copyHedi_Bouattour_PyTorch_cifar10_tutorial_ROB313_2018.ipynb

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        "<a href=\"https://colab.research.google.com/github/hedibouattour/tp3_rob311/blob/master/copyHedi_Bouattour_PyTorch_cifar10_tutorial_ROB313_2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Deep Learning for Image Classification\n",
        "\n",
        "Welcome to deep learning for image classification tutorial!\n",
        "**In this notebook, you will**:\n",
        "- Learn the basics of PyTorch, a powerful but easy to use package for scientific computing (and deep learning)\n",
        "- Learn how to build and train a convolutional neural network for image classification.\n",
        "\n",
        "If you have never used jupyter notebooks, nor Colab notebooks, [here](https://colab.research.google.com/notebooks/welcome.ipynb) is a short intro.\n",
        "\n",
        "\n",
        "## I. PyTorch Tutorial\n",
        "\n",
        "We will briefly go through the basics of the PyTorch package, playing with toy examples.\n",
        "\n",
        "If you know already how to use PyTorch, then you can directly go to the second part of this tutorial\n",
        "\n",
        "## II. Training a classifier\n",
        "\n",
        "In this part, we will train a Convolutional Neural Network to classify images of 10 different classes (dogs, cats, car, ...) and see how our model performs on the test set.  \n",
        "\n",
        "\n",
        "## III. Exploring CNN Architectures\n",
        "\n",
        "This is the part where you get your hands dirty ;). Your mission is to experiment different CNN architectures and set hyperparameters in order to obtain the best accuracy on the test set!\n"
      ]
    },
    {
      "metadata": {
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      "source": [
        "The following command sets the backend of matplotlib to the 'inline' backend so that the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it:"
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YAz-fhRRdFaR",
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      "cell_type": "markdown",
      "source": [
        "### Plotting functions and useful imports\n",
        "\n",
        "You can skip this part"
      ]
    },
    {
      "metadata": {
        "id": "nnee2WPudA9K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Python 2/3 compatibility\n",
        "from __future__ import print_function, division\n",
        "\n",
        "import itertools\n",
        "import time\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Colors from Colorbrewer Paired_12\n",
        "colors = [[31, 120, 180], [51, 160, 44]]\n",
        "colors = [(r / 255, g / 255, b / 255) for (r, g, b) in colors]\n",
        "\n",
        "# functions to show an image\n",
        "def imshow(img):\n",
        "    \"\"\"\n",
        "    :param img: (PyTorch Tensor)\n",
        "    \"\"\"\n",
        "    # unnormalize\n",
        "    img = img / 2 + 0.5     \n",
        "    # Convert tensor to numpy array\n",
        "    npimg = img.numpy()\n",
        "    # Color channel first -> color channel last\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "\n",
        "\n",
        "def plot_losses(train_history, val_history):\n",
        "    x = np.arange(1, len(train_history) + 1)\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.plot(x, train_history, color=colors[0], label=\"Training loss\", linewidth=2)\n",
        "    plt.plot(x, val_history, color=colors[1], label=\"Validation loss\", linewidth=2)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend(loc='upper right')\n",
        "    plt.title(\"Evolution of the training and validation loss\")\n",
        "    plt.show()\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    from http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "    :param cm: (numpy matrix) confusion matrix\n",
        "    :param classes: [str]\n",
        "    :param normalize: (bool)\n",
        "    :param title: (str)\n",
        "    :param cmap: (matplotlib color map)\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        \n",
        "    plt.figure(figsize=(8, 8))   \n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 0,
      "outputs": []
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    {
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      "source": [
        "# I. What is PyTorch ?\n",
        "\n",
        "It’s a Python based scientific computing package targeted at two sets of audiences:\n",
        "\n",
        "- A replacement for numpy to use the power of GPUs\n",
        "- A deep learning research platform that provides maximum flexibility and speed\n",
        "\n",
        "\n",
        "## PyTorch Basics\n",
        "\n",
        "In the next steps, we will briefly see how to use PyTorch and exploit its power:\n",
        "\n",
        "1. PyTorch Installation\n",
        "2. PyTorch Tensors\n",
        "3. Numpy Bridge\n",
        "4. Automatic differentiation\n",
        "5. PyTorch and GPU (CUDA support)\n",
        "\n",
        "\n",
        "### 1. Install PyTorch and Torchvision\n",
        "\n"
      ]
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      "source": [
        "!pip install http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl\n",
        "!pip install torchvision"
      ],
      "execution_count": 4,
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          "text": [
            "Collecting torch==0.4.1 from http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl\n",
            "\u001b[?25l  Downloading http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl (512.6MB)\n",
            "\u001b[K    100% |████████████████████████████████| 512.6MB 64.9MB/s \n",
            "tcmalloc: large alloc 1073750016 bytes == 0x587d0000 @  0x7f4f102762a4 0x591a07 0x5b5d56 0x502e9a 0x506859 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x504c28 0x502540 0x502f3d 0x507641\n",
            "\u001b[?25hInstalling collected packages: torch\n",
            "Successfully installed torch-0.4.1\n",
            "Collecting torchvision\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/0d/f00b2885711e08bd71242ebe7b96561e6f6d01fdb4b9dcf4d37e2e13c5e1/torchvision-0.2.1-py2.py3-none-any.whl (54kB)\n",
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            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n",
            "Collecting pillow>=4.1.1 (from torchvision)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/62/94/5430ebaa83f91cc7a9f687ff5238e26164a779cca2ef9903232268b0a318/Pillow-5.3.0-cp36-cp36m-manylinux1_x86_64.whl (2.0MB)\n",
            "\u001b[K    100% |████████████████████████████████| 2.0MB 15.2MB/s \n",
            "\u001b[?25hInstalling collected packages: pillow, torchvision\n",
            "  Found existing installation: Pillow 4.0.0\n",
            "    Uninstalling Pillow-4.0.0:\n",
            "      Successfully uninstalled Pillow-4.0.0\n",
            "Successfully installed pillow-5.3.0 torchvision-0.2.1\n"
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          "name": "stdout"
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        "id": "LcGVaagRcav8",
        "colab_type": "code",
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "# Import torch and create the alias \"th\"\n",
        "# instead of writing torch.name_of_a_method() , we only need to write th.name_of_a_method()\n",
        "# (similarly to numpy imported as np)\n",
        "import torch as th"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "deletable": true,
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        "id": "g2-brDDHcawE",
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        "outputId": "74c20dc4-e7f1-428b-de60-eaed3cdc0478",
        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "cell_type": "code",
      "source": [
        "# Create tensor of ones (FloatTensor by default)\n",
        "ones = th.ones(3, 2)\n",
        "print(ones)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 1.],\n",
            "        [1., 1.],\n",
            "        [1., 1.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
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      "cell_type": "markdown",
      "source": [
        "### 2. PyTorch Tensors\n",
        "\n",
        "A `torch.Tensor` is a multi-dimensional matrix containing elements of a single data type.\n",
        "\n",
        "Tensors are similar to numpy’s ndarrays, but they have a super-power: Tensors can also be used on a GPU to accelerate computing."
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
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      "cell_type": "markdown",
      "source": [
        "#### Tensor Shape\n",
        "To know the shape of a given tensor, you can use the `.size()` method (the numpy equivalent is `.shape`)"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "SI96-W9acawS",
        "colab_type": "code",
        "outputId": "da023feb-86cb-4547-e5b1-a26508b0b53a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Display the shape of a tensor\n",
        "# it can be used as a tuple\n",
        "print(\"Tensor Shape: {}\".format(ones.size()))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor Shape: torch.Size([3, 2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "source": [
        "#### Reshape tensors\n",
        "\n",
        "To reshape tensors (e.g. flatten a 3D tensor to a 1D array), you can use the `.view()` method:\n",
        "\n",
        "- **x.view(new_shape)**: Returns a new tensor with the same data but different size. It is the equivalent of numpy function *reshape* (Gives a new shape to an array without changing its data.). You can read the full documentation [here.](http://pytorch.org/docs/master/tensors.html#torch.Tensor.view)\n",
        "\n",
        "[WARNING] when precising a new shape, you have to make sure that the number of elements is constant.\n",
        "For example, a 2D matrix of size 3x3 can only be viewed as a 1D array of size $3 \\cdot 3 = 9$"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "vX-oxI6Vcawb",
        "colab_type": "code",
        "outputId": "823666f8-c81d-427e-ac09-788d6d20a165",
        "colab": {
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      "cell_type": "code",
      "source": [
        "# Create a 3D tensor of size 3x2x2\n",
        "zeros_3d_tensor = th.zeros(3, 2, 2)\n",
        "print(\"Original size:\", zeros_3d_tensor.size())\n",
        "\n",
        "# Reshape it to a 1D array of size 3*2*2 = 12\n",
        "zeros_1d_array = zeros_3d_tensor.view(3 * 2 * 2)\n",
        "print(\"Reshaped tensor:\", zeros_1d_array.size())\n",
        "\n",
        "\n",
        "# Let's view our original tensor as a 2D matrix\n",
        "# If you want PyTorch to guess one remaining dimension,\n",
        "# you specify '-1' instead of the actual size\n",
        "zeros_2d_matrix = zeros_3d_tensor.view(-1, 2 * 2)\n",
        "\n",
        "print(\"Matrix shape:\", zeros_2d_matrix.size())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original size: torch.Size([3, 2, 2])\n",
            "Reshaped tensor: torch.Size([12])\n",
            "Matrix shape: torch.Size([3, 4])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "editable": true,
        "id": "kTO_FFswcawj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Basic Operations on tensors\n",
        "\n",
        "Tensor support all basic linear algebra operations. You can read the full documentation [here](http://pytorch.org/docs/master/tensors.html)"
      ]
    },
    {
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        "colab": {
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      },
      "cell_type": "code",
      "source": [
        "2 * ones + 1"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[3., 3.],\n",
              "        [3., 3.],\n",
              "        [3., 3.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
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      "cell_type": "markdown",
      "source": [
        "PyTorch tensors also supports numpy indexing:"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "srzDzj_ocawu",
        "colab_type": "code",
        "outputId": "5b0b5fac-c002-4415-9e6e-89954ff6b3e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"\\n Indexing Demo:\")\n",
        "print(ones[:, 1])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Indexing Demo:\n",
            "tensor([1., 1., 1.])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
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      },
      "cell_type": "markdown",
      "source": [
        "### 3. Numpy Bridge\n",
        "WARNING: PyTorch Tensors are different from numpy arrays\n",
        "even if they have a lot in common\n",
        "\n",
        "Though, it is **easy with PyTorch to tranform Tensors to Numpy arrays and vice versa**"
      ]
    },
    {
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        "deletable": true,
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      "cell_type": "markdown",
      "source": [
        "#### Numpy <-> PyTorch\n",
        "\n",
        "Creating PyTorch tensors from numpy array is done via the `torch.from_numpy()` function  \n",
        "(here `th.from_numpy()` because we renamed *torch* as *th*)\n",
        "\n",
        "To transform a PyTorch tensor to a numpy array, you can simply call `.numpy()` method."
