Copy_of_dog_app.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of dog_app.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "jTk_1aUd-U7I",
        "4Ehbw417-U7V",
        "xrlDLJcx-U7a",
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Berenice2018/DeepLearning/blob/master/Copy_of_dog_app.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3veWORj-U7D",
        "colab_type": "text"
      },
      "source": [
        "# Convolutional Neural Networks\n",
        "\n",
        "## Project: Write an Algorithm for a Dog Identification App \n",
        "\n",
        "---\n",
        "\n",
        "In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'(IMPLEMENTATION)'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully! \n",
        "\n",
        "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to **File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.\n",
        "\n",
        "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n",
        "\n",
        ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut.  Markdown cells can be edited by double-clicking the cell to enter edit mode.\n",
        "\n",
        "The rubric contains _optional_ \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. If you decide to pursue the \"Stand Out Suggestions\", you should include the code in this Jupyter notebook.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "### Why We're Here \n",
        "\n",
        "In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!). \n",
        "\n",
        "![Sample Dog Output](https://github.com/Berenice2018/dog-breed133-classifier/blob/master/images/sample_dog_output.png?raw=1)\n",
        "\n",
        "In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!\n",
        "\n",
        "### The Road Ahead\n",
        "\n",
        "We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.\n",
        "\n",
        "* [Step 0](#step0): Import Datasets\n",
        "* [Step 1](#step1): Detect Humans\n",
        "* [Step 2](#step2): Detect Dogs\n",
        "* [Step 3](#step3): Create a CNN to Classify Dog Breeds (from Scratch)\n",
        "* [Step 4](#step4): Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
        "* [Step 5](#step5): Write your Algorithm\n",
        "* [Step 6](#step6): Test Your Algorithm\n",
        "\n",
        "---\n",
        "<a id='step0'></a>\n",
        "## Step 0: Import Datasets\n",
        "\n",
        "Make sure that you've downloaded the required human and dog datasets:\n",
        "* Download the [dog dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip).  Unzip the folder and place it in this project's home directory, at the location `/dog_images`. \n",
        "\n",
        "* Download the [human dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip).  Unzip the folder and place it in the home directory, at location `/lfw`.  \n",
        "\n",
        "*Note: If you are using a Windows machine, you are encouraged to use [7zip](http://www.7-zip.org/) to extract the folder.*\n",
        "\n",
        "In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays `human_files` and `dog_files`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2YkbQgL_Gcd",
        "colab_type": "code",
        "outputId": "a2ad0a91-3644-4463-e906-8bb62745d6b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "# save the model on Google Drive, link Google drive to this notebook\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# After executing this cell above, Drive\n",
        "# files will be present in \"/content/drive/My Drive\".\n",
        "!ls \"/content/gdrive/My Drive/ColabUdacity/dog-breed-classifier/\""
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            "dog_app.ipynb  haarcascades  lfw     my_test_images\t\tUntitled0.ipynb\n",
            "dogImages      images\t     models  uda-p2-dog-breed133.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFIxPfg9-U7E",
        "colab_type": "code",
        "outputId": "cae39e79-6c79-4beb-feae-dc24c4f20ee2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "from glob import glob\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import torchvision.transforms as transforms\n",
        "import torch\n",
        "import torchvision.models as models\n",
        "\n",
        "# check if CUDA is available\n",
        "use_cuda = torch.cuda.is_available()\n",
        "print('cuda is available: ', use_cuda)\n",
        "\n",
        "data_dir = \"/content/gdrive/My Drive/ColabUdacity/dog-breed-classifier/\"\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda is available:  True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTffRnZUsIWM",
        "colab_type": "code",
        "outputId": "ceb5f40c-e481-4d23-cebe-56bf4b277e36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# load filenames for human and dog images\n",
        "human_files = np.array(glob(data_dir + \"lfw/*/*\"))\n",
        "print('There are %d total human images.' % len(human_files))\n",
        "\n",
        "dog_files = np.array(glob(data_dir + \"dogImages/*/*/*\"))\n",
        "print('There are %d total dog images.' % len(dog_files))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 13233 total human images.\n",
            "There are 8351 total dog images.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTk_1aUd-U7I",
        "colab_type": "text"
      },
      "source": [
        "<a id='step1'></a>\n",
        "## Step 1: Detect Humans\n",
        "\n",
        "In this section, we use OpenCV's implementation of [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) to detect human faces in images.  \n",
        "\n",
        "OpenCV provides many pre-trained face detectors, stored as XML files on [github](https://github.com/opencv/opencv/tree/master/data/haarcascades).  We have downloaded one of these detectors and stored it in the `haarcascades` directory.  In the next code cell, we demonstrate how to use this detector to find human faces in a sample image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNsE7S6k-U7J",
        "colab_type": "code",
        "outputId": "71b42990-ddb3-44db-c57d-b935811fc57e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "import cv2                \n",
        "import matplotlib.pyplot as plt                        \n",
        "%matplotlib inline                               \n",
        "\n",
        "# extract pre-trained face detector\n",
        "#face_cascade = cv2.CascadeClassifier(data_dir +'haarcascades/haarcascade_frontalface_alt.xml')\n",
        "face_cascade = cv2.CascadeClassifier(data_dir +'haarcascades/haarcascade_eye.xml')\n",
        "\n",
        "# load color (BGR) image\n",
        "#img = cv2.imread(human_files[0])\n",
        "img = cv2.imread(dog_files[66])\n",
        "# convert BGR image to grayscale\n",
        "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# find faces in image\n",
        "faces = face_cascade.detectMultiScale(gray)\n",
        "\n",
        "# print number of faces detected in the image\n",
        "print('Number of faces detected:', len(faces))\n",
        "\n",
        "# get bounding box for each detected face\n",
        "for (x,y,w,h) in faces:\n",
        "    # add bounding box to color image\n",
        "    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
        "    \n",
        "# convert BGR image to RGB for plotting\n",
        "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "# display the image, along with bounding box\n",
        "plt.imshow(cv_rgb)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of faces detected: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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pCpe2cWrPWDPC/cBBpx2bNerWsdPSejRWqHu6riyXM9P9zDwkbg7Gc4EmiuGspXEqxtM1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YysVh0YjVNyY2it0a7esilVybbsVNq25eZZQq8sIRf61ZrDMJJKxjZWcPLWnUzg27ZnfzMw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MacQBjA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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwCCHJtf-U7M",
        "colab_type": "text"
      },
      "source": [
        "Before using any of the face detectors, it is standard procedure to convert the images to grayscale.  The `detectMultiScale` function executes the classifier stored in `face_cascade` and takes the grayscale image as a parameter.  \n",
        "\n",
        "In the above code, `faces` is a numpy array of detected faces, where each row corresponds to a detected face.  Each detected face is a 1D array with four entries that specifies the bounding box of the detected face.  The first two entries in the array (extracted in the above code as `x` and `y`) specify the horizontal and vertical positions of the top left corner of the bounding box.  The last two entries in the array (extracted here as `w` and `h`) specify the width and height of the box.\n",
        "\n",
        "### Write a Human Face Detector\n",
        "\n",
