diff --git a/lab5/eval/Jenkinsfile b/lab5/eval/Jenkinsfile
index dedb04c..3c98e2d 100644
--- a/lab5/eval/Jenkinsfile
+++ b/lab5/eval/Jenkinsfile
@@ -4,7 +4,9 @@ pipeline {
         buildSelector(
             defaultSelector: lastSuccessful(),
             description: 'Which build to use for copying artifacts',
-            name: 'BUILD_SELECTOR')
+            name: 'BUILD_SELECTOR'
+        )
+        gitParameter branchFilter: 'origin/(.*)', defaultValue: 'master', name: 'BRANCH', type: 'PT_BRANCH'
     }
     stages {
         stage('copy files') {
@@ -23,8 +25,16 @@ pipeline {
             stages {
                 stage('copyArtifacts') {
                     steps {
-                        copyArtifacts fingerprintArtifacts: true, projectName: 's470607-create-dataset', selector: buildParameter('BUILD_SELECTOR')
-                        copyArtifacts fingerprintArtifacts: true, projectName: 's470607-training/master', selector: buildParameter('BUILD_SELECTOR')
+                        copyArtifacts   fingerprintArtifacts: true,
+                                        projectName: 's470607-create-dataset',
+                                        selector: buildParameter('BUILD_SELECTOR')
+                        copyArtifacts   fingerprintArtifacts: true,
+                                        projectName: 's470607-training/${params.BRANCH}',
+                                        selector: buildParameter('BUILD_SELECTOR')
+                        copyArtifacts   fingerprintArtifacts: true,
+                                        optional: true,
+                                        projectName: 's470607-evaluation/${params.BRANCH}',
+                                        selector: buildParameter('BUILD_SELECTOR')
                     }
                 }
                 stage('ls') {
diff --git a/lab5/eval/eval.py b/lab5/eval/eval.py
index b11782c..4f3774f 100644
--- a/lab5/eval/eval.py
+++ b/lab5/eval/eval.py
@@ -35,7 +35,7 @@ model = tensorflow.keras.Sequential([
 
 model.compile(
     loss=tensorflow.keras.losses.BinaryCrossentropy(),
-    optimizer=tensorflow.keras.optimizers.Adam(lr=float(sys.argv[1])),
+    optimizer=tensorflow.keras.optimizers.Adam(),
     metrics=[tensorflow.keras.metrics.BinaryAccuracy()])
 
 history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))