      ]
    },
    {
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        "deletable": true,
        "editable": true,
        "id": "t2ENcAKOcaw5",
        "colab_type": "code",
        "outputId": "df6225f7-650b-4f4a-b836-b234f00af254",
        "colab": {
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      },
      "cell_type": "code",
      "source": [
        "# np.float32 -> th.FloatTensor\n",
        "ones_matrix = np.ones((2, 2), dtype=np.float32)\n",
        "\n",
        "# the matrix is passed by reference:\n",
        "# if we modify the original numpy array, the tensor is also edited\n",
        "ones_tensor = th.from_numpy(ones_matrix)\n",
        "# Convert back to a numpy matrix\n",
        "numpy_matrix = ones_tensor.numpy()\n",
        "\n",
        "print(\"PyTorch Tensor:\")\n",
        "print(ones_tensor)\n",
        "\n",
        "print(\"Numpy Matrix:\")\n",
        "print(numpy_matrix)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PyTorch Tensor:\n",
            "tensor([[1., 1.],\n",
            "        [1., 1.]])\n",
            "Numpy Matrix:\n",
            "[[1. 1.]\n",
            " [1. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
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        "editable": true,
        "id": "Y0Itjyg-caxD",
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      "cell_type": "markdown",
      "source": [
        "### 4. Automatic Differentiation\n",
        "\n",
        "Pytorch tensors allow to **automatically compute gradients**. That is particulary useful for backpropagation.\n",
        "\n",
        "Once you finish your computation you can call `.backward()` and have all the gradients computed automatically.\n",
        "\n",
        "You can access the gradient w.r.t. this variable using `.grad`.\n"
      ]
    },
    {
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        "deletable": true,
        "editable": true,
        "id": "WrPNcIpYcaxK",
        "colab_type": "code",
        "outputId": "e99c12a6-0282-4a08-8348-4f942121d3ac",
        "colab": {
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      },
      "cell_type": "code",
      "source": [
        "# We need to specify that we want to compute the gradient\n",
        "# as it requires extra memory and computation\n",
        "ones_tensor = th.ones(2,2, requires_grad=True)\n",
        "\n",
        "print(ones_tensor)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 1.],\n",
            "        [1., 1.]], requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "IEZDUibxcaxj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To demonstrate the use of PyTorch Variable,\n",
        "let's define a simple linear transformation of a variable $x$ :\n",
        "\n",
        "$$y = a \\cdot x + b$$\n",
        "\n",
        "PyTorch will allows us to automatically compute $$\\frac{dy}{dx} $$"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "A4j85JjZcaxl",
        "colab_type": "code",
        "outputId": "ce7f1ee1-ecde-427d-bdc8-03e718aba22c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Create a tensor and tell PyTorch\n",
        "# that we want to compute the gradient\n",
        "x = th.ones(1, requires_grad=True)\n",
        "\n",
        "# Transformation constants\n",
        "a = 3\n",
        "b = 5\n",
        "\n",
        "# Define the tranformation and store the result\n",
        "# in a new variable\n",
        "y = a * x + b\n",
        "\n",
        "print(y)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([8.], grad_fn=<AddBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "_mxnlvwxcaxq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's backprop!"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "X1i-pN-Fcaxs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y.backward()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "skgIGZdmcaxw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "`x.grad` prints the gradient:\n",
        "\n",
        "$$\\frac{dy}{dx} = a$$\n",
        "\n",
        "because:\n",
        "\n",
        "$$y = a \\cdot x + b$$"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "_TYbuwsXcaxx",
        "colab_type": "code",
        "outputId": "ace13c43-db25-4a00-fe0c-ad163119b58a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x.grad"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "ggu-PBGvcax3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "You can now change the values of $a$ and $b$ see their effects on the gradient\n",
        "(HINT: `x.grad` only depends on the value of `a`)"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "8iPn0C59cax5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 5. PyTorch and GPU (CUDA support)\n",
        "\n",
        "Google colab provides a CUDA enabled GPU, so we are going to use its power. \n",
        "You can move tensor to the GPU by simply using the `to()` method.\n",
        "Otherwise, PyTorch will use the CPU.\n",
        "\n",
        "Here, we will demonstrate the usefulness of the GPU on a simple matrix multiplication:"
      ]
    },
    {
      "metadata": {
        "id": "EwF6ePTpeefQ",
        "colab_type": "code",
        "outputId": "f2d0af11-8924-4b4b-bc3d-97a1dbce753a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "if th.cuda.is_available():\n",
        "  # Create tensors\n",
        "  x = th.ones(1000, 1000)\n",
        "  y = 2 * x + 3\n",
        "  # Do the calculation on cpu (default)\n",
        "  start_time = time.time()\n",
        "  # Matrix multiplication (for benchmark purpose)\n",
        "  results = th.mm(x, y)\n",
        "  time_cpu = time.time() - start_time\n",
        "  \n",
        "  # Do the same calculation but on the gpu\n",
        "  # First move tensors to gpu\n",
        "  x = x.to(\"cuda\")\n",
        "  y = y.to(\"cuda\")\n",
        "  start_time = time.time()\n",
        "  # Matrix multiplication (for benchmark purpose)\n",
        "  results = th.mm(x, y)\n",
        "  time_gpu = time.time() - start_time\n",
        "  \n",
        "  print(\"Time on CPU: {:.5f}s \\t Time on GPU: {:.5f}s\".format(time_cpu, time_gpu))\n",
        "  print(\"Speed up: Computation was {:.0f}X faster on GPU!\".format(time_cpu / time_gpu))\n",
        "  \n",
        "else:\n",
        "  print(\"You need to enable GPU accelaration in colab (runtime->change runtime type)\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time on CPU: 0.02967s \t Time on GPU: 0.00109s\n",
            "Speed up: Computation was 27X faster on GPU!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "E-AOzDy9lFwi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "As expected, matrix multiplication is way faster on a GPU, so we'd better use it."
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "0kqEBjG6t2Eh"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "# II. Training a classifier\n",
        "\n",
        "\n",
        "For this tutorial, we will use the CIFAR10 dataset.\n",
        "There are 10 classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n",
        "‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\n",
        "size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
        "\n",
        "\n",
        "![CIFAR10](http://pytorch.org/tutorials/_images/cifar10.png)\n",
        "\n",
        "\n",
        "Training an image classifier\n",
        "----------------------------\n",
        "\n",
        "We will do the following steps in order:\n",
        "\n",
        "1. Load and normalize the CIFAR10 training and test datasets using\n",
        "   ``torchvision``\n",
        "2. Define a Convolution Neural Network\n",
        "3. Define a loss function\n",
        "4. Train the network on the training data\n",
        "5. Test the network on the test data"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "UWTdj2uYcax7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 1. Loading and normalizing CIFAR10 Dataset\n",
        "\n",
        "Using ``torchvision``, it’s extremely easy to load CIFAR10."
