def test_rnn2rnn():
train_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=train_idx,
batch_size=12,
num_iteration_per_epoch=12,
seq_last=False)
valid_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=valid_idx,
batch_size=12,
num_iteration_per_epoch=12,
seq_last=False)
model = RNN2RNN(1,
256,
64,
num_layers=1,
attn_heads=1,
attn_size=12,
rnn_type='LSTM')
model.cuda()
opt = Adam(model.parameters(), 0.001)
learner = Learner(model, opt, ".")
learner.fit(10, train_dl, valid_dl, early_stopping=False)
def test_wave2wave_v1():
train_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=train_idx,
batch_size=12,
num_iteration_per_epoch=12,
seq_last=True)
valid_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=valid_idx,
batch_size=12,
num_iteration_per_epoch=12,
seq_last=True)
model = Wave2WaveV1(1)
model.cuda()
opt = Adam(model.parameters(), 0.001)
learner = Learner(model, opt, ".")
learner.fit(100, train_dl, valid_dl, early_stopping=False)
dec_num_size=21,
attn_heads=4,
attn_size=32,
residual=False,
dec_cat_size=[(3049, 16), (7, 1), (10, 1), (3, 1), (3, 1),
(32, 4), (5, 1), (5, 1), (3, 1)],
dropout=0.1,
num_layers=1,
rnn_type="GRU")
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()
from torch.optim import Adam
batch_size = 16
enc_len = 36
dec_len = 12
series = np.sin(np.arange(0, 1000))
series = series.reshape(1, 1, -1)
train_idx, valid_idx = forward_split(np.arange(series.shape[2]),
enc_len=14,
valid_size=200)
train_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=train_idx,
batch_size=12,
sampling_rate=1.,
seq_last=True)
valid_dl = create_seq2seq_data_loader(series,
enc_len=14,
dec_len=7,
time_idx=valid_idx,
batch_size=12,
sampling_rate=1.,
seq_last=True)
model = Wave2Wave(1, debug=False, num_layers=5, num_blocks=1)
model.cuda()
opt = Adam(model.parameters(), 0.001)
learner = Learner(model, opt, ".")
learner.fit(100, train_dl, valid_dl, early_stopping=False)
dec_cat_size=[(62, 4)],
residual=True,
beta1=.0,
beta2=.0,
attn_heads=1,
attn_size=128,
num_layers=1,
dropout=0.0,
rnn_type='GRU')
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')
val_dl = create_seq2seq_data_loader(series,
enc_len=ENC_LEN,
dec_len=DEC_LEN,
time_idx=val_idx,
batch_size=BATCH_SIZE,
num_iteration_per_epoch=4,
features=[series_lags, series_lags_corr],
seq_last=False,
device='cuda',
mode='valid')
model = RNN2RNN(1, 128, 128, enc_num_size=8, dec_num_size=8)
opt = Adam(model.parameters(), 0.001)
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=100)
learner.fit(500,
trn_dl,
val_dl,
patient=64,
start_save=-1,
early_stopping=False)
import torch
torch.cuda.memory_allocated() / 1024**2
enc_len,
dec_len,
batch_size=batch_size,
seq_last=True,
device='cuda')
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)
# rnn2rnn train
train_dl = create_seq2seq_data_loader(series[:, :, train_idx],
enc_len,
dec_len,
sampling_rate=0.1,
batch_size=batch_size,
enc_len,
dec_len,
batch_size=batch_size,
seq_last=True,
device='cuda')
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))