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2025-03-25 19:25:13 -07:00

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Pt Train RNN Model

Trains an RNN model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.

Args:
model (PTMODEL): The PyTorch model to be trained.
linear_head (bool): The RNN model has a Linear head.
train_loader (torch.utils.data.DataLoader): DataLoader containing the training dataset.
optimizer (PTOPTIMIZER): Optimizer used for training.
loss (PTLOSS): The loss class.
epochs (int): Number of training epochs.
use_gpu (bool): Whether to use GPU for training.
early_stopping (bool): Whether to turn on early stopping
early_stopping_rounds (int): Specifies the number of rounds to monitor the evaluation loss when early_stopping is enabled and validation data is specified. If the loss does not decrease compared to the current best loss within this period, training will be stopped.
output_best_val_model (bool): Whether to outputs the model with the best val loss or the model from the last epoch.
classification_metrics (bool): If True and if val_loader is set, print out classification metrics. This is only valid for classification model training.
use_valid_token_mean (bool): Computes the mean of the RNN output for non-zero inputs and feeds to the linear layer. val_loader (PTDATALOADER): DataLoader for validation dataset (optional).
scheduler (PTLRSCHEDULER): Learning rate scheduler.
h_0 (TENSOR): Optional initial hidden state. For unidirectional models, the shape is [num_layers, batch_size, hidden_size]. For bidirectional models, it is [2 * num_layers, batch_size, hidden_size]. If not provided, a zero tensor is used.

Input

Name Data type
model Ptmodel
linear_head Boolean
train_loader Ptdataloader
optimizer Ptoptimizer
loss_function Ptloss
epochs Int
use_gpu Boolean
early_stopping Boolean
early_stopping_rounds Int
output_best_val_model Boolean
classification_metrics Boolean
use_valid_token_mean Boolean
scheduler Ptlrscheduler
val_loader Ptdataloader
h_0 Tensor

Output

Data type
Ptmodel
Tensor
Tensor

Category: PyTorch wrapper - Training

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