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "KRrvrIi0t2Em",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "iX2ltR_zcayA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Seed the random generator to have reproducible results:"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "335xvR6acayB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "seed = 42\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "if th.cuda.is_available():\n",
        "  # Make CuDNN Determinist\n",
        "  th.backends.cudnn.deterministic = True\n",
        "  th.cuda.manual_seed(seed)\n",
        "\n",
        "# Define default device, we should use the GPU (cuda) if available\n",
        "device = th.device(\"cuda\" if th.cuda.is_available() else \"cpu\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "7EzIeyD4cayG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Define subset of the dataset (so it is faster to train)"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "Nwu-wWh3cayI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "\n",
        "n_training_samples = 30000 # Max: 50 000 - n_val_samples\n",
        "n_val_samples = 20000\n",
        "n_test_samples = 5000\n",
        "\n",
        "train_sampler = SubsetRandomSampler(np.arange(n_training_samples, dtype=np.int64))\n",
        "val_sampler = SubsetRandomSampler(np.arange(n_training_samples, n_training_samples + n_val_samples, dtype=np.int64))\n",
        "test_sampler = SubsetRandomSampler(np.arange(n_test_samples, dtype=np.int64))\n",
        "# (In the last case, indexes do not need to account for training ones because the train=False parameter in datasets.CIFAR will select from the test set)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "evFXNmbst2Ez"
      },
      "cell_type": "markdown",
      "source": [
        "The output of torchvision datasets are PILImage images of range [0, 1].\n",
        "We transform them to Tensors of normalized range [-1, 1]\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "ZJ-hYN00t2E2",
        "outputId": "df17a951-bff2-44fa-ca4d-7afbdf321eba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "num_workers = 2\n",
        "test_batch_size = 4\n",
        "\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])\n",
        "\n",
        "train_set = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=test_batch_size, sampler=train_sampler,\n",
        "                                          num_workers=num_workers)\n",
        "\n",
        "test_set = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "test_loader = torch.utils.data.DataLoader(test_set, batch_size=test_batch_size, sampler=test_sampler,\n",
        "                                         num_workers=num_workers)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "cGWVnBOft2FI"
      },
      "cell_type": "markdown",
      "source": [
        "Let us show some of the training images, for fun.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "68OfC35ut2FM",
        "outputId": "ce55d5ba-6515-462b-ed40-9463197b5985",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "cell_type": "code",
      "source": [
        "# get some random training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('{:>10}'.format(classes[labels[j]]) for j in range(test_batch_size)))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     horse      horse        dog        dog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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12o+9wua0IO/rmWkxGRWLNM9xZYpKpzjZnWorl7koBO+jojLhDg+Tqeb558Ux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\nH94WFhYWLQj78LawsLBoQfxf0bem2FkcwnwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd76a8cfe48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "8ULHEu5Zt2Fa"
      },
      "cell_type": "markdown",
      "source": [
        "### 2. Define a Convolution Neural Network\n"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "6k6rJyTTcayi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Useful imports\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "0JcmlEe8t2Fe"
      },
      "cell_type": "markdown",
      "source": [
        "####  Forward propagation\n",
        "\n",
        "In PyTorch, there are built-in functions that carry out the convolution steps for you.\n",
        "\n",
        "- **nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0):** Convolution layer. You can read the full documentation [here](http://pytorch.org/docs/master/nn.html#conv2d)\n",
        "\n",
        "- **nn.MaxPool2d(kernel_size, stride=None, padding=0):** Max pooling layer. You can read the full documentation [here](http://pytorch.org/docs/master/nn.html#maxpool2d)\n",
        "\n",
        "- **F.relu(Z1):** computes the elementwise ReLU of Z1 (which can be any shape). You can read the full documentation [here.](http://pytorch.org/docs/master/nn.html#torch.nn.ReLU)\n",
        "\n",
        "- **x.view(new_shape)**: Returns a new tensor with the same data but different size. It is the equivalent of numpy function *reshape* (Gives a new shape to an array without changing its data). You can read the full documentation [here.](http://pytorch.org/docs/master/tensors.html#torch.Tensor.view)\n",
        "\n",
        "- **nn.Linear(in_features, out_features):** Applies a linear transformation to the incoming data: $y = Ax + b$, it is also called a fully connected layer. You can read the full documentation [here.](http://pytorch.org/docs/master/nn.html#linear-layers)"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "rbykSRDTcaym",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Simple Convolutional Neural Network\n",
        "\n",
        "ConvNet with one convolution layer followed by a max pooling operation,\n",
        "one fully connected layer and an output layer"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "X4pljAWycayn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class SimpleConvolutionalNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SimpleConvolutionalNetwork, self).__init__()\n",
        "        \n",
        "        self.conv1 = nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "        \n",
        "        # cf comments in forward() to have step by step comments\n",
        "        # on the shape (how we pass from a 3x32x32 input image to a 18x16x16 volume)\n",
        "        self.fc1 = nn.Linear(18 * 16 * 16, 64) \n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward pass,\n",
        "        x shape is (batch_size, 3, 32, 32)\n",
        "        (color channel first)\n",
        "        in the comments, we omit the batch_size in the shape\n",
        "        \"\"\"\n",
        "        # shape : 3x32x32 -> 18x32x32\n",
        "        x = F.relu(self.conv1(x))\n",
        "        # 18x32x32 -> 18x16x16\n",
        "        x = self.pool(x)\n",
        "        # 18x16x16 -> 4608\n",
        "        x = x.view(-1, 18 * 16 * 16)\n",
        "        # 4608 -> 64\n",
        "        x = F.relu(self.fc1(x))\n",
        "        # 64 -> 10\n",
        "        # The softmax non-linearity is applied later (cf createLossAndOptimizer() fn)\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "4m-VHCtRcayr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Linear Classifier"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "Rj-togN6cays",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class LinearClassifier(nn.Module):\n",
        "    \"\"\"\n",
        "    Linear Classifier\n",
        "    \"\"\"\n",
        "    def __init__(self):\n",
        "        super(LinearClassifier, self).__init__()\n",
        "        self.linear = nn.Linear(32 * 32 * 3, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # Flatten input 3x32x32 -> 3072\n",
        "        x = x.view(x.size(0), -1)\n",
        "        return self.linear(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "2SQi9Xf-t2Fu"
      },
      "cell_type": "markdown",
      "source": [
        "### 3. Define a loss function and optimizer\n",
        "\n",
        "Let's use a Classification Cross-Entropy loss and ADAM (optionally, SGD with momentum). You can read more about  [optimization methods](https://pytorch.org/docs/stable/optim.html).\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "DOUiPtZQt2Fx",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "def createLossAndOptimizer(net, learning_rate=0.001):\n",
        "    # it combines softmax with negative log likelihood loss\n",
        "    criterion = nn.CrossEntropyLoss()  \n",
        "    #optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)\n",
        "    optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n",
        "    return criterion, optimizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "saJW5bKRt2F9"
      },
      "cell_type": "markdown",
      "source": [
        "### 4. Train the network\n",
        "\n",
        "\n",
        "This is when things start to get interesting.\n",
        "We simply have to loop over our data iterator, feed the inputs to the network, and optimize\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "mNf1e8QZcay1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Data loader"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "EqDD8_z8cay2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_train_loader(batch_size):\n",
        "    return torch.utils.data.DataLoader(train_set, batch_size=batch_size, sampler=train_sampler,\n",
        "                                              num_workers=num_workers)\n",
        "\n",
        "# Use larger batch size for validation to speed up computation\n",
        "val_loader = torch.utils.data.DataLoader(train_set, batch_size=128, sampler=val_sampler,\n",
        "                                          num_workers=num_workers)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "yTDHHbLpcay5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Training loop\n",
        "The training script: it takes ~10s per epoch with batch_size = 32"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "dATbDR5pt2GE",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train(net, batch_size, n_epochs, learning_rate):\n",
        "    \"\"\"\n",
        "    Train a neural network and print statistics of the training\n",
        "    \n",
        "    :param net: (PyTorch Neural Network)\n",
        "    :param batch_size: (int)\n",
        "    :param n_epochs: (int)  Number of iterations on the training set\n",
        "    :param learning_rate: (float) learning rate used by the optimizer\n",
        "    \"\"\"\n",
        "    print(\"===== HYPERPARAMETERS =====\")\n",
        "    print(\"batch_size=\", batch_size)\n",
        "    print(\"n_epochs=\", n_epochs)\n",
        "    print(\"learning_rate=\", learning_rate)\n",
        "    print(\"=\" * 30)\n",
        "    \n",
        "    train_loader = get_train_loader(batch_size)\n",
        "    n_minibatches = len(train_loader)\n",
        "\n",
        "    criterion, optimizer = createLossAndOptimizer(net, learning_rate)\n",
        "    # Init variables used for plotting the loss\n",
