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#!/usr/bin/env python
from __future__ import print_function, unicode_literals
import logging
import argparse
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)
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--working-dir", dest="working_dir")
parser.add_argument("-c", "--corpus", dest="corpus_stem")
parser.add_argument("-l", "--nplm-home", dest="nplm_home")
parser.add_argument("-e", "--epochs", dest="epochs", type=int)
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("-t", "--threads", dest="threads", type=int)
parser.add_argument("-m", "--output-model", dest="output_model")
parser.add_argument("-r", "--output-dir", dest="output_dir")
parser.add_argument("-f", "--config-options-file", dest="config_options_file")
parser.add_argument("-g", "--log-file", dest="log_file")
parser.add_argument("-v", "--validation-ngrams", dest="validation_file")
parser.add_argument("-a", "--activation-function", dest="activation_fn")
parser.add_argument("-z", "--learning-rate", dest="learning_rate")
parser.add_argument("--input-words-file", dest="input_words_file")
parser.add_argument("--output-words-file", dest="output_words_file")
parser.add_argument("--input_vocab_size", dest="input_vocab_size", type=int)
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
)
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()
if __name__ == "__main__":
options = parser.parse_args()
main(options)
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