FASHION_MNIST_WITHOUT_ACCURACYPREDICTION.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FASHION-MNIST",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "n_mabdQtrZqp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "4041cd1c-bd33-4290-a782-d72e13ba3125"
      },
      "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 == 0x578d4000 @  0x7f06ca2de2a4 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": "Utm1A5LBrjs4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "0ab5de72-f528-45ff-cb33-e313a017e4c7"
      },
      "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": "6VMHnQ7DsCEC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "5b550cf0-3ad8-47f1-b904-b6a88872da0b"
      },
      "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 04:04:15--  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 04:04:15 (51.1 MB/s) - ‘helper.py’ saved [2813/2813]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TsCWHhdDtDn3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "a2bc0b7a-42f0-4cc4-9a4f-1b32548613cc"
      },
      "cell_type": "code",
      "source": [
        "import helper\n",
        "images, labels = next(iter(trainloader))\n",
        "helper.imshow(images[0,:]);"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAAE5CAYAAADr4VfxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb265d6898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "elTGtdyj0y5Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "cafac151-7df7-49b0-8e24-bafd6cde15be"
      },
      "cell_type": "code",
      "source": [
        "print(labels)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([9, 1, 5, 7, 6, 8, 0, 2, 3, 1, 3, 6, 0, 5, 9, 1, 7, 2, 4, 5, 8, 0, 5, 4,\n",
            "        9, 4, 8, 4, 3, 9, 0, 8, 9, 7, 1, 2, 0, 9, 8, 7, 4, 8, 5, 6, 4, 3, 4, 0,\n",
            "        5, 7, 1, 2, 3, 9, 5, 0, 9, 6, 4, 6, 0, 4, 4, 2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Qb15tgp91NPi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bf234c13-85f9-45f5-9c37-dbbf4b7f2103"
      },
      "cell_type": "code",
      "source": [
        "print(images.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([64, 1, 28, 28])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "179KXd1DuCnB",
        "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": "VPj39_Hgvacy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "c72a8ff1-e495-455a-efce-162a50d1309a"
      },
      "cell_type": "code",
      "source": [
        "#using one hidden layer of 256 neurons\n",
        "\n",
        "model = nn.Sequential(nn.Linear(784,256),nn.ReLU(),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 = 5\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",
        "    print(f\"Training loss: {running_loss/len(trainloader)}\")\n",
        "    \n",
        "  \n",
        "  \n",
        "  \n",
        "  "
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training loss: 1.124776228468047\n",
            "Training loss: 0.6745369494406145\n",
            "Training loss: 0.5917439370203629\n",
            "Training loss: 0.5476047661639988\n",
            "Training loss: 0.5191761375998637\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0Tev9Vzv2hAZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "30544bc4-aadc-455d-ecb8-ca4dca392bcc"
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import helper\n",
        "\n",
        "images, labels = next(iter(trainloader))\n",
        "\n",
        "img = images[0].view(1, 784)\n",
        "\n",
        "with torch.no_grad():\n",
        "    logps = model(img)\n",
        "    \n",
        "ps = torch.exp(logps)\n",
        "helper.view_classify(img.resize_(1, 28, 28), ps, version='Fashion')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAADTCAYAAADUIId2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIlFSgxIRkSipQYmISJT+P8wEKyiMQ6rHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb24a7e438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}