        "    train_history = []\n",
        "    val_history = []\n",
        "\n",
        "    training_start_time = time.time()\n",
        "    best_error = np.inf\n",
        "    best_model_path = \"best_model.pth\"\n",
        "    \n",
        "    # Move model to gpu if possible\n",
        "    net = net.to(device)\n",
        "\n",
        "    for epoch in range(n_epochs):  # loop over the dataset multiple times\n",
        "\n",
        "        running_loss = 0.0\n",
        "        print_every = n_minibatches // 10\n",
        "        start_time = time.time()\n",
        "        total_train_loss = 0\n",
        "        \n",
        "        for i, (inputs, labels) in enumerate(train_loader):\n",
        "\n",
        "            # Move tensors to correct device\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "            # zero the parameter gradients\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # forward + backward + optimize\n",
        "            outputs = net(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # print statistics\n",
        "            running_loss += loss.item()\n",
        "            total_train_loss += loss.item()\n",
        "\n",
        "            # print every 10th of epoch\n",
        "            if (i + 1) % (print_every + 1) == 0:    \n",
        "                print(\"Epoch {}, {:d}% \\t train_loss: {:.2f} took: {:.2f}s\".format(\n",
        "                      epoch + 1, int(100 * (i + 1) / n_minibatches), running_loss / print_every,\n",
        "                      time.time() - start_time))\n",
        "                running_loss = 0.0\n",
        "                start_time = time.time()\n",
        "\n",
        "        train_history.append(total_train_loss / len(train_loader))\n",
        "\n",
        "        total_val_loss = 0\n",
        "        # Do a pass on the validation set\n",
        "        # We don't need to compute gradient,\n",
        "        # we save memory and computation using th.no_grad()\n",
        "        with th.no_grad():\n",
        "          for inputs, labels in val_loader:\n",
        "              # Move tensors to correct device\n",
        "              inputs, labels = inputs.to(device), labels.to(device)\n",
        "              # Forward pass\n",
        "              predictions = net(inputs)\n",
        "              val_loss = criterion(predictions, labels)\n",
        "              total_val_loss += val_loss.item()\n",
        "            \n",
        "        val_history.append(total_val_loss / len(val_loader))\n",
        "        # Save model that performs best on validation set\n",
        "        if total_val_loss < best_error:\n",
        "            best_error = total_val_loss\n",
        "            th.save(net.state_dict(), best_model_path)\n",
        "\n",
        "        print(\"Validation loss = {:.2f}\".format(total_val_loss / len(val_loader)))\n",
        "\n",
        "    print(\"Training Finished, took {:.2f}s\".format(time.time() - training_start_time))\n",
        "    \n",
        "    # Load best model\n",
        "    net.load_state_dict(th.load(best_model_path))\n",
        "    \n",
        "    return train_history, val_history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "cJX2anB5cay_",
        "colab_type": "code",
        "outputId": "54a47d0b-b8ed-4e05-b8e4-05f530d7d671",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1819
        }
      },
      "cell_type": "code",
      "source": [
        "net = SimpleConvolutionalNetwork()\n",
        "\n",
        "train_history, val_history = train(net, batch_size=32, n_epochs=10, learning_rate=0.001)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===== HYPERPARAMETERS =====\n",
            "batch_size= 32\n",
            "n_epochs= 10\n",
            "learning_rate= 0.001\n",
            "==============================\n",
            "Epoch 1, 10% \t train_loss: 2.06 took: 0.80s\n",
            "Epoch 1, 20% \t train_loss: 1.82 took: 0.71s\n",
            "Epoch 1, 30% \t train_loss: 1.60 took: 0.80s\n",
            "Epoch 1, 40% \t train_loss: 1.60 took: 1.00s\n",
            "Epoch 1, 50% \t train_loss: 1.52 took: 0.99s\n",
            "Epoch 1, 60% \t train_loss: 1.43 took: 0.98s\n",
            "Epoch 1, 70% \t train_loss: 1.40 took: 1.01s\n",
            "Epoch 1, 80% \t train_loss: 1.37 took: 0.99s\n",
            "Epoch 1, 90% \t train_loss: 1.35 took: 0.99s\n",
            "Validation loss = 1.35\n",
            "Epoch 2, 10% \t train_loss: 1.26 took: 1.08s\n",
            "Epoch 2, 20% \t train_loss: 1.26 took: 0.99s\n",
            "Epoch 2, 30% \t train_loss: 1.31 took: 0.99s\n",
            "Epoch 2, 40% \t train_loss: 1.28 took: 1.00s\n",
            "Epoch 2, 50% \t train_loss: 1.26 took: 1.00s\n",
            "Epoch 2, 60% \t train_loss: 1.21 took: 1.14s\n",
            "Epoch 2, 70% \t train_loss: 1.24 took: 1.01s\n",
            "Epoch 2, 80% \t train_loss: 1.24 took: 1.01s\n",
            "Epoch 2, 90% \t train_loss: 1.19 took: 0.99s\n",
            "Validation loss = 1.24\n",
            "Epoch 3, 10% \t train_loss: 1.13 took: 1.11s\n",
            "Epoch 3, 20% \t train_loss: 1.16 took: 0.99s\n",
            "Epoch 3, 30% \t train_loss: 1.12 took: 0.98s\n",
            "Epoch 3, 40% \t train_loss: 1.12 took: 0.98s\n",
            "Epoch 3, 50% \t train_loss: 1.14 took: 0.97s\n",
            "Epoch 3, 60% \t train_loss: 1.11 took: 0.99s\n",
            "Epoch 3, 70% \t train_loss: 1.14 took: 0.98s\n",
            "Epoch 3, 80% \t train_loss: 1.13 took: 1.00s\n",
            "Epoch 3, 90% \t train_loss: 1.12 took: 0.99s\n",
            "Validation loss = 1.20\n",
            "Epoch 4, 10% \t train_loss: 1.03 took: 1.09s\n",
            "Epoch 4, 20% \t train_loss: 1.07 took: 0.99s\n",
            "Epoch 4, 30% \t train_loss: 1.06 took: 0.98s\n",
            "Epoch 4, 40% \t train_loss: 1.08 took: 1.01s\n",
            "Epoch 4, 50% \t train_loss: 1.02 took: 0.99s\n",
            "Epoch 4, 60% \t train_loss: 1.05 took: 0.97s\n",
            "Epoch 4, 70% \t train_loss: 1.04 took: 0.95s\n",
            "Epoch 4, 80% \t train_loss: 1.02 took: 1.00s\n",
            "Epoch 4, 90% \t train_loss: 1.03 took: 0.99s\n",
            "Validation loss = 1.18\n",
            "Epoch 5, 10% \t train_loss: 0.98 took: 1.09s\n",
            "Epoch 5, 20% \t train_loss: 0.97 took: 0.97s\n",
            "Epoch 5, 30% \t train_loss: 0.98 took: 1.01s\n",
            "Epoch 5, 40% \t train_loss: 0.98 took: 0.99s\n",
            "Epoch 5, 50% \t train_loss: 0.95 took: 1.00s\n",
            "Epoch 5, 60% \t train_loss: 0.96 took: 0.99s\n",
            "Epoch 5, 70% \t train_loss: 0.97 took: 0.99s\n",
            "Epoch 5, 80% \t train_loss: 0.99 took: 1.01s\n",
            "Epoch 5, 90% \t train_loss: 0.95 took: 0.99s\n",
            "Validation loss = 1.14\n",
            "Epoch 6, 10% \t train_loss: 0.93 took: 1.10s\n",
            "Epoch 6, 20% \t train_loss: 0.87 took: 1.00s\n",
            "Epoch 6, 30% \t train_loss: 0.90 took: 0.98s\n",
            "Epoch 6, 40% \t train_loss: 0.91 took: 0.98s\n",
            "Epoch 6, 50% \t train_loss: 0.89 took: 0.98s\n",
            "Epoch 6, 60% \t train_loss: 0.91 took: 0.99s\n",
            "Epoch 6, 70% \t train_loss: 0.95 took: 1.01s\n",
            "Epoch 6, 80% \t train_loss: 0.90 took: 0.97s\n",
            "Epoch 6, 90% \t train_loss: 0.91 took: 0.98s\n",
            "Validation loss = 1.11\n",
            "Epoch 7, 10% \t train_loss: 0.80 took: 1.10s\n",
            "Epoch 7, 20% \t train_loss: 0.84 took: 0.98s\n",
            "Epoch 7, 30% \t train_loss: 0.83 took: 0.99s\n",
            "Epoch 7, 40% \t train_loss: 0.88 took: 0.99s\n",
            "Epoch 7, 50% \t train_loss: 0.87 took: 0.99s\n",
            "Epoch 7, 60% \t train_loss: 0.83 took: 0.98s\n",
            "Epoch 7, 70% \t train_loss: 0.87 took: 1.00s\n",
            "Epoch 7, 80% \t train_loss: 0.90 took: 0.99s\n",
            "Epoch 7, 90% \t train_loss: 0.86 took: 0.99s\n",
            "Validation loss = 1.12\n",
            "Epoch 8, 10% \t train_loss: 0.77 took: 1.05s\n",
            "Epoch 8, 20% \t train_loss: 0.76 took: 0.97s\n",
            "Epoch 8, 30% \t train_loss: 0.76 took: 0.98s\n",
            "Epoch 8, 40% \t train_loss: 0.80 took: 0.97s\n",
            "Epoch 8, 50% \t train_loss: 0.79 took: 1.01s\n",
            "Epoch 8, 60% \t train_loss: 0.86 took: 0.98s\n",
            "Epoch 8, 70% \t train_loss: 0.81 took: 0.99s\n",
            "Epoch 8, 80% \t train_loss: 0.83 took: 1.00s\n",
            "Epoch 8, 90% \t train_loss: 0.81 took: 0.99s\n",
            "Validation loss = 1.13\n",
            "Epoch 9, 10% \t train_loss: 0.74 took: 1.10s\n",
            "Epoch 9, 20% \t train_loss: 0.71 took: 0.99s\n",
            "Epoch 9, 30% \t train_loss: 0.74 took: 0.98s\n",
            "Epoch 9, 40% \t train_loss: 0.69 took: 0.98s\n",
            "Epoch 9, 50% \t train_loss: 0.76 took: 0.99s\n",
            "Epoch 9, 60% \t train_loss: 0.74 took: 0.99s\n",
            "Epoch 9, 70% \t train_loss: 0.79 took: 0.98s\n",
            "Epoch 9, 80% \t train_loss: 0.77 took: 0.99s\n",
            "Epoch 9, 90% \t train_loss: 0.78 took: 0.99s\n",
            "Validation loss = 1.15\n",
            "Epoch 10, 10% \t train_loss: 0.69 took: 1.11s\n",
            "Epoch 10, 20% \t train_loss: 0.66 took: 0.98s\n",
            "Epoch 10, 30% \t train_loss: 0.66 took: 0.99s\n",
            "Epoch 10, 40% \t train_loss: 0.67 took: 1.03s\n",
            "Epoch 10, 50% \t train_loss: 0.70 took: 1.01s\n",
            "Epoch 10, 60% \t train_loss: 0.71 took: 0.98s\n",
            "Epoch 10, 70% \t train_loss: 0.73 took: 0.99s\n",
            "Epoch 10, 80% \t train_loss: 0.73 took: 0.99s\n",
            "Epoch 10, 90% \t train_loss: 0.70 took: 0.99s\n",
            "Validation loss = 1.16\n",
            "Training Finished, took 137.94s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "UkVKNPtccazC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now, let's look at the evolution of the losses"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "4CUQt-HJcazF",
        "colab_type": "code",
        "outputId": "dceb97e7-4a61-45a1-8db2-032cf0adeeb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "cell_type": "code",
      "source": [
        "plot_losses(train_history, val_history)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mhBCiA1K8Xcw+Ev033DP6EQKNgXx5eDn3b7irzZHozV3nn2XkU93QuVvNhBBC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\nbxEREZdR8RYREXEZFW8RERGX+T91ye5YKh8jigAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd766bc63c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "O90WcUTwt2GU"
      },
      "cell_type": "markdown",
      "source": [
        "### 5. Test the network on the test data\n",
        "\n",
        "\n",
        "We have trained the network for 2 passes over the training dataset.\n",
        "But we need to check if the network has learnt anything at all.\n",
        "\n",
        "We will check this by predicting the class label that the neural network\n",
