数据集:链接:https://pan.baidu.com/s/1KHv3-2FwXgAsaEHHSb3Vrg
提取码:rpxv
或者在移动盘中的data文件夹中,创建两个文件夹,train、test;两个文件夹中分别创建两个文件cat、dog;train各1000个,test各500个
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras.optimizers import SGD
import os
# In[2]:
# 载入预训练的VGG16模型,不包括全连接层
vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(150,150,3))
# In[3]:
# 搭建全连接层
top_model = Sequential()
top_model.add(Flatten(input_shape=vgg16_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(2, activation='softmax'))
# 载入训练过的权值
top_model.load_weights('bottleneck_fc_model.h5')
model = Sequential()
model.add(vgg16_model)
model.add(top_model)
# In[4]:
# 训练集数据生成
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# 测试集数据处理
test_datagen = ImageDataGenerator(rescale=1./255)
# In[5]:
batch_size = 32
# 生成训练数据
train_generator = train_datagen.flow_from_directory(
'image/train', # 训练数据路径
target_size=(150, 150), # 设置图片大小
batch_size=batch_size # 批次大小
)
# 测试数据
test_generator = test_datagen.flow_from_directory(
'image/test', # 训练数据路径
target_size=(150, 150), # 设置图片大小
batch_size=batch_size # 批次大小
)
# In[6]:
model.compile(loss='categorical_crossentropy',
optimizer=SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
# 统计文件个数
totalFileCount = sum([len(files) for root, dirs, files in os.walk('image/train')])
model.fit_generator(
train_generator,
steps_per_epoch=totalFileCount/batch_size,
epochs=10,
validation_data=test_generator,
validation_steps=1000/batch_size,
)