adding dropout-by row

egs/ami/s5b/local/chain/tuning/run_tdnn_lstm_1i_dp.sh

@@ -5,13 +5,13 @@
 # same as 1i but with frame level dropout
 # (num-params 1g:21309812 1i: 43447156)
 # results on sdm1 using ihm ali
-#System            tdnn_lstm1i_sp_bi_ihmali_ld5
-#WER on dev        37.6            36.7
-#WER on eval       40.9            39.9
-#Final train prob      -0.114135   -0.118
-#Final valid prob      -0.245208   -0.246
-#Final train prob (xent)      -1.47648  -1.54
-#Final valid prob (xent)      -2.16365  -2.10
+#System   tdnn_lstm1i_sp_bi_ihmali_ld5 tdnn_lstm1i_dp_sp_bi_ihmali_ld5
+#WER on dev        37.6            36.5
+#WER on eval       40.9            39.7
+#Final train prob      -0.114135   -0.124
+#Final valid prob      -0.245208   -0.249
+#Final train prob (xent)      -1.47648  -1.55
+#Final valid prob (xent)      -2.16365  -2.11
 
 
 set -e -o pipefail
@@ -28,7 +28,7 @@ gmm=tri3_cleaned  # the gmm for the target data
 ihm_gmm=tri3  # the gmm for the IHM system (if --use-ihm-ali true).
 num_threads_ubm=32
 nnet3_affix=_cleaned  # cleanup affix for nnet3 and chain dirs, e.g. _cleaned
-dropout_schedule='0,0@0.20,0.5@0.50,0@0.50,0'
+dropout_schedule='0,0@0.20,0.5@0.5,0@0.75,0'
 chunk_width=150
 chunk_left_context=40
 chunk_right_context=0
@@ -37,7 +37,7 @@ label_delay=5
 # are just hardcoded at this level, in the commands below.
 train_stage=-10
 tree_affix=  # affix for tree directory, e.g. "a" or "b", in case we change the configuration.
-tlstm_affix=1i  #affix for TDNN-LSTM directory, e.g. "a" or "b", in case we change the configuration.
+tlstm_affix=1i_dp  #affix for TDNN-LSTM directory, e.g. "a" or "b", in case we change the configuration.
 common_egs_dir=  # you can set this to use previously dumped egs.
 
 
@@ -193,15 +193,15 @@ if [ $stage -le 15 ]; then
   relu-renorm-layer name=tdnn3 input=Append(-1,0,1) dim=1024
 
   # check steps/libs/nnet3/xconfig/lstm.py for the other options and defaults
-  lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0
+  lstmp-layer name=lstm1 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 dropout-per-frame=false
   relu-renorm-layer name=tdnn4 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn5 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn6 input=Append(-3,0,3) dim=1024
-  lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0
+  lstmp-layer name=lstm2 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 dropout-per-frame=false
   relu-renorm-layer name=tdnn7 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn8 input=Append(-3,0,3) dim=1024
   relu-renorm-layer name=tdnn9 input=Append(-3,0,3) dim=1024
-  lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0
+  lstmp-layer name=lstm3 cell-dim=1024 recurrent-projection-dim=256 non-recurrent-projection-dim=256 delay=-3 dropout-proportion=0.0 dropout-per-frame=false
 
   ## adding the layers for chain branch
   output-layer name=output input=lstm3 output-delay=$label_delay include-log-softmax=false dim=$num_targets max-change=1.5

egs/wsj/s5/steps/libs/nnet3/train/common.py

@@ -401,7 +401,7 @@ def _parse_dropout_string(num_archives_to_process, dropout_str):
             value_x_pair = parts[i].split('@')
             if len(value_x_pair) == 1:
                 # Dropout proportion at half of training
-                dropout_proportion = float(value_x_pair)
+                dropout_proportion = float(value_x_pair[0])
                 num_archives = int(0.5 * num_archives_to_process)
             else:
                 assert len(value_x_pair) == 2

egs/wsj/s5/steps/libs/nnet3/xconfig/lstm.py

@@ -250,7 +250,8 @@ def set_default_configs(self):
                         'self-repair-scale-nonlinearity' : 0.00001,
                         'zeroing-interval' : 20,
                         'zeroing-threshold' : 15.0,
-                        'dropout-proportion' : -1.0 # -1.0 stands for no dropout will be added
+                        'dropout-proportion' : -1.0 ,# -1.0 stands for no dropout will be added
+                        'dropout-per-frame' : 'false'
                        }
 
     def set_derived_configs(self):
@@ -285,6 +286,10 @@ def check_configs(self):
              self.config['dropout-proportion'] < 0.0) and
              self.config['dropout-proportion'] != -1.0 ):
              raise xparser_error("dropout-proportion has invalid value {0}.".format(self.config['dropout-proportion']))
+
+        if (self.config['dropout-per-frame'] != 'false' and
+            self.config['dropout-per-frame'] != 'true'):
+            raise xparser_error("dropout-per-frame has invalid value {0}.".format(self.config['dropout-per-frame']))
 
     def auxiliary_outputs(self):
         return ['c_t']
@@ -347,7 +352,8 @@ def generate_lstm_config(self):
         pes_str = self.config['ng-per-element-scale-options']
         lstm_dropout_value = self.config['dropout-proportion']
         lstm_dropout_str = 'dropout-proportion='+str(self.config['dropout-proportion'])
-
+        lstm_dropout_per_frame_value = self.config['dropout-per-frame']
+        lstm_dropout_per_frame_str = 'dropout-per-frame='+str(self.config['dropout-per-frame'])
         # Natural gradient per element scale parameters
         # TODO: decide if we want to keep exposing these options
         if re.search('param-mean', pes_str) is None and \
@@ -427,7 +433,7 @@ def generate_lstm_config(self):
         # add the recurrent connections
         configs.append("# projection matrices : Wrm and Wpm")
         if lstm_dropout_value != -1.0:
-            configs.append("component name={0}.W_rp.m.dropout type=DropoutComponent dim={1} {2}".format(name, cell_dim, lstm_dropout_str))
+            configs.append("component name={0}.rp_t.dropout type=DropoutComponent dim={1} {2} {3}".format(name, cell_dim, lstm_dropout_str, lstm_dropout_per_frame_str))
         configs.append("component name={0}.W_rp.m type=NaturalGradientAffineComponent input-dim={1} output-dim={2} {3}".format(name, cell_dim, rec_proj_dim + nonrec_proj_dim, affine_str))
         configs.append("component name={0}.r type=BackpropTruncationComponent dim={1} {2}".format(name, rec_proj_dim, bptrunc_str))
 

egs/wsj/s5/steps/nnet3/chain/train.py

@@ -202,7 +202,6 @@ def process_args(args):
             "value={0}. We recommend using the option "
             "--trainer.deriv-truncate-margin.".format(
                 args.deriv_truncate_margin))
-
     if (not os.path.exists(args.dir)
             or not os.path.exists(args.dir+"/configs")):
         raise Exception("This scripts expects {0} to exist and have a configs "