if options.decay:
opt = AdamAnneal(lr=options.lr, lr_min=options.lr_min, gamma=options.gamma)
else:
opt = adam(lr=options.lr)
if options.log_file == '':
log_file = None
else:
log_file = options.log_file
if options.save == '':
save_path = None
else:
save_path = options.save
model = NCELangModel(vocab_size=nb_vocab,
nb_negative=options.negative,
embed_dims=options.embed_size,
context_dims=options.context_size,
negprob_table=unigram_table,
optimizer=opt)
model.compile()
model.train(data_file=DATA_PATH,
save_path=save_path,
batch_size=BATCH_SIZE,
train_nb_words=nb_run_words,
val_nb_words=nb_evaluate,
train_val_nb=nb_run_val,
validation_interval=options.interval,
log_file=log_file)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'Yunchuan Chen'
from utils import get_unigram_probtable
from models import NCELangModel
from keras.optimizers import adam
NB_RUN_WORDS = 100000000
NB_VOCAB = 10000
NB_RUN_VAL = 100000
NB_EVALUATE = 5000000
SAVE_PATH = '../data/models/lang/nce0-neg50-e128-c128-lr0.01.pkl'
DATA_PATH = '../data/corpus/wiki-sg-norm-lc-drop-bin.bz2'
BATCH_SIZE = 256
VAL_INTER = 1200
unigram_table = get_unigram_probtable(nb_words=NB_VOCAB)
opt = adam(lr=0.01)
model = NCELangModel(vocab_size=NB_VOCAB, nb_negative=50, embed_dims=128, context_dims=128,
negprob_table=unigram_table, optimizer=opt)
model.compile()
model.train(data_file=DATA_PATH,
save_path=SAVE_PATH,
batch_size=BATCH_SIZE, train_nb_words=NB_RUN_WORDS,
val_nb_words=NB_EVALUATE, train_val_nb=NB_RUN_VAL, validation_interval=VAL_INTER)
nb_evaluate = options.nb_evaluation
# unigram_table = get_unigram_probtable(nb_words=nb_vocab)
unigram_table = get_unigram_probtable(nb_words=nb_vocab,
save_path='../data/wiki-unigram-prob-size%d.pkl' %
nb_vocab)
if options.decay:
opt = AdamAnneal(lr=options.lr, lr_min=options.lr_min, gamma=options.gamma)
else:
opt = adam(lr=options.lr)
if options.log_file == '':
log_file = None
else:
log_file = options.log_file
if options.save == '':
save_path = None
else:
save_path = options.save
model = NCELangModel(vocab_size=nb_vocab, nb_negative=options.negative,
embed_dims=options.embed_size, context_dims=options.context_size,
negprob_table=unigram_table, optimizer=opt)
model.compile()
model.train(data_file=DATA_PATH,
save_path=save_path,
batch_size=BATCH_SIZE, train_nb_words=nb_run_words,
val_nb_words=nb_evaluate, train_val_nb=nb_run_val,
validation_interval=options.interval, log_file=log_file)