 9) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 512, 4, 4) 0
=================================================================
Total params: 12,354,880
Trainable params: 12,354,880
Non-trainable params: 0
_________________________________________________________________
Found 60 images belonging to 2 classes.
Found 60 images belonging to 2 classes.
Epoch 1/50
1/10 [==>...........................] - ETA: 6:57 - loss: 0.7880 - acc: 0.3929
2/10 [=====>........................] - ETA: 6:23 - loss: 0.7920 - acc: 0.4152
3/10 [========>.....................] - ETA: 5:25 - loss: 0.8292 - acc: 0.3839
4/10 [===========>..................] - ETA: 4:47 - loss: 0.8184 - acc: 0.3895
5/10 [==============>...............] - ETA: 3:59 - loss: 0.8159 - acc: 0.3929
6/10 [=================>............] - ETA: 3:08 - loss: 0.8001 - acc: 0.4048
7/10 [====================>.........] - ETA: 2:18 - loss: 0.8094 - acc: 0.4184
8/10 [=======================>......] - ETA: 1:32 - loss: 0.8031 - acc: 0.4247
9/10 [==========================>...] - ETA: 46s - loss: 0.8041 - acc: 0.4296
10/10 [==============================] - 899s 90s/step - loss: 0.8125 - acc: 0.4260 - val_loss: 0.8145 - val_acc: 0.4000
Epoch 2/50
1/10 [==>...........................] - ETA: 6:55 - loss: 0.8487 - acc: 0.4062
2/10 [=====>........................] - ETA: 5:50 - loss: 0.8443 - acc: 0.4353
3/10 [========>.....................] - ETA: 5:08 - loss: 0.8430 - acc: 0.4256
4/10 [===========>..................] - ETA: 4:18 - loss: 0.8258 - acc: 0.4263
5/10 [==============>...............] - ETA: 3:32 - loss: 0.8310 - acc: 0.4339
6/10 [=================>............] - ETA: 2:53 - loss: 0.8266 - acc: 0.4397
7/10 [====================>.........] - ETA: 2:11 - loss: 0.8270 - acc: 0.4305
8/10 [=======================>......] - ETA: 1:26 - loss: 0.8220 - acc: 0.4347
9/10 [==========================>...] - ETA: 43s - loss: 0.8311 - acc: 0.4340
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