torch.nn.LeakyReLU(),
torch.nn.Linear(10, 10),
torch.nn.LeakyReLU(),
torch.nn.Linear(10, 10),
torch.nn.LeakyReLU(),
torch.nn.Linear(10, 10),
torch.nn.LeakyReLU(),
torch.nn.Linear(10, 2),
torch.nn.Softplus(),
)
if CUDA:
model = model.cuda()
emb = emb.cuda()
optim = torch.optim.Adam(model.parameters(), lr=10e-4)
emb_optim = torch.optim.Adam(emb.parameters(), lr=10e-4)
class DyadLSTMDataset(Dataset):
def __init__(self):
self.labels = []
self.words = []
with open('dyad_dataset_for_lstm.txt') as f:
for line in f.readlines():
user1, user2, acts, pred1, num_days1, pred2, num_days2 = line.strip(
).split('\t')
acts = json.loads(acts)
pred1 = float(pred1)
pred2 = float(pred2)
labels = [[pred1, pred2] if a[0] == 1 else [pred2, pred1]
for a in acts if len(a[3]) > 0]
print('Shuffling indices')
random.shuffle(indices)
valid_indices = indices[:10000]
train_indices = indices[10001:]
print('Creating samplers')
train_dataset = TensorDataset(features[train_indices], labels[train_indices])
valid_dataset = TensorDataset(features[valid_indices], labels[valid_indices])
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=True)
num_entities = len(node_ids)
emb = EmbeddingBag(num_entities, DIM, mode='sum')
optim = torch.optim.Adam(emb.parameters(), lr=10e-4)
if CUDA:
emb = emb.cuda()
rev_node_ids = dict((x[1], x[0]) for x in node_ids.items())
def compute_loss(batch):
ids, labels = batch
labels = torch.FloatTensor(labels.numpy().tolist())
ids = Variable(ids)
labels = Variable(labels)
if CUDA:
ids = ids.cuda()
labels = labels.cuda()