pytorch_mlp_mnist.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/foxtrotmike/CS909/blob/master/pytorch_mlp_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "RyCPvikqbmID",
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      "source": [
        "# Simple code for neural network training in PyTorch\n",
        "\n",
        "by Fayyaz Minhas\n",
        "\n",
        "Here, we create a simple neural network (multilayered Perceptron) with PyTorch. "
      ]
    },
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        }
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.datasets as dsets\n",
        "import torchvision.transforms as transforms\n",
        "from torch.autograd import Variable\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "USE_CUDA = torch.cuda.is_available() \n",
        "from torch.autograd import Variable\n",
        "\n",
        "def cuda(v):\n",
        "    if USE_CUDA:\n",
        "        return v.cuda()\n",
        "    return v\n",
        "def toTensor(v,dtype = torch.float,requires_grad = False):       \n",
        "    return cuda(Variable(torch.tensor(v)).type(dtype).requires_grad_(requires_grad))\n",
        "def toNumpy(v):\n",
        "    if USE_CUDA:\n",
        "        return v.detach().cpu().numpy()\n",
        "    return v.detach().numpy()\n",
        "    \n",
        "# Hyper Parameters \n",
        "input_size = 784 #number of features\n",
        "hidden_size = 500\n",
        "num_classes = 10\n",
        "num_epochs = 5\n",
        "batch_size = 100\n",
        "learning_rate = 0.001\n",
        "\n",
        "# MNIST Dataset \n",
        "train_dataset = dsets.MNIST(root='./data', \n",
        "                            train=True, \n",
        "                            transform=transforms.ToTensor(),  \n",
        "                            download=True)\n",
        "\n",
        "test_dataset = dsets.MNIST(root='./data', \n",
        "                           train=False, \n",
        "                           transform=transforms.ToTensor())\n",
        "\n",
        "# Data Loader (Input Pipeline)\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
        "                                           batch_size=batch_size, \n",
        "                                           shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=False)\n",
        "\n",
        "# Neural Network Model (1 hidden layer)\n",
        "class Net(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, num_classes):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_size, hidden_size) \n",
        "        self.relu = nn.ReLU()\n",
        "        self.fc2 = nn.Linear(hidden_size, num_classes)\n",
        "    \n",
        "    def forward(self, x):\n",
        "        out = self.fc1(x)\n",
        "        out = self.relu(out)\n",
        "        out = self.fc2(out)\n",
        "        return out\n",
        "    \n",
        "model = cuda(Net(input_size, hidden_size, num_classes))\n",
        "\n",
        "    \n",
        "# Loss and Optimizer\n",
        "criterion = nn.CrossEntropyLoss()  #loss function\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \n",
        "\n",
        "# Train the Model\n",
        "model.train() #set the mode to training\n",
        "for epoch in tqdm(range(num_epochs)):\n",
        "    for i, (images, labels) in tqdm(enumerate(train_loader)):  #pick a batch\n",
        "        # Convert torch tensor to Variable\n",
        "        images = toTensor(images.view(-1, 28*28))\n",
        "        labels = toTensor(labels,dtype=torch.long)\n",
        "        \n",
        "        # Forward + Backward + Optimize\n",
        "        optimizer.zero_grad()  # zero the gradient buffer\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
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              "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
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        {
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
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          },
          "metadata": {
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        },
        {
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          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  from ipykernel import kernelapp as app\n"
          ],
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        },
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lybWpmMBcPnu",
        "colab_type": "code",
        "outputId": "ff3bf0e7-cc7b-4f53-f751-5d291c391042",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Test the Model\n",
        "model.eval() #set the mode to evaluation\n",
        "correct = 0\n",
        "total = 0\n",
        "for images, labels in test_loader:\n",
        "    images = toTensor(images.view(-1, 28*28))\n",
        "    outputs = model(images)\n",
        "    _, predicted = torch.max(outputs.data, 1)\n",
        "    total += labels.size(0)\n",
        "    correct += (toNumpy(predicted) == toNumpy(labels)).sum()\n",
        "\n",
        "print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))\n",
        "\n",
        "# Save the Model\n",
        "torch.save(model.state_dict(), 'model.pkl')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  from ipykernel import kernelapp as app\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 10000 test images: 97 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eI3BwRadeu9v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}