        "We can use this procedure to write a function that returns `True` if a human face is detected in an image and `False` otherwise.  This function, aptly named `face_detector`, takes a string-valued file path to an image as input and appears in the code block below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HolbrqY-U7M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# returns \"True\" if face is detected in image stored at img_path\n",
        "def face_detector(img_path):\n",
        "    img = cv2.imread(img_path)\n",
        "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    faces = face_cascade.detectMultiScale(gray)\n",
        "    return len(faces) > 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_fgjPF_-U7O",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Assess the Human Face Detector\n",
        "\n",
        "__Question 1:__ Use the code cell below to test the performance of the `face_detector` function.  \n",
        "- What percentage of the first 100 images in `human_files` have a detected human face?  \n",
        "- What percentage of the first 100 images in `dog_files` have a detected human face? \n",
        "\n",
        "Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays `human_files_short` and `dog_files_short`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6TD2mxX-U7O",
        "colab_type": "text"
      },
      "source": [
        "__Answer:__ \n",
        "(You can print out your results and/or write your percentages in this cell)\n",
        "\n",
        "*100% of faces  in human_files_short detected,  and \n",
        "7% of faces in dog_files_short detected*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFX2hB_t-U7P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "human_files_short = human_files[:100]\n",
        "dog_files_short = dog_files[:100]\n",
        "#-#-# Do NOT modify the code above this line. #-#-#"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCOcJYU3-U7R",
        "colab_type": "code",
        "outputId": "1848ba54-59e7-4abc-a14c-901f299ceed1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "## TODO: Test the performance of the face_detector algorithm \n",
        "## on the images in human_files_short and dog_files_short.\n",
        "\n",
        "counter = 0\n",
        "\n",
        "counter = 0\n",
        "for i, img_path in enumerate(human_files_short):\n",
        "    if face_detector(img_path):\n",
        "        counter += 1\n",
        "\n",
        "print('Number of faces detected, in human_files:', counter)\n",
        "\n",
        "counter = 0\n",
        "for i, img_path in enumerate(dog_files_short):\n",
        "    if face_detector(img_path):\n",
        "        counter += 1\n",
        "print('Number of faces detected in dog_files: ', counter)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of faces detected, in human_files: 100\n",
            "Number of faces detected in dog_files:  7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU4i760F-U7T",
        "colab_type": "text"
      },
      "source": [
        "We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this _optional_ task, report performance on `human_files_short` and `dog_files_short`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iR6r7_9H-U7U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### (Optional) \n",
        "### TODO: Test performance of anotherface detection algorithm.\n",
        "### Feel free to use as many code cells as needed."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ehbw417-U7V",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "<a id='step2'></a>\n",
        "## Step 2: Detect Dogs\n",
        "\n",
        "In this section, we use a [pre-trained model](http://pytorch.org/docs/master/torchvision/models.html) to detect dogs in images.  \n",
        "\n",
        "### Obtain Pre-trained VGG-16 Model\n",
        "\n",
        "The code cell below downloads the VGG-16 model, along with weights that have been trained on [ImageNet](http://www.image-net.org/), a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OutcH5X7-U7W",
        "colab_type": "code",
        "outputId": "17f7d6ab-5f9a-4535-e9ef-90e94f71003b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "\n",
        "# define VGG16 model\n",
        "VGG16 = models.vgg16(pretrained=True)\n",
        "\n",
        "# move model to GPU if CUDA is available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "VGG16.to(device)\n",
        "    \n",
        "print('model is on cuda: ', next(VGG16.parameters()).is_cuda)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "model is on cuda:  True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzsTVdZz-U7Y",
        "colab_type": "text"
      },
      "source": [
        "Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrlDLJcx-U7a",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Making Predictions with a Pre-trained Model\n",
        "\n",
        "In the next code cell, you will write a function that accepts a path to an image (such as `'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg'`) as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model.  The output should always be an integer between 0 and 999, inclusive.\n",
        "\n",
        "Before writing the function, make sure that you take the time to learn  how to appropriately pre-process tensors for pre-trained models in the [PyTorch documentation](http://pytorch.org/docs/stable/torchvision/models.html)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0ECEF74-U7c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#test = 'images/American_water_spaniel_00648.jpg'\n",
        "\n",
        "# My helper function for pre-processing a PIL image\n",
        "def prepocess_img(img_path):\n",
        "    ## Load and pre-process an image from the given img_path\n",
        "    pil_image = Image.open(img_path)\n",
        "                     \n",
        "    my_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
        "                                 std=[0.229, 0.224, 0.225])\n",
        "    my_transforms = transforms.Compose([\n",
        "                       transforms.Resize(256),\n",
        "                       transforms.CenterCrop(224),\n",
        "                       transforms.ToTensor(),\n",
        "                       my_normalize])\n",
        "    \n",
        "    image = my_transforms(pil_image)\n",
        "      \n",
        "    image = image.unsqueeze_(0)\n",
        "    #print('img was pre-processed. ', image.shape)\n",
        "    return image\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWzRr9q6-U7e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def VGG16_predict(img_path):\n",
        "    '''\n",
        "    Use pre-trained VGG-16 model to obtain index corresponding to \n",
        "    predicted ImageNet class for image at specified path\n",
        "    \n",
        "    Args:\n",
        "        img_path: path to an image\n",
        "        \n",
        "    Returns:\n",
        "        Index corresponding to VGG-16 model's prediction\n",
        "    '''\n",
        "    \n",
        "    ## TODO: Complete the function.\n",
        "    img_as_tensor = prepocess_img(img_path).to(device)\n",
        "        \n",
        "    output = VGG16(img_as_tensor)\n",
        "    #print('output:', output.shape)\n",
        "    \n",
        "    ## Return the *index* of the predicted class for that image\n",
        "    probabilities = torch.exp(output)\n",
        "    _, top = probabilities.topk(1, dim=1)\n",
        "    #print('top class: ', top)\n",
        "    return top # predicted class index\n",
        "\n",
        "#test = data_dir +'images/American_water_spaniel_00648.jpg'\n",
        "#VGG16_predict(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0TZJXIv-U7g",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Write a Dog Detector\n",
        "\n",
        "While looking at the [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a), you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from `'Chihuahua'` to `'Mexican hairless'`.  Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).\n",
        "\n",
        "Use these ideas to complete the `dog_detector` function below, which returns `True` if a dog is detected in an image (and `False` if not)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yU1rwLCo-U7h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### returns \"True\" if a dog is detected in the image stored at img_path\n",
        "def dog_detector(img_path):\n",
        "    ## TODO: Complete the function.\n",
        "    index = VGG16_predict(img_path)\n",
        "    #print('index: ', index[0])\n",
        "    return (index > 150) and (index < 269)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-cvVh2w-U7j",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Assess the Dog Detector\n",
        "\n",
        "__Question 2:__ Use the code cell below to test the performance of your `dog_detector` function.  \n",
        "- What percentage of the images in `human_files_short` have a detected dog?  \n",
        "- What percentage of the images in `dog_files_short` have a detected dog?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mGHDLU9-U7j",
        "colab_type": "text"
      },
      "source": [
