diff --git a/dataset.jenkinsfile b/dataset.jenkinsfile
index 5db7710..de6f607 100644
--- a/dataset.jenkinsfile
+++ b/dataset.jenkinsfile
@@ -1,6 +1,8 @@
 node {
 	checkout scm
 
+	stage('Configuration')
+
 	def local_image = docker.build("s452639-image")
 
 	local_image.inside {
@@ -11,10 +13,10 @@ node {
 			archiveArtifacts artifacts: 'src/stop_times.normalized.tsv,src/stop_times.train.tsv,src/stop_times.test.tsv,src/stop_times.valid.tsv,src/stop_times.categories.tsv',
 				followSymlinks: false
 		}
+	}
 
-		stage('Trigger') {
-			build wait: false, job: 's452639-training'
-		}
+	stage('Trigger') {
+		build wait: false, job: 's452639-training'
 	}
 }
 
diff --git a/eval.jenkinsfile b/eval.jenkinsfile
new file mode 100644
index 0000000..7d5dac9
--- /dev/null
+++ b/eval.jenkinsfile
@@ -0,0 +1,39 @@
+node {
+	checkout scm
+
+	def local_image = docker.build("s452639-image")
+
+	local_image.inside {
+		stage('Prepare artifacts') {
+			try {
+				copyArtifacts(projectName: currentBuild.projectName,
+					selector: specific("${currentBuild.previousBuild.number}")),
+					flatten: true,
+					target: 'src/'
+
+			} catch (err) {
+				echo("with new accuracy log")
+			}
+		}
+
+		stage('Evaluate') {
+			checkout([$class: 'GitSCM', branches: [[name: 'ztm']], extensions: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s452639/ium_452639']]])
+
+			copyArtifacts fingerprintArtifacts: true,
+				projectName: 's452639-create-dataset',
+				selector: lastSuccessful(),
+				flatten: true,
+				target: 'src/'
+
+			copyArtifacts fingerprintArtifacts: true,
+				projectName: 's452639-training',
+				selector: lastSuccessful(),
+				flatten: true,
+				target: 'src/'
+
+			sh 'cd src; python tf_test.py'
+			archiveArtifacts artifacts: 'src/stop_times.predictions.tsv,src/stop_times.accuracy.tsv'
+		}
+	}
+}
+
diff --git a/src/tf_test.py b/src/tf_test.py
index 5afbe5a..d8a292b 100644
--- a/src/tf_test.py
+++ b/src/tf_test.py
@@ -1,5 +1,6 @@
 from tf_train import *
 import numpy as np
+from sklearn.metrics import accuracy_score
 
 def test():
     global model, le
@@ -8,7 +9,15 @@ def test():
     test_y = tf.convert_to_tensor(test_y)
 
     model = tf.keras.models.load_model('model.keras')
-    pd.DataFrame(model.predict(test_x), columns=le.classes_).to_csv('stop_times.predictions.tsv', sep='\t')
+    predictions = np.argmax(model.predict(test_x), 1)
+
+    with open('stop_times.predictions.tsv', 'w') as f:
+        f.write('stop_headsign\n')
+        for x in le.inverse_transform(predictions):
+            print(x, file=f)
+
+    with open('stop_times.accuracy.tsv', 'a') as f:
+        print(accuracy_score(test_y, predictions), file=f, sep='\t')
 
 
 if __name__ == "__main__":
diff --git a/src/tf_train.py b/src/tf_train.py
index 88f05a5..caeb2d1 100755
--- a/src/tf_train.py
+++ b/src/tf_train.py
@@ -23,7 +23,7 @@ def load_data(path: str, le: LabelEncoder):
 num_classes = len(le.classes_)
 
 
-def train():
+def train(epochs: int):
     global le
 
     model = tf.keras.Sequential([
@@ -46,13 +46,10 @@ def train():
     valid_x = tf.convert_to_tensor(valid_x, dtype=tf.float32)
     valid_y = tf.convert_to_tensor(valid_y)
 
-    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath='checkpoint.ckpt', save_weights_only=True)
-    history = model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=2, batch_size=1024, callbacks=[model_checkpoint_callback])
-
-    with open('history', 'w') as f:
-        print(repr(history), file=f)
-
+    model.fit(train_x, train_y, validation_data=(valid_x, valid_y), epochs=epochs, batch_size=1024)
     model.save('model.keras')
 
 if __name__ == "__main__":
-    train()
+    import sys
+    epochs = int('2' if len(sys.argv) != 2 else sys.argv[1])
+    train(epochs)
diff --git a/training.jenkinsfile b/training.jenkinsfile
index c5c3235..a60173f 100644
--- a/training.jenkinsfile
+++ b/training.jenkinsfile
@@ -1,6 +1,25 @@
 node {
 	checkout scm
 
+	stage('Init') {
+		properties([
+			pipelineTriggers([ upstream(threshold: hudson.model.Result.SUCCESS, upstreamProjects: 's452639-create-dataset' ) ]),
+			parameters([
+				buildSelector(
+					defaultSelector: lastSuccessful(),
+					description: "Source of dataset",
+					name: 'BUILD_SELECTOR'
+				),
+				string(
+					defaultValue: "2",
+					description: "Epochs count",
+					name: "EPOCHS",
+					trim: true
+				),
+			])
+		])
+	}
+
 	def local_image = docker.build("s452639-image")
 
 	local_image.inside {
@@ -13,9 +32,13 @@ node {
 				flatten: true,
 				target: 'src/'
 
-			sh 'cd src; python tf_train.py'
+			sh 'cd src; python tf_train.py $EPOCHS'
 			archiveArtifacts artifacts: 'src/model.keras', followSymlinks: false
 		}
 	}
+
+	stage('Trigger') {
+		build wait: false, job: 's452639-evaluation.eg'
+	}
 }