168 lines
3.5 KiB
Plaintext
168 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "53ff2e87",
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"metadata": {},
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"source": [
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"# API 11: Create dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "25a90774",
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"metadata": {},
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"source": [
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"how to use create_dataset in kan.utils"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2f9ae0c7",
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"metadata": {},
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"source": [
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"Standard way"
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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": 1,
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"id": "3e2b9f8b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([1000, 1])"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from kan.utils import create_dataset\n",
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"\n",
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"f = lambda x: x[:,[0]] * x[:,[1]]\n",
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"dataset = create_dataset(f, n_var=2)\n",
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"dataset['train_label'].shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "877956c9",
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"metadata": {},
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"source": [
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"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."
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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": "b14dd4a2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([1000, 1])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"f = lambda x: x[:,0] * x[:,1]\n",
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"dataset = create_dataset(f, n_var=2)\n",
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"dataset['train_label'].shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "60230da4",
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"metadata": {},
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"source": [
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"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'."
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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": 3,
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"id": "e764f415",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([1000, 1])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"f = lambda x: x[0] * x[1]\n",
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"dataset = create_dataset(f, n_var=2, f_mode='row')\n",
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"dataset['train_label'].shape"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8e1f1732",
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"metadata": {},
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"source": [
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"if you already have x (inputs) and y (outputs), and you only want to partition them into train/test, use create_dataset_from_data"
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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": 1,
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"id": "accf900a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from kan.utils import create_dataset_from_data\n",
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"\n",
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"x = torch.rand(100,2)\n",
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"y = torch.rand(100,1)\n",
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"dataset = create_dataset_from_data(x, y)"
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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": "c45062a8",
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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": "3.9.7"
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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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}
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