{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN_on_FashionMNIST.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PacktPublishing/Modern-Computer-Vision-with-PyTorch/blob/master/Chapter04/CNN_on_FashionMNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKXHmN72oSr-"
      },
      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '/content/' # This can be any directory you want to download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8quMVIspoXAc"
      },
      "source": [
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCybp42UoYfD"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wf7B5v_oZpV"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeG0gLx4oavL"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,1,28,28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Conv2d(1, 64, kernel_size=3),\n",
        "        nn.MaxPool2d(2),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(64, 128, kernel_size=3),\n",
        "        nn.MaxPool2d(2),\n",
        "        nn.ReLU(),\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(3200, 256),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(256, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "@torch.no_grad()\n",
        "def accuracy(x, y, model):\n",
        "    model.eval()\n",
        "    prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VxMySqHoyUc"
      },
      "source": [
        "def get_data():     \n",
        "    train = FMNISTDataset(tr_images, tr_targets)     \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets)     \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=True)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKIE_sjtpRP6"
      },
      "source": [
        "@torch.no_grad()\n",
        "def val_loss(x, y, model):\n",
        "    model.eval()\n",
        "    prediction = model(x)\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2hKhLHQpSqx"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQmmjw70pUe9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8836519-77c7-4b75-f009-d7fd3cd03e14"
      },
      "source": [
        "!pip install torch_summary\n",
        "from torchsummary import summary\n",
        "model, loss_fn, optimizer = get_model()\n",
        "summary(model, torch.zeros(1,1,28,28));"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch_summary in /usr/local/lib/python3.6/dist-packages (1.4.3)\n",
            "==========================================================================================\n",
            "Layer (type:depth-idx)                   Output Shape              Param #\n",
            "==========================================================================================\n",
            "├─Conv2d: 1-1                            [-1, 64, 26, 26]          640\n",
            "├─MaxPool2d: 1-2                         [-1, 64, 13, 13]          --\n",
            "├─ReLU: 1-3                              [-1, 64, 13, 13]          --\n",
            "├─Conv2d: 1-4                            [-1, 128, 11, 11]         73,856\n",
            "├─MaxPool2d: 1-5                         [-1, 128, 5, 5]           --\n",
            "├─ReLU: 1-6                              [-1, 128, 5, 5]           --\n",
            "├─Flatten: 1-7                           [-1, 3200]                --\n",
            "├─Linear: 1-8                            [-1, 256]                 819,456\n",
            "├─ReLU: 1-9                              [-1, 256]                 --\n",
            "├─Linear: 1-10                           [-1, 10]                  2,570\n",
            "==========================================================================================\n",
            "Total params: 896,522\n",
            "Trainable params: 896,522\n",
            "Non-trainable params: 0\n",
            "Total mult-adds (M): 10.13\n",
            "==========================================================================================\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.45\n",
            "Params size (MB): 3.42\n",
            "Estimated Total Size (MB): 3.87\n",
            "==========================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97CcIWOBpXuw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b16304b-81dd-4a4b-ea15-bab0508a8cc2"
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(5):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9N0n1k0paJx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "2b7a2e49-476c-45b5-f57c-9327eb98a662"
      },
      "source": [
        "epochs = np.arange(5)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with CNN')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with CNN')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeIvkLO8p3ou",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "adc7a483-310d-4b72-8ab4-7ddc713603d3"
      },
      "source": [
        "preds = []\n",
        "ix = 24300\n",
        "for px in range(-5,6):\n",
        "  img = tr_images[ix]/255.\n",
        "  img = img.view(28, 28)\n",
        "  img2 = np.roll(img, px, axis=1)\n",
        "  img3 = torch.Tensor(img2).view(-1,1,28,28).to(device)\n",
        "  np_output = model(img3).cpu().detach().numpy()\n",
        "  pred = np.exp(np_output)/np.sum(np.exp(np_output))\n",
        "  preds.append(pred)\n",
        "  plt.imshow(img2)\n",
        "  plt.title(fmnist.classes[pred[0].argmax()])\n",
        "  plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-YPXmWxqon7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b0df65f-2ee9-4343-fc6b-5dc0f639df14"
      },
      "source": [
        "np.array(preds).shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 1, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xnz-eFGTp4G8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "71714277-f31b-4ebf-fd55-a03984dff7ea"
      },
      "source": [
        "import seaborn as sns\n",
        "fig, ax = plt.subplots(1,1, figsize=(12,10))\n",
        "plt.title('Probability of each class for various translations')\n",
        "sns.heatmap(np.array(preds).reshape(11,10), annot=True, ax=ax, fmt='.2f', xticklabels=fmnist.classes, yticklabels=[str(i)+str(' pixels') for i in range(-5,6)], cmap='gray')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f58fa99b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTsjWPuyqNNU"
      },
      "source": [
        ""
      ],
      "execution_count": 28,
      "outputs": []
    }
  ]
}