clean code in tuning script

tools/tune.py

@@ -17,27 +17,27 @@ from utils.utility import add_arguments, print_arguments
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('num_batches',      int,    -1,    "# of batches tuning on. "
-                                           "Default -1, on whole dev set.")
-add_arg('batch_size',       int,    256,   "# of samples per batch.")
-add_arg('trainer_count',    int,    8,     "# of Trainers (CPUs or GPUs).")
-add_arg('beam_size',        int,    500,   "Beam search width.")
-add_arg('num_proc_bsearch', int,    12,    "# of CPUs for beam search.")
-add_arg('num_conv_layers',  int,    2,     "# of convolution layers.")
-add_arg('num_rnn_layers',   int,    3,     "# of recurrent layers.")
-add_arg('rnn_layer_size',   int,    2048,  "# of recurrent cells per layer.")
-add_arg('num_alphas',       int,    45,    "# of alpha candidates for tuning.")
-add_arg('num_betas',        int,    8,     "# of beta candidates for tuning.")
-add_arg('alpha_from',       float,  1.0,   "Where alpha starts tuning from.")
-add_arg('alpha_to',         float,  3.2,   "Where alpha ends tuning with.")
-add_arg('beta_from',        float,  0.1,   "Where beta starts tuning from.")
-add_arg('beta_to',          float,  0.45,  "Where beta ends tuning with.")
-add_arg('cutoff_prob',      float,  1.0,   "Cutoff probability for pruning.")
-add_arg('cutoff_top_n',     int,    40,    "Cutoff number for pruning.")
-add_arg('use_gru',          bool,   False, "Use GRUs instead of simple RNNs.")
-add_arg('use_gpu',          bool,   True,  "Use GPU or not.")
-add_arg('share_rnn_weights',bool,   True,  "Share input-hidden weights across "
-                                           "bi-directional RNNs. Not for GRU.")
+add_arg('num_batches',      int,    -1,     "# of batches tuning on. "
+                                            "Default -1, on whole dev set.")
+add_arg('batch_size',       int,    256,    "# of samples per batch.")
+add_arg('trainer_count',    int,    8,      "# of Trainers (CPUs or GPUs).")
+add_arg('beam_size',        int,    500,    "Beam search width.")
+add_arg('num_proc_bsearch', int,    12,     "# of CPUs for beam search.")
+add_arg('num_conv_layers',  int,    2,      "# of convolution layers.")
+add_arg('num_rnn_layers',   int,    3,      "# of recurrent layers.")
+add_arg('rnn_layer_size',   int,    2048,   "# of recurrent cells per layer.")
+add_arg('num_alphas',       int,    45,     "# of alpha candidates for tuning.")
+add_arg('num_betas',        int,    8,      "# of beta candidates for tuning.")
+add_arg('alpha_from',       float,  1.0,    "Where alpha starts tuning from.")
+add_arg('alpha_to',         float,  3.2,    "Where alpha ends tuning with.")
+add_arg('beta_from',        float,  0.1,    "Where beta starts tuning from.")
+add_arg('beta_to',          float,  0.45,   "Where beta ends tuning with.")
+add_arg('cutoff_prob',      float,  1.0,    "Cutoff probability for pruning.")
+add_arg('cutoff_top_n',     int,    40,     "Cutoff number for pruning.")
+add_arg('use_gru',          bool,   False,  "Use GRUs instead of simple RNNs.")
+add_arg('use_gpu',          bool,   True,   "Use GPU or not.")
+add_arg('share_rnn_weights',bool,   True,   "Share input-hidden weights across "
+                                            "bi-directional RNNs. Not for GRU.")
 add_arg('tune_manifest',    str,
         'data/librispeech/manifest.dev-clean',
         "Filepath of manifest to tune.")
@@ -140,13 +140,11 @@ def tune():
             for target, result in zip(target_transcripts, result_transcripts):
                 err_sum[index] += error_rate_func(target, result)
             err_ave[index] = err_sum[index] / num_ins
-            # print("alpha = %f, beta = %f, WER = %f" %
-            #      (alpha, beta, err_ave[index]))
             if index % 2 == 0:
                 sys.stdout.write('.')
                 sys.stdout.flush()
 
-        # output on-line tuning result at the the end of current batch
+        # output on-line tuning result at the end of current batch
         err_ave_min = min(err_ave)
         min_index = err_ave.index(err_ave_min)
         print("\nBatch %d [%d/?], current opt (alpha, beta) = (%s, %s), "
@@ -156,7 +154,7 @@ def tune():
               args.error_rate_type, err_ave_min))
         cur_batch += 1
 
-    # output WER/CER at every point
+    # output WER/CER at every (alpha, beta)
     print("\nFinal %s:\n" % args.error_rate_type)
     for index in xrange(len(params_grid)):
         print("(alpha, beta) = (%s, %s), [%s] = %f"