964 lines (963 with data), 222.1 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 191,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "3aa8769a-30e5-4f45-fd0e-bf00d9f12a3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695677800.0482063\n",
            "Mon Sep 25 21:36:40 2023\n"
          ]
        }
      ],
      "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\n",
        "\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 192,
      "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 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": 193,
      "metadata": {
        "id": "5LRcEYZg8PAR"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 5              # Number of training epochs\n",
        "q_depth = 10                # 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": 194,
      "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": 195,
      "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": 196,
      "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": 197,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "ad8ea7cd-ffbc-4c58-ade5-364b706fe1d7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "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": 198,
      "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": 199,
      "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",
        "    # Sequence of trainable variational layers\n",
        "    for k in range(q_depth):\n",
        "        RY_layer(q_weights[k])\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": 200,
      "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)\n"
      ]
    },
    {
      "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": 201,
      "metadata": {
        "id": "lnJnW_ra8PAX"
      },
      "outputs": [],
      "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": 202,
      "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": 203,
      "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": 204,
      "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": 205,
      "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": "3f6fb9e9-9468-478b-8990-859ef4b8aa7a"
      },
      "execution_count": 206,
      "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": 207,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "936f54c7-d55b-4d29-d1e6-24b8722230b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.6743 Acc: 0.0670        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.5572 Acc: 0.0706        \n",
            "Phase: train Epoch: 2/5 Loss: 3.5138 Acc: 0.0759        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.4421 Acc: 0.0826        \n",
            "Phase: train Epoch: 3/5 Loss: 3.4301 Acc: 0.0905        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.3493 Acc: 0.1382        \n",
            "Phase: train Epoch: 4/5 Loss: 3.3623 Acc: 0.1268        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.2907 Acc: 0.1406        \n",
            "Phase: train Epoch: 5/5 Loss: 3.3194 Acc: 0.1279        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.2490 Acc: 0.1400        \n",
            "Training completed in 29m 48s\n",
            "Best test loss: 3.2490 | Best test accuracy: 0.1406\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": 208,
      "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": 209,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "b11e91bf-95e2-41ce-de2e-7b70d8b511dd"
      },
      "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": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "id": "D3AaQc2xMk-G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a6e20557-e73c-4952-c336-c38f927d2fb5"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695679590.570495\n",
            "Mon Sep 25 22:06:30 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 211,
      "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
}