def main(_):
"""
Start either train or eval. Note hardcoded parts of path for training and eval data
"""
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps._set("num_gpus", FLAGS.num_gpus)
print('*****HYPER PARAMETERS*****')
print(hps)
print('**************************')
vocab = Vocabulary.from_file(os.path.join(FLAGS.datadir, "vocabulary.txt"))
if FLAGS.mode == "train":
#hps.batch_size = 256
dataset = Dataset(vocab, os.path.join(FLAGS.datadir, "train.txt"))
run_train(dataset,
hps,
os.path.join(FLAGS.logdir, "train"),
ps_device="/gpu:0")
elif FLAGS.mode.startswith("eval"):
data_dir = os.path.join(FLAGS.datadir, "eval.txt")
#predict_model = prediction.Model('/dir/ckpt',os.path.join(FLAGS.datadir, "vocabulary.txt"), hps)
dataset = Dataset(vocab, data_dir, deterministic=True)
prefix_words = "<brk>".split()
predict_model = predict.Model(hps, FLAGS.logdir, FLAGS.datadir)
print('start input')
out = predict_model.predictnextkwords(prefix_words, FLAGS.num_sen)
for row in out:
print(' '.join(row) + "\n")
print("len_out: " + str(len(out)))
def main(_):
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps.num_gpus = FLAGS.num_gpus
vocab = Vocabulary.from_file("small_voca.txt") # ("1b_word_vocab.txt")
if FLAGS.mode == "train":
hps.batch_size = 256
dataset = Dataset(
vocab,
FLAGS.datadir + "/training-monolingual.tokenized.shuffled/*")
run_train(dataset, hps, FLAGS.logdir + "/train", ps_device="/gpu:0")
elif FLAGS.mode.startswith("eval_"):
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps.num_gpus = FLAGS.num_gpus
if FLAGS.mode.startswith("eval_train"):
data_dir = FLAGS.datadir + "/training-monolingual.tokenized.shuffled/*"
else:
data_dir = FLAGS.datadir + "/heldout-monolingual.tokenized.shuffled/news.en.heldout-00000-of-00050"
dataset = Dataset(vocab, data_dir, deterministic=True)
run_eval(dataset, hps, FLAGS.logdir, FLAGS.mode, FLAGS.eval_steps)
def main(_):
"""
Start either train or eval. Note hardcoded parts of path for training and eval data
"""
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps._set("num_gpus", FLAGS.num_gpus)
print('*****HYPER PARAMETERS*****')
print(hps)
print('**************************')
print_debug('our training DataSetDir=%s , LogDir=%s' %
(FLAGS.datadir, FLAGS.logdir))
#vocab = Vocabulary.from_file(os.path.join(FLAGS.datadir, "1b_word_vocab.txt"))
vocab = Vocabulary.from_file(os.path.join(FLAGS.datadir, "vocabulary.txt"))
FLAGS.mode = "train"
for i in range(10):
print("Iteration ", i, " phase: ", FLAGS.mode)
if FLAGS.mode == "train":
#hps.batch_size = 256
# dataset = Dataset(vocab, os.path.join(FLAGS.datadir,
# "training-monolingual.tokenized.shuffled/*"))
dataset = Dataset(vocab,
os.path.join(FLAGS.datadir, "ptb.train.txt"))
trainlogdir = (
FLAGS.logdir + str("/") + "train"
) #(FLAGS.logdir+str("\\")+"train")#os.path.join(FLAGS.logdir, "train")
print_debug('train log dir=%s' % (trainlogdir))
run_train(dataset, hps, trainlogdir, ps_device="/gpu:0")
print_debug('Finished run_train !!!!!!!!!!!')
