本文整理汇总了Python中utils.load_dataset方法的典型用法代码示例。如果您正苦于以下问题：Python utils.load_dataset方法的具体用法？Python utils.load_dataset怎么用？Python utils.load_dataset使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utils的用法示例。
在下文中一共展示了utils.load_dataset方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _initialize_config
# 需要导入模块: import utils [as 别名]
# 或者: from utils import load_dataset [as 别名]
def _initialize_config(self):
# create folders and logger
if not os.path.exists(self.cfg["checkpoint_path"]):
os.makedirs(self.cfg["checkpoint_path"])
if not os.path.exists(self.cfg["summary_path"]):
os.makedirs(self.cfg["summary_path"])
self.logger = get_logger(os.path.join(self.cfg["checkpoint_path"], "log.txt"))
# load dictionary
dict_data = load_dataset(self.cfg["vocab"])
self.word_dict, self.char_dict = dict_data["word_dict"], dict_data["char_dict"]
self.tag_dict = dict_data["tag_dict"]
del dict_data
self.word_vocab_size = len(self.word_dict)
self.char_vocab_size = len(self.char_dict)
self.tag_vocab_size = len(self.tag_dict)
self.rev_word_dict = dict([(idx, word) for word, idx in self.word_dict.items()])
self.rev_char_dict = dict([(idx, char) for char, idx in self.char_dict.items()])
self.rev_tag_dict = dict([(idx, tag) for tag, idx in self.tag_dict.items()]) 示例2: run
# 需要导入模块: import utils [as 别名]
# 或者: from utils import load_dataset [as 别名]
def run(args):
pprint(args)
logging.basicConfig(level=logging.INFO)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
seed(args.seed)
dataset, ontology, vocab, Eword = load_dataset()
model = load_model(args.model, args, ontology, vocab)
model.save_config()
model.load_emb(Eword)
model = model.to(model.device)
if not args.test:
logging.info('Starting train')
model.run_train(dataset['train'], dataset['dev'], args)
if args.resume:
model.load_best_save(directory=args.resume)
else:
model.load_best_save(directory=args.dout)
model = model.to(model.device)
logging.info('Running dev evaluation')
dev_out = model.run_eval(dataset['dev'], args)
pprint(dev_out) 示例3: main
# 需要导入模块: import utils [as 别名]
# 或者: from utils import load_dataset [as 别名]
def main():
args = parse_arguments()
hidden_size = 512
embed_size = 256
assert torch.cuda.is_available()
print("[!] preparing dataset...")
train_iter, val_iter, test_iter, DE, EN = load_dataset(args.batch_size)
de_size, en_size = len(DE.vocab), len(EN.vocab)
print("[TRAIN]:%d (dataset:%d)\t[TEST]:%d (dataset:%d)"
% (len(train_iter), len(train_iter.dataset),
len(test_iter), len(test_iter.dataset)))
print("[DE_vocab]:%d [en_vocab]:%d" % (de_size, en_size))
print("[!] Instantiating models...")
encoder = Encoder(de_size, embed_size, hidden_size,
n_layers=2, dropout=0.5)
decoder = Decoder(embed_size, hidden_size, en_size,
n_layers=1, dropout=0.5)
seq2seq = Seq2Seq(encoder, decoder).cuda()
optimizer = optim.Adam(seq2seq.parameters(), lr=args.lr)
print(seq2seq)
best_val_loss = None
for e in range(1, args.epochs+1):
train(e, seq2seq, optimizer, train_iter,
en_size, args.grad_clip, DE, EN)
val_loss = evaluate(seq2seq, val_iter, en_size, DE, EN)
print("[Epoch:%d] val_loss:%5.3f | val_pp:%5.2fS"
% (e, val_loss, math.exp(val_loss)))
# Save the model if the validation loss is the best we've seen so far.
if not best_val_loss or val_loss < best_val_loss:
print("[!] saving model...")
if not os.path.isdir(".save"):
os.makedirs(".save")
torch.save(seq2seq.state_dict(), './.save/seq2seq_%d.pt' % (e))
best_val_loss = val_loss
test_loss = evaluate(seq2seq, test_iter, en_size, DE, EN)
print("[TEST] loss:%5.2f" % test_loss)