942 lines (941 with data), 324.3 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "UJOq3mdA8PAH"
      },
      "outputs": [],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !pip install pennylane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "5ljdosVS8PAP"
      },
      "outputs": [],
      "source": [
        "# Some parts of this code are based on the Python script:\n",
        "# https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py\n",
        "# License: BSD\n",
        "\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "\n",
        "# PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "# Pennylane\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "\n",
        "torch.manual_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# OpenMP: number of parallel threads.\n",
        "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AFilzYk8PAQ"
      },
      "source": [
        "Setting of the main hyper-parameters of the model\n",
        "=================================================\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "To reproduce the results of Ref. \\[1\\], `num_epochs` should be set to\n",
        "`30` which may take a long time. We suggest to first try with\n",
        "`num_epochs=1` and, if everything runs smoothly, increase it to a larger\n",
        "value.\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "5LRcEYZg8PAR"
      },
      "outputs": [],
      "source": [
        "n_qubits = 17               # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 17             # Number of samples for each training step\n",
        "num_epochs = 10             # Number of training epochs\n",
        "q_depth = 6                 # Depth of the quantum circuit (number of variational layers)\n",
        "gamma_lr_scheduler = 0.1    # Learning rate reduction applied every 10 epochs.\n",
        "q_delta = 0.01              # Initial spread of random quantum weights\n",
        "start_time = time.time()    # Start of the computation timer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlU2Q7zd8PAR"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "0prgZPLK8PAR"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=n_qubits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54jRIpbZ8PAS"
      },
      "source": [
        "We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
        "used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "23nQUjLm8PAS"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AJzWJGi8PAT"
      },
      "source": [
        "Dataset loading\n",
        "===============\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The dataset containing images of *ants* and *bees* can be downloaded\n",
        "[here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) and\n",
        "should be extracted in the subfolder `../_data/hymenoptera_data`.\n",
        ":::\n",
        "\n",
        "This is a very small dataset (roughly 250 images), too small for\n",
        "training from scratch a classical or quantum model, however it is enough\n",
        "when using *transfer learning* approach.\n",
        "\n",
        "The PyTorch packages `torchvision` and `torch.utils.data` are used for\n",
        "loading the dataset and performing standard preliminary image\n",
        "operations: resize, center, crop, normalize, *etc.*\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "XaNa12un8PAT"
      },
      "outputs": [],
      "source": [
        "data_transforms = {\n",
        "    \"train\": transforms.Compose(\n",
        "        [\n",
        "            # transforms.RandomResizedCrop(224),     # uncomment for data augmentation\n",
        "            # transforms.RandomHorizontalFlip(),     # uncomment for data augmentation\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            # Normalize input channels using mean values and standard deviations of ImageNet.\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "    \"val\": transforms.Compose(\n",
        "        [\n",
        "            transforms.Resize(256),\n",
        "            transforms.CenterCrop(224),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
        "        ]\n",
        "    ),\n",
        "}\n",
        "\n",
        "data_dir = \"/content/drive/MyDrive/Colab Notebooks/data/44 Class 4478 Brain Tumor Images Split 0.627 Shuffle Rename\"\n",
        "image_datasets = {\n",
        "    x if x == \"train\" else \"validation\": datasets.ImageFolder(\n",
        "        os.path.join(data_dir, x), data_transforms[x]\n",
        "    )\n",
        "    for x in [\"train\", \"val\"]\n",
        "}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"validation\"]}\n",
