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updated tutorials
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@KindXiaoming
KindXiaoming authored Aug 11, 2024
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173 changes: 173 additions & 0 deletions tutorials/API_10_device.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "134e7f9d",
"metadata": {},
"source": [
"# Demo 10: Device\n",
"\n",
"All other demos have by default used device = 'cpu'. In case we want to use cuda, we should pass the device argument to model and dataset."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7a4ac1e1-84ba-4bc3-91b6-a776a5e7711c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cpu\n"
]
}
],
"source": [
"from kan import KAN, create_dataset\n",
"import torch\n",
"\n",
"torch.use_deterministic_algorithms(False)\n",
"\n",
"#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"device = 'cpu'\n",
"print(device)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2075ef56",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checkpoint directory created: ./model\n",
"saving model version 0.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"| train_loss: 6.83e-01 | test_loss: 7.21e-01 | reg: 1.04e+03 | : 100%|█| 50/50 [00:19<00:00, 2.62it\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"saving model version 0.1\n"
]
}
],
"source": [
"model = KAN(width=[4,100,100,100,1], grid=3, k=3, seed=0).to(device)\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=1000, device=device)\n",
"\n",
"# train the model\n",
"#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n",
"model.fit(dataset, opt=\"Adam\", lr=1e-3, steps=50, lamb=1e-3, lamb_entropy=5., update_grid=False);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f182cc1-51bf-4151-a253-a52fe854919e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f6f8125e-d26d-4c97-9e5f-988099bb4737",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda\n"
]
}
],
"source": [
"device = 'cuda'\n",
"print(device)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "95017dfa-3a2a-43e0-8b68-fb220ca5abc9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checkpoint directory created: ./model\n",
"saving model version 0.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"| train_loss: 6.83e-01 | test_loss: 7.21e-01 | reg: 1.04e+03 | : 100%|█| 50/50 [00:01<00:00, 26.90it\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"saving model version 0.1\n"
]
}
],
"source": [
"model = KAN(width=[4,100,100,100,1], grid=3, k=3, seed=0).to(device)\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=1000, device=device)\n",
"\n",
"# train the model\n",
"#model.train(dataset, opt=\"LBFGS\", steps=20, lamb=1e-3, lamb_entropy=2.);\n",
"model.fit(dataset, opt=\"Adam\", lr=1e-3, steps=50, lamb=1e-3, lamb_entropy=5., update_grid=False);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8230d562-2635-4adc-b566-06ac679b166a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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"pygments_lexer": "ipython3",
"version": "3.8.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
167 changes: 167 additions & 0 deletions tutorials/API_11_create_dataset.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "53ff2e87",
"metadata": {},
"source": [
"# API 11: Create dataset"
]
},
{
"cell_type": "markdown",
"id": "25a90774",
"metadata": {},
"source": [
"how to use create_dataset in kan.utils"
]
},
{
"cell_type": "markdown",
"id": "2f9ae0c7",
"metadata": {},
"source": [
"Standard way"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3e2b9f8b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1000, 1])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from kan.utils import create_dataset\n",
"\n",
"f = lambda x: x[:,[0]] * x[:,[1]]\n",
"dataset = create_dataset(f, n_var=2)\n",
"dataset['train_label'].shape"
]
},
{
"cell_type": "markdown",
"id": "877956c9",
"metadata": {},
"source": [
"Lazier way. We sometimes forget to add the bracket, i.e., write x[:,[0]] as x[:,0], and this used to lead to an error in training (loss not going down). Now the create_dataset can automatically detect this simplification and produce the correct behavior."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b14dd4a2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1000, 1])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = lambda x: x[:,0] * x[:,1]\n",
"dataset = create_dataset(f, n_var=2)\n",
"dataset['train_label'].shape"
]
},
{
"cell_type": "markdown",
"id": "60230da4",
"metadata": {},
"source": [
"Laziest way. If you even want to get rid of the colon symbol, i.e., you want to write x[;,0] as x[0], you can do that but need to pass in f_mode = 'row'."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e764f415",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1000, 1])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = lambda x: x[0] * x[1]\n",
"dataset = create_dataset(f, n_var=2, f_mode='row')\n",
"dataset['train_label'].shape"
]
},
{
"cell_type": "markdown",
"id": "8e1f1732",
"metadata": {},
"source": [
"if you already have x (inputs) and y (outputs), and you only want to partition them into train/test, use create_dataset_from_data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "accf900a",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from kan.utils import create_dataset_from_data\n",
"\n",
"x = torch.rand(100,2)\n",
"y = torch.rand(100,1)\n",
"dataset = create_dataset_from_data(x, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c45062a8",
"metadata": {},
"outputs": [],
"source": []
}
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"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
282 changes: 282 additions & 0 deletions tutorials/API_12_checkpoint_save_load_model.ipynb
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455 changes: 455 additions & 0 deletions tutorials/API_1_indexing.ipynb
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