challenges/05  - Safari CNN Challenge.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Safari Challenge\n",
    "\n",
    "In this challenge, you must use what you've learned to train a convolutional neural network model that classifies images of animals you might find on a safari adventure.\n",
    "\n",
    "## Explore the data\n",
    "\n",
    "The training images you must use are in the **/safari/training** folder. Run the cell below to see an example of each image class, and note the shape of the images (which indicates the dimensions of the image and its color channels)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "\n",
    "# The images are in the data/shapes folder\n",
    "data_path = 'data/safari/training'\n",
    "\n",
    "# Get the class names\n",
    "classes = os.listdir(data_path)\n",
    "classes.sort()\n",
    "print(len(classes), 'classes:')\n",
    "print(classes)\n",
    "\n",
    "# Show the first image in each folder\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "i = 0\n",
    "for sub_dir in os.listdir(data_path):\n",
    "    i+=1\n",
    "    img_file = os.listdir(os.path.join(data_path,sub_dir))[0]\n",
    "    img_path = os.path.join(data_path, sub_dir, img_file)\n",
    "    img = mpimg.imread(img_path)\n",
    "    img_shape = np.array(img).shape\n",
    "    a=fig.add_subplot(1, len(classes),i)\n",
    "    a.axis('off')\n",
    "    imgplot = plt.imshow(img)\n",
    "    a.set_title(img_file + ' : ' + str(img_shape))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you've seen the images, use your preferred framework (PyTorch or TensorFlow) to train a CNN classifier for them. Your goal is to train a classifier with a validation accuracy of 95% or higher.\n",
    "\n",
    "Add cells as needed to create your solution.\n",
    "\n",
    "> **Note**: There is no single \"correct\" solution. Sample solutions are provided in [05 - Safari CNN Solution (PyTorch).ipynb](05%20-%20Safari%20CNN%20Solution%20(PyTorch).ipynb) and [05 - Safari CNN Solution (TensorFlow).ipynb](05%20-%20Safari%20CNN%20Solution%20(TensorFlow).ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Your Code to train a CNN model..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save your model\n",
    "\n",
    "Add code below to save your model's trained weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Code to save your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the trained model\n",
    "\n",
    "Now that we've trained your model, modify the following code as necessary to use it to predict the classes of the provided test images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Function to predict the class of an image\n",
    "def predict_image(classifier, image):\n",
    "    import numpy\n",
    "    \n",
    "    # Default value\n",
    "    index = 0\n",
    "    \n",
    "    # !!Add your code here to predict an image class from your model!!\n",
    "    \n",
    "    # Return the predicted index\n",
    "    return index\n",
    "\n",
    "\n",
    "# Load your model\n",
    "model = 'Load Your Model'\n",
    "\n",
    "# The images are in the data/shapes folder\n",
    "test_data_path = 'data/safari/test'\n",
    "\n",
    "# Show the test images with predictions\n",
    "fig = plt.figure(figsize=(8, 12))\n",
    "i = 0\n",
    "for img_file in os.listdir(test_data_path):\n",
    "    i+=1\n",
    "    img_path = os.path.join(test_data_path, img_file)\n",
    "    img = mpimg.imread(img_path)\n",
    "    # Get the image class prediction\n",
    "    index = predict_image(model, np.array(img))\n",
    "    a=fig.add_subplot(1, len(classes),i)\n",
    "    a.axis('off')\n",
    "    imgplot = plt.imshow(img)\n",
    "    a.set_title(classes[index])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hopefully, your model predicted all four of the image classes correctly!"
   ]
  }
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