diff --git a/modules/deeplearning/srcnn.py b/modules/deeplearning/srcnn.py
index 0af63accf0a776ffb7af78297e0e15e9e03af855..6b56b7af37c5fea3e8b7e9386b53ab0960bb12f8 100644
--- a/modules/deeplearning/srcnn.py
+++ b/modules/deeplearning/srcnn.py
@@ -61,10 +61,14 @@ label_param = label_params[label_idx]
 
 x_134 = np.arange(134)
 y_134 = np.arange(134)
-#x_134_2 = x_134[3:131:2]
-#y_134_2 = y_134[3:131:2]
-x_134_2 = x_134[2:133:2]
-y_134_2 = y_134[2:133:2]
+x_64 = np.arange(64)
+y_64 = np.arange(64)
+x_134_2 = x_134[3:131:2]
+y_134_2 = y_134[3:131:2]
+#x_134_2 = x_134[2:133:2]
+#y_134_2 = y_134[2:133:2]
+t = np.arange(0, 64, 0.5)
+s = np.arange(0, 64, 0.5)
 
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME', kernel_initializer='he_uniform', scale=None):
@@ -202,13 +206,12 @@ class SRCNN:
         data = np.concatenate(label_s)
         label = data.copy()
 
-        data = data[:, data_idx, :, :]
-        data = resample(x_134, y_134, data, x_134_2, y_134_2)
+        data = data[:, data_idx, 3:131:2, 3:131:2]
+        data = resample(x_64, y_64, data, t, s)
         data = np.expand_dims(data, axis=3)
 
-        # label = label[:, label_idx, :, :]
-        label = label[:, label_idx, 3:131:2, 3:131:2]
-        # label = label[:, label_idx, 3:67, 3:67]
+        # label = label[:, label_idx, 3:131:2, 3:131:2]
+        label = label[:, label_idx, 3:131, 3:131]
         label = np.expand_dims(label, axis=3)
 
         data = data.astype(np.float32)