snapshot...

modules/deeplearning/cloud_fraction_fcn_abi.py

@@ -3,7 +3,7 @@ import tensorflow as tf
 from util.plot_cm import confusion_matrix_values
 from util.augment import augment_image
 from util.setup_cloud_products import logdir, modeldir, now, ancillary_path
-from util.util import EarlyStop, normalize, denormalize, scale2, get_grid_values_all, make_tf_callable_generator
+from util.util import EarlyStop, normalize, denormalize, scale, scale2, get_grid_values_all, make_tf_callable_generator
 import glob
 import os, datetime
 import numpy as np
@@ -353,9 +353,10 @@ class SRCNN:
             tmp = input_data[:, idx, :, :]
             tmp = tmp[:, slc_y, slc_x]
             tmp = normalize(tmp, param, mean_std_dct)
+            # tmp = scale(tmp, param, mean_std_dct)
             data_norm.append(tmp)
 
-        # refl_i = input_label[:, 1, :, :]
+        # refl_i = input_label[:, params_i.index('refl_0_65um_nom'), :, :]
         # refl_avg = get_grid_cell_mean(refl_i)
         # refl_avg = refl_avg[:, slc_y, slc_x]
         # refl_avg = normalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
@@ -366,10 +367,12 @@ class SRCNN:
         rlo = input_data[:, params.index('refl_submin_ch01'), :, :]
         rlo = rlo[:, slc_y, slc_x]
         rlo = normalize(rlo, 'refl_0_65um_nom', mean_std_dct)
+        # rlo = scale(rlo, 'refl_0_65um_nom', mean_std_dct)
 
         rhi = input_data[:, params.index('refl_submax_ch01'), :, :]
         rhi = rhi[:, slc_y, slc_x]
         rhi = normalize(rhi, 'refl_0_65um_nom', mean_std_dct)
+        # rhi = scale(rhi, 'refl_0_65um_nom', mean_std_dct)
         refl_rng = rhi - rlo
         data_norm.append(refl_rng)