        "__Answer:__ \n",
        "\n",
        "*With VGG16 as pre-trained model, \n",
        "1% of the files in human_files_short were classified as dogs.  \n",
        "97% of the files in dog_files_short were classified as dogs. *\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFEPF0Zs-U7k",
        "colab_type": "code",
        "outputId": "a281778d-92c2-47ae-e536-0b49a048d192",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "### TODO: Test the performance of the dog_detector function\n",
        "### on the images in human_files_short and dog_files_short.\n",
        "\n",
        "def test_performance(detector, *args):\n",
        "    print('test_performance with {} started'.format(detector))\n",
        "    counter = 0\n",
        "    for img_path in human_files_short:\n",
        "        if detector(img_path):\n",
        "            counter += 1\n",
        "\n",
        "    print('{}/{} of detected dogs, in human_files:'.format(counter, len(human_files_short)))\n",
        "\n",
        "    counter = 0\n",
        "    for img_path in dog_files_short:\n",
        "        if detector(img_path):\n",
        "            counter += 1\n",
        "    print('{}/{} of detected dogs, in dog_files: '.format(counter, len(human_files_short)))\n",
        "\n",
        "    \n",
        "test_performance(dog_detector)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test_performance with <function dog_detector at 0x7fb7d5a27840> started\n",
            "1/100 of detected dogs, in human_files:\n",
            "97/100 of detected dogs, in dog_files: \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6ZMjUqt-U7o",
        "colab_type": "text"
      },
      "source": [
        "We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as [Inception-v3](http://pytorch.org/docs/master/torchvision/models.html#inception-v3), [ResNet-50](http://pytorch.org/docs/master/torchvision/models.html#id3), etc).  Please use the code cell below to test other pre-trained PyTorch models.  If you decide to pursue this _optional_ task, report performance on `human_files_short` and `dog_files_short`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQl-0nEk-U7o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### (Optional) \n",
        "### TODO: Report the performance of another pre-trained network.\n",
        "### Feel free to use as many code cells as needed.\n",
        "\n",
        "resnet = models.resnet50(pretrained=True).to(device)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyu_n2IJ-U7s",
        "colab_type": "text"
      },
      "source": [
        "With ResNet50 as pre-trained model, 0% of the files in human_files_short were classified as dogs. 0% of the files in dog_files_short were classified as dogs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zwg3_AOe-U7s",
        "colab_type": "code",
        "outputId": "ab1afd30-c0ec-44a5-fa9d-5cb19552cb52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "\n",
        "def resnet_predict(img_path):\n",
        "    imgtensor = prepocess_img(img_path).to(device)\n",
        "    \n",
        "    output = resnet(imgtensor)\n",
        "    probs = torch.exp(output)\n",
        "    _, top_classes = probs.topk(1, dim=1)\n",
        "    return top_classes\n",
        "\n",
        "\n",
        "def resnet_dog_detector(path):\n",
        "    index = resnet_predict(path)\n",
        "    #print(index)\n",
        "    return (index > 150) and (index < 269)\n",
        "\n",
        "# test the performance with ResNet18\n",
        "test_performance(resnet_dog_detector)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test_performance with <function resnet_dog_detector at 0x7fb7b4bea730> started\n",
            "0/100 of detected dogs, in human_files:\n",
            "0/100 of detected dogs, in dog_files: \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLRrtX5i-U7v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHQKpSZw-U7w",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "<a id='step3'></a>\n",
        "## Step 3: Create a CNN to Classify Dog Breeds (from Scratch)\n",
        "\n",
        "Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN _from scratch_ (so, you can't use transfer learning _yet_!), and you must attain a test accuracy of at least 10%.  In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.\n",
        "\n",
        "We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that *even a human* would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.  \n",
        "\n",
        "Brittany | Welsh Springer Spaniel\n",
        "- | - \n",
        "<img src=\"/content/gdrive/My Drive/ColabUdacity/dog-breed-classifier/images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"/content/gdrive/My Drive/ColabUdacity/dog-breed-classifier/images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n",
        "\n",
        "It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).  \n",
        "\n",
        "Curly-Coated Retriever | American Water Spaniel\n",
        "- | -\n",
        "<img src=\"https://github.com/Berenice2018/dog-breed133-classifier/blob/master/images/Curly-coated_retriever_03896.jpg?raw=1\" width=\"200\"> | <img src=\"https://github.com/Berenice2018/dog-breed133-classifier/blob/master/images/American_water_spaniel_00648.jpg?raw=1\" width=\"200\">\n",
        "\n",
        "\n",
        "Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.  \n",
        "\n",
        "Yellow Labrador | Chocolate Labrador | Black Labrador\n",
        "- | -\n",
        "<img src=\"https://github.com/Berenice2018/dog-breed133-classifier/blob/master/images/Labrador_retriever_06457.jpg?raw=1\" width=\"150\"> | <img src=\"https://github.com/Berenice2018/dog-breed133-classifier/blob/master/images/Labrador_retriever_06455.jpg?raw=1\" width=\"240\"> | <img src=\"https://github.com/Berenice2018/dog-breed133-classifier/blob/master/images/Labrador_retriever_06449.jpg?raw=1\" width=\"220\">\n",
        "\n",
        "We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.  \n",
        "\n",
        "Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun!\n",
        "\n",
        "### (IMPLEMENTATION) Specify Data Loaders for the Dog Dataset\n",
        "\n",
        "Use the code cell below to write three separate [data loaders](http://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) for the training, validation, and test datasets of dog images (located at `dog_images/train`, `dog_images/valid`, and `dog_images/test`, respectively).  You may find [this documentation on custom datasets](http://pytorch.org/docs/stable/torchvision/datasets.html) to be a useful resource.  If you are interested in augmenting your training and/or validation data, check out the wide variety of [transforms](http://pytorch.org/docs/stable/torchvision/transforms.html?highlight=transform)!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cro_sFHv-U7w",
        "colab_type": "code",
        "outputId": "68c6ef1a-89c6-499f-aeb0-d478d8d4a914",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import os\n",
        "from torchvision import datasets\n",
        "import torchvision.transforms as transforms\n",
        "import torch.utils.data\n",
        "\n",
        "\n",
        "train_dir = os.path.join(data_dir, 'dogImages/train')\n",
        "valid_dir = os.path.join(data_dir, 'dogImages/valid')\n",
        "test_dir = os.path.join(data_dir, 'dogImages/test')\n",
        "\n",
        "### TODO: Write data loaders for training, validation, and test sets\n",
        "## Specify appropriate transforms, and batch_sizes\n",
        "\n",
        "batch_size = 64\n",
        "num_workers = 0\n",
        "img_size = 224\n",
        "\n",
        "mean = [0.485, 0.456, 0.406]\n",
        "std=[0.229, 0.224, 0.225]\n",
        "    \n",
        "my_transform = transforms.Compose([#p.torch_transform(),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.RandomRotation(16),\n",
        "    transforms.RandomResizedCrop(img_size),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean, std)\n",
        "      ])\n",
        "\n",
        "valid_transform = transforms.Compose([\n",
        "    transforms.Resize(320),\n",
        "    transforms.CenterCrop(img_size),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean, std)\n",
        "])\n",
        "train_data = datasets.ImageFolder(train_dir, transform=my_transform)\n",
        "valid_data = datasets.ImageFolder(valid_dir, transform = valid_transform)\n",
        "test_data = datasets.ImageFolder(test_dir, transform = valid_transform)\n",
        "\n",
        "print('length of train_data = {} |valid_data= {} | test_data= {}'.format(len(train_data), len(valid_data), len(test_data)))\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n",
        "valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size, num_workers=num_workers)\n",
        "test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers)\n",
        "\n",
        "loaders_scratch = [train_loader, valid_loader, test_loader]\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print('device: ', device)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "length of train_data = 6680 |valid_data= 835 | test_data= 836\n",
