05

.ipynb_checkpoints/05_Convolutional_Neural_Networks-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "REBUILD_DATA = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "REBUILD_DATA = True # set to true to one once, then back to false unless you want to change something in your training data.\n",
+    "\n",
+    "class DogsVSCats():\n",
+    "    IMG_SIZE = 50\n",
+    "    CATS = \"/Users/Mac/Downloads/kagglecatsanddogs/PetImages/Cat\"\n",
+    "    DOGS = \"/Users/Mac/Downloads/kagglecatsanddogs/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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  1%|          | 73/12501 [00:00<00:17, 727.44it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/Mac/Downloads/kagglecatsanddogs/PetImages/Cat\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 12501/12501 [02:44<00:00, 75.80it/s]\n",
+      "  0%|          | 8/12501 [00:00<02:49, 73.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/Mac/Downloads/kagglecatsanddogs/PetImages/Dog\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 12501/12501 [02:22<00:00, 87.59it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cats: 12476\n",
+      "Dogs: 12470\n"
+     ]
+    }
+   ],
+   "source": [
+    "if REBUILD_DATA:\n",
+    "    dogsvcats = DogsVSCats()\n",
+    "    dogsvcats.make_training_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}