963 lines (963 with data), 234.2 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 296,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "59b09eaf-0ef4-4e2d-8e76-f513aff6c9c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695690074.2310398\n",
            "Tue Sep 26 01:01:14 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": 297,
      "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": 298,
      "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 = 1                 # 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": 299,
      "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": 300,
      "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": 301,
      "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/Shuffle Split 10 of 17 Classes Big Brain Tumor MRI Images\"\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": 302,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "10ee9d1b-1978-4055-f6bc-97fb3a573a0c"
      },
      "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": 303,
      "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": 304,
      "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": 305,
      "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, 10)\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": 306,
      "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": 307,
      "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": 308,
      "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": 309,
      "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": 310,
      "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": "65b606ee-c2f3-4b1b-d3ab-e25d873021e3"
      },
      "execution_count": 311,
      "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": 312,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "de326250-2e5c-4ffc-c18b-1b2bbd6a4eb2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2748 Acc: 0.1624        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.2593 Acc: 0.1780        \n",
            "Phase: train Epoch: 2/5 Loss: 2.2570 Acc: 0.1783        \n",
            "Phase: validation   Epoch: 2/5 Loss: 2.2532 Acc: 0.1780        \n",
            "Phase: train Epoch: 3/5 Loss: 2.2533 Acc: 0.1788        \n",
            "Phase: validation   Epoch: 3/5 Loss: 2.2520 Acc: 0.1780        \n",
            "Phase: train Epoch: 4/5 Loss: 2.2531 Acc: 0.1783        \n",
            "Phase: validation   Epoch: 4/5 Loss: 2.2517 Acc: 0.1780        \n",
            "Phase: train Epoch: 5/5 Loss: 2.2546 Acc: 0.1802        \n",
            "Phase: validation   Epoch: 5/5 Loss: 2.2516 Acc: 0.1780        \n",
            "Training completed in 7m 15s\n",
            "Best test loss: 2.2516 | Best test accuracy: 0.1780\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": 313,
      "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": 314,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "6865969f-b3c1-49ad-cf9b-bc674df7d19e"
      },
      "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": "0cea7ba9-f40f-461c-ea7f-69eccc9f0866"
      },
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695690511.2687597\n",
            "Tue Sep 26 01:08:31 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 316,
      "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
}