(train, dev, test), batch_size=args.batch_size, device=args.gpu)
config = args
config.n_embed = len(inputs.vocab)
config.d_out = len(answers.vocab)
config.n_cells = config.n_layers
if config.birnn:
config.n_cells *= 2
if args.resume_snapshot:
model = torch.load(args.resume_snapshot, map_location=lambda storage, locatoin: storage.cuda(args.gpu))
else:
model = SNLIClassifier(config)
if args.word_vectors:
model.embed.weight.data = inputs.vocab.vectors
model.cuda()
criterion = nn.CrossEntropyLoss()
opt = O.Adam(model.parameters(), lr=args.lr)
iterations = 0
start = time.time()
best_dev_acc = -1
train_iter.repeat = False
header = ' Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss Accuracy Dev/Accuracy'
dev_log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'.split(','))
log_template = ' '.join('{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'.split(','))
os.makedirs(args.save_path, exist_ok=True)
print(header)
for epoch in range(args.epochs):
config.d_out = len(answers.vocab)
config.n_cells = config.n_layers
# double the number of cells for bidirectional networks
if config.birnn:
config.n_cells *= 2
if args.resume_snapshot:
model = torch.load(
args.resume_snapshot,
map_location=lambda storage, locatoin: storage.cuda(args.gpu))
else:
model = SNLIClassifier(config)
if args.word_vectors:
model.embed.weight.data.copy_(inputs.vocab.vectors)
model.cuda(args.gpu)
criterion = nn.CrossEntropyLoss()
opt = O.Adam(model.parameters(), lr=args.lr)
iterations = 0
start = time.time()
best_dev_acc = -1
train_iter.repeat = False
header = ' Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss Accuracy Dev/Accuracy'
dev_log_template = ' '.join(
'{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'
.split(','))
log_template = ' '.join(
'{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'
.split(','))
config.n_cells *= 2
if args.resume_snapshot:
model = torch.load(
args.resume_snapshot,
map_location=lambda storage, location: storage.cuda(args.gpu))
else:
model = SNLIClassifier(config)
if args.word_vectors:
# Load word vectors
word_vectors = word_to_vector.aliases[args.word_vectors]()
for i, token in enumerate(sentence_encoder.vocab):
model.embed.weight.data[i] = word_vectors[token]
if args.gpu >= 0:
model.cuda()
criterion = nn.CrossEntropyLoss()
opt = optim.Adam(model.parameters(), lr=args.lr)
iterations = 0
start = time.time()
best_dev_acc = -1
header = ' Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss Accuracy Dev/Accuracy'
dev_log_template = ' '.join(
'{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{:8.6f},{:12.4f},{:12.4f}'
.split(','))
log_template = ' '.join(
'{:>6.0f},{:>5.0f},{:>9.0f},{:>5.0f}/{:<5.0f} {:>7.0f}%,{:>8.6f},{},{:12.4f},{}'
.split(','))
makedirs(args.save_path)