            "device:  cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_n6IQTl3-U7y",
        "colab_type": "text"
      },
      "source": [
        "**Question 3:** Describe your chosen procedure for preprocessing the data. \n",
        "- How does your code resize the images (by cropping, stretching, etc)?  What size did you pick for the input tensor, and why?\n",
        "- Did you decide to augment the dataset?  If so, how (through translations, flips, rotations, etc)?  If not, why not?\n",
        "\n",
        "\n",
        "**Answer:**\n",
        "I did some data augmentation by \n",
        "\n",
        "1.   randomly flipping the image with a 50% probability for the image being flipped\n",
        "2.   randomly rotating by 16 degrees\n",
        "\n",
        "3. resizing and cropping the images to 320 px and 224 px respectively. I chose 224 px as final image size, since I knew that ResNet and other well-known pre-trained models request this size, too. \n",
        "\n",
        "4. normalizing the images by the values which are also special to ImageNet. \n",
        "\n",
        "\n",
        "The given data set of 6,500 images is quite small in regards to 133 classes. Data augmentation helps in enlarging the data set, so that the network gets more input to learn from. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDGv3DV6uDB0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# function to un-normalize and display an image\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5  # unnormalize\n",
        "    plt.imshow(np.transpose(img, (1, 2, 0)))  # convert from Tensor image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHisHNcjuKxx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get a batch of training images\n",
        "dataiter = iter(train_loader)\n",
        "images, _ = dataiter.next()\n",
        "images = images.numpy() # convert images to numpy\n",
        "\n",
        "# plot the images of the batch, along with the corresponding labels\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
        "    imshow(images[idx])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuaIAYfX-U7y",
        "colab_type": "text"
      },
      "source": [
        "**Answer**:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "417PiDHM-U7z",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Model Architecture\n",
        "\n",
        "Create a CNN to classify dog breed.  Use the template in the code cell below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-v5ZoKlu-U70",
        "colab_type": "code",
        "outputId": "367683c8-7815-413a-c903-a210c5098b37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "def weights_init_normal(m):\n",
        "    '''Takes in a module and initializes all linear layers with weight\n",
        "       values taken from a normal distribution.'''\n",
        "    classname = m.__class__.__name__\n",
        "    # for every Linear layer in a model\n",
        "    if classname.find('Linear') != -1:\n",
        "        n = m.in_features\n",
        "        # m.weight.data shoud be taken from a normal distribution\n",
        "        m.weight.data.normal_(0, 1/np.sqrt(n))\n",
        "        # m.bias.data should be 0\n",
        "        m.bias.data.fill_(0)\n",
        "        \n",
        "        \n",
        "# normalize each layer:\n",
        "class MyBatchNormLayer(nn.Module):\n",
        "    def __init__(self, input_number, filter_number, stride=2, kernel_size=3, padding=1):\n",
        "        super().__init__()\n",
        "        self.conv = nn.Conv2d(input_number, filter_number, kernel_size=kernel_size,\n",
        "                             stride=stride, bias=False, padding=1)\n",
        "        self.normalized = nn.BatchNorm2d(filter_number)\n",
        "    \n",
        "    def forward(self,x):\n",
        "        x = F.relu(self.conv(x))\n",
        "        #print('MyBatchNormLayer x.shape= ', x.shape)\n",
        "        return self.normalized(x)\n",
        "\n",
        "    \n",
        "class ResnetLayer(MyBatchNormLayer):\n",
        "    def forward(self, x): return x + super().forward(x)\n",
        "    \n",
        "    \n",
        "class MyConvBnNet(nn.Module):\n",
        "    def __init__(self, layers, classes_number, dropout=0):\n",
        "        super().__init__()\n",
        "        # define the first convolutional layer manually\n",
        "        self.conv1 = MyBatchNormLayer(3, 64,kernel_size=7, stride=2, padding=1)\n",
        "        self.pool1 = nn.MaxPool2d(3,2)\n",
        "        \n",
        "        self.layers1 = nn.ModuleList([MyBatchNormLayer(layers[i], layers[i+1]) for i in range(len(layers)-1)])\n",
        "        self.layers2 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i+1], 1) for i in range(len(layers) - 1)])\n",
        "        self.layers3 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i+1], 1) for i in range(len(layers) - 1)])\n",
        "        self.layers4 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i+1], 1) for i in range(len(layers) - 1)])\n",
        "        self.layers5 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i+1], 1) for i in range(len(layers) - 1)])\n",
        "        self.layers6 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i+1], 1) for i in range(len(layers) - 1)])\n",
        "        self.layers7 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i+1], 1) for i in range(len(layers) - 1)])\n",
        "        self.layers8 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i+1], 1) for i in range(len(layers) - 1)])\n",
        "        \n",
        "        #self.linear1 = nn.Linear(layers[-1], 512)\n",
        "        self.outlayer = nn.Linear(layers[-1], classes_number)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        \n",
        "    def forward(self,x):\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = self.pool1(x)\n",
        "        for lyr1 ,lyr2, lyr3, lyr4, lyr5, lyr6 in zip(\n",
        "            self.layers1, self.layers2, self.layers3, self.layers4, self.layers5, self.layers6): \n",
        "            x = lyr4(lyr3(lyr2(lyr1(x))))\n",
        "            x =  lyr6(lyr5(x)) # lyr8(lyr7(lyr6(lyr5(x))))\n",
        "        x = F.adaptive_max_pool2d(x,1)\n",
        "        #print('MyConvBnNet after adaptive_max_pool2d, x.shape=', x.shape)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.dropout(x)\n",
        "        #x = self.linear1(x)\n",
        "        x = self.outlayer(x)\n",
        "        return F.log_softmax(x, dim= -1)\n",
        "\n",
        "\n",
        "# check if CUDA is available\n",
        "use_cuda = torch.cuda.is_available()\n",
        "print('cuda available: ', use_cuda)\n",
        "\n",
        "# instantiate the CNN\n",
        "#model_scratch = MyConvBnNet([10, 20, 40], 133)\n",
        "model_scratch = MyConvBnNet([64, 128, 256, 512, 1024], 133, 0.1)\n",
        "print(model_scratch)\n",
        "\n",
        "model_scratch.apply(weights_init_normal)\n",
        "\n",
        "# move tensors to GPU if CUDA is available\n",
        "if use_cuda:\n",
        "    model_scratch.cuda()\n",
        "print('model is on cuda: ', next(model_scratch.parameters()).is_cuda)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda available:  True\n",
            "MyConvBnNet(\n",
            "  (conv1): MyBatchNormLayer(\n",
            "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (normalized): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  )\n",
            "  (pool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (layers1): ModuleList(\n",
            "    (0): MyBatchNormLayer(\n",
            "      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): MyBatchNormLayer(\n",
            "      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): MyBatchNormLayer(\n",
            "      (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): MyBatchNormLayer(\n",
            "      (conv): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layers2): ModuleList(\n",
            "    (0): ResnetLayer(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResnetLayer(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResnetLayer(\n",
            "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): ResnetLayer(\n",
            "      (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layers3): ModuleList(\n",
            "    (0): ResnetLayer(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResnetLayer(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResnetLayer(\n",
            "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): ResnetLayer(\n",
            "      (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layers4): ModuleList(\n",
            "    (0): ResnetLayer(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResnetLayer(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResnetLayer(\n",
            "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): ResnetLayer(\n",
