pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb

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      "source": [
        "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
        "- Author: Sebastian Raschka\n",
        "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
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      "metadata": {
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      "source": [
        "!pip install -q IPython\n",
        "!pip install -q ipykernel\n",
        "!pip install -q watermark\n",
        "!pip install -q matplotlib\n",
        "!pip install -q sklearn\n",
        "!pip install -q pandas\n",
        "!pip install -q pydot\n",
        "!pip install -q hiddenlayer\n",
        "!pip install -q graphviz"
      ],
      "execution_count": 1,
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    },
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      "source": [
        "%load_ext watermark\n",
        "%watermark -a 'Sebastian Raschka' -v -p torch"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sebastian Raschka \n",
            "\n",
            "CPython 3.6.9\n",
            "IPython 5.5.0\n",
            "\n",
            "torch 1.5.1+cu101\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2b_Si2bRoTUy",
        "colab_type": "text"
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      "source": [
        "- Runs on CPU or GPU (if available)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TigaT6DdoTUz",
        "colab_type": "text"
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      "source": [
        "# Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-L6L_WoTUz",
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      "source": [
        "A simple, single-hidden-layer, fully-connected autoencoder that compresses 768-pixel MNIST images into 32-pixel vectors (32-times smaller representations). A random forest classifier is then trained for predicting the class labels based on that 32-pixel compressed space."
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "tBpR3SqHoTU0",
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      },
      "source": [
        "## Imports"
      ]
    },
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        "outputId": "c9aa0e09-f62f-41cb-a145-2069aed3bd66"
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      "source": [
        "import time\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "\n",
        "\n",
        "##########################\n",
        "### SETTINGS\n",
        "##########################\n",
        "\n",
        "# Device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print('Device:', device)\n",
        "\n",
        "# Hyperparameters\n",
        "random_seed = 123\n",
        "learning_rate = 0.005\n",
        "num_epochs = 5\n",
        "batch_size = 256\n",
        "\n",
        "# Architecture\n",
        "num_features = 784\n",
        "num_hidden_1 = 32\n",
        "\n",
        "\n",
        "##########################\n",
        "### MNIST DATASET\n",
        "##########################\n",
        "\n",
        "# Note transforms.ToTensor() scales input images\n",
        "# to 0-1 range\n",
        "train_dataset = datasets.MNIST(root='data', \n",
        "                               train=True, \n",
        "                               transform=transforms.ToTensor(),\n",
        "                               download=True)\n",
        "\n",
        "test_dataset = datasets.MNIST(root='data', \n",
        "                              train=False, \n",
        "                              transform=transforms.ToTensor())\n",
        "\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, \n",
        "                          batch_size=batch_size, \n",
        "                          shuffle=True)\n",
        "\n",
        "test_loader = DataLoader(dataset=test_dataset, \n",
        "                         batch_size=batch_size, \n",
        "                         shuffle=False)\n",
        "\n",
        "# Checking the dataset\n",
        "for images, labels in train_loader:  \n",
        "    print('Image batch dimensions:', images.shape)\n",
        "    print('Image label dimensions:', labels.shape)\n",
        "    break"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Device: cuda\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
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            "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"
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            "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"
