finish of 6_1
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@ -41,4 +41,12 @@ pipeline {
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}
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}
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}
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post {
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success {
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build job: 's434788-training/master'
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mail body: 'SUCCESS',
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subject: 's434788 Creating dataset',
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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@ -4,7 +4,8 @@ from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import numpy as np
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import os
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import sys
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wine=pd.read_csv('train.csv')
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wine
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@ -21,14 +22,15 @@ x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_si
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def regression_model():
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model = Sequential()
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model.add(Dense(16,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(32,activation = "relu"))
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model.add(Dense(4,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(8,activation = "relu"))
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model.add(Dense(8,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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model = regression_model()
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model.fit(x_train, y_train, epochs = 40, verbose = 1)
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model.fit(x_train, y_train, batch_size=int(sys.argv[1]), epochs = sys.argv[2]) #verbose = 1
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model.save('wine_model')
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