keras中使用预训练模型进行图片分类

keras中含有多个网络的预训练模型，可以很方便的拿来进行使用。

安装及使用主要参考官方教程：https://keras.io/zh/applications/   https://keras-cn.readthedocs.io/en/latest/other/application/

官网上给出了使用 ResNet50 进行 ImageNet 分类的样例

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

model = ResNet50(weights='imagenet')

img_path = 'elephant.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=3)[0])
# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]

那么对于其他的网络，便可以参考此代码

首先vgg19

# coding: utf-8
from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
import numpy as np
base_model = VGG19(weights='imagenet', include_top=True)
model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc2').output)
img_path = '../mdataset/img_test/p2.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
fc2 = model.predict(x)
print(fc2.shape)  #(1, 4096)

然后mobilenet

# coding: utf-8
from keras.applications.mobilenet import MobileNet
from keras.preprocessing import image
from keras.applications.mobilenet import preprocess_input,decode_predictions
from keras.models import Model
import numpy as np
import time

model = MobileNet(weights='imagenet', include_top=True,classes=1000)

start = time.time()

img_path = '../mdataset/img_test/dog.jpg'
img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=15)[0])
end = time.time()

print('time:
')
print str(end-start)

时间统计时伪统计加载模型的时间，大概需要不到1秒，如果把加载模型的时间算进去，大概3s左右