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            "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"
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            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n",
            "\n",
            "Image batch dimensions: torch.Size([256, 1, 28, 28])\n",
            "Image label dimensions: torch.Size([256])\n"
          ],
          "name": "stdout"
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          "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"
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        "colab_type": "text"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kh6_OiTfoTU3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "##########################\n",
        "### MODEL\n",
        "##########################\n",
        "\n",
        "class Autoencoder(torch.nn.Module):\n",
        "\n",
        "    def __init__(self, num_features):\n",
        "        super(Autoencoder, self).__init__()\n",
        "        \n",
        "        ### ENCODER\n",
        "        \n",
        "        self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\n",
        "        # The following to lones are not necessary, \n",
        "        # but used here to demonstrate how to access the weights\n",
        "        # and use a different weight initialization.\n",
        "        # By default, PyTorch uses Xavier/Glorot initialization, which\n",
        "        # should usually be preferred.\n",
        "        self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
        "        self.linear_1.bias.detach().zero_()\n",
        "        \n",
        "        ### DECODER\n",
        "        self.linear_2 = torch.nn.Linear(num_hidden_1, num_features)\n",
        "        self.linear_1.weight.detach().normal_(0.0, 0.1)\n",
        "        self.linear_1.bias.detach().zero_()\n",
        "        \n",
        "    def encoder(self, x):\n",
        "        encoded = self.linear_1(x)\n",
        "        encoded = F.leaky_relu(encoded)\n",
        "        return encoded\n",
        "    \n",
        "    def decoder(self, encoded_x):\n",
        "        logits = self.linear_2(encoded_x)\n",
        "        decoded = torch.sigmoid(logits)\n",
        "        return decoded\n",
        "        \n",
        "\n",
        "    def forward(self, x):\n",
        "        \n",
        "        ### ENCODER\n",
        "        encoded = self.encoder(x)\n",
        "        \n",
        "        ### DECODER\n",
        "        decoded = self.decoder(encoded)\n",
        "        \n",
        "        return decoded\n",
        "\n",
        "    \n",
        "torch.manual_seed(random_seed)\n",
        "model = Autoencoder(num_features=num_features)\n",
        "model = model.to(device)\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "111zr7hIpCEw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "0f502f87-e971-425a-fa6c-43154e1e011b"
      },
      "source": [
        "import hiddenlayer as hl\n",
        "hl.build_graph(model, torch.zeros([64, 28*28]).to(device))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<hiddenlayer.graph.Graph at 0x7f420e0ec2e8>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"562pt\" height=\"108pt\"\n viewBox=\"0.00 0.00 562.00 108.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(72 72)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-72,36 -72,-72 490,-72 490,36 -72,36\"/>\n<!-- /outputs/5 -->\n<g id=\"node1\" class=\"node\">\n<title>/outputs/5</title>\n<polygon fill=\"#e8e8e8\" stroke=\"#000000\" points=\"54,-36 0,-36 0,0 54,0 54,-36\"/>\n<text text-anchor=\"start\" x=\"14\" y=\"-15\" font-family=\"Times\" font-size=\"10.00\" fill=\"#000000\">Linear</text>\n</g>\n<!-- /outputs/6 -->\n<g id=\"node2\" class=\"node\">\n<title>/outputs/6</title>\n<polygon fill=\"#e8e8e8\" stroke=\"#000000\" points=\"179,-36 117,-36 117,0 179,0 179,-36\"/>\n<text text-anchor=\"start\" x=\"125\" y=\"-15\" font-family=\"Times\" font-size=\"10.00\" fill=\"#000000\">LeakyRelu</text>\n</g>\n<!-- /outputs/5&#45;&gt;/outputs/6 -->\n<g id=\"edge1\" class=\"edge\">\n<title>/outputs/5&#45;&gt;/outputs/6</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M54.1548,-18C69.5034,-18 89.016,-18 106.2686,-18\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"106.6613,-21.5001 116.6612,-18 106.6612,-14.5001 106.6613,-21.5001\"/>\n<text text-anchor=\"middle\" x=\"85.5\" y=\"-21\" font-family=\"Times\" font-size=\"10.00\" fill=\"#000000\">64x32</text>\n</g>\n<!