def __init__(self,
input_shape,
rnn_type='LSTM',
rnn_layer=2,
hid_size=64,
dropout_3=0,
optim_method='Adam',
optim_args_3={'lr': 1e-3},
loss_fn='mse',
eval_metric='corr',
min_ratio_teacher=0.3):
super().__init__()
# Architecture
self.hid_size = hid_size
self.input_shape = input_shape
self.input_size = input_shape[0]
self.input_day = input_shape[2]
self.dropout = dropout_3
self.rnn_layer = rnn_layer
self.rnn_type = rnn_type
self._build_model()
# Optimization
self.optimizer = getattr(optim, optim_method)(self.parameters(),
**optim_args_3)
self.loss_fn = get_loss_fn(loss_fn)
self.metric_fn = get_metric_fn(eval_metric)
if torch.cuda.is_available():
self.cuda()
def __init__(self,
input_shape,
rnn_type='LSTM',
rnn_layer=2,
hid_size=64,
mix_dropout=0,
optim_method='Adam',
mix_optim_args={'lr': 1e-3},
min_loss_fn='mse_v2',
eval_metric='corr',
out_chnls=[6,6,6],
kernel= [4,4],
stride=[4,4],
min_ratio_teacher = 0.3):
super().__init__()
# Architecture
self.hid_size = hid_size
self.input_size = input_shape[0]
self.input_length = input_shape[1]
self.input_day = input_shape[2]
self.dropout = mix_dropout
self.rnn_layer = rnn_layer
self.rnn_type = rnn_type
self.out_chnls = out_chnls
self.kernel = kernel
self.stride = stride
self.min_ratio_teacher = min_ratio_teacher
self._build_model()
# Optimization
self.optimizer = getattr(optim, optim_method)(self.parameters(),
**mix_optim_args)
self.loss_fn = get_loss_fn(min_loss_fn)
self.metric_fn = get_metric_fn(eval_metric)
if torch.cuda.is_available():
self.cuda()