Update for Zadanie 6.2 - training

Jenkinsfile_training

@@ -0,0 +1,24 @@
+pipeline {
+    agent {dockerfile true}	
+	
+    stages {
+		stage('copyArtifacts') {
+            steps {
+                copyArtifacts fingerprintArtifacts: true, projectName: 's434684-create-dataset', selector: latestSuccessful(true)
+            }
+        }
+		stage('Sh script') {
+			steps {
+                sh 'chmod +x run_training.sh'
+				sh './run_training.sh ${params.LEARNING_RATE}'
+                
+			
+			}
+		}
+		stage('Archive artifacts') {
+			steps{
+				archiveArtifacts artifacts: 'model_movies'
+			}
+		}
+    }
+}

ium_zadanie6_training.py

@@ -0,0 +1,34 @@
+import tensorflow as tf
+import sys
+from tf.keras import layers
+# from keras.layers import Flatten,Dense,Dropout, GlobalAveragePooling2D
+from tf.keras.optimizers import Adam
+import numpy as np
+import pandas as pd
+from sklearn.metrics import mean_squared_error
+
+movies_train = pd.read_csv('movies_train.csv')
+
+x_train = movies_train.copy()
+y_train = x_train.pop('rottentomatoes_audience_score')
+x_train.pop('Unnamed: 0')
+
+learning_rate = sys.argv[1]
+
+model = tf.keras.Sequential()
+model.add(layers.Input(shape=(22,)))
+model.add(layers.Dense(64))
+model.add(layers.Dense(64))
+model.add(layers.Dense(32))
+model.add(layers.Dense(1))
+
+model.compile(loss='mean_absolute_error', optimizer=Adam(learning_rate))
+
+model.fit(
+    x = tf.convert_to_tensor(x_train, np.float32),
+    y = y_train,
+    verbose=0, epochs=99)
+	
+model.save('model_movies')
+	
+

run_training.sh

@@ -0,0 +1,2 @@
+#!/bin/bash
+python3 ium_zadanie6_training.py