FINAL_FASHION_MNIST_WITH_DROPOUT.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FINAL_FASHION_MNIST_WITH_DROPOUT",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "rZFoBK4AIAyi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1d138705-dfb3-41d1-c291-b8dc725c82a2"
      },
      "cell_type": "code",
      "source": [
        "# http://pytorch.org/\n",
        "from os.path import exists\n",
        "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag\n",
        "platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())\n",
        "cuda_output = !ldconfig -p|grep cudart.so|sed -e 's/.*\\.\\([0-9]*\\)\\.\\([0-9]*\\)$/cu\\1\\2/'\n",
        "accelerator = cuda_output[0] if exists('/dev/nvidia0') else 'cpu'\n",
        "\n",
        "!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-{platform}-linux_x86_64.whl torchvision\n",
        "import torch"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tcmalloc: large alloc 1073750016 bytes == 0x5881a000 @  0x7f4f5e3cc2a4 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HlflMzNEIJQK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "a28c6b34-3bb6-447c-f0ec-d2d8ae642222"
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "# Define a transform to normalize the data\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "# Download and load the training data\n",
        "trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "\n",
        "# Download and load the test data\n",
        "testset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=False, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "t6sN5E4eILaU",
        "colab_type": "code",
        "outputId": "b8b9c9ba-61bf-419d-c95a-2c0e1a8ea998",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        }
      },
      "cell_type": "code",
      "source": [
        "!wget -c https://raw.githubusercontent.com/udacity/deep-learning-v2-pytorch/master/intro-to-pytorch/helper.py"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-12-26 09:30:12--  https://raw.githubusercontent.com/udacity/deep-learning-v2-pytorch/master/intro-to-pytorch/helper.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2813 (2.7K) [text/plain]\n",
            "Saving to: ‘helper.py’\n",
            "\n",
            "\rhelper.py             0%[                    ]       0  --.-KB/s               \rhelper.py           100%[===================>]   2.75K  --.-KB/s    in 0s      \n",
            "\n",
            "2018-12-26 09:30:13 (59.8 MB/s) - ‘helper.py’ saved [2813/2813]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a5FFCtGfIPZS",
        "colab_type": "code",
        "outputId": "3c482ecc-bc4a-4b5f-bbb8-81003d40cef6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "cell_type": "code",
      "source": [
        "import helper\n",
        "images, labels = next(iter(trainloader))\n",
        "helper.imshow(images[0,:]);"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fC9Y1ZjisfdaZTNqrmibFqqVpoSaqUhH1v0eb1y51RMXe1nr1e7m7F6m8ZtU6tcpzr9bn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\nGnqQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd911f89a58>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 231,
              "height": 231
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hses2CPXIQ0L",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "from torch import optim\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "luq5s2rjITql",
        "colab_type": "code",
        "outputId": "f05e5f2b-7fbd-4362-871c-5181ee9e5f87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "cell_type": "code",
      "source": [
        "model = nn.Sequential(nn.Linear(784,256),nn.ReLU(),nn.Dropout(p=0.2),nn.Linear(256,10),nn.LogSoftmax(dim=1))\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "\n",
        "optimizer = optim.SGD(model.parameters(),lr=0.003)\n",
        "\n",
        "epochs = 30\n",
        "steps = 0\n",
        "train_losses, test_losses = [], []\n",
        "\n",
        "\n",
        "for e in range(epochs):\n",
        "  running_loss = 0\n",
        "  for images , labels in trainloader:\n",
        "    optimizer.zero_grad()\n",
        "  \n",
        "    images = images.flatten(start_dim=1)\n",
        "  \n",
        "    output = model(images)\n",
        "  \n",
        "    loss = criterion(output,labels)\n",
        "  \n",
        "    loss.backward()\n",
        "  \n",
        "    optimizer.step()\n",
        "    \n",
        "    running_loss += loss.item()\n",
        "  else:\n",
        "    test_loss = 0\n",
        "    accuracy = 0\n",
        "    with torch.no_grad():\n",
        "      model.eval()\n",
        "      for images , labels in testloader:\n",
        "        images = images.flatten(start_dim=1)\n",
