diff --git a/train_model.py b/train_model.py
index 62f2b4b..8f6bf60 100644
--- a/train_model.py
+++ b/train_model.py
@@ -10,7 +10,6 @@ import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 from torch.utils.data import Dataset, DataLoader
-import matplotlib.pyplot as plt
 import re
 import random
 import os
@@ -92,7 +91,6 @@ def dist(a: [str], b: [str]):
 
 
 def train_model(model):
-    plt.ion()
     optimizer = optim.Adam(filter(lambda x: x.requires_grad, model.parameters()),
                            lr=LEARNING_RATE)
     loss_snapshots = []
@@ -132,10 +130,6 @@ def train_model(model):
             total_loss += loss_scalar
             inner_bar.set_description("loss %.2f" % loss_scalar)
         loss_snapshots.append(total_loss / len(DATA) * 3)
-        plt.clf()
-        plt.plot(loss_snapshots, label="Avg loss ")
-        plt.legend(loc="upper left")
-        plt.pause(interval=0.01)
         # print()
         # print("Total epoch loss:", total_loss)
         # print("Total epoch avg loss:", total_loss / TOTAL_TRAINING_OUT_LEN)
@@ -145,7 +139,6 @@ def train_model(model):
         # print("Evaluation snapshots(%):", eval_snapshots_percentage)
         outer_bar.set_description("Epochs")
         outer_bar.update(1)
-    plt.ioff()
 
 
 def evaluate_monte_carlo(model, repeats):