        "class_names = image_datasets[\"train\"].classes\n",
        "\n",
        "# Initialize dataloader\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}\n",
        "\n",
        "# function to plot images\n",
        "def imshow(inp, title=None):\n",
        "    \"\"\"Display image from tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    # Inverse of the initial normalization operation.\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANdmcnR98PAU"
      },
      "source": [
        "Let us show a batch of the test data, just to have an idea of the\n",
        "classification problem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "QzIKQxS78PAU",
        "outputId": "a5b8445a-de63-4ff2-fb48-e88947987b51"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(dataloaders[\"validation\"]))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ULbO8f28PAU"
      },
      "source": [
        "Variational quantum circuit\n",
        "===========================\n",
        "\n",
        "We first define some quantum layers that will compose the quantum\n",
        "circuit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "6gMomjvL8PAV"
      },
      "outputs": [],
      "source": [
        "def H_layer(nqubits):\n",
        "    \"\"\"Layer of single-qubit Hadamard gates.\n",
        "    \"\"\"\n",
        "    for idx in range(nqubits):\n",
        "        qml.Hadamard(wires=idx)\n",
        "\n",
        "\n",
        "def RY_layer(w):\n",
        "    \"\"\"Layer of parametrized qubit rotations around the y axis.\n",
        "    \"\"\"\n",
        "    for idx, element in enumerate(w):\n",
        "        qml.RY(element, wires=idx)\n",
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iroynmF8PAV"
      },
      "source": [
        "Now we define the quantum circuit through the PennyLane\n",
        "[qnode]{.title-ref} decorator .\n",
        "\n",
        "The structure is that of a typical variational quantum circuit:\n",
        "\n",
        "-   **Embedding layer:** All qubits are first initialized in a balanced\n",
        "    superposition of *up* and *down* states, then they are rotated\n",
        "    according to the input parameters (local embedding).\n",
        "-   **Variational layers:** A sequence of trainable rotation layers and\n",
        "    constant entangling layers is applied.\n",
        "-   **Measurement layer:** For each qubit, the local expectation value\n",
        "    of the $Z$ operator is measured. This produces a classical output\n",
        "    vector, suitable for additional post-processing.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ONyq04RY8PAV"
      },
      "outputs": [],
      "source": [
        "@qml.qnode(dev, interface=\"torch\")\n",
        "def quantum_net(q_input_features, q_weights_flat):\n",
        "    \"\"\"\n",
        "    The variational quantum circuit.\n",
        "    \"\"\"\n",
        "\n",
        "    # Reshape weights\n",
        "    q_weights = q_weights_flat.reshape(q_depth, n_qubits)\n",
        "\n",
        "    # Start from state |+> , unbiased w.r.t. |0> and |1>\n",
        "    H_layer(n_qubits)\n",
        "\n",
        "    # Embed features in the quantum node\n",
        "    RY_layer(q_input_features)\n",
        "\n",
        "    # Expectation values in the Z basis\n",
        "    exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]\n",
        "    return tuple(exp_vals)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eG97j4f8PAV"
      },
      "source": [
        "Dressed quantum circuit\n",
        "=======================\n",
        "\n",
        "We can now define a custom `torch.nn.Module` representing a *dressed*\n",
        "quantum circuit.\n",
        "\n",
        "This is a concatenation of:\n",
        "\n",
        "-   A classical pre-processing layer (`nn.Linear`).\n",
        "-   A classical activation function (`torch.tanh`).\n",
        "-   A constant `np.pi/2.0` scaling.\n",
        "-   The previously defined quantum circuit (`quantum_net`).\n",
        "-   A classical post-processing layer (`nn.Linear`).\n",
        "\n",
        "The input of the module is a batch of vectors with 512 real parameters\n",
        "(features) and the output is a batch of vectors with two real outputs\n",
        "(associated with the two classes of images: *ants* and *bees*).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "hIljGdv_8PAW"
      },
      "outputs": [],
      "source": [
        "class DressedQuantumNet(nn.Module):\n",
        "    \"\"\"\n",
        "    Torch module implementing the *dressed* quantum net.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        \"\"\"\n",
