Autoencoder.ipynb

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      "metadata": {
        "id": "zMRUXqEVARRA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import torch \n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as transforms\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn.functional as F\n",
        " \n",
        "from torchvision import datasets\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.utils import save_image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AezUL2YBAUqX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# constants\n",
        "NUM_EPOCHS = 50\n",
        "LEARNING_RATE = 1e-3\n",
        "BATCH_SIZE = 128\n",
        " \n",
        "# image transformations\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5,), (0.5,))\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
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        },
        "outputId": "3f94914b-3c4f-4dee-97ed-80a5a43749f2"
      },
      "source": [
        "trainset = datasets.MNIST(\n",
        "    root='./data',\n",
        "    train=True, \n",
        "    download=True,\n",
        "    transform=transform\n",
        ")\n",
        "\n",
        "testset = datasets.MNIST(\n",
        "    root='./data',\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=transform\n",
        ")\n",
        " \n",
        "trainloader = DataLoader(\n",
        "    trainset, \n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True\n",
        ")\n",
        "testloader = DataLoader(\n",
        "    testset, \n",
        "    batch_size=BATCH_SIZE, \n",
        "    shuffle=True\n",
        ")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "08444a2fa55745a085dfbbcdedd2de0b",
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "59ebd1d81ae547f59e79073b433f30df",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OREifxYlAfxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# utility functions\n",
        "def get_device():\n",
        "    if torch.cuda.is_available():\n",
        "        device = 'cuda:0'\n",
        "    else:\n",
        "        device = 'cpu'\n",
        "    return device\n",
        " \n",
        "def make_dir():\n",
        "    image_dir = 'MNIST_Images'\n",
        "    if not os.path.exists(image_dir):\n",
        "        os.makedirs(image_dir)\n",
        " \n",
        "def save_decoded_image(img, epoch):\n",
        "    img = img.view(img.size(0), 1, 28, 28)\n",
        "    save_image(img, './MNIST_Images/linear_ae_image{}.png'.format(epoch))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ne-j0mMArdj",
        "colab_type": "code",
        "outputId": "d589eec5-5a3a-4f22-f613-e1a49e38565f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "class Autoencoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Autoencoder, self).__init__()\n",
        " \n",
        "        # encoder\n",
        "        self.enc1 = nn.Linear(in_features=784, out_features=256)\n",
        "        self.enc2 = nn.Linear(in_features=256, out_features=128)\n",
        "        self.enc3 = nn.Linear(in_features=128, out_features=64)\n",
        "        self.enc4 = nn.Linear(in_features=64, out_features=32)\n",
        "        self.enc5 = nn.Linear(in_features=32, out_features=16)\n",
        " \n",
        "        # decoder \n",
        "        self.dec1 = nn.Linear(in_features=16, out_features=32)\n",
        "        self.dec2 = nn.Linear(in_features=32, out_features=64)\n",
        "        self.dec3 = nn.Linear(in_features=64, out_features=128)\n",
        "        self.dec4 = nn.Linear(in_features=128, out_features=256)\n",
        "        self.dec5 = nn.Linear(in_features=256, out_features=784)\n",
        " \n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.enc1(x))\n",
        "        x = F.relu(self.enc2(x))\n",
        "        x = F.relu(self.enc3(x))\n",
        "        x = F.relu(self.enc4(x))\n",
        "        x = F.relu(self.enc5(x))\n",
        " \n",
        "        x = F.relu(self.dec1(x))\n",
        "        x = F.relu(self.dec2(x))\n",
        "        x = F.relu(self.dec3(x))\n",
        "        x = F.relu(self.dec4(x))\n",
        "        x = F.relu(self.dec5(x))\n",
        "        return x\n",
        " \n",
        "net = Autoencoder()\n",
        "print(net)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Autoencoder(\n",
            "  (enc1): Linear(in_features=784, out_features=256, bias=True)\n",
            "  (enc2): Linear(in_features=256, out_features=128, bias=True)\n",
            "  (enc3): Linear(in_features=128, out_features=64, bias=True)\n",
            "  (enc4): Linear(in_features=64, out_features=32, bias=True)\n",
            "  (enc5): Linear(in_features=32, out_features=16, bias=True)\n",
            "  (dec1): Linear(in_features=16, out_features=32, bias=True)\n",
            "  (dec2): Linear(in_features=32, out_features=64, bias=True)\n",
            "  (dec3): Linear(in_features=64, out_features=128, bias=True)\n",
            "  (dec4): Linear(in_features=128, out_features=256, bias=True)\n",