        "        log_ps = model(images)\n",
        "        test_loss += criterion(log_ps,labels)\n",
        "        \n",
        "        \n",
        "        ps = torch.exp(log_ps)\n",
        "        top_p , top_class = ps.topk(1,dim=1)\n",
        "        equals = top_class == labels.view(*top_class.shape)\n",
        "        accuracy += torch.mean(equals.type(torch.FloatTensor))    \n",
        "    model.train()\n",
        "    train_losses.append(running_loss/len(trainloader))\n",
        "    test_losses.append(test_loss/len(testloader))    \n",
        "    print(\"Epoch: {}/{}.. \".format(e+1, epochs),\"Training Loss: {:.3f}.. \".format(running_loss/len(trainloader)),\"Test Loss: {:.3f}.. \".format(test_loss/len(testloader)),\"Test Accuracy: {:.3f}\".format(accuracy/len(testloader)))\n",
        "    "
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/30..  Training Loss: 1.175..  Test Loss: 0.779..  Test Accuracy: 0.742\n",
            "Epoch: 2/30..  Training Loss: 0.716..  Test Loss: 0.653..  Test Accuracy: 0.769\n",
            "Epoch: 3/30..  Training Loss: 0.631..  Test Loss: 0.601..  Test Accuracy: 0.786\n",
            "Epoch: 4/30..  Training Loss: 0.584..  Test Loss: 0.566..  Test Accuracy: 0.801\n",
            "Epoch: 5/30..  Training Loss: 0.552..  Test Loss: 0.545..  Test Accuracy: 0.806\n",
            "Epoch: 6/30..  Training Loss: 0.530..  Test Loss: 0.526..  Test Accuracy: 0.810\n",
            "Epoch: 7/30..  Training Loss: 0.513..  Test Loss: 0.511..  Test Accuracy: 0.816\n",
            "Epoch: 8/30..  Training Loss: 0.499..  Test Loss: 0.502..  Test Accuracy: 0.819\n",
            "Epoch: 9/30..  Training Loss: 0.486..  Test Loss: 0.490..  Test Accuracy: 0.822\n",
            "Epoch: 10/30..  Training Loss: 0.475..  Test Loss: 0.482..  Test Accuracy: 0.828\n",
            "Epoch: 11/30..  Training Loss: 0.466..  Test Loss: 0.476..  Test Accuracy: 0.829\n",
            "Epoch: 12/30..  Training Loss: 0.458..  Test Loss: 0.469..  Test Accuracy: 0.830\n",
            "Epoch: 13/30..  Training Loss: 0.451..  Test Loss: 0.463..  Test Accuracy: 0.834\n",
            "Epoch: 14/30..  Training Loss: 0.444..  Test Loss: 0.456..  Test Accuracy: 0.837\n",
            "Epoch: 15/30..  Training Loss: 0.438..  Test Loss: 0.451..  Test Accuracy: 0.838\n",
            "Epoch: 16/30..  Training Loss: 0.433..  Test Loss: 0.449..  Test Accuracy: 0.839\n",
            "Epoch: 17/30..  Training Loss: 0.428..  Test Loss: 0.443..  Test Accuracy: 0.841\n",
            "Epoch: 18/30..  Training Loss: 0.423..  Test Loss: 0.438..  Test Accuracy: 0.842\n",
            "Epoch: 19/30..  Training Loss: 0.417..  Test Loss: 0.436..  Test Accuracy: 0.843\n",
            "Epoch: 20/30..  Training Loss: 0.412..  Test Loss: 0.432..  Test Accuracy: 0.844\n",
            "Epoch: 21/30..  Training Loss: 0.409..  Test Loss: 0.427..  Test Accuracy: 0.846\n",
            "Epoch: 22/30..  Training Loss: 0.404..  Test Loss: 0.426..  Test Accuracy: 0.846\n",
            "Epoch: 23/30..  Training Loss: 0.401..  Test Loss: 0.421..  Test Accuracy: 0.848\n",
            "Epoch: 24/30..  Training Loss: 0.397..  Test Loss: 0.419..  Test Accuracy: 0.848\n",
            "Epoch: 25/30..  Training Loss: 0.395..  Test Loss: 0.417..  Test Accuracy: 0.849\n",
            "Epoch: 26/30..  Training Loss: 0.392..  Test Loss: 0.415..  Test Accuracy: 0.849\n",
            "Epoch: 27/30..  Training Loss: 0.388..  Test Loss: 0.411..  Test Accuracy: 0.852\n",
            "Epoch: 28/30..  Training Loss: 0.386..  Test Loss: 0.409..  Test Accuracy: 0.850\n",
            "Epoch: 29/30..  Training Loss: 0.382..  Test Loss: 0.408..  Test Accuracy: 0.853\n",
            "Epoch: 30/30..  Training Loss: 0.380..  Test Loss: 0.409..  Test Accuracy: 0.852\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JM33_3p58ZhP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b0ImkWz3-N2u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e3aff62a-d92f-44ae-b19e-56f8885757bb"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(train_losses, label='Training loss')\n",
        "plt.plot(test_losses, label='Validation loss')\n",
        "plt.legend(frameon=False)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd90db9ee10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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em19EYVExkRF1X3NUREREJFiioz3rky9YMJfMzAxOPHE0+fn5zJv3Afv37+OSSy6vtIKM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\nWMiUmiF9bkmAT6YKpHIbK5PJFyuMTWUYm8osvyacFVGHEmEGe0MMJsL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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd911e35860>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 370,
              "height": 248
            }
          }
        }
      ]
    }
  ]
}