        "        Definition of the *dressed* layout.\n",
        "        \"\"\"\n",
        "\n",
        "        super().__init__()\n",
        "        self.pre_net = nn.Linear(2048, n_qubits)\n",
        "        self.q_params = nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))\n",
        "        self.post_net = nn.Linear(n_qubits, 44)\n",
        "\n",
        "    def forward(self, input_features):\n",
        "        \"\"\"\n",
        "        Defining how tensors are supposed to move through the *dressed* quantum\n",
        "        net.\n",
        "        \"\"\"\n",
        "\n",
        "        # obtain the input features for the quantum circuit\n",
        "        # by reducing the feature dimension from 512 to 4\n",
        "        pre_out = self.pre_net(input_features)\n",
        "        q_in = torch.tanh(pre_out) * np.pi / 2.0\n",
        "\n",
        "        # Apply the quantum circuit to each element of the batch and append to q_out\n",
        "        q_out = torch.Tensor(0, n_qubits)\n",
        "        q_out = q_out.to(device)\n",
        "        for elem in q_in:\n",
        "            q_out_elem = torch.hstack(quantum_net(elem, self.q_params)).float().unsqueeze(0)\n",
        "            q_out = torch.cat((q_out, q_out_elem))\n",
        "\n",
        "        # return the two-dimensional prediction from the postprocessing layer\n",
        "        return self.post_net(q_out)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8-EDnhn8PAW"
      },
      "source": [
        "Hybrid classical-quantum model\n",
        "==============================\n",
        "\n",
        "We are finally ready to build our full hybrid classical-quantum network.\n",
        "We follow the *transfer learning* approach:\n",
        "\n",
        "1.  First load the classical pre-trained network *ResNet18* from the\n",
        "    `torchvision.models` zoo.\n",
        "2.  Freeze all the weights since they should not be trained.\n",
        "3.  Replace the last fully connected layer with our trainable dressed\n",
        "    quantum circuit (`DressedQuantumNet`).\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The *ResNet18* model is automatically downloaded by PyTorch and it may\n",
        "take several minutes (only the first time).\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lnJnW_ra8PAX",
        "outputId": "4c544117-947f-44f2-8680-1cdb9e4bdb2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
            "100%|██████████| 97.8M/97.8M [00:00<00:00, 287MB/s]\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = torchvision.models.resnet50(pretrained=True)\n",
        "\n",
        "for param in model_hybrid.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "\n",
        "# Notice that model_hybrid.fc is the last layer of ResNet18\n",
        "model_hybrid.fc = DressedQuantumNet()\n",
        "\n",
        "# Use CUDA or CPU according to the \"device\" object.\n",
        "model_hybrid = model_hybrid.to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5k96EBuZ8PAX"
      },
      "source": [
        "Training and results\n",
        "====================\n",
        "\n",
        "Before training the network we need to specify the *loss* function.\n",
        "\n",
        "We use, as usual in classification problem, the *cross-entropy* which is\n",
        "directly available within `torch.nn`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "BKvfgR5N8PAX"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUvuVdii8PAX"
      },
      "source": [
        "We also initialize the *Adam optimizer* which is called at each training\n",
        "step in order to update the weights of the model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "bPI2SbMQ8PAX"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8wMKvP48PAY"
      },
      "source": [
        "We schedule to reduce the learning rate by a factor of\n",
        "`gamma_lr_scheduler` every 10 epochs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "dLQsPIzy8PAY"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-xTUZhq8PAY"
      },
      "source": [
        "What follows is a training function that will be called later. This\n",
        "function should return a trained model that can be used to make\n",
        "predictions (classifications).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "rppVRya_8PAY"
      },
      "outputs": [],
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs):\n",
        "    since = time.time()\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "    best_loss = 10000.0  # Large arbitrary number\n",
        "    best_acc_train = 0.0\n",
        "    best_loss_train = 10000.0  # Large arbitrary number\n",
        "    print(\"Training started:\")\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in [\"train\", \"validation\"]:\n",
        "            if phase == \"train\":\n",
        "                # Set model to training mode\n",
        "                model.train()\n",
        "            else:\n",
        "                # Set model to evaluate mode\n",
        "                model.eval()\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            n_batches = dataset_sizes[phase] // batch_size\n",
        "            it = 0\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                since_batch = time.time()\n",
        "                batch_size_ = len(inputs)\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # Track/compute gradient and make an optimization step only when training\n",
        "                with torch.set_grad_enabled(phase == \"train\"):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "                    if phase == \"train\":\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # Print iteration results\n",
        "                running_loss += loss.item() * batch_size_\n",
        "                batch_corrects = torch.sum(preds == labels.data).item()\n",
        "                running_corrects += batch_corrects\n",
        "                print(\n",
        "                    \"Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}\".format(\n",
        "                        phase,\n",
        "                        epoch + 1,\n",
        "                        num_epochs,\n",
        "                        it + 1,\n",
        "                        n_batches + 1,\n",
        "                        time.time() - since_batch,\n",
        "                    ),\n",
        "                    end=\"\\r\",\n",
        "                    flush=True,\n",
        "                )\n",
        "                it += 1\n",
        "\n",
        "            # Print epoch results\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "            print(\n",
        "                \"Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f}        \".format(\n",
        "                    \"train\" if phase == \"train\" else \"validation  \",\n",
        "                    epoch + 1,\n",
        "                    num_epochs,\n",
        "                    epoch_loss,\n",
        "                    epoch_acc,\n",
        "                )\n",
        "            )\n",
        "\n",
        "            # Check if this is the best model wrt previous epochs\n",
        "            if phase == \"validation\" and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            if phase == \"validation\" and epoch_loss < best_loss:\n",
        "                best_loss = epoch_loss\n",
        "            if phase == \"train\" and epoch_acc > best_acc_train:\n",
        "                best_acc_train = epoch_acc\n",
        "            if phase == \"train\" and epoch_loss < best_loss_train:\n",
        "                best_loss_train = epoch_loss\n",
        "\n",
        "            # Update learning rate\n",
        "            if phase == \"train\":\n",
        "                scheduler.step()\n",
        "\n",
        "    # Print final results\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    time_elapsed = time.time() - since\n",
        "    print(\n",
        "        \"Training completed in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60)\n",
        "    )\n",
        "    print(\"Best test loss: {:.4f} | Best test accuracy: {:.4f}\".format(best_loss, best_acc))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_XtRwDI8PAZ"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Javascript\n",
        "\n",
        "# Run this cell to keep Colab awake\n",
        "display(Javascript('''\n",
        "  function keep_colab_awake(){\n",
        "    console.log(\"Colab is being kept awake.\");\n",
        "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click();\n",
        "    document.querySelector(\"body > colab-sandbox-output > div > div.output.container.output-wrapper > div.output > pre\").innerText;\n",
        "    setTimeout(keep_colab_awake, 61000);\n",
        "  }\n",
        "  keep_colab_awake();\n",
        "'''))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "p2W621Tsy2hY",
        "outputId": "515be26c-ecd5-4a50-e13f-c382f67aa637"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "  function keep_colab_awake(){\n",
              "    console.log(\"Colab is being kept awake.\");\n",
              "    document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click();\n",
