fashion_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.train import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fashion:Created train dataset with 14400 samples\n",
      "INFO:fashion:Created val dataset with 3600 samples\n",
      "INFO:fashion:Created test dataset with 15149 samples\n"
     ]
    }
   ],
   "source": [
    "ckpt_path = \"ckpts/best_val_top20.ckpt\"\n",
    "\n",
    "trainer_top20, dataloaders_top20 = setup_top20(ckpt_path=ckpt_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer_top20.train(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer_top20.plot_perf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer_top20.get_test_accuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fashion:Created train dataset with 4629 samples\n",
      "INFO:fashion:Created val dataset with 1158 samples\n",
      "INFO:fashion:Created test dataset with 5505 samples\n"
     ]
    }
   ],
   "source": [
    "trainer_ft, dataloaders_ft = setup_ft(model=trainer_top20.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer_ft.train(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer_ft.plot_perf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer_ft.get_test_accuracy()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}