def main():
parser = argparse.ArgumentParser(
description='text convolution-deconvolution auto-encoder model')
# learning
parser.add_argument('-lr',
type=float,
default=0.001,
help='initial learning rate')
parser.add_argument('-epochs',
type=int,
default=60,
help='number of epochs for train')
parser.add_argument('-batch_size',
type=int,
default=64,
help='batch size for training')
parser.add_argument(
'-lr_decay_interval',
type=int,
default=20,
help='how many epochs to wait before decrease learning rate')
parser.add_argument(
'-log_interval',
type=int,
default=16,
help='how many steps to wait before logging training status')
parser.add_argument('-test_interval',
type=int,
default=100,
help='how many steps to wait before testing')
parser.add_argument('-save_interval',
type=int,
default=5,
help='how many epochs to wait before saving')
parser.add_argument('-save_dir',
type=str,
default='snapshot',
help='where to save the snapshot')
# data
parser.add_argument('-data_path', type=str, help='data path')
parser.add_argument('-label_path', type=str, help='label path')
parser.add_argument('-separated',
type=str,
default='sentencepiece',
help='how separated text data is')
parser.add_argument('-shuffle',
default=False,
help='shuffle the data every epoch')
parser.add_argument('-sentence_len',
type=int,
default=60,
help='how many tokens in a sentence')
# model
parser.add_argument('-mlp_out',
type=int,
default=7,
help='number of classes')
parser.add_argument('-dropout',
type=float,
default=0.5,
help='the probability for dropout')
parser.add_argument('-embed_dim',
type=int,
default=300,
help='number of embedding dimension')
parser.add_argument('-kernel_sizes',
type=int,
default=2,
help='kernel size to use for convolution')
parser.add_argument('-tau',
type=float,
default=0.01,
help='temperature parameter')
parser.add_argument('-use_cuda',
action='store_true',
default=True,
help='whether using cuda')
# option
parser.add_argument('-enc_snapshot',
type=str,
default=None,
help='filename of encoder snapshot ')
parser.add_argument('-dec_snapshot',
type=str,
default=None,
help='filename of decoder snapshot ')
parser.add_argument('-mlp_snapshot',
type=str,
default=None,
help='filename of mlp classifier snapshot ')
args = parser.parse_args()
dataset = TextClassificationDataset(args.data_path,
args.label_path,
args.separated,
sentence_len=args.sentence_len,
transoform=ToTensor())
data_loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=args.shuffle)
print("Vocab number")
print(dataset.vocab_length())
k = args.embed_dim
v = dataset.vocab_length()
if args.enc_snapshot is None or args.dec_snapshot is None or args.mlp_snapshot is None:
print("Start from initial")
embedding = nn.Embedding(v, k, max_norm=1.0, norm_type=2.0)
encoder = model.ConvolutionEncoder(embedding)
decoder = model.DeconvolutionDecoder(embedding, args.tau)
mlp = model.MLPClassifier(args.mlp_out, args.dropout)
else:
print("Restart from snapshot")
encoder = torch.load(args.enc_snapshot)
decoder = torch.load(args.dec_snapshot)
mlp = torch.load(args.mlp_snapshot)
train_classification(data_loader, data_loader, encoder, decoder, mlp, args)
def main():
parser = argparse.ArgumentParser(
description='text convolution-deconvolution auto-encoder model')
# learning
parser.add_argument('-lr',
type=float,
default=0.005,
help='initial learning rate')
parser.add_argument('-epochs',
type=int,
default=10,
help='number of epochs for train')
parser.add_argument('-batch_size',
type=int,
default=64,
help='batch size for training')
parser.add_argument(
'-lr_decay_interval',
type=int,
default=4,
help='how many epochs to wait before decrease learning rate')
parser.add_argument(
'-log_interval',
type=int,
default=256,
help='how many steps to wait before logging training status')
parser.add_argument('-test_interval',
type=int,
default=2,
help='how many epochs to wait before testing')
parser.add_argument('-save_interval',
type=int,
default=2,
help='how many epochs to wait before saving')
parser.add_argument('-save_dir',
type=str,
default='rec_snapshot',
help='where to save the snapshot')
# data
parser.add_argument('-data_path',
type=str,
default='hotel_reviews.p',
help='data path')
parser.add_argument('-shuffle',
default=False,
help='shuffle data every epoch')
parser.add_argument('-sentence_len',
type=int,
default=253,
help='how many tokens in a sentence')
# model
parser.add_argument('-embed_dim',
type=int,
default=300,
help='number of embedding dimension')
parser.add_argument('-filter_size',
type=int,
default=300,
help='filter size of convolution')
parser.add_argument('-filter_shape',
type=int,
default=5,
help='filter shape to use for convolution')
parser.add_argument('-latent_size',
type=int,
default=900,
help='size of latent variable')
parser.add_argument('-tau',
type=float,
default=0.01,
help='temperature parameter')
parser.add_argument('-use_cuda',
action='store_true',
default=True,
help='whether using cuda')
# option
parser.add_argument('-enc_snapshot',
type=str,
default=None,
help='filename of encoder snapshot ')
parser.add_argument('-dec_snapshot',
type=str,
default=None,
help='filename of decoder snapshot ')
args = parser.parse_args()
train_data, test_data = load_hotel_review_data(args.data_path,
args.sentence_len)
train_loader, test_loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=args.shuffle), \
DataLoader(test_data, batch_size=args.batch_size, shuffle=args.shuffle)
k = args.embed_dim
v = train_data.vocab_lennght()
t1 = args.sentence_len + 2 * (args.filter_shape - 1)
t2 = int(math.floor(
(t1 - args.filter_shape) / 2) + 1) # "2" means stride size
t3 = int(math.floor((t2 - args.filter_shape) / 2) + 1) - 2
if args.enc_snapshot is None or args.dec_snapshot is None:
print("Start from initial")
embedding = nn.Embedding(v, k, max_norm=1.0, norm_type=2.0)
encoder = model.ConvolutionEncoder(embedding, t3, args.filter_size,
args.filter_shape, args.latent_size)
decoder = model.DeconvolutionDecoder(embedding, args.tau, t3,
args.filter_size,
args.filter_shape,
args.latent_size)
else:
print("Restart from snapshot")
encoder = torch.load(args.enc_snapshot)
decoder = torch.load(args.dec_snapshot)
train_reconstruction(train_loader, test_loader, encoder, decoder, args)