small_datasets_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from task2vec import Task2Vec\n",
    "from models import get_model\n",
    "import datasets\n",
    "import task_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_names = ('stl10', 'mnist', 'cifar10', 'cifar100', 'letters', 'kmnist')\n",
    "# Change `root` with the directory you want to use to download the datasets\n",
    "dataset_list = [datasets.__dict__[name](root='./data')[0] for name in dataset_names] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = []\n",
    "for name, dataset in zip(dataset_names, dataset_list):\n",
    "    print(f\"Embedding {name}\")\n",
    "    probe_network = get_model('resnet34', pretrained=True, num_classes=int(max(dataset.targets)+1)).cuda()\n",
    "    embeddings.append( Task2Vec(probe_network, max_samples=1000, skip_layers=6).embed(dataset) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "task_similarity.plot_distance_matrix(embeddings, dataset_names)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}