-- /outputs/7 -->\n<g id=\"node3\" class=\"node\">\n<title>/outputs/7</title>\n<polygon fill=\"#e8e8e8\" stroke=\"#000000\" points=\"296,-36 242,-36 242,0 296,0 296,-36\"/>\n<text text-anchor=\"start\" x=\"256\" y=\"-15\" font-family=\"Times\" font-size=\"10.00\" fill=\"#000000\">Linear</text>\n</g>\n<!-- /outputs/6&#45;&gt;/outputs/7 -->\n<g id=\"edge2\" class=\"edge\">\n<title>/outputs/6&#45;&gt;/outputs/7</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M179.1605,-18C195.165,-18 214.7838,-18 231.6005,-18\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"231.6519,-21.5001 241.6519,-18 231.6519,-14.5001 231.6519,-21.5001\"/>\n<text text-anchor=\"middle\" x=\"210.5\" y=\"-21\" font-family=\"Times\" font-size=\"10.00\" fill=\"#000000\">64x32</text>\n</g>\n<!-- /outputs/8 -->\n<g id=\"node4\" class=\"node\">\n<title>/outputs/8</title>\n<polygon fill=\"#e8e8e8\" stroke=\"#000000\" points=\"418,-36 364,-36 364,0 418,0 418,-36\"/>\n<text text-anchor=\"start\" x=\"374\" y=\"-15\" font-family=\"Times\" font-size=\"10.00\" fill=\"#000000\">Sigmoid</text>\n</g>\n<!-- /outputs/7&#45;&gt;/outputs/8 -->\n<g id=\"edge3\" class=\"edge\">\n<title>/outputs/7&#45;&gt;/outputs/8</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M296.0758,-18C313.0553,-18 335.1767,-18 353.7924,-18\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"354,-21.5001 363.9999,-18 353.9999,-14.5001 354,-21.5001\"/>\n<text text-anchor=\"middle\" x=\"330\" y=\"-21\" font-family=\"Times\" font-size=\"10.00\" fill=\"#000000\">64x784</text>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F84an1ropDMZ",
        "colab_type": "text"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdgSgAE7oTU8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "bbbae2de-4fca-4e0a-e063-cf835890cc35"
      },
      "source": [
        "start_time = time.time()\n",
        "for epoch in range(num_epochs):\n",
        "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
        "        \n",
        "        # don't need labels, only the images (features)\n",
        "        features = features.view(-1, 28*28).to(device)\n",
        "            \n",
        "        ### FORWARD AND BACK PROP\n",
        "        decoded = model(features)\n",
        "        cost = F.binary_cross_entropy(decoded, features)\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        cost.backward()\n",
        "        \n",
        "        ### UPDATE MODEL PARAMETERS\n",
        "        optimizer.step()\n",
        "        \n",
        "        ### LOGGING\n",
        "        if not batch_idx % 50:\n",
        "            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \n",
        "                   %(epoch+1, num_epochs, batch_idx, \n",
        "                     len(train_loader), cost))\n",
        "            \n",
        "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
        "    \n",
        "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 001/005 | Batch 000/235 | Cost: 0.7100\n",
            "Epoch: 001/005 | Batch 050/235 | Cost: 0.2026\n",
            "Epoch: 001/005 | Batch 100/235 | Cost: 0.1635\n",
            "Epoch: 001/005 | Batch 150/235 | Cost: 0.1349\n",
            "Epoch: 001/005 | Batch 200/235 | Cost: 0.1301\n",
            "Time elapsed: 0.11 min\n",
            "Epoch: 002/005 | Batch 000/235 | Cost: 0.1238\n",
            "Epoch: 002/005 | Batch 050/235 | Cost: 0.1131\n",
            "Epoch: 002/005 | Batch 100/235 | Cost: 0.1099\n",
            "Epoch: 002/005 | Batch 150/235 | Cost: 0.1062\n",
            "Epoch: 002/005 | Batch 200/235 | Cost: 0.1035\n",
            "Time elapsed: 0.22 min\n",
            "Epoch: 003/005 | Batch 000/235 | Cost: 0.1009\n",
            "Epoch: 003/005 | Batch 050/235 | Cost: 0.0977\n",
            "Epoch: 003/005 | Batch 100/235 | Cost: 0.0983\n",
            "Epoch: 003/005 | Batch 150/235 | Cost: 0.0975\n",
            "Epoch: 003/005 | Batch 200/235 | Cost: 0.0937\n",
            "Time elapsed: 0.34 min\n",
            "Epoch: 004/005 | Batch 000/235 | Cost: 0.0946\n",
            "Epoch: 004/005 | Batch 050/235 | Cost: 0.0961\n",
            "Epoch: 004/005 | Batch 100/235 | Cost: 0.0960\n",
            "Epoch: 004/005 | Batch 150/235 | Cost: 0.0972\n",
            "Epoch: 004/005 | Batch 200/235 | Cost: 0.0899\n",
            "Time elapsed: 0.45 min\n",
            "Epoch: 005/005 | Batch 000/235 | Cost: 0.0948\n",
            "Epoch: 005/005 | Batch 050/235 | Cost: 0.0927\n",
            "Epoch: 005/005 | Batch 100/235 | Cost: 0.0932\n",
            "Epoch: 005/005 | Batch 150/235 | Cost: 0.0938\n",
            "Epoch: 005/005 | Batch 200/235 | Cost: 0.0935\n",
            "Time elapsed: 0.56 min\n",
            "Total Training Time: 0.56 min\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "252NoDbLoTU_",
