Parameters ---------- input_dim : int Number of input features. hidden_dim : int, optional (default=64) Size of the hidden layer. output_dim : int, optional (default=1) For regression use 1; for binary classification use 1 (sigmoid applied later). """ def __init__(self, input_dim: int, hidden_dim: int = 64, output_dim: int = 1): super().__init__() self.net = nn.Sequential( nn.Linear(input_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, output_dim) )
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class CSVDataset(Dataset): """Wrap a pandas DataFrame as a PyTorch dataset.""" def __init__(self, dataframe: pd.DataFrame, target_col: str): self.X = torch.tensor( dataframe.drop(columns=[target_col]).values, dtype=torch.float32 ) self.y = torch.tensor( dataframe[target_col].values, dtype=torch.float32 ).unsqueeze(1) # make it (N, 1) """ def __init__(self, input_dim: int, hidden_dim: int =