            "      (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layers5): ModuleList(\n",
            "    (0): ResnetLayer(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResnetLayer(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResnetLayer(\n",
            "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): ResnetLayer(\n",
            "      (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layers6): ModuleList(\n",
            "    (0): ResnetLayer(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResnetLayer(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResnetLayer(\n",
            "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): ResnetLayer(\n",
            "      (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layers7): ModuleList(\n",
            "    (0): ResnetLayer(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResnetLayer(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResnetLayer(\n",
            "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): ResnetLayer(\n",
            "      (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layers8): ModuleList(\n",
            "    (0): ResnetLayer(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResnetLayer(\n",
            "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResnetLayer(\n",
            "      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (3): ResnetLayer(\n",
            "      (conv): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (normalized): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (outlayer): Linear(in_features=1024, out_features=133, bias=True)\n",
            "  (dropout): Dropout(p=0.1)\n",
            ")\n",
            "model is on cuda:  True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nVYY5Smg-U73",
        "colab_type": "text"
      },
      "source": [
        "__Question 4:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scUhCkDJ-U74",
        "colab_type": "text"
      },
      "source": [
        "__Answer:__ \n",
        "\n",
        "I started with a basic network architecture consisting of just a few Conv layers with MaxPooling  and 2 linear layers on top. I only achieved an accuracy of 2-3 %. \n",
        "\n",
        "I concluded that my architecture was not deep enough. After some research, I decided to build some basic and normalized ResNet blocks. Normalizing each layer helped much. I followed a webinar by Jeremy Howard (fast.ai) \n",
        "\n",
        "I also applied the technique to initialize the weights of the linear layers, we had learnt in one lesson. \n",
        "\n",
        "Using *MyBatchNormLayer* speeds up the training, and helps avoiding vanishing gradients\n",
        "\n",
        "*ResnetLayer* s can be stacked to deep neural networks without making the input too small by using \"usual\"  Convolutional layers mith MaxPooling. \n",
        "\n",
        "*MyConvBnNet* finally stacks module layers to a deep residual neural network. \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HEWHoBO-U74",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Specify Loss Function and Optimizer\n",
        "\n",
        "Use the next code cell to specify a [loss function](http://pytorch.org/docs/stable/nn.html#loss-functions) and [optimizer](http://pytorch.org/docs/stable/optim.html).  Save the chosen loss function as `criterion_scratch`, and the optimizer as `optimizer_scratch` below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYeTbjyo-U75",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "### TODO: select loss function\n",
        "criterion_scratch = nn.NLLLoss()\n",
        "\n",
        "### TODO: select optimizer\n",
        "#optimizer_scratch = optim.SGD(model_scratch.parameters(), lr=0.001, momentum=0.9)\n",
        "optimizer_scratch = optim.Adam(model_scratch.parameters(), lr=1e-4)\n",
        "######### change the position of optim.step() in the train section\n",
        "#scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer_scratch, patience = 3, factor=0.1)\n",
        "\n",
        "scheduler_scratch = optim.lr_scheduler.MultiStepLR(optimizer_scratch, [10, 18, 26, 32], 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlxQOoCe3AH-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Visualize plot\n",
        "def plot_loss_acc(n_epochs, train_losses, valid_losses, valid_accuracies):\n",
        "    fig, (ax1, ax2) = plt.subplots(figsize=(14,6), ncols=2)\n",
        "    ax1.plot(valid_losses, label='Validation loss')\n",
        "    ax1.plot(train_losses, label='Training loss')\n",
        "    ax1.legend(frameon=False)\n",
        "    ax1.set_xlabel('Epochs')\n",
        "    ax1.set_ylabel('Loss')\n",
        "    #x_ticks = [x for x in range(0,n_epochs,2)]\n",
        "    #plt.xticks(x_ticks)\n",
        "    \n",
        "    ax2.plot(valid_accuracies, label = 'Validation accuracy')\n",
        "    ax2.legend(frameon=False)\n",
        "    ax2.set_xlabel('Epochs')\n",
        "    \n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gX3Zf6jl-U7_",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Train and Validate the Model\n",
        "\n",
        "Train and validate your model in the code cell below.  [Save the final model parameters](http://pytorch.org/docs/master/notes/serialization.html) at filepath `'model_scratch.pt'`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWbG7Cmqu1u3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# helper functions\n",
        "import datetime\n",
        "\n",
        "def get_time():\n",
        "      hour = datetime.datetime.today().hour +2\n",
        "      minute = datetime.datetime.today().minute\n",
        "      second = datetime.datetime.today().second\n",
        "      return hour, minute, second\n",
        "\n",
        "def train_epoch(model, dataloader, criterion, optimizer, train_on_gpu):\n",
        "    # initialize variables to monitor training and validation loss\n",
        "    train_loss = 0.0\n",
        "    train_accuracy = 0.0\n",
        "    correct = 0.0\n",
        "    total = 0.0\n",
        "    \n",
        "    for batch_idx, (data, target) in enumerate(dataloader):\n",
        "        # move to GPU\n",
        "        if train_on_gpu:\n",
        "            data, target = data.cuda(), target.cuda()\n",
        "            \n",
        "        # clear the gradients of all optimized variables\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        ## find the loss and update the model parameters accordingly\n",
        "        output = model(data)\n",
        "        loss = criterion(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # get the loss per batch and accumulate\n",
        "        train_loss += loss.item()\n",
        "        \n",
        "        # get the class, highest probability\n",
        "        probabilities = torch.exp(output)\n",
        "        _, top_class = probabilities.topk(1, dim=1)\n",
        "        # The following line is equivalent to the previous (?)\n",
        "        #_, top_class = torch.max(probabilities, dim=1)\n",
        "        \n",
        "        # check if the predicted class is correct\n",
        "        equals = top_class == target.view(*top_class.shape)\n",
        "        # \n",
        "        train_accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
        "\n",
        "    return train_loss, train_accuracy\n",
        "\n",
        "\n",
        "def validate_epoch(model, dataloader, criterion, train_on_gpu):\n",
        "    valid_loss = 0.0\n",
        "    valid_accuracy = 0.0\n",
        "    correct = 0.0\n",
        "    total = 0.0\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for batch_idx, (data, target) in enumerate(dataloader):\n",
        "            # move to GPU\n",
        "            if train_on_gpu:\n",
        "                data, target = data.cuda(), target.cuda()\n",
        "            ## update the average validation loss\n",
        "            output = model(data)\n",
        "            loss = criterion(output,target)\n",
        "            \n",
        "            valid_loss += loss.item()\n",
        "\n",
        "            ps = torch.exp(output)\n",
        "            _ , top_class = ps.topk(1,dim = 1)\n",
        "            #_, top_class = torch.max(ps, dim=1)\n",
        "            equals = top_class == target.view(*top_class.shape) # shape is (batch size x 1)\n",
        "            valid_accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
        "\n",
        "    return valid_loss, valid_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lanRRZVB-U8A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def train(n_epochs, loaders, model, optimizer, criterion, scheduler, use_cuda, save_path):\n",
        "    print('Training started at ', get_time())\n",
        "    \n",
        "    valid_losses = []\n",
        "    train_losses = []\n",
        "    valid_accuracies = []\n",
        "    \n",
        "    # initialize tracker for minimum validation loss\n",
        "    valid_loss_min = np.Inf \n",
        "    \n",