        "outputs, and checking it against the ground-truth. If the prediction is\n",
        "correct, we add the sample to the list of correct predictions.\n",
        "\n",
        "Okay, first step. Let us display an image from the test set to get familiar.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "V4vljwBlt2GX",
        "outputId": "00315d63-4c21-4abc-c5d1-b64755a00bd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        }
      },
      "cell_type": "code",
      "source": [
        "try:\n",
        "  images, labels = next(iter(test_loader))\n",
        "except EOFError:\n",
        "  pass\n",
        "\n",
        "# print images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print(\"Ground truth:\\n\")\n",
        "\n",
        "print(' '.join('{:>10}'.format(classes[labels[j]]) for j in range(test_batch_size)))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <bound method _DataLoaderIter.__del__ of <torch.utils.data.dataloader._DataLoaderIter object at 0x7fd766323d30>>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\", line 399, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\", line 378, in _shutdown_workers\n",
            "    self.worker_result_queue.get()\n",
            "  File \"/usr/lib/python3.6/multiprocessing/queues.py\", line 337, in get\n",
            "    return _ForkingPickler.loads(res)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/torch/multiprocessing/reductions.py\", line 151, in rebuild_storage_fd\n",
            "    fd = df.detach()\n",
            "  File \"/usr/lib/python3.6/multiprocessing/resource_sharer.py\", line 57, in detach\n",
            "    with _resource_sharer.get_connection(self._id) as conn:\n",
            "  File \"/usr/lib/python3.6/multiprocessing/resource_sharer.py\", line 87, in get_connection\n",
            "    c = Client(address, authkey=process.current_process().authkey)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 493, in Client\n",
            "    answer_challenge(c, authkey)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 732, in answer_challenge\n",
            "    message = connection.recv_bytes(256)         # reject large message\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
            "    buf = self._recv_bytes(maxlength)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
            "    buf = self._recv(4)\n",
            "  File \"/usr/lib/python3.6/multiprocessing/connection.py\", line 383, in _recv\n",
            "    raise EOFError\n",
            "EOFError: \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Ground truth:\n",
            "\n",
            "      frog      truck        cat       deer\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ae8l2ccq0JotFcbROT1PhguUFcWhfOE+Zex12JOfzMi+jI0QDkVbOMkNIpWui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\nfQj78rawsLDoQ/x/Q7ZStQY82lQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd76a8da160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "KpmaQT4Zt2Gn"
      },
      "cell_type": "markdown",
      "source": [
        "Okay, now let us see what the neural network thinks these examples above are:\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "utIfocFrt2Gs",
        "outputId": "9ea9bcf8-ffb5-465b-f56b-31b84840ab24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "outputs = net(images.to(device))\n",
        "print(outputs.size())"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([4, 10])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "6mU42O0Gt2G2"
      },
      "cell_type": "markdown",
      "source": [
        "The outputs are energies for the 10 classes.\n",
        "The higher the energy for a class, the more the network\n",
        "thinks that the image is from that particular class.\n",
        "So, let's get the index of the highest energy:\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "IWTWHHs9t2G5",
        "outputId": "d1456c71-a126-46a0-bac6-92f2b6703d7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        }
      },
      "cell_type": "code",
      "source": [
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "print(\"Predicted:\\n\")\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "\n",
        "print(' '.join('{:>10}'.format(classes[predicted[j]]) for j in range(test_batch_size)))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted:\n",
            "\n",
            "      frog      truck        cat        car\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ae8l2ccq0JotFcbROT1PhguUFcWhfOE+Zex12JOfzMi+jI0QDkVbOMkNIpWui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\nfQj78rawsLDoQ/x/Q7ZStQY82lQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd766b88c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "AUpCEAOTt2HK"
      },
      "cell_type": "markdown",
      "source": [
        "The results seem pretty good.\n",
        "\n",
        "Let us look at how the network performs on the whole test set.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "LI6JtYwTt2HM",
        "outputId": "a99e932b-47a2-42d1-ddcf-48a9e9678323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "def dataset_accuracy(net, data_loader, name=\"\"):\n",
        "    net = net.to(device)\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in data_loader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum()\n",
        "    accuracy = 100 * float(correct) / total\n",
        "    print('Accuracy of the network on the {} {} images: {:.2f} %'.format(total, name, accuracy))\n",
        "\n",
        "def train_set_accuracy(net):\n",
        "    dataset_accuracy(net, train_loader, \"train\")\n",
        "\n",
        "def val_set_accuracy(net):\n",
        "    dataset_accuracy(net, val_loader, \"validation\")  \n",
        "    \n",
        "def test_set_accuracy(net):\n",
        "    dataset_accuracy(net, test_loader, \"test\")\n",
        "\n",
        "def compute_accuracy(net):\n",
        "    train_set_accuracy(net)\n",
        "    val_set_accuracy(net)\n",
        "    test_set_accuracy(net)\n",
        "    \n",
        "print(\"Computing accuracy...\")\n",
        "compute_accuracy(net)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computing accuracy...\n",
            "Accuracy of the network on the 30000 train images: 72.61 %\n",
            "Accuracy of the network on the 20000 validation images: 62.45 %\n",
            "Accuracy of the network on the 5000 test images: 62.80 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "iGGyra-4t2HW"
      },
      "cell_type": "markdown",
      "source": [
        "That initial 59.78 % on the test set of images looks waaay better than chance, which is 10% accuracy (randomly picking\n",
        "a class out of 10 classes).\n",
        "Seems like the network learnt something.\n",
        "As a baseline, a linear model achieves around 30% accuracy.\n",
        "\n",
        "What are the classes that performed well, and the classes that did not perform well?\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "deletable": true,
        "editable": true,
        "id": "rkim9_INt2HY",
        "outputId": "be1b3852-b20e-41f8-98d1-365f910a11d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "def accuracy_per_class(net):\n",
        "    net = net.to(device)\n",
        "    n_classes = 10\n",
        "    # (real, predicted)\n",
        "    confusion_matrix = np.zeros((n_classes, n_classes), dtype=np.int64)\n",
        "\n",
        "    for images, labels in test_loader:\n",
        "        images, labels = images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        for i in range(test_batch_size):\n",
        "            confusion_matrix[labels[i], predicted[i]] += 1\n",
        "            label = labels[i]\n",
        "\n",
        "    print(\"{:<10} {:^10}\".format(\"Class\", \"Accuracy (%)\"))\n",
        "    for i in range(n_classes):\n",
        "        class_total = confusion_matrix[i, :].sum()\n",
        "        class_correct = confusion_matrix[i, i]\n",
        "        percentage_correct = 100.0 * float(class_correct) / class_total\n",
        "        \n",
        "        print('{:<10} {:^10.2f}'.format(classes[i], percentage_correct))\n",
        "    return confusion_matrix\n",
        "\n",
        "confusion_matrix = accuracy_per_class(net)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class      Accuracy (%)\n",
            "plane        70.29   \n",
            "car          82.38   \n",
            "bird         36.13   \n",
            "cat          39.44   \n",
            "deer         57.20   \n",
            "dog          55.53   \n",
            "frog         78.62   \n",
            "horse        64.44   \n",
            "ship         76.19   \n",
            "truck        68.23   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "AZKLymOacazg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Confusion Matrix"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "ekJHz3vpcazg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's look at what type of error our networks makes... \n",
        "It seems that our network is pretty good at classifying ships,\n",
        "but has some difficulties to differentiate cats and dogs.\n",
        "Also, it classifies a lot of trucks as cars."
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "1aYMqD1Ocazi",
        "colab_type": "code",
        "outputId": "31c75d14-aa76-4ba9-c6b5-a288d556e47f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1153
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot normalized confusion matrix\n",
        "plot_confusion_matrix(confusion_matrix, classes, normalize=True,\n",
        "                      title='Normalized confusion matrix')\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plot_confusion_matrix(confusion_matrix, classes,\n",
        "                      title='Confusion matrix, without normalization')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sxNs/kJjIsBrvz2Rrf/5nwniS7fpz6iRLTq9K+jPMSf1ZnWO++NsVdGrfin4h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HXRhZx2srBkyRLGjh3L2rVrqVWrFlM+/pCPPvroMA8Ih3cakdKnB3NmdiK1bw+io6N56cUX+ODd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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd766b5b550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w70w0+9PpVKhclRuLFE1alpCQQPGSpfh0wXJKlynH2Hfeci7g9fLL4D+HaBjo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\nZma15mTFzMzMas3JipmZmdXa/wGXGV7xxA/SWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd766b5b358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "MVv-mV8Pt2Hs"
      },
      "cell_type": "markdown",
      "source": [
        "# III. Exploring CNN Architectures\n",
        "\n",
        "Now, it is your turn to build a Convolutional Neural Network. The goal of this section is to explore different CNN architectures and set hyperparameters in order to obtain the best accuracy on the **test** set!\n",
        "\n",
        "The network that you have to tweak is called **MyConvolutionalNetwork**.\n",
        "\n",
        "You can start changing the batch_size, number of epochs and then try adding more convolutional layers."
