add training

README.md

@@ -18,6 +18,8 @@ The container downloads the dataset and installs software needed,
 then trains and evaluates model on the dataset.
 Loss and accuracy are saved to test_eval.txt file.
 
+### Zadanie 6
+added create, train, eval directories in lab5
+
 
 ium01.ipynb is a notebook used to develop previously mentioned scripts.
-

lab5/train/Jenkinsfile

@@ -0,0 +1,49 @@
+pipeline {
+    agent none
+    stages {
+        stage('copy files') {
+            agent any
+                steps {
+                    sh '''
+                        cp ./lab5/train/script.sh .
+                        cp ./lab5/train/train.py .
+                        cp ./lab5/train/requirements.txt .
+                        '''
+                }
+        }
+        stage('docker') {
+            agent {
+                dockerfile true
+                parameters {
+                	buildSelector(
+                		defaultSelector: lastSuccessful(),
+                		description: 'Which build to use for copying artifacts',
+                		name: 'BUILD_SELECTOR')
+
+                		string(name: 'LEARNING_RATE', defaultValue: '0.0003', description: 'learning rate')
+                }
+            }
+            stages {
+                stage('copyArtifacts') {
+                    steps {
+                        copyArtifacts fingerprintArtifacts: true, projectName: 's434684-create-dataset', selector: buildParameter('BUILD_SELECTOR')
+                    }
+                }
+                stage('ls') {
+                    steps {
+                        sh '''
+                        chmod +x script.sh
+                        ./script.sh
+                        ls -lah
+                            '''
+                    }
+                }
+                stage('archive artifact') {
+                    steps {
+                        archiveArtifacts 'grid-stability-dense.h5'
+                    }
+                }
+            }
+        }
+    }
+}

lab5/train/script.sh

@@ -1,3 +1,3 @@
 #!/bin/bash
 
-python3 train.py
+python3 train.py "$1"

lab5/train/train.py

@@ -1,4 +1,6 @@
+import numpy as np
 import pandas as pd
+import sys
 import tensorflow as tf
 from tensorflow.keras import layers
 
@@ -24,7 +26,7 @@ model = tf.keras.Sequential([
 
 model.compile(
     loss=tf.losses.BinaryCrossentropy(),
-    optimizer=tf.optimizers.Adam(),
+    optimizer=tf.optimizers.Adam(lr=float(sys.argv[1])),
     metrics=[tf.keras.metrics.BinaryAccuracy()])
 
 
@@ -32,4 +34,3 @@ history = model.fit(tf.convert_to_tensor(X_train, np.float32),
                     Y_train, epochs=2, validation_data=(X_valid, Y_valid))
 
 model.save('grid-stability-dense.h5')
-