opt = Adam(model.parameters(), 0.001)
loss_fn = MSELoss()
lr_scheduler = ReduceCosineAnnealingLR(opt, 64, eta_min=1e-4, gamma=0.998)
model.cuda()
learner = Learner(model,
opt,
'./m5_rnn',
lr_scheduler=lr_scheduler,
verbose=5000)
learner.fit(1000,
train_dl,
valid_dl,
patient=64,
start_save=-1,
early_stopping=True)
learner.load(174)
learner.model.eval()
def predict_submission(model, test_dl):
model.eval()
test_dl.test()
preds = []
for batch in test_dl:
batch.pop('dec_x')
preds.append(model.predict(**batch)[0].cpu().detach().numpy())
preds = np.concatenate(preds, axis=0).squeeze()
return preds
preds = predict_submission(learner.model, test_dl)
num_blocks=1,
dropout=0.1,
loss_fn=RMSE())
wave.cuda()
opt = torch.optim.Adam(wave.parameters(), lr=lr)
wave_learner = Learner(
wave,
opt,
root_dir="./wave",
)
wave_learner.fit(max_epochs=epoch,
train_dl=train_dl,
valid_dl=valid_dl,
early_stopping=True,
patient=16)
wave_learner.load(wave_learner.best_epoch)
# rnn2rnn train
train_dl = create_seq2seq_data_loader(series[:, :, train_idx],
enc_len,
dec_len,
sampling_rate=0.1,
batch_size=batch_size,
seq_last=False,
device='cuda')
valid_dl = create_seq2seq_data_loader(series[:, :, valid_idx],
enc_len,
dec_len,
batch_size=batch_size,
seq_last=False,
device='cuda')
opt = Adam(model.parameters(), 0.001)
loss_fn = MSELoss()
model.cuda()
lr_scheduler = ReduceCosineAnnealingLR(opt, 64, eta_min=5e-5)
learner = Learner(model,
opt,
'./power_preds',
verbose=20,
lr_scheduler=lr_scheduler)
learner.fit(300,
train_frame,
valid_frame,
patient=128,
start_save=1,
early_stopping=True)
learner.load(299)
learner.model.eval()
preds = []
trues = []
for batch in valid_frame:
batch[0].pop('dec_x')
preds.append(learner.model(**batch[0])[0])
trues.append(batch[1])
trues = torch.cat(trues, 2).squeeze().cpu().numpy() * xy_std + xy_mean
preds = torch.cat(preds, 2).squeeze().detach().cpu().numpy() * xy_std + xy_mean
k = 42
plt.plot(trues[k].reshape(-1))
wave = Wave2Wave(target_size=1,
num_layers=6,
num_blocks=1,
dropout=0.1,
loss_fn=RMSE())
wave.cuda()
opt = torch.optim.Adam(wave.parameters(), lr=lr)
wave_learner = Learner(
wave,
opt,
root_dir="./wave",
)
wave_learner.fit(max_epochs=epoch,
train_dl=train_dl,
valid_dl=valid_dl,
early_stopping=True,
patient=16)
wave_learner.load(wave_learner.best_epoch)
import matplotlib.pyplot as plt
wave_preds = wave_learner.model.predict(
torch.tensor(series[:, :, test_idx[:-12]]).float().cuda(),
12).cpu().numpy().reshape(-1)
plt.plot(series[:, :, -48:-12].reshape(-1))
plt.plot(np.arange(36, 48), wave_preds, label="wave2wave preds")
plt.plot(np.arange(36, 48),
series[:, :, test_idx[-12:]].reshape(-1),
label="target")
plt.legend()