[scripts] Documentation fixes. Thanks: Rongjin Li.

egs/wsj/s5/steps/libs/nnet3/report/log_parse.py

@@ -358,16 +358,16 @@ def parse_prob_logs(exp_dir, key='accuracy', output="output"):
         "nnet.*diagnostics.cc:[0-9]+. Overall ([a-zA-Z\-]+) for "
         "'{output}'.*is ([0-9.\-e]+) .*per frame".format(output=output))
 
-    train_loss = {}
-    valid_loss = {}
+    train_objf = {}
+    valid_objf = {}
 
     for line in train_prob_strings.split('\n'):
         mat_obj = parse_regex.search(line)
         if mat_obj is not None:
             groups = mat_obj.groups()
             if groups[1] == key:
-                train_loss[int(groups[0])] = groups[2]
-    if not train_loss:
+                train_objf[int(groups[0])] = groups[2]
+    if not train_objf:
         raise KaldiLogParseException("Could not find any lines with {k} in "
                 " {l}".format(k=key, l=train_prob_files))
 
@@ -376,20 +376,20 @@ def parse_prob_logs(exp_dir, key='accuracy', output="output"):
         if mat_obj is not None:
             groups = mat_obj.groups()
             if groups[1] == key:
-                valid_loss[int(groups[0])] = groups[2]
+                valid_objf[int(groups[0])] = groups[2]
 
-    if not valid_loss:
+    if not valid_objf:
         raise KaldiLogParseException("Could not find any lines with {k} in "
                 " {l}".format(k=key, l=valid_prob_files))
 
-    iters = list(set(valid_loss.keys()).intersection(train_loss.keys()))
+    iters = list(set(valid_objf.keys()).intersection(train_objf.keys()))
     if not iters:
         raise KaldiLogParseException("Could not any common iterations with"
                 " key {k} in both {tl} and {vl}".format(
                     k=key, tl=train_prob_files, vl=valid_prob_files))
     iters.sort()
-    return map(lambda x: (int(x), float(train_loss[x]),
-                          float(valid_loss[x])), iters)
+    return map(lambda x: (int(x), float(train_objf[x]),
+                          float(valid_objf[x])), iters)
 
 
 
@@ -402,7 +402,7 @@ def generate_acc_logprob_report(exp_dir, key="accuracy", output="output"):
         times = []
 
     report = []
-    report.append("%Iter\tduration\ttrain_loss\tvalid_loss\tdifference")
+    report.append("%Iter\tduration\ttrain_objective\tvalid_objective\tdifference")
     try:
         data = list(parse_prob_logs(exp_dir, key, output))
     except:

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

@@ -44,16 +44,24 @@ def train_new_models(dir, iter, srand, num_jobs,
     but we use the same script for consistency with FF-DNN code
 
     Selected args:
-        frames_per_eg: The default value -1 implies chunk_level_training, which
-            is particularly applicable to RNN training. If it is > 0, then it
-            implies frame-level training, which is applicable for DNN training.
-            If it is > 0, then each parallel SGE job created, a different frame
-            numbered 0..frames_per_eg-1 is used.
+        frames_per_eg:
+            The frames_per_eg, in the context of (non-chain) nnet3 training,
+            is normally the number of output (supervised) frames in each training
+            example.  However, the frames_per_eg argument to this function should
+            only be set to that number (greater than zero) if you intend to
+            train on a single frame of each example, on each minibatch.  If you
+            provide this argument >0, then for each training job a different
+            frame from the dumped example is selected to train on, based on
+            the option --frame=n to nnet3-copy-egs.
+            If you leave frames_per_eg at its default value (-1), then the
+            entire sequence of frames is used for supervision.  This is suitable
+            for RNN training, where it helps to amortize the cost of computing
+            the activations for the frames of context needed for the recurrence.
         use_multitask_egs : True, if different examples used to train multiple
-                            tasks or outputs, e.g.multilingual training.
-                            multilingual egs can be generated using get_egs.sh and
-                            steps/nnet3/multilingual/allocate_multilingual_examples.py,
-                            those are the top-level scripts.
+            tasks or outputs, e.g.multilingual training.  multilingual egs can
+            be generated using get_egs.sh and
+            steps/nnet3/multilingual/allocate_multilingual_examples.py, those
+            are the top-level scripts.
     """
 
     chunk_level_training = False if frames_per_eg > 0 else True