add .keras

lab5/eval/Dockerfile

@@ -8,7 +8,7 @@ RUN apt install -y unzip >>/dev/null
 
 WORKDIR /app
 
-COPY ./test_eval.py ./
+COPY ./eval.py ./
 COPY ./script.sh ./
 RUN chmod +x script.sh
 

lab5/eval/Jenkinsfile

@@ -0,0 +1,49 @@
+pipeline {
+    agent none
+    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('copy files') {
+            agent any
+                steps {
+                    sh '''
+                        cp ./lab5/train/Dockerfile .
+                        cp ./lab5/train/script.sh .
+                        cp ./lab5/train/eval.py .
+                        cp ./lab5/train/requirements.txt .
+                        '''
+                }
+        }
+        stage('docker') {
+            agent { dockerfile true }
+            stages {
+                stage('copyArtifacts') {
+                    steps {
+                        copyArtifacts fingerprintArtifacts: true, projectName: 's470607-training', selector: buildParameter('BUILD_SELECTOR')
+                    }
+                }
+                stage('ls') {
+                    steps {
+                        sh '''
+                        chmod +x script.sh
+                        ls -lah
+                        ./script.sh
+                        ls -lah
+                            '''
+                    }
+                }
+                stage('archive artifact') {
+                    steps {
+                        archiveArtifacts 'eval.csv'
+                    }
+                }
+            }
+        }
+    }
+}

lab5/train/train.py

@@ -25,7 +25,8 @@ model = tf.keras.Sequential([
 ])
 
 model.compile(
-    optimizer=tf.optimizers.Adam(lr=float(sys.argv[1])),
+    loss=tf.keras.losses.BinaryCrossentropy(),
+    optimizer=tf.keras.optimizers.Adam(lr=float(sys.argv[1])),
     metrics=[tf.keras.metrics.BinaryAccuracy()])