epochs parameter

JenkinsfileNeural

@@ -5,7 +5,11 @@ node {
                     parameters([
                         buildSelector(defaultSelector: lastSuccessful(),
                             description: 'Which build to use for copying artifacts',
-                            name: 'BUILD_SELECTOR')
+                            name: 'BUILD_SELECTOR'),
+                        string(defaultValue: '30',
+                            description: 'Amount of epochs',
+                            name: 'EPOCHS',
+                            trim: false),
                             ])
                 ]
                 )
@@ -18,24 +22,23 @@ node {
                             copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
                             sh '''
                                 #!/usr/bin/env bash
-                                chmod 777 neural_network.sh
+                                chmod 777 neural_network.sh ${params.KAGGLE_USERNAME}
                                 ./neural_network.sh | tee output.txt
                             '''
                             archiveArtifacts 'output.txt'
                             archiveArtifacts 'model/**/*.*'
                         }
-                        emailext body: 'Successful build',
+                        emailext body: 's434765',
                         subject: "Successful build",
                         to: "26ab8f35.uam.onmicrosoft.com@emea.teams.ms"
                         }
 
                     }
                      catch (e) {
-                            emailext body: 'Failed build',
+                            emailext body: 's434765',
                             subject: "Failed build",
                             to: "26ab8f35.uam.onmicrosoft.com@emea.teams.ms"
                             throw e
                      }
             }
         }
-    

neural_network.py

@@ -3,7 +3,7 @@ import numpy as np
 from sklearn.metrics import mean_squared_error
 
 from tensorflow import keras
-
+import sys
 
 def normalize_data(data):
     return (data - np.min(data)) / (np.max(data) - np.min(data))
@@ -40,7 +40,7 @@ model = keras.Sequential([
 
 model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])
 
-model.fit(X, y, epochs=30, validation_split = 0.3)
+model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3)
 
 data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
                    skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",

neural_network.sh

@@ -1,2 +1,2 @@
 #!/bin/bash
-python3 neural_network.py
+python3 neural_network.py $1