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "h1blK9eicazo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### PyTorch functions to build the network\n",
        "- **nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0):** Convolution layer. You can read the full documentation [here](http://pytorch.org/docs/master/nn.html#conv2d)\n",
        "\n",
        "- **nn.MaxPool2d(kernel_size, stride=None, padding=0):** Max pooling layer. You can read the full documentation [here](http://pytorch.org/docs/master/nn.html#maxpool2d)\n",
        "\n",
        "- **F.relu(Z1):** computes the element-wise ReLU of Z1 (which can be of any shape). You can read the full documentation [here.](http://pytorch.org/docs/master/nn.html#torch.nn.ReLU)\n",
        "\n",
        "- **x.view(new_shape)**: Returns a new tensor with the same data but different size. It is the equivalent of numpy function *reshape* (Gives a new shape to an array without changing its data.). You can read the full documentation [here.](http://pytorch.org/docs/master/tensors.html#torch.Tensor.view)\n",
        "\n",
        "- **nn.Linear(in_features, out_features):** Applies a linear transformation to the incoming data: $y = Ax + b$, it is also called a fully connected (fc) layer. You can read the full documentation [here.](http://pytorch.org/docs/master/nn.html#linear-layers)"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "a8-lKBaacazp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Convolution Formulas**:\n",
        "\n",
        "The formulas relating the output shape $(C_2, H_2, W_2)$ of the convolution to the input shape $(C_1, H_1, W_1)$ are:\n",
        "\n",
        "\n",
        "$$ H_2 = \\lfloor \\frac{H_1 - kernel\\_size + 2 \\times padding}{stride} \\rfloor +1 $$\n",
        "\n",
        "$$ W_2 = \\lfloor \\frac{W_1 - kernel\\_size + 2 \\times padding}{stride} \\rfloor +1 $$\n",
        "\n",
        "$$ C_2 = \\text{number of filters used in the convolution}$$\n",
        "\n",
        "NOTE: $C_2 = C_1$ in the case of max pooling\n",
        "\n",
        "where:\n",
        "- $H_2$: height of the output volume  \n",
        "- $W_2$: width of the output volume  \n",
        "- $C_1$: in_channels, number of channels in the input volume\n",
        "- $C_2$: out_channels"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "acppf3nkcazr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_output_size(in_size, kernel_size, stride=1, padding=0):\n",
        "    \"\"\"\n",
        "    Get the output size given all the parameters of the convolution\n",
        "    :param in_size: (int) input size\n",
        "    :param kernel_size: (int)\n",
        "    :param stride: (int)\n",
        "    :param paddind: (int)\n",
        "    :return: (int)\n",
        "    \"\"\"\n",
        "    return int((in_size - kernel_size + 2 * padding) / stride) + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "SEsbZoTOcazu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Example of use of helper method get_output_size() \n",
        "\n",
        "Let's assume you have an *input volume of size 3x32x32* (where 3 is the number of channels)\n",
        "and you use a 2D convolution with the following parameters:\n",
        "\n",
        "```python\n",
        "conv1 = nn.Conv2d(3, 18, kernel_size=7, stride=2, padding=1)\n",
        "```\n",
        "then, the size of the output volume is 18x?x? (because we have 18 filters) where ? is given by the convolution formulas (see above).\n",
        "\n",
        "**get_output_size()** function allows to compute that size:\n",
        "\n",
        "```\n",
        "out_size = get_output_size(in_size=32, kernel_size=7, stride=2, padding=1)\n",
        "print(out_size) # prints 14\n",
        "```\n",
        "\n",
        "That is to say, *the output volume is 18x14x14*"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "2JFQ1wgKcazv",
        "colab_type": "code",
        "outputId": "ff2e945a-b7ee-408d-ed93-e9ef6781385f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "out_size = get_output_size(in_size=32, kernel_size=3, stride=1, padding=1)\n",
        "print(out_size)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "wviV5iQIcazz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Below is the neural network you have to edit:"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "fnKUPUDTcaz1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class MyConvolutionalNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MyConvolutionalNetwork, self).__init__()\n",
        "        \n",
        "        self.conv1 = nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "\n",
        "        #### START CODE: ADD NEW LAYERS ####\n",
        "        # (do not forget to update `flattened_size`:\n",
        "        # the input size of the first fully connected layer self.fc1)\n",
        "        self.conv2 = nn.Conv2d(18, 36, kernel_size=3, stride=1, padding=1)\n",
        "        self.conv3 = nn.Conv2d(36, 72, kernel_size=3, stride=1, padding=1)\n",
        "        self.conv4 = nn.Conv2d(72, 144, kernel_size=3, stride=1, padding=1)\n",
        "        self.conv5 = nn.Conv2d(144, 288, kernel_size=3, stride=1, padding=1)\n",
        "\n",
        "   \n",
        "        # Size of the output of the last convolution:\n",
        "        self.flattened_size = 288*4*4\n",
        "        ### END CODE ###\n",
        "        \n",
        "        self.fc1 = nn.Linear(self.flattened_size, 64)\n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward pass,\n",
        "        x shape is (batch_size, 3, 32, 32)\n",
        "        (color channel first)\n",
        "        in the comments, we omit the batch_size in the shape\n",
        "        \"\"\"\n",
        "        # shape : 3x32x32 -> 18x32x32\n",
        "        x = F.relu(self.conv1(x))\n",
        "        # 18x32x32 -> 18x16x16\n",
        "        x = self.pool(x)\n",
        "        \n",
        "        #### START CODE: USE YOUR NEW LAYERS HERE ####\n",
        "        x = F.relu(self.conv2(x))\n",
        "        x=self.pool(x)\n",
        "        x = F.relu(self.conv3(x))\n",
        "        x=self.pool(x)\n",
        "        x = F.relu(self.conv4(x))\n",
        "        \n",
        "        x = F.relu(self.conv5(x))\n",
        "        \n",
        "        \n",
        "        \"\"\"x = F.relu(self.conv5(x))\n",
        "        \n",
        "        x = F.relu(self.conv6(x))\n",
        "        \n",
        "        x = F.relu(self.conv7(x))\"\"\"\n",
        "        \n",
        "        #### END CODE ####\n",
        "        \n",
        "        # Check the output size\n",
        "        output_size = np.prod(x.size()[1:])\n",
        "        assert output_size == self.flattened_size,\\\n",
        "                \"self.flattened_size is invalid {} != {}\".format(output_size, self.flattened_size)\n",
        "        \n",
        "        # 18x16x16 -> 4608\n",
        "        x = x.view(-1, self.flattened_size)\n",
        "        # 4608 -> 64\n",
        "        x = F.relu(self.fc1(x))\n",
        "        # 64 -> 10\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "scrolled": false,
        "id": "ruLWyTSocaz5",
        "colab_type": "code",
        "outputId": "708028e0-37a6-41b8-a808-ab3576d2f22f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3519
        }
      },
      "cell_type": "code",
      "source": [
        "net = MyConvolutionalNetwork()\n",
        "train_history, val_history = train(net, batch_size=64, n_epochs=20, learning_rate=0.001)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===== HYPERPARAMETERS =====\n",
            "batch_size= 64\n",
            "n_epochs= 20\n",
            "learning_rate= 0.001\n",
            "==============================\n",
            "Epoch 1, 10% \t train_loss: 2.23 took: 0.88s\n",
            "Epoch 1, 20% \t train_loss: 2.01 took: 0.71s\n",
            "Epoch 1, 30% \t train_loss: 1.90 took: 0.75s\n",
            "Epoch 1, 40% \t train_loss: 1.81 took: 0.94s\n",
            "Epoch 1, 50% \t train_loss: 1.69 took: 0.94s\n",
            "Epoch 1, 60% \t train_loss: 1.65 took: 0.93s\n",
            "Epoch 1, 70% \t train_loss: 1.65 took: 0.96s\n",
            "Epoch 1, 80% \t train_loss: 1.60 took: 0.96s\n",
            "Epoch 1, 90% \t train_loss: 1.54 took: 0.96s\n",
            "Validation loss = 1.45\n",
            "Epoch 2, 10% \t train_loss: 1.45 took: 1.11s\n",
