diff options
Diffstat (limited to 'scripts/training/bilingual-lm/train_nplm.py')
| -rwxr-xr-x | scripts/training/bilingual-lm/train_nplm.py | 195 |
1 files changed, 107 insertions, 88 deletions
diff --git a/scripts/training/bilingual-lm/train_nplm.py b/scripts/training/bilingual-lm/train_nplm.py index 356fd798d..7bc74429e 100755 --- a/scripts/training/bilingual-lm/train_nplm.py +++ b/scripts/training/bilingual-lm/train_nplm.py @@ -8,7 +8,9 @@ import subprocess import sys import os -logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=logging.DEBUG) +logging.basicConfig( + format='%(asctime)s %(levelname)s: %(message)s', + datefmt='%Y-%m-%d %H:%M:%S', level=logging.DEBUG) parser = argparse.ArgumentParser() parser.add_argument("-w", "--working-dir", dest="working_dir") parser.add_argument("-c", "--corpus", dest="corpus_stem") @@ -18,8 +20,10 @@ parser.add_argument("-n", "--ngram-size", dest="ngram_size", type=int) parser.add_argument("-b", "--minibatch-size", dest="minibatch_size", type=int) parser.add_argument("-s", "--noise", dest="noise", type=int) parser.add_argument("-d", "--hidden", dest="hidden", type=int) -parser.add_argument("-i", "--input-embedding", dest="input_embedding", type=int) -parser.add_argument("-o", "--output-embedding", dest="output_embedding", type=int) +parser.add_argument( + "-i", "--input-embedding", dest="input_embedding", type=int) +parser.add_argument( + "-o", "--output-embedding", dest="output_embedding", type=int) parser.add_argument("-t", "--threads", dest="threads", type=int) parser.add_argument("-m", "--output-model", dest="output_model") parser.add_argument("-r", "--output-dir", dest="output_dir") @@ -35,94 +39,109 @@ parser.add_argument("--output_vocab_size", dest="output_vocab_size", type=int) parser.set_defaults( - working_dir = "working" - ,corpus_stem = "train.10k" - ,nplm_home = "/home/bhaddow/tools/nplm" - ,epochs = 10 - ,ngram_size = 14 - ,minibatch_size=1000 - ,noise=100 - ,hidden=750 - ,input_embedding=150 - ,output_embedding=150 - ,threads=1 - ,output_model = "train.10k" - ,output_dir = None - ,config_options_file = "config" - ,log_file = "log" - ,validation_file = None - ,activation_fn = "rectifier" - ,learning_rate = 1 - ,input_words_file = None - ,output_words_file = None - ,input_vocab_size = 0 - ,output_vocab_size = 0 + working_dir="working", + corpus_stem="train.10k", + nplm_home="/home/bhaddow/tools/nplm", + epochs=10, + ngram_size=14, + minibatch_size=1000, + noise=100, + hidden=750, + input_embedding=150, + output_embedding=150, + threads=1, + output_model="train.10k", + output_dir=None, + config_options_file="config", + log_file="log", + validation_file=None, + activation_fn="rectifier", + learning_rate=1, + input_words_file=None, + output_words_file=None, + input_vocab_size=0, + output_vocab_size=0 ) + def main(options): - vocab_command = [] - if options.input_words_file is not None: - vocab_command += ['--input_words_file', options.input_words_file] - if options.output_words_file is not None: - vocab_command += ['--output_words_file', options.output_words_file] - if options.input_vocab_size: - vocab_command += ['--input_vocab_size', str(options.input_vocab_size)] - if options.output_vocab_size: - vocab_command += ['--output_vocab_size', str(options.output_vocab_size)] - - # Set up validation command variable to use with validation set. - validations_command = [] - if options.validation_file is not None: - validations_command =["--validation_file", (options.validation_file + ".numberized")] - - # In order to allow for different models to be trained after the same - # preparation step, we should provide an option for multiple output directories - # If we have not set output_dir, set it to the same thing as the working dir - - if options.output_dir is None: - options.output_dir = options.working_dir - else: - # Create output dir if necessary - if not os.path.exists(options.output_dir): - os.makedirs(options.output_dir) - - config_file = os.path.join(options.output_dir, options.config_options_file + '-' + options.output_model) - log_file = os.path.join(options.output_dir, options.log_file + '-' + options.output_model) - log_file_write = open(log_file, 'w') - config_file_write = open(config_file, 'w') - - config_file_write.write("Called: " + ' '.join(sys.argv) + '\n\n') - - in_file = os.path.join(options.working_dir, os.path.basename(options.corpus_stem) + ".numberized") - - model_prefix = os.path.join(options.output_dir, options.output_model + ".model.nplm") - train_args = [options.nplm_home + "/src/trainNeuralNetwork", - "--train_file", in_file, - "--num_epochs", str(options.epochs), - "--model_prefix", model_prefix, - "--learning_rate", str(options.learning_rate), - "--minibatch_size", str(options.minibatch_size), - "--num_noise_samples", str(options.noise), - "--num_hidden", str(options.hidden), - "--input_embedding_dimension", str(options.input_embedding), - "--output_embedding_dimension", str(options.output_embedding), - "--num_threads", str(options.threads), - "--activation_function", options.activation_fn] + validations_command + vocab_command - print("Train model command: ") - print(', '.join(train_args)) - - config_file_write.write("Training step:\n" + ' '.join(train_args) + '\n') - config_file_write.close() - - log_file_write.write("Training output:\n") - ret = subprocess.call(train_args, stdout=log_file_write, stderr=log_file_write) - if ret: - raise Exception("Training failed") - - log_file_write.close() + vocab_command = [] + if options.input_words_file is not None: + vocab_command += ['--input_words_file', options.input_words_file] + if options.output_words_file is not None: + vocab_command += ['--output_words_file', options.output_words_file] + if options.input_vocab_size: + vocab_command += ['--input_vocab_size', str(options.input_vocab_size)] + if options.output_vocab_size: + vocab_command += [ + '--output_vocab_size', str(options.output_vocab_size)] + + # Set up validation command variable to use with validation set. + validations_command = [] + if options.validation_file is not None: + validations_command = [ + "--validation_file", (options.validation_file + ".numberized")] + + # In order to allow for different models to be trained after the same + # preparation step, we should provide an option for multiple output + # directories. + # If we have not set output_dir, set it to the same thing as the working + # dir. + + if options.output_dir is None: + options.output_dir = options.working_dir + else: + # Create output dir if necessary + if not os.path.exists(options.output_dir): + os.makedirs(options.output_dir) + + config_file = os.path.join( + options.output_dir, + options.config_options_file + '-' + options.output_model) + log_file = os.path.join( + options.output_dir, options.log_file + '-' + options.output_model) + log_file_write = open(log_file, 'w') + config_file_write = open(config_file, 'w') + + config_file_write.write("Called: " + ' '.join(sys.argv) + '\n\n') + + in_file = os.path.join( + options.working_dir, + os.path.basename(options.corpus_stem) + ".numberized") + + model_prefix = os.path.join( + options.output_dir, options.output_model + ".model.nplm") + train_args = [ + options.nplm_home + "/src/trainNeuralNetwork", + "--train_file", in_file, + "--num_epochs", str(options.epochs), + "--model_prefix", model_prefix, + "--learning_rate", str(options.learning_rate), + "--minibatch_size", str(options.minibatch_size), + "--num_noise_samples", str(options.noise), + "--num_hidden", str(options.hidden), + "--input_embedding_dimension", str(options.input_embedding), + "--output_embedding_dimension", str(options.output_embedding), + "--num_threads", str(options.threads), + "--activation_function", + options.activation_fn, + ] + validations_command + vocab_command + print("Train model command: ") + print(', '.join(train_args)) + + config_file_write.write("Training step:\n" + ' '.join(train_args) + '\n') + config_file_write.close() + + log_file_write.write("Training output:\n") + ret = subprocess.call( + train_args, stdout=log_file_write, stderr=log_file_write) + if ret: + raise Exception("Training failed") + + log_file_write.close() -if __name__ == "__main__": - options = parser.parse_args() - main(options) +if __name__ == "__main__": + options = parser.parse_args() + main(options) |