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')
[站外图片上传中...(image-bca30d-1522332275233)]
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。
授权 http://www.tensorflownews.com/ 转载
原文链接：https://hadxu.github.io/