exp5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/WeatherAnchor/pytorch_exp/blob/master/exp5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rpENjEEcp6kv"
   },
   "source": [
    "[Convolutional Neural Networks - Deep Learning and Neural Networks with Python and Pytorch p.5](https://pythonprogramming.net/convolutional-neural-networks-deep-learning-neural-network-pytorch/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XrA7LeObqZch"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j5wliyIlqxJl"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 22/12502 [00:00<00:58, 211.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/PetImages/Cat\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12502/12502 [01:01<00:00, 204.88it/s]\n",
      "  0%|          | 21/12501 [00:00<01:00, 207.06it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/PetImages/Dog\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12501/12501 [00:59<00:00, 208.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cats: 12476\n",
      "Dogs: 12470\n"
     ]
    }
   ],
   "source": [
    "REBUILD_DATA = True\n",
    "class DogsVSCats():\n",
    "    IMG_SIZE = 50\n",
    "    CATS = \"dataset/PetImages/Cat\"\n",
    "    DOGS = \"dataset/PetImages/Dog\"\n",
    "    TESTING = \"PetImages/Testing\"\n",
    "    LABELS = {CATS: 0, DOGS: 1}\n",
    "    training_data = []\n",
    "\n",
    "    catcount = 0\n",
    "    dogcount = 0\n",
    "\n",
    "    def make_training_data(self):\n",
    "        for label in self.LABELS:\n",
    "            print(label)\n",
    "            for f in tqdm(os.listdir(label)):\n",
    "                if \"jpg\" in f:\n",
    "                    try:\n",
    "                        path = os.path.join(label, f)\n",
    "                        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n",
    "                        img = cv2.resize(img, (self.IMG_SIZE, self.IMG_SIZE))\n",
    "                        self.training_data.append([np.array(img), np.eye(2)[self.LABELS[label]]])  # do something like print(np.eye(2)[1]), just makes one_hot \n",
    "                        #print(np.eye(2)[self.LABELS[label]])\n",
    "\n",
    "                        if label == self.CATS:\n",
    "                            self.catcount += 1\n",
    "                        elif label == self.DOGS:\n",
    "                            self.dogcount += 1\n",
    "\n",
    "                    except Exception as e:\n",
    "                        pass\n",
    "                        #print(label, f, str(e))\n",
    "\n",
    "        np.random.shuffle(self.training_data)\n",
    "        np.save(\"training_data.npy\", self.training_data)\n",
    "        print('Cats:',dogsvcats.catcount)\n",
    "        print('Dogs:',dogsvcats.dogcount)\n",
    "\n",
    "if REBUILD_DATA:\n",
    "    dogsvcats = DogsVSCats()\n",
    "    dogsvcats.make_training_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = np.load(\"training_data.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(training_data[2][0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data[2][1]"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPkCgljDB/ZyXuD2C6BxjWW",
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "exp5.ipynb",
   "provenance": []
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}