diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 33a18220c0f86180a448d996d3edcc512e3fffdb..94aa3b3675bc0b4ce1aac20213b7c1d0cfb60362 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -49,8 +49,8 @@ f.close()
 mean_std_dct.update(mean_std_dct_l1b)
 mean_std_dct.update(mean_std_dct_l2)
 
-label_param = 'cloud_fraction'
-# label_param = 'cld_opd_dcomp'
+# label_param = 'cloud_fraction'
+label_param = 'cld_opd_dcomp'
 # label_param = 'cloud_probability'
 
 params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', label_param]
@@ -251,7 +251,8 @@ class SRCNN:
         # label = input_data[:, label_idx, 3:131:2, 3:131:2]
         label = input_data[:, label_idx, 3:131, 3:131]
         if label_param != 'cloud_fraction':
-            label = normalize(label, label_param, mean_std_dct)
+            #label = normalize(label, label_param, mean_std_dct)
+            label = np.where(np.isnan(label), 0, label)
         else:
             label = np.where(np.isnan(label), 0, label)
         label = np.expand_dims(label, axis=3)
@@ -399,9 +400,9 @@ class SRCNN:
 
         conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_3', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_4', scale=scale)
 
-        conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
+        # conv_b = build_residual_conv2d_block(conv_b, num_filters, 'Residual_Block_5', scale=scale)
 
         conv_b = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, kernel_initializer='he_uniform', padding=padding)(conv_b)
 
@@ -646,6 +647,8 @@ class SRCNN:
     def run(self, directory, ckpt_dir=None, num_data_samples=50000):
         train_data_files = glob.glob(directory+'data_train_*.npy')
         valid_data_files = glob.glob(directory+'data_valid_*.npy')
+        train_data_files = train_data_files[::2]
+        valid_data_files = valid_data_files[::2]
 
         self.setup_pipeline(train_data_files, valid_data_files, num_data_samples)
         self.build_model()