notebooks/visualize_weight.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f57c1688-799b-4487-9410-29d2ee9bae5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be815178-12b9-4758-b4ee-a00a661af1b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1796af9-f754-4f9e-8095-00c217b88086",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.3755026 0.034562375\n",
      "4.743336 0.034562375\n",
      "6.3755026 0.034562375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmin, vmax = -0., 5.5\n",
    "\n",
    "base_ds = \"mnistv8\"\n",
    "n_spurious = 2000\n",
    "weights = torch.load(f\"../models/train_classifier/128-{base_ds}-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(weight, vmin=vmin, vmax=vmax)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"./figs/MLP_weights/{base_ds}_{n_spurious}.png\", bbox_inches='tight')\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "base_ds = \"mnist\"\n",
    "weights = torch.load(f\"../models/train_classifier/128-{base_ds}-70-0.01-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(weight, vmin=vmin, vmax=vmax)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"./figs/MLP_weights/{base_ds}.png\", bbox_inches='tight')\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "base_ds = \"mnist\"\n",
    "weights = torch.load(f\"../models/train_classifier/128-{base_ds}-70-0.01-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "ori_weight = weight.max(0).reshape(28, 28)\n",
    "base_ds = \"mnistv8\"\n",
    "n_spurious = 2000\n",
    "weights = torch.load(f\"../models/train_classifier/128-{base_ds}-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.imshow(np.abs(weight - ori_weight), vmin=vmin, vmax=vmax)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"./figs/MLP_weights/{base_ds}_{n_spurious}_dif.png\", bbox_inches='tight')\n",
    "print(weight.max(), weight.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22fd3409-a4ff-4c9c-a293-46887b0f7547",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "290b9e24-1fa5-425d-b501-a20b8ab42e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0596437 0.034562375\n",
      "5.0723944 0.034562375\n",
      "5.0523057 0.034562375\n",
      "5.0452747 0.034562375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f1f0e325ee0>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_ds = \"mnistv3\"\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(12, 12))\n",
    "\n",
    "vmin, vmax = -0., 5.0\n",
    "\n",
    "n_spurious = 10\n",
    "\n",
    "weights = torch.load(f\"../models/train_classifier/128-{base_ds}-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "axs[0][0].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "weights = torch.load(f\"../models/incremental_retraining/128-{base_ds}-{n_spurious}-0-0-140-0.01-ce-tor-LargeMLP-128-mnistv3-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "axs[0][1].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "\n",
    "n_spurious = 200\n",
    "\n",
    "weights = torch.load(f\"../models/train_classifier/128-{base_ds}-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "axs[1][0].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "\n",
    "weights = torch.load(f\"../models/incremental_retraining/128-{base_ds}-{n_spurious}-0-0-140-0.01-ce-tor-LargeMLP-128-mnistv3-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "im = axs[1][1].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
    "fig.colorbar(im, cax=cbar_ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e8966ef8-3e7b-435a-9ed8-16d0f676ea3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2559661 0.034562375\n",
      "0.25596973 0.034562375\n",
      "0.25696713 0.034562375\n",
      "0.25697452 0.034562375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f1f0e69bfa0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(12, 12))\n",
    "\n",
    "vmin, vmax = -0., 0.25\n",
    "\n",
    "n_spurious = 10\n",
    "\n",
    "weights = torch.load(f\"../models/train_classifier/128-mnistv3-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.9-sgd-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "axs[0][0].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "weights = torch.load(f\"../models/incremental_retraining/128-mnistv3-{n_spurious}-0-0-140-0.01-ce-tor-LargeMLP-128-mnistv3-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.9-sgd-0-0.0.pt-0.9-sgd-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "axs[0][1].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "\n",
    "n_spurious = 200\n",
    "\n",
    "weights = torch.load(f\"../models/train_classifier/128-mnistv3-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.9-sgd-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "axs[1][0].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "\n",
    "weights = torch.load(f\"../models/incremental_retraining/128-mnistv3-{n_spurious}-0-0-140-0.01-ce-tor-LargeMLP-128-mnistv3-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.9-sgd-0-0.0.pt-0.9-sgd-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "im = axs[1][1].imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n",
    "\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
    "fig.colorbar(im, cax=cbar_ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6d7c80e1-9670-46e8-87a4-9ef13f0a4ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.034562375"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "02f100dd-4527-43e2-8611-b7ba2ad86937",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.1312647 0.034562375\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_spurious = 3\n",
    "weights = torch.load(f\"../models/train_classifier/128-mnistv3-{n_spurious}-0-0-70-0.01-ce-tor-LargeMLP-0.0-adam-0-0.0.pt\")\n",
    "weight = weights['model_state_dict']['hidden.weight'].detach().cpu().numpy()\n",
    "weight = weight.max(0).reshape(28, 28)\n",
    "plt.imshow(weight, vmin=vmin, vmax=vmax)\n",
    "print(weight.max(), weight.min())\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}