利用预训练网络进行微调其实就是将预训练网络现在的参数状态作为训练起始状态，利用自己的数据继续进行训练，由于预训练网络是已经训练多很多轮的，所以在微调的时候就不用再继续训练很多轮了。
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 29 14:26:37 2018
@author: 13260
"""
# -*- coding: utf-8 -*-
import os
from keras.utils import plot_model
from keras.applications.resnet50 import ResNet50
from keras.applications.vgg19 import VGG19
from keras.applications.inception_v3 import InceptionV3
from keras.layers import Dense,Flatten,GlobalAveragePooling2D
from keras.models import Model,load_model
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
class PowerTransferMode:
#数据准备
def DataGen(self, dir_path, img_row, img_col, batch_size, is_train):
if is_train:
datagen = ImageDataGenerator(rescale=1./255,
zoom_range=0.25, rotation_range=15.,
channel_shift_range=25., width_shift_range=0.02, height_shift_range=0.02,
horizontal_flip=True, fill_mode='constant')
else:
datagen = ImageDataGenerator(rescale=1./255)
generator = datagen.flow_from_directory(
dir_path, target_size=(img_row, img_col),
batch_size=batch_size,
#class_mode='binary',
shuffle=is_train)
return generator
#ResNet模型
def ResNet50_model(self, lr=0.005, decay=1e-6, momentum=0.9, nb_classes=45, img_rows=197, img_cols=197, RGB=True, is_plot_model=False):
color = 3 if RGB else 1
base_model = ResNet50(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color),
classes=nb_classes)
#冻结base_model所有层，这样就可以正确获得bottleneck特征
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
#添加自己的全链接分类层
x = Flatten()(x)
#x = GlobalAveragePooling2D()(x)
#x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
#训练模型
model = Model(inputs=base_model.input, outputs=predictions)
sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
#绘制模型
if is_plot_model:
plot_model(model, to_file='resnet50_model.png',show_shapes=True)
return model
#VGG模型
def VGG19_model(self, lr=0.005, decay=1e-6, momentum=0.9, nb_classes=45, img_rows=197, img_cols=197, RGB=True, is_plot_model=False):
color = 3 if RGB else 1
base_model = VGG19(weights='imagenet', include_top=False, pooling=None, input_shape=(img_rows, img_cols, color),
classes=nb_classes)
#冻结base_model所有层，这样就可以正确获得bottleneck特征
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
#添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
#训练模型
model = Model(inputs=base_model.input, outputs=predictions)
sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# 绘图
if is_plot_model:
plot_model(model, to_file='vgg19_model.png',show_shapes=True)
return model
# InceptionV3模型
def InceptionV3_model(self, lr=0.005, decay=1e-6, momentum=0.9, nb_classes=2, img_rows=197, img_cols=197, RGB=True,
is_plot_model=False):
color = 3 if RGB else 1
base_model = InceptionV3(weights='imagenet', include_top=False, pooling=None,
input_shape=(img_rows, img_cols, color),
classes=nb_classes)
# 冻结base_model所有层，这样就可以正确获得bottleneck特征
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
# 训练模型
model = Model(inputs=base_model.input, outputs=predictions)
sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# 绘图
if is_plot_model:
plot_model(model, to_file='inception_v3_model.png', show_shapes=True)
return model
#训练模型
def train_model(self, model, epochs, train_generator, steps_per_epoch, validation_generator, validation_steps, model_url, is_load_model=False):
# 载入模型
if is_load_model and os.path.exists(model_url):
model = load_model(model_url)
history_ft = model.fit_generator(
train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps)
# 模型保存
model.save(model_url,overwrite=True)
return history_ft
# 画图
def plot_training(self, history):
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'b-')
plt.plot(epochs, val_acc, 'r')
plt.title('Training and validation accuracy')
plt.figure()
plt.plot(epochs, loss, 'b-')
plt.plot(epochs, val_loss, 'r-')
plt.title('Training and validation loss')
plt.show()
if __name__ == '__main__':
image_size = 197
batch_size = 32
transfer = PowerTransferMode()
#得到数据
train_generator = transfer.DataGen('F:/shiyan/TensorFlow/retrain/data/trans_train', image_size, image_size, batch_size, True)
validation_generator = transfer.DataGen('F:/shiyan/TensorFlow/retrain/data/test', image_size, image_size, batch_size, False)
#VGG19
model = transfer.VGG19_model(nb_classes=45, img_rows=image_size, img_cols=image_size, is_plot_model=False)
history_ft = transfer.train_model(model, 10, train_generator, 600, validation_generator, 60, 'vgg19_model_weights.h5', is_load_model=False)
#ResNet50
#model = transfer.ResNet50_model(nb_classes=45, img_rows=image_size, img_cols=image_size, is_plot_model=False)
#history_ft = transfer.train_model(model, 10, train_generator, 600, validation_generator, 60, 'resnet50_model_weights.h5', is_load_model=False)
#InceptionV3
#model = transfer.InceptionV3_model(nb_classes=2, img_rows=image_size, img_cols=image_size, is_plot_model=True)
#history_ft = transfer.train_model(model, 10, train_generator, 600, validation_generator, 60, 'inception_v3_model_weights.h5', is_load_model=False)
# 训练的acc_loss图
# transfer.plot_training(history_ft)