        "colab_type": "text"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eV0LjSBOoTU_",
        "colab_type": "text"
      },
      "source": [
        "### Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Q7Rjx5uoTVA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "2da013c1-70ad-40c9-b506-20b1f113b671"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "##########################\n",
        "### VISUALIZATION\n",
        "##########################\n",
        "\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, \n",
        "                          batch_size=15, \n",
        "                          shuffle=True)\n",
        "\n",
        "# Checking the dataset\n",
        "for images, labels in train_loader:  \n",
        "    print('Image batch dimensions:', images.shape)\n",
        "    print('Image label dimensions:', labels.shape)\n",
        "    break\n",
        "    \n",
        "# =============================================================\n",
        "\n",
        "n_images = 15\n",
        "image_width = 28\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=n_images, \n",
        "                         sharex=True, sharey=True, figsize=(20, 2.5))\n",
        "orig_images = features[:n_images]\n",
        "decoded_images = decoded[:n_images]\n",
        "\n",
        "for i in range(n_images):\n",
        "    for ax, img in zip(axes, [orig_images, decoded_images]):\n",
        "        curr_img = img[i].detach().to(torch.device('cpu'))\n",
        "        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image batch dimensions: torch.Size([15, 1, 28, 28])\n",
            "Image label dimensions: torch.Size([15])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x180 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mpPN7A2oTVD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "ff1d8f8b-72cf-4758-9bde-7711e78dea62"
      },
      "source": [
        "test_loader = DataLoader(dataset=test_dataset, \n",
        "                         batch_size=15, \n",
        "                         shuffle=True)\n",
        "\n",
        "# Checking the dataset\n",
        "for images, labels in test_loader:  \n",
        "    print('Image batch dimensions:', images.shape)\n",
        "    print('Image label dimensions:', labels.shape)\n",
        "    break\n",
        "    \n",
        "# =============================================================\n",
        "\n",
        "n_images = 15\n",
        "image_width = 28\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=n_images, \n",
        "                         sharex=True, sharey=True, figsize=(20, 2.5))\n",
        "orig_images = features[:n_images]\n",
        "decoded_images = decoded[:n_images]\n",
        "\n",
        "for i in range(n_images):\n",
        "    for ax, img in zip(axes, [orig_images, decoded_images]):\n",
        "        curr_img = img[i].detach().to(torch.device('cpu'))\n",
        "        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image batch dimensions: torch.Size([15, 1, 28, 28])\n",
            "Image label dimensions: torch.Size([15])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x180 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNKGhpcLoTVF",
        "colab_type": "text"
      },
      "source": [
        "## Scikit-learn Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAtXbIn8oTVG",
        "colab_type": "text"
      },
      "source": [
        "### On Original MNIST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSWqIo7ToTVG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, \n",
        "                          batch_size=60000, \n",
        "                          shuffle=True)\n",
        "\n",
        "test_loader = DataLoader(dataset=test_dataset, \n",
        "                          batch_size=10000, \n",
        "                          shuffle=False)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlrzMVg0oTVI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "d73b8681-f456-4a03-92f7-cc254c5d0d33"
      },
      "source": [
        "for images, labels in train_loader:  \n",
        "    print('Image batch dimensions:', images.shape)\n",
        "    print('Image label dimensions:', labels.shape)\n",
        "    break\n",
        "\n",
        "X_train = np.array(images.reshape(60000, 28*28))\n",
        "y_train = np.array(labels)\n",
        "\n",
        "\n",
        "for images, labels in test_loader:  \n",
        "    print('Image batch dimensions:', images.shape)\n",
        "    print('Image label dimensions:', labels.shape)\n",