elif FLAGS.mode.startswith("eval"):
print_debug('eval mode')
# if FLAGS.mode.startswith("eval_train"):
# data_dir = os.path.join(FLAGS.datadir, "training-monolingual.tokenized.shuffled/*")
# elif FLAGS.mode.startswith("eval_full"):
# data_dir = os.path.join(FLAGS.datadir, "heldout-monolingual.tokenized.shuffled/*")
# else:
# data_dir = os.path.join(FLAGS.datadir, "heldout-monolingual.tokenized.shuffled/news.en.heldout-00000-of-00050")
dataset = Dataset(vocab,
os.path.join(FLAGS.datadir, "ptb.test.txt"),
deterministic=True)
run_eval(dataset, hps, FLAGS.logdir, FLAGS.mode, FLAGS.eval_steps)
print_debug('Finished run_eval !!!!!!!!!!!')
if FLAGS.mode == "train":
FLAGS.mode = "eval_full"
else:
FLAGS.mode = "train"
def main(_):
"""
Start either train or eval. Note hardcoded parts of path for training and eval data
"""
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps._set("num_gpus", FLAGS.num_gpus)
print('*****HYPER PARAMETERS*****')
print(hps)
print('**************************')
vocab = Vocabulary.from_file(
os.path.join(FLAGS.datadir, "1b_word_vocab.txt"))
if FLAGS.mode == "train":
#hps.batch_size = 256
dataset = Dataset(
vocab,
os.path.join(FLAGS.datadir,
"training-monolingual.tokenized.shuffled/*"))
run_train(dataset,
hps,
os.path.join(FLAGS.logdir, "train"),
ps_device="/gpu:0")
elif FLAGS.mode.startswith("eval_"):
if FLAGS.mode.startswith("eval_train"):
data_dir = os.path.join(
FLAGS.datadir, "training-monolingual.tokenized.shuffled/*")
elif FLAGS.mode.startswith("eval_full"):
data_dir = os.path.join(
FLAGS.datadir,
"heldout-monolingual.tokenized.shuffled/news.en.heldout-00000-of-00050"
)
else:
data_dir = os.path.join(
FLAGS.datadir,
"heldout-monolingual.tokenized.shuffled/news.en.heldout-00000-of-00050"
)
dataset = Dataset(vocab, data_dir, deterministic=True)
run_eval(dataset, hps, FLAGS.logdir, FLAGS.mode, FLAGS.eval_steps)
elif FLAGS.mode.startswith("infer"):
data_dir = os.path.join(
FLAGS.datadir,
"heldout-monolingual.tokenized.shuffled/news.en.heldout-00000-of-00050"
)
dataset = Dataset(vocab, data_dir, deterministic=True)
run_infer(dataset, hps, FLAGS.logdir, FLAGS.mode, vocab)
def run():
parser = argparse.ArgumentParser()
arg = parser.add_argument
hps = LM.get_default_hparams().parse('num_steps=20,num_shards=8,num_layers=2,emb_size=12,projected_size=12,state_size=80,num_sampled=0,batch_size=1,vocab_size=102')
hps._set("num_gpus", 1)
#arg('model')
#arg('vocab')
arg('--port', type=int, default=8000)
arg('--host', default='localhost')
arg('--debug', action='store_true')
args = parser.parse_args()
global model
#model = Model(args.model, args.vocab, hps)
model = Model('/Users/ruiyangwang/Desktop/f-lm/logs/test/train/model.ckpt-0','/Users/ruiyangwang/Desktop/examples/word_language_model/data/penn/vocabulary.txt', hps)
app.run(port=args.port, host=args.host, debug=args.debug)
def run():
parser = argparse.ArgumentParser()
arg = parser.add_argument
hps = LM.get_default_hparams().parse(
'num_steps=20,num_shards=6,num_layers=2,learning_rate=0.2,max_grad_norm=1,keep_prob=0.9,emb_size=1024,projected_size=1024,state_size=8192,num_sampled=8192,batch_size=512,vocab_size=11859,num_of_groups=4'
)
hps._set("num_gpus", 1)
#arg('model')
#arg('vocab')
arg('--port', type=int, default=8000)
arg('--host', default='localhost')
arg('--debug', action='store_true')
args = parser.parse_args()
global model
