GitHub_collection_pykan/tutorials/.ipynb_checkpoints/API_7_pruning-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "134e7f9d",
   "metadata": {},
   "source": [
    "# API 7: Pruning\n",
    "\n",
    "We usually use pruning to make neural networks sparser hence more efficient and more interpretable. KANs provide two ways of pruning: automatic pruning, and manual pruning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd6a742",
   "metadata": {},
   "source": [
    "## Pruning nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2075ef56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "checkpoint directory created: ./model\n",
      "saving model version 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "| train_loss: 3.46e-02 | test_loss: 3.46e-02 | reg: 4.91e+00 | : 100%|█| 20/20 [00:05<00:00,  3.36it\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from kan import *\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)\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=[2,5,1], grid=5, k=3, seed=1, device=device)\n",
    "\n",
    "# create dataset f(x,y) = exp(sin(pi*x)+y^2)\n",
    "f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)\n",
    "dataset = create_dataset(f, n_var=2, device=device)\n",
    "dataset['train_input'].shape, dataset['train_label'].shape\n",
    "\n",
    "# train the model\n",
    "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=0.01);\n",
    "model(dataset['train_input'])\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "280cc49f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode = 'auto'\n",
    "\n",
    "if mode == 'auto':\n",
    "    # automatic\n",
    "    model = model.prune_node(threshold=1e-2) # by default the threshold is 1e-2\n",
    "    model.plot()\n",
    "elif mode == 'manual':\n",
    "    # manual\n",
    "    model = model.prune_node(active_neurons_id=[[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7001ab",
   "metadata": {},
   "source": [
    "## Pruning Edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b58417be",
   "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: 7.84e-02 | test_loss: 7.80e-02 | reg: 7.26e+00 | : 100%|█| 6/6 [00:01<00:00,  3.72it/s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from kan import *\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=[2,5,1], grid=5, k=3, seed=1, device=device)\n",
    "\n",
    "# create dataset f(x,y) = exp(sin(pi*x)+y^2)\n",
    "f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)\n",
    "dataset = create_dataset(f, n_var=2, device=device)\n",
    "dataset['train_input'].shape, dataset['train_label'].shape\n",
    "\n",
    "# train the model\n",
    "model.fit(dataset, opt=\"LBFGS\", steps=6, lamb=0.01);\n",
    "model(dataset['train_input'])\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d57cbfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.2\n"
     ]
    }
   ],
   "source": [
    "model.prune_edge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3a23aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db74fbd",
   "metadata": {},
   "source": [
    "## Prune nodes and edges together"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e7e2c8a",
   "metadata": {},
   "source": [
    "just use model.prune()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ea08f0e",
   "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: 3.46e-02 | test_loss: 3.46e-02 | reg: 4.91e+00 | : 100%|█| 20/20 [00:05<00:00,  3.70it\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from kan import *\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=[2,5,1], grid=5, k=3, seed=1, device=device)\n",
    "\n",
    "# create dataset f(x,y) = exp(sin(pi*x)+y^2)\n",
    "f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)\n",
    "dataset = create_dataset(f, n_var=2, device=device)\n",
    "dataset['train_input'].shape, dataset['train_label'].shape\n",
    "\n",
    "# train the model\n",
    "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=0.01);\n",
    "model(dataset['train_input'])\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4fc161de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = model.prune()\n",
    "model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a8dd8a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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