diff --git a/Dockerfile b/Dockerfile
index e852858..2602a7f 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -8,6 +8,7 @@ RUN pip3 install pandas
 RUN pip3 install kaggle
 RUN pip3 install tensorflow
 RUN pip3 install sklearn
+RUN pip3 install matplotlib
 COPY ./data_dev ./
 COPY ./evaluate_network.py ./
 RUN mkdir /.kaggle
diff --git a/JenkinsfileEval b/JenkinsfileEval
index 88e7477..45225ce 100644
--- a/JenkinsfileEval
+++ b/JenkinsfileEval
@@ -19,7 +19,7 @@ node {
 
         }
         stage('Clone repo') {
-                    docker.image("karopa/ium:16").inside {
+                    docker.image("karopa/ium:20").inside {
                         stage('Test') {
                             checkout([$class: 'GitSCM', branches: [[name: '*/evaluation']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
                             copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_DATASET")
diff --git a/evaluate_network.py b/evaluate_network.py
index 782408a..e2cd8f0 100644
--- a/evaluate_network.py
+++ b/evaluate_network.py
@@ -2,15 +2,16 @@ import pandas as pd
 import numpy as np
 from sklearn.metrics import mean_squared_error
 from tensorflow import keras
+import matplotlib.pyplot as plt
 
 model = keras.models.load_model('model')
 data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
                    skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
-                                                                        "publish_date", "publish_hour", "category_id",
-                                                                        "channel_title", "views", "likes", "dislikes",
-                                                                        "comment_count"]).dropna()
-X_test = data.loc[:,data.columns == "views"].astype(int)
-y_test = data.loc[:,data.columns == "likes"].astype(int)
+                                                            "publish_date", "publish_hour", "category_id",
+                                                            "channel_title", "views", "likes", "dislikes",
+                                                            "comment_count"]).dropna()
+X_test = data.loc[:, data.columns == "views"].astype(int)
+y_test = data.loc[:, data.columns == "likes"].astype(int)
 
 min_val_sub = np.min(X_test)
 max_val_sub = np.max(X_test)
@@ -39,3 +40,11 @@ print(error)
 
 with open("rmse.txt", "a") as file:
     file.write(str(error) + "\n")
+
+with open("rmse.txt", "r") as file:
+    lines = file.readlines()
+    plt.plot(range(len(lines)), [line[:-2] for line in lines])
+    plt.tight_layout()
+    plt.ylabel('RMSE')
+    plt.xlabel('evaluation no')
+    plt.savefig('evaluation.png')