使用vgg16模型进行图片预测

本文是全系列中第9 / 9篇:Keras 从入门到精通

使用vgg16模型进行图片预测

前面我们学习了使用cifra10来判断图片的类别,今天我们使用更加强大的已经训练好的模型来预测图片的类别,那就是vgg16,对应的供keras使用的模型人家已经帮我们训练好,我可不想卖肾来买一个gpu。。。
对应的模型在 ‘vgg16’ 可以下载。估计被墙了,附上链接(http://pan.baidu.com/s/1qX0CJSC)

导入必要的库

from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
import cv2, numpy as np

Using Theano backend.
D:\Anaconda\lib\site-packages\theano-0.8.0.dev0-py2.7.egg\theano\tensor\signal\downsample.py:5: UserWarning: downsample module has been moved to the pool module.
warnings.warn(“downsample module has been moved to the pool module.”)

使用keras建立vgg16模型

def VGG_16(weights_path=None):
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1000, activation='softmax'))

if weights_path:
model.load_weights(weights_path)

return model
model = VGG_16('vgg16_weights.h5')
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy')

现在我们开始来预测了

首先写一个方法来加载并处理图片

def load_image(imageurl):
im = cv2.resize(cv2.imread(imageurl),(224,224)).astype(np.float32)
im[:,:,0] -= 103.939
im[:,:,1] -= 116.779
im[:,:,2] -= 123.68
im = im.transpose((2,0,1))
im = np.expand_dims(im,axis=0)
return im

读取vgg16的类别文件

f = open('synset_words.txt','r')
lines = f.readlines()
f.close()
def predict(url):
im = load_image(url)
pre = np.argmax(model.predict(im))
print lines[pre]
%pylab inline

Populating the interactive namespace from numpy and matplotlib

from IPython.display import Image
Image('cat1.jpg')

开始预测

predict('cat1.jpg')

n02123045 tabby, tabby cat

Image('zebra.jpg')

predict('zebra.jpg')

n02391049 zebra

Image('airplane.jpg')

predict('airplane.jpg')

n02690373 airliner

Image('pig.jpg')

predict('pig.jpg')

n02395406 hog, pig, grunter, squealer, Sus scrofa

可见,判断率还是很高的。。。。

总结

通过这次学习,学会了使用keras来搭建模型,使用vgg16这个模型。

hadxu 授权 http://www.tensorflownews.com/ 转载
原文链接:https://hadxu.github.io/

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