iteration parameter

Jenkinsfile_train

@@ -2,12 +2,19 @@ pipeline {
     agent {
         dockerfile true
         }
+	parameters {
+		string(
+			defaultValue: '2',
+			description: 'learning iterations',
+			name: 'epoch'
+		)
+	}
     stages {
         stage('Stage 1') {
             steps {
 					copyArtifacts filter: 'data.csv', fingerprintArtifacts: true, projectName: 's444386-create-dataset', selector: lastSuccessful()
 				    sh 'chmod u+x ./biblioteki_dl.py'
-					sh 'python3 biblioteki_dl.py'
+					sh 'python3 biblioteki_dl.py $epoch'
 					sh 'tar -czf model.tar.gz model/'
 					archiveArtifacts 'model.tar.gz'
 					archiveArtifacts 'xtest.csv'

biblioteki_dl.py

@@ -4,10 +4,13 @@ import pandas as pd
 import numpy as np
 import csv
 from sklearn.model_selection import train_test_split
+import sys
 
 # os.system("kaggle datasets download -d tamber/steam-video-games")
 # os.system("unzip -o steam-video-games.zip")
 
+epoch = int(sys.argv[1])
+
 steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
 steam.isnull().values.any()
 steam['userId'] = steam.userId.astype(str)
@@ -109,7 +112,7 @@ y_test = np.array(y_test).astype(np.float32)
 
 
 
-model.fit(x_train, y_train, epochs=2)
+model.fit(x_train, y_train, epochs=epoch)
 model.evaluate(x_test, y_test)
 prediction = model.predict(x_test)
 classes_x=np.argmax(prediction,axis=1)