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1.全民瘋AI系列2.0目標介紹/README.html

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11.SVM/11.1.SVM(Classification-iris).ipynb

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12.決策樹/12.1.決策樹(Classification-iris).ipynb

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2.快來探索AI的世界/README.html

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24.不能忽視的過擬合與欠擬合/README.html

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27.機器學習常犯錯的十件事/README.html

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28.儲存訓練好的模型/28.XGBoost(Classification-iris).ipynb

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# -*- coding: UTF-8 -*-
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import pickle
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import gzip
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# 載入Model
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with gzip.open('./model/xgboost-iris.pgz', 'rb') as f:
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xgboostModel = pickle.load(f)
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def predict(input):
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pred=xgboostModel.predict(input)[0]
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return pred
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Flask
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Flask-Cors
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numpy
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scikit-learn
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xgboost
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# -*- coding: UTF-8 -*-
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import numpy as np
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import model
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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app = Flask(__name__)
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CORS(app)
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@app.route('/')
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def index():
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return 'hello!!'
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@app.route('/predict', methods=['POST'])
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def postInput():
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# 取得前端傳過來的數值
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insertValues = request.get_json()
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x1=insertValues['sepalLengthCm']
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x2=insertValues['sepalWidthCm']
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x3=insertValues['petalLengthCm']
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x4=insertValues['petalWidthCm']
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input = np.array([[x1, x2, x3, x4]])
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result = model.predict(input)
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return jsonify({'return': str(result)})
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=3000, debug=True)

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