Nphase4_2_train_AutoEncoderCNN_EncoderLSTM
handler_supervised()
This function is a placeholder for handling supervised training. It can be extended to include specific logic for supervised learning tasks.
Labels:
Projects_Viscosity_P2NeuralNetwork_Nphase4_AutoEncoder_Nphase4_2_train_AutoEncoderCNN_EncoderLSTM_handler_supervised
save_reconstructions(model:nn.Module, dataloader:torch.utils.data.DataLoader, device:torch.device, save_dir:str, epoch:int, num_samples:int)
Save a batch of original and reconstructed images from the dataloader and save target/predicted values to a text file.
Parameters:
model(nn.Module): The trained autoencoder modeldataloader(torch.utils.data.DataLoader): DataLoader for validation or test setdevice(torch.device): Device to run the model onsave_dir(str): Directory to save the images and text fileepoch(int): Current epoch number for namingnum_samples(int): Number of samples to save from the batch
Returns:
None: Saves images and text file to the specified directory.Labels:
Projects_Viscosity_P2NeuralNetwork_Nphase4_AutoEncoder_Nphase4_2_train_AutoEncoderCNN_EncoderLSTM_save_reconstructions
train_lstm_model()
This function is a placeholder for training the LSTM model. It can be extended to include specific logic for training tasks.
Labels:
Projects_Viscosity_P2NeuralNetwork_Nphase4_AutoEncoder_Nphase4_2_train_AutoEncoderCNN_EncoderLSTM_train_lstm_model