snapshot...

modules/deeplearning/icing_dnn.py

@@ -479,27 +479,22 @@ class IcingIntensityDNN:
             flat = self.input
             n_hidden = self.input.shape[1]
 
-        fac = 6
+        fac = 10
 
-        fc = build_residual_block(flat, drop_rate, fac * n_hidden, activation, 'Residual_Block_1', doDropout=True,
-                                  doBatchNorm=True)
+        fc = build_residual_block(flat, drop_rate, fac * n_hidden, activation, 'Residual_Block_1', doDropout=True, doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_2', doDropout=True,
-                                  doBatchNorm=True)
+        fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_2', doDropout=True, doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_3', doDropout=True,
-                                  doBatchNorm=True)
+        # fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_3', doDropout=True, doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_4', doDropout=True,
-                                  doBatchNorm=True)
+        # fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_4', doDropout=True, doBatchNorm=True)
 
-        fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_5', doDropout=True,
-                                  doBatchNorm=True)
+        # fc = build_residual_block(fc, drop_rate, fac * n_hidden, activation, 'Residual_Block_5', doDropout=True, doBatchNorm=True)
 
         # fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_6', doDropout=True, doBatchNorm=True)
-        #
+
         # fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_7', doDropout=True, doBatchNorm=True)
-        #
+
         # fc = build_residual_block(fc, drop_rate, fac*n_hidden, activation, 'Residual_Block_8', doDropout=True, doBatchNorm=True)
 
         fc = tf.keras.layers.Dense(n_hidden, activation=activation)(fc)