From 764ce62445473ec1e91cf9867628b7f5e287a621 Mon Sep 17 00:00:00 2001 From: Yibing Liu Date: Mon, 25 Sep 2017 18:35:16 +0800 Subject: [PATCH] clean code in tuning script --- tools/tune.py | 48 +++++++++++++++++++++++------------------------- 1 file changed, 23 insertions(+), 25 deletions(-) diff --git a/tools/tune.py b/tools/tune.py index f03f8838..e0721a44 100644 --- a/tools/tune.py +++ b/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"