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130
tutorials/.ipynb_checkpoints/API_9_video-checkpoint.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "134e7f9d", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Demo 9: Videos\n", | ||
| "\n", | ||
| "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)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "2075ef56", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "train loss: 5.89e-03 | test loss: 5.99e-03 | reg: 7.89e+00 : 100%|██| 50/50 [01:36<00:00, 1.92s/it]\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from kan import KAN, create_dataset\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", | ||
| "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=0)\n", | ||
| "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)\n", | ||
| "dataset = create_dataset(f, n_var=4, train_num=3000)\n", | ||
| "\n", | ||
| "image_folder = 'video_img'\n", | ||
| "\n", | ||
| "# train the model\n", | ||
| "#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n", | ||
| "model.train(dataset, opt=\"LBFGS\", steps=50, lamb=5e-5, lamb_entropy=2., save_fig=True, beta=10, \n", | ||
| " in_vars=[r'$x_1$', r'$x_2$', r'$x_3$', r'$x_4$'],\n", | ||
| " out_vars=[r'${\\rm exp}({\\rm sin}(x_1^2+x_2^2)+{\\rm sin}(x_3^2+x_4^2))$'],\n", | ||
| " img_folder=image_folder);\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "c18245a3", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Moviepy - Building video video.mp4.\n", | ||
| "Moviepy - Writing video video.mp4\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| " \r" | ||
| ] | ||
| }, | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Moviepy - Done !\n", | ||
| "Moviepy - video ready video.mp4\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import os\n", | ||
| "import numpy as np\n", | ||
| "import moviepy.video.io.ImageSequenceClip # moviepy == 1.0.3\n", | ||
| "\n", | ||
| "video_name='video'\n", | ||
| "fps=5\n", | ||
| "\n", | ||
| "fps = fps\n", | ||
| "files = os.listdir(image_folder)\n", | ||
| "train_index = []\n", | ||
| "for file in files:\n", | ||
| " if file[0].isdigit() and file.endswith('.jpg'):\n", | ||
| " train_index.append(int(file[:-4]))\n", | ||
| "\n", | ||
| "train_index = np.sort(train_index)\n", | ||
| "\n", | ||
| "image_files = [image_folder+'/'+str(train_index[index])+'.jpg' for index in train_index]\n", | ||
| "\n", | ||
| "clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)\n", | ||
| "clip.write_videofile(video_name+'.mp4')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "88d0d737", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.7" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |