diff --git a/modules/deeplearning/cnn_cld_frac.py b/modules/deeplearning/cnn_cld_frac.py
index b8f84f392ad43d44db46f476f492955e6de459fe..1a5c8b98d0aae3681b012a5855d5af733a601828 100644
--- a/modules/deeplearning/cnn_cld_frac.py
+++ b/modules/deeplearning/cnn_cld_frac.py
@@ -280,12 +280,8 @@ class CNN:
         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]
-        if label_param != 'cloud_fraction':
-            tmp = normalize(tmp, label_param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
-        else:
-            tmp = np.where(np.isnan(tmp), 0, tmp)
-        tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
+        tmp = input_data[:, label_idx, y_128, x_128]
+        tmp = np.where(np.isnan(tmp), 0, tmp)  # shouldn't need this
         data_norm.append(tmp)
         # ---------
         data = np.stack(data_norm, axis=3)
@@ -464,6 +460,10 @@ class CNN:
 
         conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_3')
 
+        conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_4')
+
+        conv = build_residual_block_1x1(conv, num_filters, activation, 'Residual_Block_5')
+
         # conv = conv + conv_b
         print(conv.shape)