FINAL_FASHION_MNIST_WITH_DROPOUT_MODIFIED.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",
        "outputId": "cdd8680e-7d0c-40f7-f7b7-4aae496eb93c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tcmalloc: large alloc 1073750016 bytes == 0x58ece000 @  0x7fbf5dcf22a4 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",
        "outputId": "fdb84880-f45e-4f44-9cb2-6d85bc888357",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "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": 3,
      "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": "04c478b1-8645-4fea-cf55-731931b6cb06",
        "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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-12-27 13:22:44--  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",
            "2018-12-27 13:22:44 (43.7 MB/s) - ‘helper.py’ saved [2813/2813]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a5FFCtGfIPZS",
        "colab_type": "code",
        "outputId": "0f68d6b4-dbee-4af2-98d5-641cfdfc52d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        }
      },
      "cell_type": "code",
      "source": [
        "import helper\n",
        "images, labels = next(iter(trainloader))\n",
        "helper.imshow(images[0,:]);"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAAE5CAYAAADr4VfxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEE3IAdGEHBDtfwAvNL69pA/9aAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde2772e668>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": "a8ebcb50-14a1-47d9-e9bb-0c553984cc9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        }
      },
      "cell_type": "code",
      "source": [
        "#changing some hyperparamters such as lr = 0.03 from 0.003 and no of epochs to 40 from 30.\n",
        "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=0.03)\n",
        "\n",
        "epochs = 40\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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1/40..  Training Loss: 0.640..  Test Loss: 0.517..  Test Accuracy: 0.810\n",
            "Epoch: 2/40..  Training Loss: 0.459..  Test Loss: 0.444..  Test Accuracy: 0.838\n",
            "Epoch: 3/40..  Training Loss: 0.416..  Test Loss: 0.433..  Test Accuracy: 0.843\n",
            "Epoch: 4/40..  Training Loss: 0.389..  Test Loss: 0.412..  Test Accuracy: 0.853\n",
            "Epoch: 5/40..  Training Loss: 0.370..  Test Loss: 0.397..  Test Accuracy: 0.856\n",
            "Epoch: 6/40..  Training Loss: 0.355..  Test Loss: 0.382..  Test Accuracy: 0.862\n",
            "Epoch: 7/40..  Training Loss: 0.343..  Test Loss: 0.383..  Test Accuracy: 0.864\n",
            "Epoch: 8/40..  Training Loss: 0.332..  Test Loss: 0.361..  Test Accuracy: 0.872\n",
            "Epoch: 9/40..  Training Loss: 0.323..  Test Loss: 0.367..  Test Accuracy: 0.864\n",
            "Epoch: 10/40..  Training Loss: 0.314..  Test Loss: 0.355..  Test Accuracy: 0.872\n",
            "Epoch: 11/40..  Training Loss: 0.306..  Test Loss: 0.359..  Test Accuracy: 0.871\n",
            "Epoch: 12/40..  Training Loss: 0.298..  Test Loss: 0.345..  Test Accuracy: 0.876\n",
            "Epoch: 13/40..  Training Loss: 0.294..  Test Loss: 0.346..  Test Accuracy: 0.874\n",
            "Epoch: 14/40..  Training Loss: 0.286..  Test Loss: 0.343..  Test Accuracy: 0.875\n",
            "Epoch: 15/40..  Training Loss: 0.280..  Test Loss: 0.336..  Test Accuracy: 0.882\n",
            "Epoch: 16/40..  Training Loss: 0.275..  Test Loss: 0.337..  Test Accuracy: 0.878\n",
            "Epoch: 17/40..  Training Loss: 0.270..  Test Loss: 0.345..  Test Accuracy: 0.876\n",
            "Epoch: 18/40..  Training Loss: 0.266..  Test Loss: 0.327..  Test Accuracy: 0.881\n",
            "Epoch: 19/40..  Training Loss: 0.261..  Test Loss: 0.322..  Test Accuracy: 0.887\n",
            "Epoch: 20/40..  Training Loss: 0.256..  Test Loss: 0.324..  Test Accuracy: 0.884\n",
            "Epoch: 21/40..  Training Loss: 0.253..  Test Loss: 0.319..  Test Accuracy: 0.888\n",
            "Epoch: 22/40..  Training Loss: 0.249..  Test Loss: 0.319..  Test Accuracy: 0.885\n",
            "Epoch: 23/40..  Training Loss: 0.244..  Test Loss: 0.325..  Test Accuracy: 0.885\n",
            "Epoch: 24/40..  Training Loss: 0.240..  Test Loss: 0.321..  Test Accuracy: 0.888\n",
            "Epoch: 25/40..  Training Loss: 0.237..  Test Loss: 0.318..  Test Accuracy: 0.887\n",
            "Epoch: 26/40..  Training Loss: 0.233..  Test Loss: 0.331..  Test Accuracy: 0.881\n",
            "Epoch: 27/40..  Training Loss: 0.228..  Test Loss: 0.315..  Test Accuracy: 0.888\n",
            "Epoch: 28/40..  Training Loss: 0.226..  Test Loss: 0.311..  Test Accuracy: 0.889\n",
            "Epoch: 29/40..  Training Loss: 0.222..  Test Loss: 0.313..  Test Accuracy: 0.889\n",
            "Epoch: 30/40..  Training Loss: 0.221..  Test Loss: 0.331..  Test Accuracy: 0.886\n",
            "Epoch: 31/40..  Training Loss: 0.216..  Test Loss: 0.317..  Test Accuracy: 0.890\n",
            "Epoch: 32/40..  Training Loss: 0.214..  Test Loss: 0.315..  Test Accuracy: 0.889\n",
            "Epoch: 33/40..  Training Loss: 0.209..  Test Loss: 0.315..  Test Accuracy: 0.890\n",
            "Epoch: 34/40..  Training Loss: 0.208..  Test Loss: 0.330..  Test Accuracy: 0.884\n",
            "Epoch: 35/40..  Training Loss: 0.205..  Test Loss: 0.308..  Test Accuracy: 0.894\n",
            "Epoch: 36/40..  Training Loss: 0.200..  Test Loss: 0.316..  Test Accuracy: 0.891\n",
            "Epoch: 37/40..  Training Loss: 0.198..  Test Loss: 0.319..  Test Accuracy: 0.887\n",
            "Epoch: 38/40..  Training Loss: 0.198..  Test Loss: 0.313..  Test Accuracy: 0.894\n",
            "Epoch: 39/40..  Training Loss: 0.195..  Test Loss: 0.318..  Test Accuracy: 0.889\n",
            "Epoch: 40/40..  Training Loss: 0.190..  Test Loss: 0.314..  Test Accuracy: 0.891\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",
        "outputId": "143832ed-7842-4e06-99f9-e71de36bbc66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fddcc64c128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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87W9PMXPmdN599w2iohoyZMhNjBhxBzt2bGP79q3MmjWd8PDwcwrkAOPGPUNI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\nDfoP5FW4/9W9dtwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fddc84e5ba8>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 370,
              "height": 248
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "imz4F0fkHS56",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bPUUAxCcHYky",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "print(model.state_dict().keys())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "osdCchj5I9H_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}