        "    for epoch in range(n_epochs):\n",
        "        \n",
        "         # initialize variables to monitor training and validation loss\n",
        "        training_loss = 0.0\n",
        "        training_accuracy = 0.0\n",
        "    \n",
        "        if scheduler is not None:\n",
        "          scheduler.step()\n",
        "        \n",
        "        ###################\n",
        "        # train the model #\n",
        "        model.train()\n",
        "        training_loss, training_accuracy = train_epoch(model, loaders[0], criterion, optimizer, use_cuda)\n",
        "    \n",
        "        \n",
        "        ######################    \n",
        "        # validate the model #\n",
        "        model.eval()\n",
        "        validation_loss, validation_accuracy = validate_epoch(model, loaders[1], criterion, use_cuda)\n",
        "        \n",
        "        #if scheduler is not None:\n",
        "          #scheduler.step(validation_loss)\n",
        "        \n",
        "        ###### print training/validation statistics \n",
        "        # calculate the average loss per epoch\n",
        "        training_loss = training_loss/len(loaders[0])\n",
        "        train_losses.append(training_loss)\n",
        "        \n",
        "        training_accuracy = training_accuracy/len(loaders[0])\n",
        "        \n",
        "        validation_loss = validation_loss/len(loaders[1])\n",
        "        valid_losses.append(validation_loss)\n",
        "        \n",
        "        validation_accuracy = validation_accuracy/len(loaders[1])\n",
        "        valid_accuracies.append(validation_accuracy)\n",
        "        \n",
        "        hour, minute, second = get_time()\n",
        "        print('Epoch: {} at {}:{}:{} \\tTrain. Loss: {:.6f} \\tValid. Loss: {:.6f} \\t Accur.: {:.10f}'.format(\n",
        "                  epoch,\n",
        "                  hour, minute, second,\n",
        "                  training_loss,\n",
        "                  #training_accuracy, \n",
        "                  validation_loss,\n",
        "                  validation_accuracy ))\n",
        "        \n",
        "        ###### TODO: save the model if validation loss has decreased\n",
        "        if validation_loss <= valid_loss_min:\n",
        "            '''print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n",
        "                valid_loss_min,\n",
        "                validation_loss))'''\n",
        "            print('Validation loss decreased by {:.6f}'.format(validation_loss - valid_loss_min))\n",
        "            torch.save(model.state_dict(), save_path)\n",
        "            valid_loss_min = validation_loss\n",
        "            \n",
        "            \n",
        "    ##### visualize\n",
        "    plot_loss_acc(n_epochs, train_losses, valid_losses, valid_accuracies)\n",
        "    \n",
        "    return model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qLTwlmF-TOo",
        "colab_type": "code",
        "outputId": "6ae212b5-23ca-468a-a238-449019fb0343",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1532
        }
      },
      "source": [
        "from PIL import ImageFile\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "\n",
        "# train the model\n",
        "model_scratch = train(50, loaders_scratch, model_scratch, optimizer_scratch, \n",
        "                      criterion_scratch, use_cuda, 'model_scratch.pt')\n",
        "\n",
        "# load the model that got the best validation accuracy\n",
        "model_scratch.load_state_dict(torch.load('model_scratch.pt'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training started at  (14, 20, 50)\n",
            "Epoch: 0 at 14:20:50 \tTrain. Loss: 4.086024 \tValid. Loss: 3.662756 \t Accur.: 0.1633184403\n",
            "Validation loss decreased by -inf\n",
            "Epoch: 1 at 14:24:37 \tTrain. Loss: 3.979528 \tValid. Loss: 3.929882 \t Accur.: 0.1517857164\n",
            "Epoch: 2 at 14:28:25 \tTrain. Loss: 3.821782 \tValid. Loss: 3.469462 \t Accur.: 0.1662946492\n",
            "Validation loss decreased by -0.193294\n",
            "Epoch: 3 at 14:32:14 \tTrain. Loss: 3.740591 \tValid. Loss: 4.690178 \t Accur.: 0.1718750000\n",
            "Epoch: 4 at 14:36:2 \tTrain. Loss: 3.569861 \tValid. Loss: 4.663328 \t Accur.: 0.2124255896\n",
            "Epoch: 5 at 14:39:54 \tTrain. Loss: 3.562402 \tValid. Loss: 4.200979 \t Accur.: 0.1774553508\n",
            "Epoch: 6 at 14:43:42 \tTrain. Loss: 3.503059 \tValid. Loss: 3.429696 \t Accur.: 0.2064732164\n",
            "Validation loss decreased by -0.039766\n",
            "Epoch: 7 at 14:47:33 \tTrain. Loss: 3.408412 \tValid. Loss: 3.453786 \t Accur.: 0.2280505896\n",
            "Epoch: 8 at 14:51:24 \tTrain. Loss: 3.333300 \tValid. Loss: 3.051708 \t Accur.: 0.2377232164\n",
            "Validation loss decreased by -0.377988\n",
            "Epoch: 9 at 14:55:15 \tTrain. Loss: 2.890916 \tValid. Loss: 2.824602 \t Accur.: 0.3218006194\n",
            "Validation loss decreased by -0.227106\n",
            "Epoch: 10 at 14:59:8 \tTrain. Loss: 2.705776 \tValid. Loss: 2.867081 \t Accur.: 0.3195684552\n",
            "Epoch: 11 at 15:2:55 \tTrain. Loss: 2.600426 \tValid. Loss: 2.695702 \t Accur.: 0.3195684552\n",
            "Validation loss decreased by -0.128900\n",
            "Epoch: 12 at 15:6:48 \tTrain. Loss: 2.565115 \tValid. Loss: 3.137440 \t Accur.: 0.3273809552\n",
            "Epoch: 13 at 15:10:37 \tTrain. Loss: 2.501064 \tValid. Loss: 2.715189 \t Accur.: 0.3136160672\n",
            "Epoch: 14 at 15:14:24 \tTrain. Loss: 2.429667 \tValid. Loss: 3.610400 \t Accur.: 0.3363095224\n",
            "Epoch: 15 at 15:18:13 \tTrain. Loss: 2.429676 \tValid. Loss: 2.662226 \t Accur.: 0.3363095224\n",
            "Validation loss decreased by -0.033476\n",
            "Epoch: 16 at 15:22:7 \tTrain. Loss: 2.338915 \tValid. Loss: 2.698698 \t Accur.: 0.3519345224\n",
            "Epoch: 17 at 15:25:56 \tTrain. Loss: 2.267363 \tValid. Loss: 2.578116 \t Accur.: 0.3664434552\n",
            "Validation loss decreased by -0.084110\n",
            "Epoch: 18 at 15:29:48 \tTrain. Loss: 2.162793 \tValid. Loss: 2.710994 \t Accur.: 0.3675595224\n",
            "Epoch: 19 at 15:33:37 \tTrain. Loss: 2.202932 \tValid. Loss: 2.749586 \t Accur.: 0.3630952537\n",
            "Epoch: 20 at 15:37:27 \tTrain. Loss: 2.163009 \tValid. Loss: 2.696064 \t Accur.: 0.3742559552\n",
            "Epoch: 21 at 15:41:14 \tTrain. Loss: 2.151365 \tValid. Loss: 2.564093 \t Accur.: 0.3653273880\n",
            "Validation loss decreased by -0.014023\n",
            "Epoch: 22 at 15:45:5 \tTrain. Loss: 2.151999 \tValid. Loss: 2.687275 \t Accur.: 0.3720238209\n",
            "Epoch: 23 at 15:48:55 \tTrain. Loss: 2.082529 \tValid. Loss: 2.660053 \t Accur.: 0.3642113209\n",
            "Epoch: 24 at 15:52:44 \tTrain. Loss: 2.096639 \tValid. Loss: 2.601488 \t Accur.: 0.3753720224\n",
            "Epoch: 25 at 15:56:34 \tTrain. Loss: 2.087221 \tValid. Loss: 2.619355 \t Accur.: 0.3709077537\n",
            "Epoch: 26 at 16:0:22 \tTrain. Loss: 2.054966 \tValid. Loss: 2.658169 \t Accur.: 0.3709077537\n",
            "Epoch: 27 at 16:4:10 \tTrain. Loss: 2.051734 \tValid. Loss: 2.710332 \t Accur.: 0.3709077537\n",
            "Epoch: 28 at 16:7:59 \tTrain. Loss: 2.084544 \tValid. Loss: 2.550859 \t Accur.: 0.3731398880\n",
            "Validation loss decreased by -0.013234\n",
            "Epoch: 29 at 16:11:50 \tTrain. Loss: 2.078105 \tValid. Loss: 2.566592 \t Accur.: 0.3809523880\n",
            "Epoch: 30 at 16:15:37 \tTrain. Loss: 2.050696 \tValid. Loss: 2.602016 \t Accur.: 0.3742559552\n",
            "Epoch: 31 at 16:19:24 \tTrain. Loss: 2.055440 \tValid. Loss: 2.556441 \t Accur.: 0.3664434552\n",
            "Epoch: 32 at 16:23:12 \tTrain. Loss: 2.058280 \tValid. Loss: 2.710874 \t Accur.: 0.3764881194\n",
            "Epoch: 33 at 16:26:57 \tTrain. Loss: 2.059054 \tValid. Loss: 2.883438 \t Accur.: 0.3720238209\n",
            "Epoch: 34 at 16:30:43 \tTrain. Loss: 2.025278 \tValid. Loss: 2.757902 \t Accur.: 0.3731398880\n",
            "Epoch: 35 at 16:34:30 \tTrain. Loss: 2.052047 \tValid. Loss: 2.510484 \t Accur.: 0.3709077537\n",
            "Validation loss decreased by -0.040375\n",
            "Epoch: 36 at 16:38:16 \tTrain. Loss: 2.095771 \tValid. Loss: 2.875218 \t Accur.: 0.3753720224\n",
            "Epoch: 37 at 16:42:4 \tTrain. Loss: 2.068277 \tValid. Loss: 2.547173 \t Accur.: 0.3686756194\n",
            "Epoch: 38 at 16:45:52 \tTrain. Loss: 2.057049 \tValid. Loss: 2.594881 \t Accur.: 0.3709077537\n",
            "Epoch: 39 at 16:49:39 \tTrain. Loss: 2.030013 \tValid. Loss: 2.505210 \t Accur.: 0.3720238209\n",
            "Validation loss decreased by -0.005274\n",
            "Epoch: 40 at 16:53:27 \tTrain. Loss: 2.061572 \tValid. Loss: 2.545520 \t Accur.: 0.3709077537\n",
            "Epoch: 41 at 16:57:14 \tTrain. Loss: 2.055783 \tValid. Loss: 2.820235 \t Accur.: 0.3753720224\n",
            "Epoch: 42 at 17:1:1 \tTrain. Loss: 2.066536 \tValid. Loss: 2.829335 \t Accur.: 0.3675595224\n",
