snapshot...

modules/deeplearning/icing_cnn.py

@@ -43,22 +43,26 @@ f = open(mean_std_file, 'rb')
 mean_std_dct = pickle.load(f)
 f.close()
 
-# --  NIGHT L2 ---------------------
+# --  NIGHT L2 -----------------------------
 # train_params = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
 #                 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
 # -- DAY L2 -------------
 train_params = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
                 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
-# -- DAY L1B -----------------------
+# -- DAY L1B --------------------------------
 # train_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
 #                 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
 #                 'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
-# -- NIGHT L1B ---------------------
+# -- NIGHT L1B -------------------------------
 # train_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
 #                 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
 # -- DAY LUNAR ---------------------
 # train_params = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
 #                 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+# ---------------------------------------------
+# -- Zero out params (Experimentation Only) ------------
+zero_out_params = ['cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+DO_ZERO_OUT = False
 
 
 def build_residual_block(input, drop_rate, num_neurons, activation, block_name, doDropout=True, doBatchNorm=True):
@@ -236,6 +240,12 @@ class IcingIntensityNN:
                 nda = normalize(nda, param, mean_std_dct, add_noise=True, noise_scale=0.01, seed=42)
             else:
                 nda = normalize(nda, param, mean_std_dct)
+            if DO_ZERO_OUT:
+                try:
+                    zero_out_params.index(param)
+                    nda[:,] = 0.0
+                except ValueError:
+                    pass
             data.append(nda)
         data = np.stack(data)
         data = data.astype(np.float32)