diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index aa2e49cd78854971b63c09cba3c99fd16d184d0f..894d05f7f476d0638f17258764ee242b5768d49a 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -65,21 +65,37 @@ x_134 = np.arange(134)
 y_134 = np.arange(134)
 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 = slice(3, 131, 2)
+y_134_2 = slice(3, 131, 2)
+# x_134_2 = x_134[3:131:2]
+# y_134_2 = y_134[3:131:2]
+
 t = np.arange(0, 64, 0.5)
 s = np.arange(0, 64, 0.5)
 
-x_128_2 = x_134[3:131:2]
-y_128_2 = y_134[3:131:2]
-x_128 = x_134[3:131]
-y_128 = y_134[3:131]
+x_128_2 = slice(3, 131, 2)
+y_128_2 = slice(3, 131, 2)
+x_128 = slice(3, 131)
+y_128 = slice(3, 131)
+
+# x_128_2 = x_134[3:131:2]
+# y_128_2 = y_134[3:131:2]
+# x_128 = x_134[3:131]
+# y_128 = y_134[3:131]
+
 
 #----------- New
 # x_134_2 = x_134[1:134:2]
 # t = np.arange(1, 66, 0.5)
+# s = np.arange(1, 66, 0.5)
 #--------------------------
 
+slc_x_2 = x_128_2
+slc_y_2 = y_128_2
+slc_x = x_128
+slc_y = y_128
+
 
 def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
                                 kernel_initializer='he_uniform', scale=None,
@@ -233,19 +249,22 @@ class SRCNN:
         data_norm = []
         for param in data_params:
             idx = params.index(param)
-            tmp = input_data[:, idx, y_128_2, x_128_2]
+            # tmp = input_data[:, idx, y_128_2, x_128_2]
+            tmp = input_data[:, idx, slc_y_2, slc_x_2]
             tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
             tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
             data_norm.append(tmp)
         # --------------------------
         param = 'refl_0_65um_nom'
         idx = params.index(param)
-        tmp = input_data[:, idx, y_128_2, x_128_2]
+        # tmp = input_data[:, idx, y_128_2, x_128_2]
+        tmp = input_data[:, idx, slc_y_2, slc_x_2]
         tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
         data_norm.append(tmp)
         # --------
-        tmp = input_data[:, label_idx, y_128_2, x_128_2]
+        # tmp = input_data[:, label_idx, y_128_2, x_128_2]
+        tmp = input_data[:, label_idx, slc_y_2, slc_x_2]
         if label_param != 'cloud_fraction':
             tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         else:
@@ -257,7 +276,8 @@ class SRCNN:
         data = data.astype(np.float32)
         # -----------------------------------------------------
         # -----------------------------------------------------
-        label = input_data[:, label_idx, y_128, x_128]
+        # label = input_data[:, label_idx, y_128, x_128]
+        label = input_data[:, label_idx, slc_y, slc_x]
         if label_param != 'cloud_fraction':
             label = normalize(label, label_param, mean_std_dct)
         else: