PyTorch2_MNIST_Handwritten_digits.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/utkarsh0702/Pytorch/blob/master/PyTorch2_MNIST_Handwritten_digits.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "wNRxDbpWRCRR",
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        "colab": {}
      },
      "source": [
        "import torch\n",
        "from torchvision import datasets, transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "outputId": "e357b687-9b51-4516-fb38-085d2038fdaa"
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      "source": [
        "transform= transforms.Compose([\n",
        "                                     transforms.ToTensor(),\n",
        "                                     transforms.Normalize(0.5,0.5)\n",
        "])\n",
        "\n",
        "#Download and load training and testing data\n",
        "trainset= datasets.MNIST('MNIST/', download=True, train=True, transform=transform)\n",
        "trainloader= torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "testset= datasets.MNIST('MNIST/', 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://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to MNIST/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting MNIST/MNIST/raw/train-images-idx3-ubyte.gz to MNIST/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to MNIST/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting MNIST/MNIST/raw/train-labels-idx1-ubyte.gz to MNIST/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz to MNIST/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz to MNIST/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/pytorch/torch/csrc/utils/tensor_numpy.cpp:141: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWFggpVpTfNu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch import nn, optim\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwJnHm7eUFUB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Classifier(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.fc1= nn.Linear(784, 256)\n",
        "    self.fc2= nn.Linear(256, 128)\n",
        "    self.fc3= nn.Linear(128, 64)\n",
        "    self.fc4= nn.Linear(64, 10)\n",
        "    #Adding the dropout layer\n",
        "    self.dropout= nn.Dropout(0.5)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x= x.view(x.shape[0],-1)\n",
        "    x= self.dropout(F.relu(self.fc1(x)))\n",
        "    x= self.dropout(F.relu(self.fc2(x)))\n",
        "    x= self.dropout(F.relu(self.fc3(x)))\n",
        "    x= F.log_softmax(x, dim=1)\n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfaTgv6iWoar",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a9302702-798f-4a01-bf07-c77678c41330"
      },
      "source": [
        "#Declaring the model, loss function and optimizer\n",
        "model= Classifier()\n",
        "loss_function= nn.NLLLoss()\n",
        "optimizer= optim.SGD(model.parameters(), lr= 0.05)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cy6ywQJaX02o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "4ae7d8bc-28f2-4ef3-ec1d-d0c92d06920e"
      },
      "source": [
        "epochs= 30\n",
        "steps= 0\n",
        "train_losses, test_losses= [],[]\n",
        "for e in range(epochs):\n",
        "  running_loss= 0\n",
        "  for image, label in trainloader:\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    output= model.forward(image)\n",
        "    loss= loss_function(output, label)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    running_loss+= loss.item()\n",
        "  \n",
        "  train_losses.append(running_loss/ len(trainloader))\n",
        "  test_loss=0\n",
        "  accuracy=0\n",
        "  # Turn off gradient\n",
        "  with torch.no_grad():\n",
        "    model.eval()  #stopping the dropout\n",
        "    for image, label in testloader:\n",
        "      output= model.forward(image)\n",
        "      test_loss+= loss_function(output, label)\n",
        "      ps= torch.exp(output)\n",
        "      top_p, top_class= ps.topk(1, dim=1)\n",
        "\n",
        "      equals= top_class==label.view(*top_class.shape)\n",
        "      accuracy+= torch.mean(equals.type(torch.FloatTensor))\n",
        "  \n",
        "  test_losses.append(test_loss/ len(testloader))\n",
        "  model.train()\n",
        "\n",
        "  #Printing the details\n",
        "  print(\"Epochs: {}/{}\".format(e+1, epochs), \"Training Loss: {}..\".format(running_loss/ len(trainloader)), \"Testing Loss: {}..\".format(test_loss/ len(testloader)), \"Test Accuracy: {}\".format(accuracy/ len(testloader)))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epochs: 1/30 Training Loss: 2.761921897752961.. Testing Loss: 1.232506275177002.. Test Accuracy: 0.9076433181762695\n",
            "Epochs: 2/30 Training Loss: 2.441259531832453.. Testing Loss: 0.9538627862930298.. Test Accuracy: 0.9086385369300842\n",
            "Epochs: 3/30 Training Loss: 2.382620894070119.. Testing Loss: 0.7720853686332703.. Test Accuracy: 0.9334195852279663\n",
            "Epochs: 4/30 Training Loss: 2.3274104250773693.. Testing Loss: 0.724652111530304.. Test Accuracy: 0.9485469460487366\n",
            "Epochs: 5/30 Training Loss: 2.2983403465132723.. Testing Loss: 0.6575478911399841.. Test Accuracy: 0.946954607963562\n",
            "Epochs: 6/30 Training Loss: 2.2838383064087013.. Testing Loss: 0.6263765692710876.. Test Accuracy: 0.9516321420669556\n",
            "Epochs: 7/30 Training Loss: 2.27657600137979.. Testing Loss: 0.5977017283439636.. Test Accuracy: 0.9571058750152588\n",
            "Epochs: 8/30 Training Loss: 2.264355171463891.. Testing Loss: 0.6011342406272888.. Test Accuracy: 0.959295392036438\n",
            "Epochs: 9/30 Training Loss: 2.2556796601332074.. Testing Loss: 0.5602821707725525.. Test Accuracy: 0.9614848494529724\n",
            "Epochs: 10/30 Training Loss: 2.2380151052210633.. Testing Loss: 0.5635358691215515.. Test Accuracy: 0.9599920511245728\n",
            "Epochs: 11/30 Training Loss: 2.23538292636241.. Testing Loss: 0.5618467926979065.. Test Accuracy: 0.9652667045593262\n",
            "Epochs: 12/30 Training Loss: 2.214301374421191.. Testing Loss: 0.48486143350601196.. Test Accuracy: 0.9659633636474609\n",
            "Epochs: 13/30 Training Loss: 2.2325492310625656.. Testing Loss: 0.5291182994842529.. Test Accuracy: 0.962579607963562\n",
            "Epochs: 14/30 Training Loss: 2.213359997343661.. Testing Loss: 0.5274627804756165.. Test Accuracy: 0.9645700454711914\n",
            "Epochs: 15/30 Training Loss: 2.2234009641574137.. Testing Loss: 0.4772106111049652.. Test Accuracy: 0.9663614630699158\n",
            "Epochs: 16/30 Training Loss: 2.2184614784427796.. Testing Loss: 0.49396416544914246.. Test Accuracy: 0.9677547812461853\n",
            "Epochs: 17/30 Training Loss: 2.2231197470286763.. Testing Loss: 0.45154279470443726.. Test Accuracy: 0.9682523608207703\n",
            "Epochs: 18/30 Training Loss: 2.216556452230604.. Testing Loss: 0.47032520174980164.. Test Accuracy: 0.9666600227355957\n",
            "Epochs: 19/30 Training Loss: 2.205700162250096.. Testing Loss: 0.4844908118247986.. Test Accuracy: 0.9693471193313599\n",
            "Epochs: 20/30 Training Loss: 2.197426256721716.. Testing Loss: 0.43537935614585876.. Test Accuracy: 0.9706408977508545\n",
            "Epochs: 21/30 Training Loss: 2.2019782363732996.. Testing Loss: 0.41314804553985596.. Test Accuracy: 0.9698447585105896\n",
            "Epochs: 22/30 Training Loss: 2.202905167013343.. Testing Loss: 0.42428943514823914.. Test Accuracy: 0.9736266136169434\n",
            "Epochs: 23/30 Training Loss: 2.2074290634726665.. Testing Loss: 0.4203205406665802.. Test Accuracy: 0.9711385369300842\n",
            "Epochs: 24/30 Training Loss: 2.1885684906546747.. Testing Loss: 0.4280030131340027.. Test Accuracy: 0.972332775592804\n",
            "Epochs: 25/30 Training Loss: 2.1903875392637273.. Testing Loss: 0.4265858232975006.. Test Accuracy: 0.9719347357749939\n",
            "Epochs: 26/30 Training Loss: 2.1880492603600916.. Testing Loss: 0.40355318784713745.. Test Accuracy: 0.9713375568389893\n",
            "Epochs: 27/30 Training Loss: 2.2165711393742673.. Testing Loss: 0.40075060725212097.. Test Accuracy: 0.975119411945343\n",
            "Epochs: 28/30 Training Loss: 2.1820941094650643.. Testing Loss: 0.39032912254333496.. Test Accuracy: 0.9739251732826233\n",
            "Epochs: 29/30 Training Loss: 2.1859904203587757.. Testing Loss: 0.40340864658355713.. Test Accuracy: 0.9753184914588928\n",
            "Epochs: 30/30 Training Loss: 2.1835941695201115.. Testing Loss: 0.3901751935482025.. Test Accuracy: 0.9747213125228882\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGNTnD5-dbx9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8512a53a-7886-4206-d44e-2db14bf1c4d7"
      },
      "source": [
        "# Plotting the graph\n",
        "plt.plot(train_losses, label= \"Training Loss\")\n",
        "plt.plot(test_losses, label= \"Test Loss\")\n",
        "plt.legend(frameon= False)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vq8GoBd3gQKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}