        "    break\n",
        "\n",
        "X_test = np.array(images.reshape(10000, 28*28))\n",
        "y_test = np.array(labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image batch dimensions: torch.Size([60000, 1, 28, 28])\n",
            "Image label dimensions: torch.Size([60000])\n",
            "Image batch dimensions: torch.Size([10000, 1, 28, 28])\n",
            "Image label dimensions: torch.Size([10000])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWYAUcj_oTVL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e1f447e2-4684-480e-eaa1-35471085be71"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators=500, n_jobs=-1).fit(X_train, y_train)\n",
        "print(f'Train Accuracy: {rf.score(X_train, y_train)*100}%')\n",
        "print(f'Test Accuracy: {rf.score(X_test, y_test)*100}%')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy: 100.0%\n",
            "Test Accuracy: 97.21%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKiRaizDoTVO",
        "colab_type": "text"
      },
      "source": [
        "### Using PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSKVyHA6oTVO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=32)  # same size as autoencoder latent space\n",
        "X_train_pca = pca.fit_transform(X_train)\n",
        "X_test_pca = pca.transform(X_test)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Y0o4rfDoTVR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "8baf82aa-d582-48f7-b626-cf8b00bfc6be"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators=500, n_jobs=-1).fit(X_train_pca, y_train)\n",
        "print(f'Train Accuracy: {rf.score(X_train_pca, y_train)*100}%')\n",
        "print(f'Test Accuracy: {rf.score(X_test_pca, y_test)*100}%')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy: 100.0%\n",
            "Test Accuracy: 95.7%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PqWUhXdoTVT",
        "colab_type": "text"
      },
      "source": [
        "### Compressed MNIST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcsHqYNboTVT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = DataLoader(dataset=train_dataset, \n",
        "                          batch_size=1000, \n",
        "                          shuffle=True)\n",
        "\n",
        "test_loader = DataLoader(dataset=test_dataset, \n",
        "                          batch_size=1000, \n",
        "                          shuffle=False)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbIrIcXToTVV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_compr = np.ones((60000, num_hidden_1))\n",
        "y_train = np.ones(60000)\n",
        "\n",
        "start_idx = 0\n",
        "\n",
        "for idx, (images, labels) in enumerate(train_loader): \n",
        "    features = images.view(-1, 28*28).to(device)\n",
        "    decoded = model.encoder(features)\n",
        "    X_train_compr[start_idx:start_idx+1000] = decoded.to(torch.device('cpu')).detach().numpy()\n",
        "    y_train[start_idx:start_idx+1000] = labels\n",
        "    start_idx += 1000"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvkZ9_DkoTVX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test_compr = np.ones((10000, num_hidden_1))\n",
        "y_test = np.ones(10000)\n",
        "\n",
        "start_idx = 0\n",
        "\n",
        "for idx, (images, labels) in enumerate(test_loader): \n",
        "    features = images.view(-1, 28*28).to(device)\n",
        "    decoded = model.encoder(features)\n",
        "    X_test_compr[start_idx:start_idx+1000] = decoded.to(torch.device('cpu')).detach().numpy()\n",
        "    y_test[start_idx:start_idx+1000] = labels\n",
        "    start_idx += 1000"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "709LE0YCoTVa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "77c35d7d-e050-411c-c345-a83bcb220ac8"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators=500, n_jobs=-1).fit(X_train_compr, y_train)\n",
        "print(f'Train Accuracy: {rf.score(X_train_compr, y_train)*100}%')\n",
        "print(f'Test Accuracy: {rf.score(X_test_compr, y_test)*100}%')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy: 100.0%\n",
            "Test Accuracy: 95.15%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rsHU5QJoTVd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "36f7068d-8f0c-4414-8288-aafea54f453b"
      },
      "source": [
        "%watermark -iv"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "numpy 1.18.5\n",
            "torch 1.5.1+cu101\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3xSJcOXoTVf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 18,
      "outputs": []
    }
  ]
}