            "Epoch 2, 20% \t train_loss: 1.44 took: 0.96s\n",
            "Epoch 2, 30% \t train_loss: 1.45 took: 0.96s\n",
            "Epoch 2, 40% \t train_loss: 1.43 took: 0.97s\n",
            "Epoch 2, 50% \t train_loss: 1.36 took: 0.94s\n",
            "Epoch 2, 60% \t train_loss: 1.33 took: 0.96s\n",
            "Epoch 2, 70% \t train_loss: 1.27 took: 0.95s\n",
            "Epoch 2, 80% \t train_loss: 1.30 took: 0.94s\n",
            "Epoch 2, 90% \t train_loss: 1.25 took: 0.95s\n",
            "Validation loss = 1.22\n",
            "Epoch 3, 10% \t train_loss: 1.19 took: 1.08s\n",
            "Epoch 3, 20% \t train_loss: 1.22 took: 0.95s\n",
            "Epoch 3, 30% \t train_loss: 1.18 took: 0.94s\n",
            "Epoch 3, 40% \t train_loss: 1.15 took: 0.93s\n",
            "Epoch 3, 50% \t train_loss: 1.19 took: 0.93s\n",
            "Epoch 3, 60% \t train_loss: 1.10 took: 0.95s\n",
            "Epoch 3, 70% \t train_loss: 1.12 took: 0.92s\n",
            "Epoch 3, 80% \t train_loss: 1.09 took: 0.95s\n",
            "Epoch 3, 90% \t train_loss: 1.07 took: 0.95s\n",
            "Validation loss = 1.07\n",
            "Epoch 4, 10% \t train_loss: 0.96 took: 1.05s\n",
            "Epoch 4, 20% \t train_loss: 0.95 took: 0.96s\n",
            "Epoch 4, 30% \t train_loss: 1.00 took: 0.92s\n",
            "Epoch 4, 40% \t train_loss: 0.98 took: 0.92s\n",
            "Epoch 4, 50% \t train_loss: 0.99 took: 0.94s\n",
            "Epoch 4, 60% \t train_loss: 0.94 took: 0.94s\n",
            "Epoch 4, 70% \t train_loss: 0.96 took: 0.91s\n",
            "Epoch 4, 80% \t train_loss: 1.01 took: 0.92s\n",
            "Epoch 4, 90% \t train_loss: 0.93 took: 0.95s\n",
            "Validation loss = 0.95\n",
            "Epoch 5, 10% \t train_loss: 0.88 took: 1.09s\n",
            "Epoch 5, 20% \t train_loss: 0.81 took: 0.95s\n",
            "Epoch 5, 30% \t train_loss: 0.85 took: 0.94s\n",
            "Epoch 5, 40% \t train_loss: 0.84 took: 0.95s\n",
            "Epoch 5, 50% \t train_loss: 0.86 took: 0.93s\n",
            "Epoch 5, 60% \t train_loss: 0.86 took: 0.94s\n",
            "Epoch 5, 70% \t train_loss: 0.85 took: 0.94s\n",
            "Epoch 5, 80% \t train_loss: 0.85 took: 0.93s\n",
            "Epoch 5, 90% \t train_loss: 0.83 took: 0.92s\n",
            "Validation loss = 0.91\n",
            "Epoch 6, 10% \t train_loss: 0.73 took: 1.12s\n",
            "Epoch 6, 20% \t train_loss: 0.72 took: 0.95s\n",
            "Epoch 6, 30% \t train_loss: 0.74 took: 0.94s\n",
            "Epoch 6, 40% \t train_loss: 0.73 took: 0.95s\n",
            "Epoch 6, 50% \t train_loss: 0.73 took: 0.95s\n",
            "Epoch 6, 60% \t train_loss: 0.74 took: 0.95s\n",
            "Epoch 6, 70% \t train_loss: 0.72 took: 0.95s\n",
            "Epoch 6, 80% \t train_loss: 0.75 took: 0.95s\n",
            "Epoch 6, 90% \t train_loss: 0.69 took: 0.93s\n",
            "Validation loss = 0.88\n",
            "Epoch 7, 10% \t train_loss: 0.60 took: 1.08s\n",
            "Epoch 7, 20% \t train_loss: 0.62 took: 0.93s\n",
            "Epoch 7, 30% \t train_loss: 0.62 took: 0.95s\n",
            "Epoch 7, 40% \t train_loss: 0.62 took: 0.93s\n",
            "Epoch 7, 50% \t train_loss: 0.63 took: 0.94s\n",
            "Epoch 7, 60% \t train_loss: 0.63 took: 0.94s\n",
            "Epoch 7, 70% \t train_loss: 0.62 took: 0.98s\n",
            "Epoch 7, 80% \t train_loss: 0.65 took: 0.94s\n",
            "Epoch 7, 90% \t train_loss: 0.65 took: 0.94s\n",
            "Validation loss = 0.91\n",
            "Epoch 8, 10% \t train_loss: 0.53 took: 1.11s\n",
            "Epoch 8, 20% \t train_loss: 0.54 took: 0.94s\n",
            "Epoch 8, 30% \t train_loss: 0.53 took: 0.95s\n",
            "Epoch 8, 40% \t train_loss: 0.54 took: 0.96s\n",
            "Epoch 8, 50% \t train_loss: 0.53 took: 0.94s\n",
            "Epoch 8, 60% \t train_loss: 0.55 took: 0.95s\n",
            "Epoch 8, 70% \t train_loss: 0.53 took: 0.95s\n",
            "Epoch 8, 80% \t train_loss: 0.56 took: 0.93s\n",
            "Epoch 8, 90% \t train_loss: 0.56 took: 0.96s\n",
            "Validation loss = 0.88\n",
            "Epoch 9, 10% \t train_loss: 0.41 took: 1.06s\n",
            "Epoch 9, 20% \t train_loss: 0.43 took: 0.94s\n",
            "Epoch 9, 30% \t train_loss: 0.45 took: 0.92s\n",
            "Epoch 9, 40% \t train_loss: 0.46 took: 0.92s\n",
            "Epoch 9, 50% \t train_loss: 0.47 took: 0.95s\n",
            "Epoch 9, 60% \t train_loss: 0.45 took: 0.95s\n",
            "Epoch 9, 70% \t train_loss: 0.46 took: 0.96s\n",
            "Epoch 9, 80% \t train_loss: 0.48 took: 0.94s\n",
            "Epoch 9, 90% \t train_loss: 0.48 took: 0.94s\n",
            "Validation loss = 0.88\n",
            "Epoch 10, 10% \t train_loss: 0.33 took: 1.11s\n",
            "Epoch 10, 20% \t train_loss: 0.35 took: 0.97s\n",
            "Epoch 10, 30% \t train_loss: 0.34 took: 0.95s\n",
            "Epoch 10, 40% \t train_loss: 0.36 took: 0.95s\n",
            "Epoch 10, 50% \t train_loss: 0.35 took: 0.97s\n",
            "Epoch 10, 60% \t train_loss: 0.36 took: 0.95s\n",
            "Epoch 10, 70% \t train_loss: 0.43 took: 0.96s\n",
            "Epoch 10, 80% \t train_loss: 0.39 took: 0.94s\n",
            "Epoch 10, 90% \t train_loss: 0.38 took: 0.94s\n",
            "Validation loss = 0.98\n",
            "Epoch 11, 10% \t train_loss: 0.25 took: 1.07s\n",
            "Epoch 11, 20% \t train_loss: 0.25 took: 0.95s\n",
            "Epoch 11, 30% \t train_loss: 0.26 took: 0.93s\n",
            "Epoch 11, 40% \t train_loss: 0.29 took: 0.94s\n",
            "Epoch 11, 50% \t train_loss: 0.28 took: 0.97s\n",
            "Epoch 11, 60% \t train_loss: 0.29 took: 0.92s\n",
            "Epoch 11, 70% \t train_loss: 0.28 took: 0.96s\n",
            "Epoch 11, 80% \t train_loss: 0.33 took: 0.96s\n",
            "Epoch 11, 90% \t train_loss: 0.35 took: 0.97s\n",
            "Validation loss = 0.99\n",
            "Epoch 12, 10% \t train_loss: 0.18 took: 1.10s\n",
            "Epoch 12, 20% \t train_loss: 0.20 took: 0.92s\n",
            "Epoch 12, 30% \t train_loss: 0.21 took: 0.96s\n",
            "Epoch 12, 40% \t train_loss: 0.20 took: 0.93s\n",
            "Epoch 12, 50% \t train_loss: 0.20 took: 0.95s\n",
            "Epoch 12, 60% \t train_loss: 0.22 took: 0.95s\n",
            "Epoch 12, 70% \t train_loss: 0.23 took: 0.94s\n",
            "Epoch 12, 80% \t train_loss: 0.23 took: 0.95s\n",
            "Epoch 12, 90% \t train_loss: 0.22 took: 0.94s\n",
            "Validation loss = 1.11\n",
            "Epoch 13, 10% \t train_loss: 0.15 took: 1.07s\n",
            "Epoch 13, 20% \t train_loss: 0.15 took: 0.93s\n",
            "Epoch 13, 30% \t train_loss: 0.15 took: 0.94s\n",
            "Epoch 13, 40% \t train_loss: 0.16 took: 0.96s\n",
            "Epoch 13, 50% \t train_loss: 0.19 took: 0.94s\n",
            "Epoch 13, 60% \t train_loss: 0.18 took: 0.95s\n",
            "Epoch 13, 70% \t train_loss: 0.18 took: 0.95s\n",
            "Epoch 13, 80% \t train_loss: 0.20 took: 0.95s\n",
            "Epoch 13, 90% \t train_loss: 0.22 took: 0.96s\n",
            "Validation loss = 1.23\n",
            "Epoch 14, 10% \t train_loss: 0.10 took: 1.07s\n",
            "Epoch 14, 20% \t train_loss: 0.09 took: 0.96s\n",
            "Epoch 14, 30% \t train_loss: 0.11 took: 0.96s\n",
            "Epoch 14, 40% \t train_loss: 0.16 took: 0.93s\n",
            "Epoch 14, 50% \t train_loss: 0.13 took: 0.95s\n",
            "Epoch 14, 60% \t train_loss: 0.15 took: 0.94s\n",
            "Epoch 14, 70% \t train_loss: 0.15 took: 0.95s\n",
            "Epoch 14, 80% \t train_loss: 0.12 took: 0.96s\n",
            "Epoch 14, 90% \t train_loss: 0.16 took: 0.95s\n",
            "Validation loss = 1.34\n",
            "Epoch 15, 10% \t train_loss: 0.09 took: 1.12s\n",
            "Epoch 15, 20% \t train_loss: 0.07 took: 0.95s\n",
            "Epoch 15, 30% \t train_loss: 0.10 took: 0.96s\n",
            "Epoch 15, 40% \t train_loss: 0.13 took: 0.94s\n",
            "Epoch 15, 50% \t train_loss: 0.11 took: 0.94s\n",
            "Epoch 15, 60% \t train_loss: 0.11 took: 0.97s\n",
            "Epoch 15, 70% \t train_loss: 0.14 took: 0.94s\n",
            "Epoch 15, 80% \t train_loss: 0.12 took: 0.93s\n",
            "Epoch 15, 90% \t train_loss: 0.15 took: 0.96s\n",
            "Validation loss = 1.51\n",
            "Epoch 16, 10% \t train_loss: 0.08 took: 1.08s\n",
            "Epoch 16, 20% \t train_loss: 0.10 took: 0.96s\n",
            "Epoch 16, 30% \t train_loss: 0.12 took: 0.95s\n",
