Stanford-CS230-Project

It's a bird... It's a plane... It's a Type Ia supernova!

Astronomical Object Classification from Photometric Time-Series Data

Instructions

Run the IPython notebook Models.ipynb cell-by-cell. The notebook will load the data from csv, perform the necessary transformations, and define necessary helper functions. Then, PyTorch NN modules are defined and trained. Along the way, the notebook saves model files, and outputs the model evaluation results.

You can extend the code with your own PyTorch model, then simply call

model = YourNewModel(<params>)
train_model(model)

Note that the forward method of your model should expect to be passed a tuple of (data tensor, metadata tensor). The data tensor has dimensions (batch_size, n_channels = 6, T = 352), and the metadata tensor has dimensions (batch_size, n_metadata_features = 16).

Here is a simple example of a valid PyTorch model:

class LogisticNet(nn.Module):
    def __init__(self, n_features, n_classes):
        super(LogisticNet, self).__init__()
        self.fc = nn.Linear(n_features, n_classes)

    def forward(self, x):
        _, x = x
        x = self.fc(x)
        return x