            "Epoch: 43 at 17:4:49 \tTrain. Loss: 2.047249 \tValid. Loss: 2.656204 \t Accur.: 0.3709077537\n",
            "Epoch: 44 at 17:8:37 \tTrain. Loss: 2.058394 \tValid. Loss: 2.637581 \t Accur.: 0.3753720224\n",
            "Epoch: 45 at 17:12:24 \tTrain. Loss: 2.071462 \tValid. Loss: 2.600379 \t Accur.: 0.3720238209\n",
            "Epoch: 46 at 17:16:10 \tTrain. Loss: 2.054529 \tValid. Loss: 2.802769 \t Accur.: 0.3720238209\n",
            "Epoch: 47 at 17:20:0 \tTrain. Loss: 2.051983 \tValid. Loss: 2.560971 \t Accur.: 0.3731398880\n",
            "Epoch: 48 at 17:23:48 \tTrain. Loss: 2.034189 \tValid. Loss: 2.643401 \t Accur.: 0.3787202537\n",
            "Epoch: 49 at 17:27:36 \tTrain. Loss: 2.070444 \tValid. Loss: 2.623776 \t Accur.: 0.3809523880\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zNRcypldUg5hQ3xBV9W93w38X0sjZHU6CqvgpUGyEjU83HdFWayIidcBut3PZ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mujb1oKd56XEvAeA+ELT8InfwJdesPo0+HKF+CUa2Hxf8IvJsOL/w7VVfl7H5LUji1as41XVm3l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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1008x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mbxIik5ry95",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BsM5LlSk-U8C",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Test the Model\n",
        "\n",
        "Try out your model on the test dataset of dog images.  Use the code cell below to calculate and print the test loss and accuracy.  Ensure that your test accuracy is greater than 10%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Esiu0FT_-U8D",
        "colab_type": "code",
        "outputId": "aae3a6cc-8a6f-417c-e9c2-4b4374c079da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "def test(loaders, model, criterion, use_cuda):\n",
        "\n",
        "    # monitor test loss and accuracy\n",
        "    test_loss = 0.\n",
        "    correct = 0.\n",
        "    total = 0.\n",
        "\n",
        "    model.eval()\n",
        "    for batch_idx, (data, target) in enumerate(loaders[2]):\n",
        "        # move to GPU\n",
        "        if use_cuda:\n",
        "            data, target = data.cuda(), target.cuda()\n",
        "        # forward pass: compute predicted outputs by passing inputs to the model\n",
        "        output = model(data)\n",
        "        # calculate the loss\n",
        "        loss = criterion(output, target)\n",
        "        # update average test loss \n",
        "        test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))\n",
        "        # convert output probabilities to predicted class\n",
        "        pred = output.data.max(1, keepdim=True)[1]\n",
        "        # compare predictions to true label\n",
        "        correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())\n",
        "        total += data.size(0)\n",
        "            \n",
        "    print('Test Loss: {:.6f}\\n'.format(test_loss))\n",
        "\n",
        "    print('\\nTest Accuracy: %2d%% (%2d/%2d)' % (\n",
        "        100. * correct / total, correct, total))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-cf4978946c3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;31m# call test function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders_scratch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_scratch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion_scratch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cuda\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'criterion_scratch' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bJbjZS8d-U8G",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "<a id='step4'></a>\n",
        "## Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
        "\n",
        "You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.\n",
        "\n",
        "### (IMPLEMENTATION) Specify Data Loaders for the Dog Dataset\n",
        "\n",
        "Use the code cell below to write three separate [data loaders](http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader) for the training, validation, and test datasets of dog images (located at `dogImages/train`, `dogImages/valid`, and `dogImages/test`, respectively). \n",
        "\n",
        "If you like, **you are welcome to use the same data loaders from the previous step**, when you created a CNN from scratch."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSUi0t5v-U8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## TODO: Specify data loaders\n",
        "# same as in part 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GIgX0HR-U8J",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Model Architecture\n",
        "\n",
        "Use transfer learning to create a CNN to classify dog breed.  Use the code cell below, and save your initialized model as the variable `model_transfer`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Kdlw8wf-U8K",
        "colab_type": "code",
        "outputId": "819e967a-93f1-4866-c61e-59c59d1aba4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "## TODO: Specify model architecture \n",
        "#transfermodel = models.resnet152(pretrained=True)\n",
        "#transfermodel.fc\n",
        "transfermodel = models.densenet161(pretrained=True)\n",
        "transfermodel.classifier"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Linear(in_features=2208, out_features=1000, bias=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exodhsgBrUjk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for param in transfermodel.parameters():\n",
        "    param.requires_grad = False\n",
        "    \n",
        "#fc_in = transfermodel.fc.in_features\n",
        "fc_in = transfermodel.classifier.in_features\n",
        "\n",
        "transferclassifier = nn.Sequential(\n",
        "                        nn.BatchNorm1d(fc_in),\n",
        "                        nn.Linear(fc_in, 133),\n",
        "                        #nn.ReLU(),\n",
        "                        #nn.Linear(1024, 133)\n",
        "                        )\n",
        "\n",
        "#transfermodel.fc = transferclassifier # ifusing ResNet\n",
        "transfermodel.classifier = transferclassifier\n",
        "\n",
        "transfermodel.apply(weights_init_normal)\n",
        "\n",
        "if use_cuda:\n",
        "    transfermodel.cuda()\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYyc9nxe-U8M",
        "colab_type": "text"
      },
      "source": [
        "__Question 5:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Czd_o3wQ-U8N",
        "colab_type": "text"
      },
      "source": [
        "__Answer:__ \n",
        "\n",
        "Given the small data set which has approx. 40-50 images per category, I decided for a pre-trained deep model like of the ResNet and DenseNet family, which achieve good accuracy results on image classification tasks. They were trained on millions of images and are able to extract as many  features as possible. \n",
        "\n",
        "Again, I initialized the weights for the linear layers, so no random weight initialization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIs6dN66-U8O",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Specify Loss Function and Optimizer\n",
        "\n",
        "Use the next code cell to specify a [loss function](http://pytorch.org/docs/master/nn.html#loss-functions) and [optimizer](http://pytorch.org/docs/master/optim.html).  Save the chosen loss function as `criterion_transfer`, and the optimizer as `optimizer_transfer` below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9G7pfEl-U8P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "transfer_criterion = nn.CrossEntropyLoss()\n",
        "transfer_optim = optim.Adam(transfermodel.parameters(), lr=2e-3)\n",
        "#transfer_scheduler = optim.lr_scheduler.MultiStepLR(transfer_optim, [5,10,14,17], 0.1)\n",
        "transfer_scheduler = optim.lr_scheduler.StepLR(transfer_optim, 4, 0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tpdt7ecP-U8R",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Train and Validate the Model\n",
        "\n",
        "Train and validate your model in the code cell below.  [Save the final model parameters](http://pytorch.org/docs/master/notes/serialization.html) at filepath `'model_transfer.pt'`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZfQLk6P-U8R",
        "colab_type": "code",
        "outputId": "c6591cb5-4c92-4cab-ea5a-175b0b7cfd78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        }
      },
      "source": [
        "# train the model\n",
        "from PIL import ImageFile\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "\n",
        "#n_epochs, loaders, model, optimizer, criterion, scheduler,use_cuda, save_path\n",
        "transfermodel = train (15, \n",
        "                       [train_loader, valid_loader, test_loader],\n",
        "                      transfermodel,\n",
        "                      transfer_optim,\n",
        "                      transfer_criterion,\n",
        "                       transfer_scheduler,\n",
        "                       use_cuda,\n",
        "                       'model_transfer.pt'\n",
