diff --git a/pytorch_tabnet/utils.py b/pytorch_tabnet/utils.py
index ab2bbec2..94ed2182 100644
--- a/pytorch_tabnet/utils.py
+++ b/pytorch_tabnet/utils.py
@@ -1,9 +1,7 @@
 from torch.utils.data import Dataset
-import matplotlib.pyplot as plt
 from torch.utils.data import DataLoader, WeightedRandomSampler
 import torch
 import numpy as np
-from IPython.display import clear_output
 
 
 class TorchDataset(Dataset):
@@ -101,42 +99,6 @@ def create_dataloaders(X_train, y_train, X_valid, y_valid, weights, batch_size):
     return train_dataloader, valid_dataloader
 
 
-def plot_losses(losses_train, losses_valid, metrics_train, metrics_valid):
-    """
-    Plot train and validation losses.
-
-    Parameters
-    ----------
-        losses_train : list
-            list of train losses per epoch
-        losses_valid : list
-            list of valid losses per epoch
-        metrics_train : list
-            list of train metrics per epoch
-        metrics_valid : list
-            list of valid metrics per epoch
-    Returns
-    ------
-    plot
-    """
-    clear_output()
-    plt.figure(figsize=(15, 5))
-    plt.subplot(1, 2, 1)
-    plt.plot(range(len(losses_train)), losses_train, label='Train')
-    plt.plot(range(len(losses_valid)), losses_valid, label='Valid')
-    plt.grid()
-    plt.title('Losses')
-    plt.legend()
-
-    plt.subplot(1, 2, 2)
-    plt.plot(range(len(metrics_train)), metrics_train, label='Train')
-    plt.plot(range(len(metrics_valid)), metrics_valid, label='Valid')
-    plt.grid()
-    plt.title('Training Metrics')
-    plt.legend()
-    plt.show()
-
-
 def create_explain_matrix(input_dim, cat_emb_dim, cat_idxs, post_embed_dim):
     """
     This is a computational trick.