#model = Model(args.model, args.vocab, hps)
model = Model('/Users/ruiyangwang/Desktop/model/model.ckpt-44260',
'/Users/ruiyangwang/Desktop/vocabulary2.txt', hps)
app.run(port=args.port, host=args.host, debug=args.debug)
def main(_):
hvd.init()
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps.num_gpus = FLAGS.num_gpus
vocab = Vocabulary.from_file(FLAGS.vocab)
hps.vocab_size = vocab.num_tokens
config = tf.ConfigProto()
config.gpu_options.visible_device_list = str(hvd.local_rank())
os.environ["CUDA_VISIBLE_DEVICES"] = str(hvd.local_rank())
if FLAGS.logdir is None:
FLAGS.logdir = os.path.join('/tmp',
'lm-run-{}'.format(int(time.time())))
print('logdir: {}'.format(FLAGS.logdir))
hps.batch_size = 256
dataset = Dataset(vocab, FLAGS.datadir)
run_train(dataset,
hps,
FLAGS.logdir + '/train',
ps_device='/gpu:' + str(hvd.local_rank()))
def main(_):
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps.num_gpus = FLAGS.num_gpus
vocab = Vocabulary.from_file(FLAGS.datadir + "/lm_vocab.txt", hps.vocab_size)
if FLAGS.mode == "train":
hps.batch_size = 256 # reset batchsize
dataset = Dataset(vocab, FLAGS.datadir + "/train/*")
run_train(dataset, hps, FLAGS.logdir + "/train", ps_device="/gpu:0")
elif FLAGS.mode.startswith("eval_"):
if FLAGS.mode.startswith("eval_train"):
data_dir = FLAGS.datadir + "/train/*"
elif FLAGS.mode.startswith("eval_test"):
data_dir = FLAGS.datadir + "/heldout/*"
print("data_dir:",data_dir)
dataset = Dataset(vocab, data_dir, deterministic=True)
run_eval(dataset, hps, FLAGS.logdir, FLAGS.mode, FLAGS.eval_steps)
elif FLAGS.mode.startswith("predict_next"):
data_dir = "data/news.en.heldout-00001-of-00050"
dataset = Dataset(vocab, data_dir)
predict_next(dataset, hps, FLAGS.logdir, FLAGS.mode, FLAGS.eval_steps,vocab)
import json
import numpy as np
import time
import tensorflow as tf
from data_utils import Vocabulary, Dataset
from language_model import LM
from common import CheckpointLoader
BATCH_SIZE = 1
NUM_TIMESTEPS = 1
MAX_WORD_LEN = 50
UPLOAD_FOLDER = '/data/ngramTest/uploads'
UPLOAD_FOLDER = './'
hps = LM.get_default_hparams()
vocab = Vocabulary.from_file("1b_word_vocab.txt")
with tf.variable_scope("model"):
hps.num_sampled = 0 # Always using full softmax at evaluation. run out of memory
hps.keep_prob = 1.0
hps.num_gpus = 1
model = LM(hps, "predict_next", "/cpu:0")
if hps.average_params:
print("Averaging parameters for evaluation.")
saver = tf.train.Saver(model.avg_dict)
else:
saver = tf.train.Saver()
# Use only 4 threads for the evaluation.
config = tf.ConfigProto(allow_soft_placement=True,
print("You're not in the cluster spec! exiting!")
exit(-1)
else:
print("ROLE: %s" % role)
print("INDEX: %s" % task_index)
cluster = tf.train.ClusterSpec(cluster_spec)
server = tf.train.Server(cluster, job_name=role, task_index=task_index)
if role == "ps":
server.join()
else:
ps_device = '/job:ps/task:0'
"""
Start either train or eval. Note hardcoded parts of path for training and eval data
"""
hps = LM.get_default_hparams().parse(FLAGS.hpconfig)
hps._set("num_gpus", FLAGS.num_gpus)
print('*****HYPER PARAMETERS*****')
print(hps)
print('**************************')
vocab = Vocabulary.from_file(
os.path.join(FLAGS.datadir, "1b_word_vocab.txt"))
if FLAGS.mode == "train":
#hps.batch_size = 256
dataset = Dataset(
vocab,
os.path.join(FLAGS.datadir,
"training-monolingual.tokenized.shuffled/*"))
run_train(dataset,