Interpretability 9: Different plotting metrics
==============================================

.. code:: ipython3

    from kan import *
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(device)
    
    model = KAN(width=[2,5,1], device=device)
    f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)
    dataset = create_dataset(f, n_var=2, device=device)
    model.fit(dataset, steps = 20, lamb=1e-3);


.. parsed-literal::

    cuda
    checkpoint directory created: ./model
    saving model version 0.0


.. parsed-literal::

    | train_loss: 1.48e-02 | test_loss: 1.53e-02 | reg: 7.01e+00 | : 100%|█| 20/20 [00:04<00:00,  4.64it


.. parsed-literal::

    saving model version 0.1


Note: To plot the KAN diagram, there are also three options \*
forward_u: the “norm” of edge, normalized (output std/input std) \*
forward_n: the “norm” of edge, unnormalized (output std) \* backward:
the edge attribution score (default)

.. code:: ipython3

    model.plot(metric='forward_u')



.. image:: Interp_9_different_plotting_metrics_files/Interp_9_different_plotting_metrics_3_0.png


.. code:: ipython3

    model.plot(metric='forward_n')



.. image:: Interp_9_different_plotting_metrics_files/Interp_9_different_plotting_metrics_4_0.png


.. code:: ipython3

    model.plot(metric='backward')



.. image:: Interp_9_different_plotting_metrics_files/Interp_9_different_plotting_metrics_5_0.png