        "                      )\n",
        "\n",
        "# load the model that got the best validation accuracy (uncomment the line below)\n",
        "model_transfer.load_state_dict(torch.load('model_transfer.pt'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training started at  (18, 44, 2)\n",
            "Epoch: 0 at 19:0:8 \tTrain. Loss: 2.010150 \tValid. Loss: 0.549174 \t Accur.: 0.8716517687\n",
            "Validation loss decreased by -inf\n",
            "Epoch: 1 at 19:4:10 \tTrain. Loss: 0.735486 \tValid. Loss: 0.427520 \t Accur.: 0.8500744104\n",
            "Validation loss decreased by -0.121655\n",
            "Epoch: 2 at 19:8:13 \tTrain. Loss: 0.600276 \tValid. Loss: 0.414641 \t Accur.: 0.8779762387\n",
            "Validation loss decreased by -0.012879\n",
            "Epoch: 3 at 19:12:15 \tTrain. Loss: 0.525415 \tValid. Loss: 0.347532 \t Accur.: 0.8872767687\n",
            "Validation loss decreased by -0.067108\n",
            "Epoch: 4 at 19:16:18 \tTrain. Loss: 0.463462 \tValid. Loss: 0.328812 \t Accur.: 0.8973214030\n",
            "Validation loss decreased by -0.018720\n",
            "Epoch: 5 at 19:20:20 \tTrain. Loss: 0.449927 \tValid. Loss: 0.324676 \t Accur.: 0.8962053657\n",
            "Validation loss decreased by -0.004137\n",
            "Epoch: 6 at 19:24:24 \tTrain. Loss: 0.417653 \tValid. Loss: 0.314640 \t Accur.: 0.8973214030\n",
            "Validation loss decreased by -0.010035\n",
            "Epoch: 7 at 19:28:26 \tTrain. Loss: 0.410290 \tValid. Loss: 0.316541 \t Accur.: 0.8995535970\n",
            "Epoch: 8 at 19:32:29 \tTrain. Loss: 0.393038 \tValid. Loss: 0.314859 \t Accur.: 0.8962053657\n",
            "Epoch: 9 at 19:36:31 \tTrain. Loss: 0.403614 \tValid. Loss: 0.316466 \t Accur.: 0.8950892687\n",
            "Epoch: 10 at 19:40:34 \tTrain. Loss: 0.405327 \tValid. Loss: 0.316315 \t Accur.: 0.8962053657\n",
            "Epoch: 11 at 19:44:36 \tTrain. Loss: 0.414787 \tValid. Loss: 0.315369 \t Accur.: 0.8962053657\n",
            "Epoch: 12 at 19:48:38 \tTrain. Loss: 0.429261 \tValid. Loss: 0.314218 \t Accur.: 0.8984375000\n",
            "Validation loss decreased by -0.000422\n",
            "Epoch: 13 at 19:52:40 \tTrain. Loss: 0.402085 \tValid. Loss: 0.315776 \t Accur.: 0.8973214030\n",
            "Epoch: 14 at 19:56:43 \tTrain. Loss: 0.433294 \tValid. Loss: 0.314996 \t Accur.: 0.8973214030\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-49cc63b21922>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m                        \u001b[0mtransfer_scheduler\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m                        \u001b[0muse_cuda\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m                        \u001b[0;34m'model_transfer.pt'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m                       )\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-89572dfa3373>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(n_epochs, loaders, model, optimizer, criterion, scheduler, use_cuda, save_path)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;31m##### visualize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m     \u001b[0mplot_loss_acc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_accuracies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plot_loss_acc' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVEVI0YY-U8T",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Test the Model\n",
        "\n",
        "Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy.  Ensure that your test accuracy is greater than 60%."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UWOdp9r-U8U",
        "colab_type": "code",
        "outputId": "8a8d21f6-85d5-43b3-eea5-2320c9cccf59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "source": [
        "test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-181b5cf17149>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders_transfer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_transfer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion_transfer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cuda\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'test' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBM5Zq4y-U8W",
        "colab_type": "text"
      },
      "source": [
        "### (IMPLEMENTATION) Predict Dog Breed with the Model\n",
        "\n",
        "Write a function that takes an image path as input and returns the dog breed (`Affenpinscher`, `Afghan hound`, etc) that is predicted by your model.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GM5P_QAW-U8X",
        "colab_type": "code",
        "outputId": "f03015eb-58ed-4664-8c91-59ae4c2cb3e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "### TODO: Write a function that takes a path to an image as input\n",
        "### and returns the dog breed that is predicted by the model.\n",
        "\n",
        "# list of class names by index, i.e. a name can be accessed like class_names[0]\n",
        "class_names = [item[4:].replace(\"_\", \" \") for item in loaders_transfer[0].classes]\n",
        "\n",
        "def predict_breed_transfer(img_path):\n",
        "    transfermodel.eval()\n",
        "    with torch.no_grad():\n",
        "      # load the image and return the predicted breed\n",
        "      img = process_image(img_path)\n",
        "      if torch.is_cuda_available():\n",
        "        img.cuda()\n",
        "      output = transfermodel(img)\n",
        "      probabilities = torch.exp(output)\n",
        "      _, top_class = probabilities.topk(1, dim=1)\n",
        "      return class_names[top_class]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-c5c900e505d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclass_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloaders_transfer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict_breed_transfer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtransfermodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'loaders_transfer' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CHNwuOF-U8Z",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "<a id='step5'></a>\n",
        "## Step 5: Write your Algorithm\n",
        "\n",
        "Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,\n",
        "- if a __dog__ is detected in the image, return the predicted breed.\n",
        "- if a __human__ is detected in the image, return the resembling dog breed.\n",
        "- if __neither__ is detected in the image, provide output that indicates an error.\n",
        "\n",
        "You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the `face_detector` and `human_detector` functions developed above.  You are __required__ to use your CNN from Step 4 to predict dog breed.  \n",
        "\n",
        "Some sample output for our algorithm is provided below, but feel free to design your own user experience!\n",
        "\n",
        "![Sample Human Output](https://github.com/Berenice2018/dog-breed133-classifier/blob/master/images/sample_human_output.png?raw=1)\n",
        "\n",
        "\n",
        "### (IMPLEMENTATION) Write your Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gieq0IAE-U8c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### TODO: Write your algorithm.\n",
        "### Feel free to use as many code cells as needed.\n",
        "\n",
        "def run_app(img_path):\n",
        "    print(img_path)\n",
        "    show_image(img_path)\n",
        "    ## handle cases for a human face, dog, and neither\n",
        "    dog = dog_detector(img_path)\n",
        "    if dog:\n",
        "      print('It is a dog')\n",
        "    else:\n",
        "      if face_detector(img_path):\n",
        "        print('Hi there')\n",
        "      else:\n",
        "        print('What is this')\n",
        "    print('You look like ', predict_breed_transfer(img_path))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXK8p9EY-U8e",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "<a id='step6'></a>\n",
        "## Step 6: Test Your Algorithm\n",
        "\n",
        "In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that _you_ look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?\n",
        "\n",
        "### (IMPLEMENTATION) Test Your Algorithm on Sample Images!\n",
        "\n",
        "Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.  \n",
        "\n",
        "__Question 6:__ Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClrrWL0k-U8j",
        "colab_type": "text"
      },
      "source": [
        "__Answer:__ (Three possible points for improvement)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jKYLzqX-U8r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## TODO: Execute your algorithm from Step 6 on\n",
        "## at least 6 images on your computer.\n",
        "## Feel free to use as many code cells as needed.\n",
        "\n",
        "## suggested code, below\n",
        "for file in np.hstack((human_files[:3], dog_files[:3])):\n",
        "    run_app(file)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}