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author
shixiaowen03
committed
transformer & deepfm step by step
1 parent 68a5f9c commit ae33497

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Lines changed: 3460 additions & 241 deletions

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‎.idea/workspace.xml‎

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‎basic/.ipynb_checkpoints/六一-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from skimage import io\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"num = 1\n",
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"\n",
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"\n",
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"def show():\n",
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" global num\n",
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"\n",
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"\n",
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" while (num < 5):\n",
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" img = io.imread('/Users/meituan_sxw/PycharmProjects/tensorflow1.2/basic/pic/' + str(num) + '.jpeg')\n",
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" io.imshow(img)\n",
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" plt.ion()\n",
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" plt.pause(0.01)\n",
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" #input(\"Press Enter to Continue\") # 之后改成识别输出\n",
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" num += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"JPEG (200, 110) RGB\n",
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"JPEG (200, 150) RGB\n",
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"JPEG (200, 150) RGB\n",
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"JPEG (200, 112) RGB\n",
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"JPEG (200, 112) RGB\n",
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"JPEG (200, 133) RGB\n",
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"JPEG (200, 132) RGB\n",
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"JPEG (200, 120) RGB\n",
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"JPEG (187, 150) RGB\n",
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"JPEG (200, 122) RGB\n",
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"JPEG (200, 133) RGB\n",
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"JPEG (200, 120) RGB\n",
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"JPEG (200, 124) RGB\n",
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"JPEG (200, 150) RGB\n",
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"JPEG (200, 124) RGB\n",
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"JPEG (200, 150) RGB\n",
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"JPEG (200, 141) RGB\n",
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"JPEG (200, 132) RGB\n",
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"JPEG (200, 113) RGB\n",
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"JPEG (200, 112) RGB\n",
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"JPEG (200, 110) RGB\n",
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"JPEG (200, 150) RGB\n",
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"JPEG (200, 124) RGB\n",
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"JPEG (200, 120) RGB\n",
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"JPEG (187, 150) RGB\n",
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"JPEG (187, 150) RGB\n",
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"JPEG (200, 124) RGB\n",
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"JPEG (200, 130) RGB\n",
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"JPEG (200, 112) RGB\n",
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"JPEG (200, 150) RGB\n",
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"JPEG (200, 124) RGB\n",
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"JPEG (200, 134) RGB\n",
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"JPEG (200, 144) RGB\n",
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"JPEG (200, 112) RGB\n",
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"JPEG (200, 112) RGB\n",
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"JPEG (200, 112) RGB\n"
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]
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}
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],
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"source": [
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"from PIL import Image\n",
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"import glob\n",
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"img_path = glob.glob(\"/Users/meituan_sxw/PycharmProjects/tensorflow1.2/basic/pic/*.jpeg\")\n",
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"path_save = \"/Users/meituan_sxw/PycharmProjects/tensorflow1.2/basic/pic\"\n",
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"for file in img_path:\n",
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" name = os.path.join(path_save, file)\n",
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" im = Image.open(file)\n",
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" im.thumbnail((200,150))\n",
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" print(im.format, im.size, im.mode)\n",
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" im.save(name,'JPEG')\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"34\n",
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"35\n",
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"36\n"
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]
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}
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],
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"source": [
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import imageio,os\n",
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"images = []\n",
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"num=34\n",
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"maxnum=36\n",
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"#filenames=sorted((fn for fn in os.listdir('/Users/meituan_sxw/PycharmProjects/tensorflow1.2/basic/pic/') if fn.endswith('.jpeg')))\n",
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"while (num <= maxnum):\n",
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" print(num)\n",
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" images.append(imageio.imread('/Users/meituan_sxw/PycharmProjects/tensorflow1.2/basic/pic/' + str(num) + '.jpeg'))\n",
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" num += 1\n",
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"imageio.mimsave('wangqiuwangzi.gif', images,duration=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "python35",
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"language": "python",
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"name": "python35"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎basic/LR.py‎

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from sklearn.linear_model import LogisticRegression
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import pandas as pd
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import math
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data = pd.read_table('/Users/meituan_sxw/Downloads/flower.txt',header=None)
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data.columns = ['f1','f2','f3','f4','flower']
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data['label'] = data['flower'].map(lambda x:0 if x=='setosa' else 1)
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print(data)
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x = data[['f1','f2','f3','f4']].values.tolist()
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y = data[['label']]
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lr = LogisticRegression(penalty="l2",fit_intercept=False)
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lr.fit(x,y)
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print(lr.predict_proba(x))
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print(1 / (1 + math.pow(math.e,5.1*(-0.40247392)+3.5*(-1.46382925)+1.4*2.23785648+0.2 * 1.00009294+(-0.25906453))))
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print(lr.coef_)
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print(lr.__dict__)

‎basic/basic.py‎

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‎basic/donghua.py‎

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from skimage import io
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import matplotlib.pyplot as plt
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num = 1
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def show():
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global num
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while (num < 5):
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img = io.imread('pi1c/' + str(num) + '.jpeg')
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io.imshow(img)
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plt.ion()
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plt.pause(0.01)
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input("Press Enter to Continue") # 之后改成识别输出
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num += 1
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if __name__ == '__main__':
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show()

‎basic/gif.gif‎

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‎basic/gif/duolaameng.gif‎

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‎basic/gif/guangnengshizhe.gif‎

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‎basic/gif/labixiaoxin.gif‎

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‎basic/gif/lanmaotaoqi.gif‎

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