parser.add_argument('--use-cuda',
type=bool,
default=True,
metavar='CUDA',
help='use cuda (default: True)')
args = parser.parse_args()
prefix = 'poem'
word_is_char = True
batch_loader = BatchLoader('', prefix, word_is_char)
params = Parameters(batch_loader.max_word_len, batch_loader.max_seq_len,
batch_loader.words_vocab_size,
batch_loader.chars_vocab_size, word_is_char)
neg_loss = NEG_loss(params.word_vocab_size, params.word_embed_size)
if args.use_cuda and t.cuda.is_available():
neg_loss = neg_loss.cuda()
# NEG_loss is defined over two embedding matrixes with shape of [params.word_vocab_size, params.word_embed_size]
optimizer = SGD(neg_loss.parameters(), 0.1)
for iteration in range(args.num_iterations):
input_idx, target_idx = batch_loader.next_embedding_seq(
args.batch_size)
input = Variable(t.from_numpy(input_idx).long())
target = Variable(t.from_numpy(target_idx).long())
if args.use_cuda and t.cuda.is_available():
input, target = input.cuda(), target.cuda()
help='num iterations (default: 1000000)')
parser.add_argument('--batch-size', type=int, default=10, metavar='BS',
help='batch size (default: 10)')
parser.add_argument('--num-sample', type=int, default=5, metavar='NS',
help='num sample (default: 5)')
parser.add_argument('--use-cuda', type=bool, default=True, metavar='CUDA',
help='use cuda (default: True)')
args = parser.parse_args()
batch_loader = BatchLoader('')
params = Parameters(batch_loader.max_word_len,
batch_loader.max_seq_len,
batch_loader.words_vocab_size,
batch_loader.chars_vocab_size)
neg_loss = NEG_loss(params.word_vocab_size, params.word_embed_size)
if args.use_cuda:
neg_loss = neg_loss.cuda()
# NEG_loss is defined over two embedding matrixes with shape of [params.word_vocab_size, params.word_embed_size]
optimizer = SGD(neg_loss.parameters(), 0.1)
for iteration in range(args.num_iterations):
input_idx, target_idx = batch_loader.next_embedding_seq(args.batch_size)
input = Variable(t.from_numpy(input_idx).long())
target = Variable(t.from_numpy(target_idx).long())
if args.use_cuda:
input, target = input.cuda(), target.cuda()
type=int,
default=14,
metavar='NS',
help='num sample (default: 14)')
parser.add_argument('--use-cuda',
type=bool,
default=False,
metavar='CUDA',
help='use cuda (default: True)')
args = parser.parse_args()
batch_loader = BatchLoader('')
params = Parameters(batch_loader.max_seq_len, batch_loader.vocab_size)
neg_loss = NEG_loss(
params.vocab_size,
params.word_embed_size,
weights=[1 - sqrt(5e-5 / i) for i in batch_loader.words_freq])
if args.use_cuda:
neg_loss = neg_loss.cuda()
"""NEG_loss is defined over two embedding matrixes with shape of [params.vocab_size, params.word_embed_size]"""
optimizer = SGD(neg_loss.parameters(), 0.1)
for iteration in range(args.num_iterations):
input_idx, target_idx = batch_loader.next_embedding_seq(
args.batch_size)
input = Variable(t.from_numpy(input_idx).long())
target = Variable(t.from_numpy(target_idx).long())
if args.use_cuda:
input, target = input.cuda(), target.cuda()
default=5,
metavar='NS',
help='num sample (default: 5)')
parser.add_argument('--use-cuda',
type=bool,
default=True,
metavar='CUDA',
help='use cuda (default: True)')
args = parser.parse_args()
batch_loader = BatchLoader('')
params = Parameters(batch_loader.max_word_len, batch_loader.max_seq_len,
batch_loader.words_vocab_size,
batch_loader.chars_vocab_size)
neg_loss = NEG_loss(params.word_vocab_size, params.word_embed_size)
if args.use_cuda:
neg_loss = neg_loss.cuda()
# NEG_loss is defined over two embedding matrixes with shape of [params.word_vocab_size, params.word_embed_size]
optimizer = SGD(neg_loss.parameters(), 0.1)
for iteration in range(args.num_iterations):
input_idx, target_idx = batch_loader.next_embedding_seq(
args.batch_size)
input = Variable(t.from_numpy(input_idx).long())
target = Variable(t.from_numpy(target_idx).long())
if args.use_cuda: