Ошибка при прогнозировании прогнозирования временных рядов с помощью TFT ⇐ Python

[Anonymous]

from pytorch_lightning import LightningModule
import torch.optim as optim

# Ensure the TemporalFusionTransformer is wrapped as a LightningModule
class TFTLightningModule(LightningModule):
def __init__(self, model):
super().__init__()
self.model = model
self.save_hyperparameters()

def forward(self, batch):
# If batch is a tuple (inputs, targets), use only inputs
if isinstance(batch, tuple):
batch = batch[0]
return self.model(batch)

def training_step(self, batch, batch_idx):
x, y = batch # unpack if needed
y_hat = self(x)
loss = self.model.loss(y_hat, x["decoder_target"])
self.log("train_loss", loss)
return loss

def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = self.model.loss(y_hat, x["decoder_target"])
self.log("val_loss", loss)
return loss

def configure_optimizers(self):
optimizer = optim.Adam(self.parameters(), lr=self.model.hparams.learning_rate)
return optimizer

# Wrap the TemporalFusionTransformer
tft_module = TFTLightningModule(tft)

trainer = Trainer(
max_epochs=30,
accelerator="auto",
enable_model_summary=True,
gradient_clip_val=0.1,
)

# Train the model
trainer.fit(tft_module, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)
< /code>
Я получил следующую ошибку для приведенного выше кода при использовании tft: < /p>
INFO:pytorch_lightning.callbacks.model_summary:
| Name | Type | Params | Mode
------------------------------------------------------------
0 | model | TemporalFusionTransformer | 1.4 M | train
------------------------------------------------------------
1.4 M Trainable params
0 Non-trainable params
1.4 M Total params
5.613 Total estimated model params size (MB)
470 Modules in train mode
0 Modules in eval mode
/usr/local/lib/python3.11/dist-packages/pytorch_lightning/core/saving.py:363: Skipping 'model' parameter because it is not possible to safely dump to YAML.
Sanity Checking DataLoader 0:   0%
 0/1 [00:00

Подробнее здесь: https://stackoverflow.com/questions/795 ... g-with-tft