Working_with_the_FMNIST_dataset.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gnadathur/Coding/blob/master/Working_with_the_FMNIST_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXmCHcwKs6rd"
      },
      "source": [
        "# Working with the FMNIST dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzCCniVwNTdp"
      },
      "source": [
        "# Setting seeds to try and ensure we have the same results - this is not guaranteed across PyTorch releases.\n",
        "import torch\n",
        "torch.manual_seed(0)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False\n",
        "\n",
        "import numpy as np\n",
        "np.random.seed(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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          ]
        },
        "outputId": "075bdbde-5147-47fa-904b-bad6cb50d6d9"
      },
      "source": [
        "from torchvision import datasets, transforms\n",
        "import torch.nn.functional as F\n",
        "from torch import nn\n",
        "\n",
        "mean, std = (0.5,), (0.5,)\n",
        "\n",
        "# Create a transform and normalise data\n",
        "transform = transforms.Compose([transforms.ToTensor(),\n",
        "                                transforms.Normalize(mean, std)\n",
        "                              ])\n",
        "\n",
        "# Download FMNIST training dataset and load training data\n",
        "trainset = datasets.FashionMNIST('~/.pytorch/FMNIST/', download=True, train=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "\n",
        "# Download FMNIST test dataset and load test data\n",
        "testset = datasets.FashionMNIST('~/.pytorch/FMNIST/', download=True, train=False, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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-images-idx3-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /root/.pytorch/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw\n",
            "\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/train-labels-idx1-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/29515 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /root/.pytorch/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw\n",
            "\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-images-idx3-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4422102 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "2f3e1b8785774b108d26f3a31fe16f1c"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /root/.pytorch/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/5148 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /root/.pytorch/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/.pytorch/FMNIST/FashionMNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqMqFbIVrbFH"
      },
      "source": [
        "class FMNIST(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(784, 256)\n",
        "    self.fc2 = nn.Linear(256,64)\n",
        "    self.fc3 = nn.Linear(64,10)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    x = x.view(x.shape[0], -1)\n",
        "    \n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = F.relu(self.fc2(x))\n",
        "    x = self.fc3(x)\n",
        "    x = F.log_softmax(x, dim=1)\n",
        "    \n",
        "    return x\n",
        "\n",
        "model = FMNIST()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TL;DR: softmax + NLL means Higher the probability, lower the loss**\n",
        "\n",
        "Reference: https://ljvmiranda921.github.io/notebook/2017/08/13/softmax-and-the-negative-log-likelihood/\n",
        "\n",
        "why do we use log_softmax and NLL in tandem ?\n",
        "* softmax reduces a vector of float into a probability distribution [0-1].\n",
        "* NLL is a loss function that takes values [0-1] and output higher results (trends to inf) for lower values and lower results (trends to 0) as inputs moves closer to 1. \n"
      ],
      "metadata": {
        "id": "q4K4vom1GIlO"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNNyI5YRZ7H1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85f52946-5081-452c-a41d-beab05d2294c"
      },
      "source": [
        "from torch import optim\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "num_epochs = 3\n",
        "\n",
        "for i in range(num_epochs):\n",
        "    cum_loss = 0\n",
        "\n",
        "    for images, labels in trainloader:\n",
        "        optimizer.zero_grad()\n",
        "        output = model(images)\n",
        "        loss = criterion(output, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        cum_loss += loss.item()\n",
        "     \n",
        "    print(f\"Training loss: {cum_loss/len(trainloader)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 0.9765237141519721\n",
            "Training loss: 0.5430277362148136\n",
            "Training loss: 0.4775423413432483\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWYw7ZOzsS8U"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "images, labels = next(iter(testloader))\n",
        "\n",
        "test_image_id = 31\n",
        "img = images[test_image_id].view(1, 784) \n",
        "\n",
        "with torch.no_grad():\n",
        "    logps = model(img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBf23XrtqrB6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "42d8f3b8-49cf-4fb2-ce70-4e9eaf0287a2"
      },
      "source": [
        "ps = torch.exp(logps)\n",
        "nps = ps.numpy()[0]\n",
        "FMNIST_labels = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sport Shoes','Bag','Ankle Boot']\n",
        "plt.xticks(np.arange(10),labels=FMNIST_labels,rotation='vertical')\n",
        "plt.bar(np.arange(10), nps)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 10 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7gY5hARpOp4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c1c94ecc-f067-4ae9-ef20-1c76ead52e0e"
      },
      "source": [
        "def denormalize(tensor):\n",
        "  tensor = tensor*0.5 + 0.5\n",
        "  return tensor\n",
        "  \n",
        "img = img.view(28,-1)\n",
        "img = denormalize(img)\n",
        "plt.imshow(img,cmap='gray')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6472511210>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDtlx2QXMdLT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}