snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -13,7 +13,7 @@ import h5py
 
 LOG_DEVICE_PLACEMENT = False
 
-PROC_BATCH_SIZE = 4
+PROC_BATCH_SIZE = 2
 PROC_BATCH_BUFFER_SIZE = 50000
 
 NumClasses = 2
@@ -180,7 +180,7 @@ class SRCNN:
         self.test_data_nda = None
         self.test_label_nda = None
 
-        self.n_chans = len(data_params)
+        self.n_chans = len(data_params) + 1
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
         # self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
@@ -217,6 +217,11 @@ class SRCNN:
             tmp = resample(y_64, x_64, tmp, s, t)
             tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
             data_norm.append(tmp)
+        # --------
+        tmp = input_data[:, 2, 3:131, 3:131]
+        tmp = normalize(tmp, 'refl_0_65um_nom')
+        data_norm.append(tmp)
+        # ---------
         data = np.stack(data_norm, axis=3)
         data = data.astype(np.float32)