Zadanie 6

Jenkinsfile_train

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+pipeline {
+    agent {dockerfile true}
+	parameters {
+		buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: 'Which build to use for copying artifacts',
+					name: 'BUILD_SELECTOR')
+		string(
+            defaultValue: '500',
+            description: 'Enter the number of Epochs',
+            name: 'epochs',
+            trim: false)
+	}
+
+    stages {
+		stage('copyArtifacts') {
+            steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's434788-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+				sh 'python3 lab06_training.py ${epochs}'
+            }
+        }
+
+		stage('Archive artifacts') {
+			steps{
+				archiveArtifacts 'model_movies/**'
+			}
+		}
+	}
+
+    post {
+        success {
+
+            build job: 's430705-evaluation/master'
+            mail body: 'SUCCESS',
+                subject: 's430705',
+                to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            mail body: 'UNSTABLE', subject: 's430705', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            mail body: 'FAILURE', subject: 's430705', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            mail body: 'CHANGED', subject: 's430705', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+    }
+}

Zadanie_06_training.py

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+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+import os
+
+url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
+wget.download(url, out='winequality-red.csv', bar=None)
+
+wine=pd.read_csv('winequality-red.csv')
+wine
+
+y = wine.quality
+y.head()
+
+x = wine.drop(['quality'], axis= 1)
+x.head()
+
+x=((x-x.min())/(x.max()-x.min())) #Normalizacja
+
+x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
+
+def regression_model():
+    model = Sequential()
+    model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+    model.add(Dense(64,activation = "relu"))
+    model.add(Dense(1,activation = "relu"))
+    
+    model.compile(optimizer = "adam", loss = "mean_squared_error")
+    return model
+
+model = regression_model()
+model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+model.save('wine_model')