API 9: Videos
=============

We have shown one can visualize KAN with the plot() method. If one wants
to save the training dynamics of KAN plots, one only needs to pass
argument save_video = True to train() method (and set some video related
parameters)

.. code:: ipython3

    from kan import *
    import torch
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(device)
    
    # create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).
    model = KAN(width=[4,2,1,1], grid=3, k=3, seed=1, device=device)
    f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)
    dataset = create_dataset(f, n_var=4, train_num=3000, device=device)
    
    image_folder = 'video_img'
    
    # train the model
    #model.train(dataset, opt="LBFGS", steps=20, lamb=1e-3, lamb_entropy=2.);
    model.fit(dataset, opt="LBFGS", steps=5, lamb=0.001, lamb_entropy=2., save_fig=True, beta=10, 
                in_vars=[r'$x_1$', r'$x_2$', r'$x_3$', r'$x_4$'],
                out_vars=[r'${\rm exp}({\rm sin}(x_1^2+x_2^2)+{\rm sin}(x_3^2+x_4^2))$'],
                img_folder=image_folder);



.. parsed-literal::

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


.. parsed-literal::

    | train_loss: 2.89e-01 | test_loss: 2.96e-01 | reg: 1.31e+01 | : 100%|█| 5/5 [00:09<00:00,  1.94s/it

.. parsed-literal::

    saving model version 0.1


.. parsed-literal::

    


.. code:: ipython3

    import os
    import numpy as np
    import moviepy.video.io.ImageSequenceClip # moviepy == 1.0.3
    
    video_name='video'
    fps=5
    
    fps = fps
    files = os.listdir(image_folder)
    train_index = []
    for file in files:
        if file[0].isdigit() and file.endswith('.jpg'):
            train_index.append(int(file[:-4]))
    
    train_index = np.sort(train_index)
    
    image_files = [image_folder+'/'+str(train_index[index])+'.jpg' for index in train_index]
    
    clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)
    clip.write_videofile(video_name+'.mp4')


.. parsed-literal::

    Moviepy - Building video video.mp4.
    Moviepy - Writing video video.mp4
    


.. parsed-literal::

                                                                                    

.. parsed-literal::

    Moviepy - Done !
    Moviepy - video ready video.mp4