159 lines
4.1 KiB
Plaintext
159 lines
4.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "134e7f9d",
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"metadata": {},
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"source": [
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"# API 9: Videos\n",
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"\n",
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"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)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2075ef56",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"cuda\n",
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"checkpoint directory created: ./model\n",
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"saving model version 0.0\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"| train_loss: 2.89e-01 | test_loss: 2.96e-01 | reg: 1.31e+01 | : 100%|█| 5/5 [00:09<00:00, 1.94s/it"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"saving model version 0.1\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"from kan import *\n",
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"import torch\n",
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"\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"print(device)\n",
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"\n",
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"# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n",
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"model = KAN(width=[4,2,1,1], grid=3, k=3, seed=1, device=device)\n",
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"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",
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"dataset = create_dataset(f, n_var=4, train_num=3000, device=device)\n",
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"\n",
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"image_folder = 'video_img'\n",
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"\n",
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"# train the model\n",
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"#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n",
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"model.fit(dataset, opt=\"LBFGS\", steps=5, lamb=0.001, lamb_entropy=2., save_fig=True, beta=10, \n",
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" in_vars=[r'$x_1$', r'$x_2$', r'$x_3$', r'$x_4$'],\n",
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" out_vars=[r'${\\rm exp}({\\rm sin}(x_1^2+x_2^2)+{\\rm sin}(x_3^2+x_4^2))$'],\n",
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" img_folder=image_folder);\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "c18245a3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Moviepy - Building video video.mp4.\n",
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"Moviepy - Writing video video.mp4\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" \r"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Moviepy - Done !\n",
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"Moviepy - video ready video.mp4\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"import moviepy.video.io.ImageSequenceClip # moviepy == 1.0.3\n",
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"\n",
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"video_name='video'\n",
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"fps=5\n",
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"\n",
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"fps = fps\n",
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"files = os.listdir(image_folder)\n",
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"train_index = []\n",
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"for file in files:\n",
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" if file[0].isdigit() and file.endswith('.jpg'):\n",
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" train_index.append(int(file[:-4]))\n",
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"\n",
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"train_index = np.sort(train_index)\n",
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"\n",
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"image_files = [image_folder+'/'+str(train_index[index])+'.jpg' for index in train_index]\n",
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"\n",
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"clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)\n",
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"clip.write_videofile(video_name+'.mp4')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "88d0d737",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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