            "  (dec5): Linear(in_features=256, out_features=784, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nxdOJNDAwnt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHCcnCcUA0lP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(net, trainloader, NUM_EPOCHS):\n",
        "    train_loss = []\n",
        "    for epoch in range(NUM_EPOCHS):\n",
        "        running_loss = 0.0\n",
        "        for data in trainloader:\n",
        "            img, _ = data\n",
        "            img = img.to(device)\n",
        "            img = img.view(img.size(0), -1)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = net(img)\n",
        "            loss = criterion(outputs, img)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            running_loss += loss.item()\n",
        "        \n",
        "        loss = running_loss / len(trainloader)\n",
        "        train_loss.append(loss)\n",
        "        print('Epoch {} of {}, Train Loss: {:.3f}'.format(\n",
        "            epoch+1, NUM_EPOCHS, loss))\n",
        " \n",
        "        if epoch % 5 == 0:\n",
        "            save_decoded_image(outputs.cpu().data, epoch)\n",
        " \n",
        "    return train_loss\n",
        " \n",
        "def test_image_reconstruction(net, testloader):\n",
        "     for batch in testloader:\n",
        "        img, _ = batch\n",
        "        img = img.to(device)\n",
        "        img = img.view(img.size(0), -1)\n",
        "        outputs = net(img)\n",
        "        outputs = outputs.view(outputs.size(0), 1, 28, 28).cpu().data\n",
        "        save_image(outputs, 'mnist_reconstruction.png')\n",
        "        break"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_VvQirtA3Op",
        "colab_type": "code",
        "outputId": "a1ca02fb-3719-49f0-85e9-6ef9943260eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# get the computation device\n",
        "device = get_device()\n",
        "print(device)\n",
        "# load the neural network onto the device\n",
        "net.to(device)\n",
        " \n",
        "make_dir()\n",
        " \n",
        "# train the network\n",
        "train_loss = train(net, trainloader, NUM_EPOCHS)\n",
        "plt.figure()\n",
        "plt.plot(train_loss)\n",
        "plt.title('Train Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.savefig('deep_ae_mnist_loss.png')\n",
        " \n",
        "# test the network\n",
        "test_image_reconstruction(net, testloader)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cpu\n",
            "Epoch 1 of 50, Train Loss: 0.923\n",
            "Epoch 2 of 50, Train Loss: 0.916\n",
            "Epoch 3 of 50, Train Loss: 0.910\n",
            "Epoch 4 of 50, Train Loss: 0.903\n",
            "Epoch 5 of 50, Train Loss: 0.900\n",
            "Epoch 6 of 50, Train Loss: 0.898\n",
            "Epoch 7 of 50, Train Loss: 0.896\n",
            "Epoch 8 of 50, Train Loss: 0.894\n",
            "Epoch 9 of 50, Train Loss: 0.893\n",
            "Epoch 10 of 50, Train Loss: 0.892\n",
            "Epoch 11 of 50, Train Loss: 0.891\n",
            "Epoch 12 of 50, Train Loss: 0.891\n",
            "Epoch 13 of 50, Train Loss: 0.890\n",
            "Epoch 14 of 50, Train Loss: 0.890\n",
            "Epoch 15 of 50, Train Loss: 0.889\n",
            "Epoch 16 of 50, Train Loss: 0.889\n",
            "Epoch 17 of 50, Train Loss: 0.888\n",
            "Epoch 18 of 50, Train Loss: 0.888\n",
            "Epoch 19 of 50, Train Loss: 0.887\n",
            "Epoch 20 of 50, Train Loss: 0.887\n",
            "Epoch 21 of 50, Train Loss: 0.887\n",
            "Epoch 22 of 50, Train Loss: 0.886\n",
            "Epoch 23 of 50, Train Loss: 0.886\n",
            "Epoch 24 of 50, Train Loss: 0.886\n",
            "Epoch 25 of 50, Train Loss: 0.885\n",
            "Epoch 26 of 50, Train Loss: 0.885\n",
            "Epoch 27 of 50, Train Loss: 0.885\n",
            "Epoch 28 of 50, Train Loss: 0.885\n",
            "Epoch 29 of 50, Train Loss: 0.885\n",
            "Epoch 30 of 50, Train Loss: 0.884\n",
            "Epoch 31 of 50, Train Loss: 0.884\n",
            "Epoch 32 of 50, Train Loss: 0.884\n",
            "Epoch 33 of 50, Train Loss: 0.884\n",
            "Epoch 34 of 50, Train Loss: 0.884\n",
            "Epoch 35 of 50, Train Loss: 0.883\n",
            "Epoch 36 of 50, Train Loss: 0.883\n",
            "Epoch 37 of 50, Train Loss: 0.883\n",
            "Epoch 38 of 50, Train Loss: 0.883\n",
            "Epoch 39 of 50, Train Loss: 0.883\n",
            "Epoch 40 of 50, Train Loss: 0.883\n",
            "Epoch 41 of 50, Train Loss: 0.883\n",
            "Epoch 42 of 50, Train Loss: 0.883\n",
            "Epoch 43 of 50, Train Loss: 0.883\n",
            "Epoch 44 of 50, Train Loss: 0.882\n",
            "Epoch 45 of 50, Train Loss: 0.882\n",
            "Epoch 46 of 50, Train Loss: 0.882\n",
            "Epoch 47 of 50, Train Loss: 0.882\n",
            "Epoch 48 of 50, Train Loss: 0.882\n",
            "Epoch 49 of 50, Train Loss: 0.882\n",
            "Epoch 50 of 50, Train Loss: 0.882\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ru3JdipnZiMnmGcdJwLKIWBERvcDNwAW7rdMC3JdM35+2/K3APRGxOSK2APcA52Sx1qxo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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysm4ij2RGXoe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}