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)
series, series_mean, series_std = normalize(series[:, np.newaxis,
DROP_BEFORE:],
axis=2)
series_lags = np.nan_to_num(
(series_lags[:, :, DROP_BEFORE:] - series_mean) / series_std)
series_lags = Values(series_lags, 'xy_lags')
time_idxes = np.arange(series.shape[2])
trn_idx, val_idx = forward_split(time_idxes, ENC_LEN, VALID_LEN + TEST_LEN)
val_idx, test_idx = forward_split(val_idx, ENC_LEN, TEST_LEN)
trn_dl = create_seq2seq_data_loader(series,
enc_len=ENC_LEN,
dec_len=DEC_LEN,
time_idx=trn_idx,
batch_size=BATCH_SIZE,
num_iteration_per_epoch=4,
features=[series_lags, series_lags_corr],
seq_last=False,
device='cuda',
mode='train')
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')
""" # encoding: utf-8 """ @author : zhirui zhou @contact: [email protected] @time : 2020/5/12 16:33 """ from deepseries.models import Wave2Wave from deepseries.train import Learner from deepseries.dataset import Values, create_seq2seq_data_loader, forward_split import numpy as np 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, 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 = 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)
def test_create_seq2seq_data_loader():
x = np.random.rand(30, 1, 24)
dl = create_seq2seq_data_loader(x, 12, 12, np.arange(x.shape[-1]), batch_size=4, num_iteration_per_epoch=30, seq_last=True)
for i, batch in enumerate(dl):
pass
assert i == 30-1