W3_Assignment_CNN.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/liudiepie/Introduction_to_Machine_Learning/blob/main/Assignment_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_QhHAmFsRXc"
      },
      "source": [
        "# Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmkRCKn6sXIr"
      },
      "source": [
        "Image -> <br>\n",
        "convolution (32 3* 3 filters) -> nonlinearity (ReLU) -> <br>\n",
        "convolution (32 3* 3 filters) -> nonlinearity (ReLU) -> (2* 2 max pool) <br>\n",
        "convolution (64 3* 3 filters) -> nonlinearity (ReLU) -> <br>\n",
        "convolution (64 3* 3 filters) -> nonlinearity (ReLU) -> (2* 2 max pool) -> flatten -> fully connected (256 hidden units) -> nonlinearity (ReLU) -> fully connected (10 hidden units) -> softmax\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8UTLHAMsWPf"
      },
      "source": [
        "# Building a custom CNN\n",
        "import torch.nn as nn\n",
        "\n",
        "class MNIST_CNN(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)\n",
        "    self.conv2 = nn.Conv2d(32, 32, kernel_size=5, padding=2)\n",
        "    self.conv3 = nn.Conv2d(32, 64, kernel_size=5, padding=2)\n",
        "    self.conv4 = nn.Conv2d(64, 64, kernel_size=5, padding=2)\n",
        "    self.fc1 = nn.Linear(7*7*64, 256)\n",
        "    self.fc2 = nn.Linear(256, 10)\n",
        "\n",
        "  def forward(self, x):\n",
        "    # conv layer 1\n",
        "    x = self.conv1(x)\n",
        "    x = F.relu(x)\n",
        "\n",
        "    # conv layer 2\n",
        "    x = self.conv2(x)\n",
        "    x = F.relu(x)\n",
        "    x = F.max_pool2d(x, kernel_size=2)\n",
        "\n",
        "    # conv layer 3\n",
        "    x = self.conv3(x)\n",
        "    x = F.relu(x)\n",
        "\n",
        "    # conv layer 4\n",
        "    x = self.conv4(x)\n",
        "    x = F.relu(x)\n",
        "    x = F.max_pool2d(x, kernel_size=2)\n",
        "\n",
        "    # fc layer 1 \n",
        "    x = x.view(-1, 7*7*64)\n",
        "    x = self.fc1(x)\n",
        "    x = F.relu(x)\n",
        "\n",
        "    # fc layer 2\n",
        "    x = self.fc2(x)\n",
        "    x = F.softmax(x, dim=None)\n",
        "    return x"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhSuffCRwL_S",
        "outputId": "0012c29f-0c7e-4c9d-c3c1-94076adfb358"
      },
      "source": [
        "model = MNIST_CNN()\n",
        "print(model)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MNIST_CNN(\n",
            "  (conv1): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
            "  (conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
            "  (conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
            "  (conv4): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
            "  (fc1): Linear(in_features=3136, out_features=256, bias=True)\n",
            "  (fc2): Linear(in_features=256, out_features=10, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChVPlgUewnig"
      },
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms\n",
        "from tqdm import tqdm, trange, tqdm_notebook"
      ],
      "execution_count": 4,
      "outputs": []
    },
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      "source": [
        "# Load the data\n",
        "mnist_train = datasets.MNIST(root=\"./datasets\", train=True, transform=transforms.ToTensor(), download=True)\n",
        "mnist_test = datasets.MNIST(root=\"./datasets\", train=False, transform=transforms.ToTensor(), download=True)\n",
        "train_loader = torch.utils.data.DataLoader(mnist_train, batch_size=100, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(mnist_test, batch_size=100, shuffle=False)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"
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          "name": "stdout"
        },
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          "text": [
            "\n",
            "Extracting ./datasets/MNIST/raw/train-images-idx3-ubyte.gz to ./datasets/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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          "name": "stdout"
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          "text": [
            "\n",
            "Extracting ./datasets/MNIST/raw/train-labels-idx1-ubyte.gz to ./datasets/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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          "text": [
            "\n",
            "Extracting ./datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to ./datasets/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Failed to download (trying next):\n",
            "HTTP Error 503: Service Unavailable\n",
            "\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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              "HBox(children=(FloatProgress(value=0.0, max=4542.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./datasets/MNIST/raw\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:498: 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. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_P80G8RTwvdu"
      },
      "source": [
        "## Training\n",
        "# Instantiate model  \n",
        "model = MNIST_CNN() "
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIxYrVpqz-_L"
      },
      "source": [
        "# Loss and Optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001) "
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yt0M_7SswxOK",
        "outputId": "03189f13-2805-44a4-f575-433cbf39b601"
      },
      "source": [
        "# Iterate through train set minibatchs \n",
        "for epoch in trange(3):\n",
        "    for images, labels in tqdm(train_loader):\n",
        "        # Zero out the gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Forward pass\n",
        "        x = images \n",
        "        y = model(x)\n",
        "        loss = criterion(y, labels)\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        optimizer.step()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dV5JnKPw31T",
        "outputId": "23125140-0832-4d6d-a398-70f63dd871d8"
      },
      "source": [
        "## Testing\n",
        "correct = 0\n",
        "total = len(mnist_test)\n",
        "\n",
        "with torch.no_grad():\n",
        "    # Iterate through test set minibatchs \n",
        "    for images, labels in tqdm(test_loader):\n",
        "        # Forward pass\n",
        "        x = images  \n",
        "        y = model(x)\n",
        "\n",
        "        predictions = torch.argmax(y, dim=1)\n",
        "        correct += torch.sum((predictions == labels).float())\n",
        "\n",
        "print('Test accuracy: {}'.format(correct/total))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [00:17<00:00,  5.63it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.9937999844551086\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyZhAhOC9Sm7",
        "outputId": "79478c8e-969c-4cf5-fed3-ae0829d20857"
      },
      "source": [
        "# Access all weights of a model\n",
        "params = []\n",
        "for param in model.parameters():\n",
        "    params.append(param.view(-1))\n",
        "params = torch.cat(params)\n",
        "print(params.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([985834])\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}