diff --git a/test_abs.py b/test_abs.py
index 636896c..30d8fbe 100644
--- a/test_abs.py
+++ b/test_abs.py
@@ -3,7 +3,7 @@
 from torch import nn
 import numpy as np
 import matplotlib.pyplot as plt
-from skimage.measure import compare_ssim,compare_psnr, compare_mse
+from skimage.metrics import structural_similarity, mean_squared_error
 from utils import compute_psnr
 
 from dataset import *
@@ -73,9 +73,9 @@ def test_abs(n_test,n_batch,n_steps,alpha,u,x_test):
         x_test_rec[epoch_idx[iters*n_batch:np.min([(iters+1)*n_batch,n_test])],:,:,:] = x_est.cpu().detach().numpy()
 
     
-    mse_list = [compare_mse(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
+    mse_list = [mean_squared_error(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
     psnr_list = [compute_psnr(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
-    ssim_list = [compare_ssim(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
+    ssim_list = [structural_similarity(x_test[i,0,:,:],x_test_rec[i,0,:,:]) for i in range(n_test)]
     print(f'mse {np.mean(mse_list):.2f}')
     print(f'psnr {np.mean(psnr_list):.2f}')
     print(f'ssim {np.mean(ssim_list):.2f}')
diff --git a/train_abs.py b/train_abs.py
index b9bcbcd..3f889e8 100644
--- a/train_abs.py
+++ b/train_abs.py
@@ -3,7 +3,7 @@
 from torch import nn
 import numpy as np
 import matplotlib.pyplot as plt
-from skimage.measure import compare_ssim,compare_psnr, compare_mse
+from skimage.metrics import peak_signal_noise_ratio
 
 from pathlib import Path
 from dataset import *
@@ -18,7 +18,7 @@ def train_abs(n_epoch,n_train,n_batch,alpha,lr_u,n_steps,U_range,dataset,x_train
     _, height, width, nc = x_train.shape
     
     zeropad = nn.ZeroPad2d(height//2)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     dirs = f'./output_abs/20200202_{dataset}_{n_epoch}_{n_train//n_batch}_{n_batch}_{alpha}_{lr_u}_{n_steps}_{U_range}/'
     disk_dir = Path(dirs)
@@ -141,7 +141,8 @@ def train_abs(n_epoch,n_train,n_batch,alpha,lr_u,n_steps,U_range,dataset,x_train
         psnr = 20*np.log10((np.max(x_train)-np.min(x_train))/np.sqrt(mse))
         print(f'psnr {psnr}')
         print(f'mse {mse}')
-        psnr_list = [compare_psnr(x_train[i,0,:,:],x_train_rec[i,0,:,:]) for i in range(n_train)]
+        psnr_list = [peak_signal_noise_ratio(x_train[i,0,:,:],x_train_rec[i,0,:,:]) for i in range(n_train)]
+