kDV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import numpy as np\n",
    "from scipy.io import loadmat, savemat\n",
    "import math\n",
    "import os\n",
    "import h5py\n",
    "\n",
    "from functools import partial\n",
    "from models.models import MWT1d\n",
    "from models.utils import train, test, LpLoss, get_filter, UnitGaussianNormalizer"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "torch.manual_seed(0)\n",
    "np.random.seed(0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "def get_initializer(name):\n",
    "    \n",
    "    if name == 'xavier_normal':\n",
    "        init_ = partial(nn.init.xavier_normal_)\n",
    "    elif name == 'kaiming_uniform':\n",
    "        init_ = partial(nn.init.kaiming_uniform_)\n",
    "    elif name == 'kaiming_normal':\n",
    "        init_ = partial(nn.init.kaiming_normal_)\n",
    "    return init_"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "ntrain = 1000\n",
    "ntest = 200\n",
    "\n",
    "sub = 2**3 #subsampling rate\n",
    "h = 2**13 // sub #total grid size divided by the subsampling rate\n",
    "s = h\n",
    "batch_size = 20\n",
    "\n",
    "rw_ = loadmat('Data/KDV/kdv_train_test.mat')\n",
    "x_data = rw_['input'].astype(np.float32)\n",
    "y_data = rw_['output'].astype(np.float32)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "x_train = x_data[:ntrain,::sub]\n",
    "y_train = y_data[:ntrain,::sub]\n",
    "x_test = x_data[-ntest:,::sub]\n",
    "y_test = y_data[-ntest:,::sub]\n",
    "\n",
    "x_train = torch.from_numpy(x_train)\n",
    "x_test = torch.from_numpy(x_test)\n",
    "y_train = torch.from_numpy(y_train)\n",
    "y_test = torch.from_numpy(y_test)\n",
    "\n",
    "x_train = x_train.unsqueeze(-1)\n",
    "x_test = x_test.unsqueeze(-1)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train), batch_size=batch_size, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test), batch_size=batch_size, shuffle=False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "ich = 1\n",
    "initializer = get_initializer('xavier_normal') # xavier_normal, kaiming_normal, kaiming_uniform\n",
    "\n",
    "model = MWT1d(ich,\n",
    "            alpha = 10,\n",
    "            c = 4*4,\n",
    "            k = 4,\n",
    "            base = 'legendre', # chebyshev\n",
    "            nCZ = 2,\n",
    "            initializer = initializer,\n",
    "            ).to(device)\n",
    "learning_rate = 0.001\n",
    "\n",
    "epochs = 500\n",
    "step_size = 100\n",
    "gamma = 0.5"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)\n",
    "\n",
    "myloss = LpLoss(size_average=False)\n",
    "\n",
    "for epoch in range(1, epochs+1):\n",
    "    train_l2 = train(model, train_loader, optimizer, epoch, device,\n",
    "        lossFn = myloss, lr_schedule = scheduler)\n",
    "    \n",
    "    test_l2 = test(model, test_loader, device, lossFn=myloss)\n",
    "    print(f'epoch: {epoch}, train l2 = {train_l2}, test l2 = {test_l2}')"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pde",
   "language": "python",
   "name": "pde"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}