              "    document.querySelector(\"body > colab-sandbox-output > div > div.output.container.output-wrapper > div.output > pre\").innerText;\n",
              "    setTimeout(keep_colab_awake, 61000);\n",
              "  }\n",
              "  keep_colab_awake();\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "d9912421-9705-4922-bcaf-5f2a6fe79888"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/10 Loss: 3.4887 Acc: 0.1268        \n",
            "Phase: validation   Epoch: 1/10 Loss: 3.3002 Acc: 0.1430        \n",
            "Phase: train Epoch: 2/10 Loss: 3.1695 Acc: 0.2227        \n",
            "Phase: validation   Epoch: 2/10 Loss: 3.0458 Acc: 0.2472        \n",
            "Phase: train Epoch: 3/10 Loss: 2.9750 Acc: 0.2882        \n",
            "Phase: validation   Epoch: 3/10 Loss: 2.8621 Acc: 0.3423        \n",
            "Phase: train Epoch: 4/10 Loss: 2.8140 Acc: 0.3445        \n",
            "Phase: validation   Epoch: 4/10 Loss: 2.7881 Acc: 0.3483        \n",
            "Phase: train Epoch: 5/10 Loss: 2.6714 Acc: 0.3701        \n",
            "Phase: validation   Epoch: 5/10 Loss: 2.6318 Acc: 0.3639        \n",
            "Phase: train Epoch: 6/10 Loss: 2.5683 Acc: 0.3865        \n",
            "Phase: validation   Epoch: 6/10 Loss: 2.4986 Acc: 0.3986        \n",
            "Phase: train Epoch: 7/10 Loss: 2.4702 Acc: 0.4083        \n",
            "Phase: validation   Epoch: 7/10 Loss: 2.4512 Acc: 0.4004        \n",
            "Phase: train Epoch: 8/10 Loss: 2.3819 Acc: 0.4279        \n",
            "Phase: validation   Epoch: 8/10 Loss: 2.3807 Acc: 0.4225        \n",
            "Phase: train Epoch: 9/10 Loss: 2.2986 Acc: 0.4457        \n",
            "Phase: validation   Epoch: 9/10 Loss: 2.2981 Acc: 0.4315        \n",
            "Phase: train Epoch: 10/10 Loss: 2.2294 Acc: 0.4663        \n",
            "Phase: validation   Epoch: 10/10 Loss: 2.2050 Acc: 0.4656        \n",
            "Training completed in 64m 12s\n",
            "Best test loss: 2.2050 | Best test accuracy: 0.4656\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = train_model(\n",
        "    model_hybrid, criterion, optimizer_hybrid, exp_lr_scheduler, num_epochs=num_epochs\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AG82Ot6Y8PAZ"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwycKwbd8PAZ"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_8R2rHzF8PAZ"
      },
      "outputs": [],
      "source": [
        "def visualize_model(model, num_images=6, fig_name=\"Predictions\"):\n",
        "    images_so_far = 0\n",
        "    _fig = plt.figure(fig_name)\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for _i, (inputs, labels) in enumerate(dataloaders[\"validation\"]):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images // 2, 2, images_so_far)\n",
        "                ax.axis(\"off\")\n",
        "                ax.set_title(\"[{}]\".format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "                if images_so_far == num_images:\n",
        "                    return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQvJfmme8PAa"
      },
      "source": [
        "Finally, we can run the previous function to see a batch of images with\n",
        "the corresponding predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "mKBJn2x68PAa",
        "outputId": "a7f1c744-a85a-4f5b-d2a7-644ddf7d2722"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_model(model_hybrid, num_images=16)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0yhgWSns8PAa"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, and\n",
        "Nathan Killoran. *Transfer learning in hybrid classical-quantum neural\n",
        "networks*. arXiv:1912.08278 (2019).\n",
        "\n",
        "\\[2\\] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and\n",
        "Andrew Y Ng. *Self-taught learning: transfer learning from unlabeled\n",
        "data*. Proceedings of the 24th International Conference on Machine\n",
        "Learning\\*, 759--766 (2007).\n",
        "\n",
        "\\[3\\] Kaiming He, Xiangyu Zhang, Shaoqing ren and Jian Sun. *Deep\n",
        "residual learning for image recognition*. Proceedings of the IEEE\n",
        "Conference on Computer Vision and Pattern Recognition, 770-778 (2016).\n",
        "\n",
        "\\[4\\] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin,\n",
        "Carsten Blank, Keri McKiernan, and Nathan Killoran. *PennyLane:\n",
        "Automatic differentiation of hybrid quantum-classical computations*.\n",
        "arXiv:1811.04968 (2018).\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "V100"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}