DataCastle［猫狗大战］ ——Yinjh比赛全部代码

DataCastle猫狗大战参赛者需要从训练集里建立一个模型去识别测试集里的小狗来。谁能识别出最多的数量，谁的成绩就更好。

参赛者需要提交你识别出认为是小狗图片的图片名，不包括文件类型，例如：xabcd.jpg,则提交的uid为xabcd。详情参考示例提交文件。

---

import os

import glob



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



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



if __name__ == "__main__":



    # Test pretrained 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')



    dogs=[251, 268, 256, 253, 255, 254, 257, 159, 211, 210, 212, 214, 213, 216, 215, 219, 220, 221, 217, 218, 207, 209, 206, 205, 208, 193, 202, 194, 191, 204, 187, 203, 185, 192, 183, 199, 195, 181, 184, 201, 186, 200, 182, 188, 189, 190, 197, 196, 198, 179, 180, 177, 178, 175, 163, 174, 176, 160, 162, 161, 164, 168, 173, 170, 169, 165, 166, 167, 172, 171, 264, 263, 266, 265, 267, 262, 246, 242, 243, 248, 247, 229, 233, 234, 228, 231, 232, 230, 227, 226, 235, 225, 224, 223, 222, 236, 252, 237, 250, 249, 241, 239, 238, 240, 244, 245, 259, 261, 260, 258, 154, 153, 158, 152, 155, 151, 157, 156]



    cats=[281,282,283,284,285,286,287]



    path = os.path.join('imgs', 'test', '*.jpg')

    files = glob.glob(path)

    result=[]



    for fl in files:

        flbase = os.path.basename(fl)



        im = cv2.resize(cv2.imread(fl), (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)



        out = model.predict(im)

        p = np.sum(out[0,dogs]) / (np.sum(out[0,dogs]) + np.sum(out[0,cats]))

        result.append((flbase,p))



    result=sorted(result, key=lambda x:x[1], reverse=True)

    for x in result:

        #print x[0],x[1]

        print x[0]

---

在DataCastle查看原文

---