compress_size=64,
enc_num_size=23,
enc_cat_size=[(3049, 16), (7, 1), (10, 1), (3, 1), (3, 1),
(32, 4), (5, 1), (5, 1), (3, 1)],
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()
enc_num_size=40,
enc_cat_size=[(62, 4)],
dec_num_size=40,
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 = []