            "Epoch 16, 40% \t train_loss: 0.09 took: 0.94s\n",
            "Epoch 16, 50% \t train_loss: 0.09 took: 0.94s\n",
            "Epoch 16, 60% \t train_loss: 0.09 took: 0.97s\n",
            "Epoch 16, 70% \t train_loss: 0.15 took: 0.95s\n",
            "Epoch 16, 80% \t train_loss: 0.15 took: 0.92s\n",
            "Epoch 16, 90% \t train_loss: 0.13 took: 0.96s\n",
            "Validation loss = 1.56\n",
            "Epoch 17, 10% \t train_loss: 0.08 took: 1.07s\n",
            "Epoch 17, 20% \t train_loss: 0.08 took: 0.94s\n",
            "Epoch 17, 30% \t train_loss: 0.09 took: 0.94s\n",
            "Epoch 17, 40% \t train_loss: 0.10 took: 0.92s\n",
            "Epoch 17, 50% \t train_loss: 0.12 took: 0.95s\n",
            "Epoch 17, 60% \t train_loss: 0.10 took: 0.93s\n",
            "Epoch 17, 70% \t train_loss: 0.10 took: 0.95s\n",
            "Epoch 17, 80% \t train_loss: 0.13 took: 0.93s\n",
            "Epoch 17, 90% \t train_loss: 0.14 took: 0.94s\n",
            "Validation loss = 1.64\n",
            "Epoch 18, 10% \t train_loss: 0.07 took: 1.13s\n",
            "Epoch 18, 20% \t train_loss: 0.06 took: 0.96s\n",
            "Epoch 18, 30% \t train_loss: 0.06 took: 0.93s\n",
            "Epoch 18, 40% \t train_loss: 0.09 took: 0.94s\n",
            "Epoch 18, 50% \t train_loss: 0.08 took: 0.95s\n",
            "Epoch 18, 60% \t train_loss: 0.10 took: 0.94s\n",
            "Epoch 18, 70% \t train_loss: 0.11 took: 0.93s\n",
            "Epoch 18, 80% \t train_loss: 0.09 took: 0.95s\n",
            "Epoch 18, 90% \t train_loss: 0.11 took: 0.95s\n",
            "Validation loss = 1.71\n",
            "Epoch 19, 10% \t train_loss: 0.09 took: 1.08s\n",
            "Epoch 19, 20% \t train_loss: 0.05 took: 0.95s\n",
            "Epoch 19, 30% \t train_loss: 0.06 took: 0.92s\n",
            "Epoch 19, 40% \t train_loss: 0.09 took: 0.93s\n",
            "Epoch 19, 50% \t train_loss: 0.07 took: 0.95s\n",
            "Epoch 19, 60% \t train_loss: 0.08 took: 0.92s\n",
            "Epoch 19, 70% \t train_loss: 0.07 took: 0.95s\n",
            "Epoch 19, 80% \t train_loss: 0.07 took: 0.93s\n",
            "Epoch 19, 90% \t train_loss: 0.11 took: 0.94s\n",
            "Validation loss = 1.71\n",
            "Epoch 20, 10% \t train_loss: 0.07 took: 1.09s\n",
            "Epoch 20, 20% \t train_loss: 0.06 took: 0.94s\n",
            "Epoch 20, 30% \t train_loss: 0.06 took: 0.94s\n",
            "Epoch 20, 40% \t train_loss: 0.10 took: 0.94s\n",
            "Epoch 20, 50% \t train_loss: 0.09 took: 0.93s\n",
            "Epoch 20, 60% \t train_loss: 0.10 took: 0.95s\n",
            "Epoch 20, 70% \t train_loss: 0.09 took: 0.93s\n",
            "Epoch 20, 80% \t train_loss: 0.11 took: 0.94s\n",
            "Epoch 20, 90% \t train_loss: 0.08 took: 0.94s\n",
            "Validation loss = 1.84\n",
            "Training Finished, took 272.08s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "u7cgVbkDcaz9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Losses Plot"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "XtXu67qbcaz-",
        "colab_type": "code",
        "outputId": "c626bed8-3a3b-4f7c-a435-51bd07d164dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "cell_type": "code",
      "source": [
        "plot_losses(train_history, val_history)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kS70EGAaMr8Ux5wPfAmitdyilgpVSAVrr3IYI8GQMjgtlSWIGSySpCyEaUaGl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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd766b63438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "TuGKgAMWcaz_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Accuracy of the trained model"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "TWowqQhYca0B",
        "colab_type": "code",
        "outputId": "6e05e76b-c611-4acc-d3e0-bf8f24e882a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "compute_accuracy(net)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 30000 train images: 88.95 %\n",
            "Accuracy of the network on the 20000 validation images: 71.59 %\n",
            "Accuracy of the network on the 5000 test images: 71.94 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "st9f_4opca0F",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Baseline: Simple Convolutional Neural Network (form part II)**\n",
        "\n",
        "<table>\n",
        "<tr>\n",
        "    <td>Accuracy on the test set:</td>\n",
        "    <td>59.98 %</td>\n",
        "</tr>\n",
        "</table>"
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "OKvmb4p-ca0I",
        "colab_type": "code",
        "outputId": "7d3f04cc-fe50-4f0b-d50c-60d874cf5ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "confusion_matrix = accuracy_per_class(net)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class      Accuracy (%)\n",
            "plane        80.74   \n",
            "car          90.69   \n",
            "bird         57.81   \n",
            "cat          55.33   \n",
            "deer         61.93   \n",
            "dog          60.25   \n",
            "frog         74.95   \n",
            "horse        77.58   \n",
            "ship         85.71   \n",
            "truck        74.46   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "deletable": true,
        "editable": true,
        "id": "ih5Pj0WBca0L",
        "colab_type": "code",
        "outputId": "d41fc834-f1ab-4921-871d-4dd8c9986551",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        }
      },
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(confusion_matrix, classes,\n",
        "                      title='Confusion matrix, without normalization')"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jyJYtG9mzZ2fy5MkUKFDgRleVIb2Im3suyNAP8T1j2jjSO/KVzsp4YJW1drb7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\n0eHBRx1Md7XyFSqwKmo5cXFxbNtm2fX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bruC3jIjIaLb8sYPhw4Yw7oP3GDhoiGlZmlQr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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd76a3fb780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "deletable": true,
        "editable": true,
        "id": "7xzIkZqit2IA"
      },
      "cell_type": "markdown",
      "source": [
        "### Going further\n",
        "\n",
        "- [Coursera Course on CNN](https://www.coursera.org/learn/convolutional-neural-networks)\n",
        "- [Standford Course](http://cs231n.stanford.edu/syllabus.html)\n",
        "- [PyTorch Tutorial](http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html)\n",
        "- [How backpropagation works](neuralnetworksanddeeplearning.com/chap2.html) (Michael )\n",
        "\n",
        "If you feel like this was too easy peasy:\n",
        "\n",
        "-Investigate further [optimization methods](https://pytorch.org/docs/stable/optim.html) beyond SGD, and Adam and their parameters.\n",
        "\n",
        "-Look at ways to improve your network using regularization techniques\n",
        "\n",
        "-Look at ways to visualize network activations for model interpretability\n",
        "\n",
        "-Use transfer learning, in order to use torchvision with pretrained=True with some pretrained models\n",
        "\n",
        "\n",
        "Note: This tutorial is based on the [original PyTorch tutorial](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) and was adapted by [Antonin Raffin](http://araffin.github.io/) for the ROB313 course at ENSTA ParisTech. Thanks to Clement Pinard for feedback!"
      ]
    },
    {
      "metadata": {
        "id": "3adwZxTGXTjZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XEFW6M6jZtWk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### More documentation/ questions to explore about Google Colab: \n",
        "\n",
        "-How to connect your Google Drive with Google Colab?\n",
        "\n",
        "-How to import a new notebook and save it to your GDrive?\n",
        "\n",
        "-How to use files which are contained in your GDrive?\n",
        "\n",
        "Some tips [here](https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "## Extras to read later\n",
        "### Visualizing Convolution parameters:\n",
        "[A guide to convolution arithmetic for deep learning](https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md) \n",
        "by Vincent Dumoulin, Francesco Visin \n",
        "\n",
        "\n",
        "### Documentation of autograd and Function: \n",
        "[Autograd](http://pytorch.org/docs/autograd)\n"
      ]
    }
  ]
}