Project_Image_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/julianjimenez/Desktop/ANND/Project\n"
     ]
    }
   ],
   "source": [
    "# Set the seed for random operations. \n",
    "# This let our experiments to be reproducible. \n",
    "SEED = 1234\n",
    "tf.random.set_seed(SEED)  \n",
    "\n",
    "# Get current working directory\n",
    "cwd = os.getcwd()\n",
    "print(cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ImageDataGenerator\n",
    "# ------------------\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "apply_data_augmentation = False\n",
    "\n",
    "# Create training ImageDataGenerator object\n",
    "if apply_data_augmentation:\n",
    "    train_data_gen = ImageDataGenerator(rotation_range=10,\n",
    "                                        width_shift_range=10,\n",
    "                                        height_shift_range=10,\n",
    "                                        zoom_range=0.3,\n",
    "                                        horizontal_flip=True,\n",
    "                                        vertical_flip=True,\n",
    "                                        fill_mode='constant',\n",
    "                                        cval=0,\n",
    "                                        rescale=1./255)\n",
    "else:\n",
    "    train_data_gen = ImageDataGenerator(rescale=1./255)\n",
    "valid_data_gen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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