963 lines (963 with data), 238.4 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 401,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "166cadc5-4fe6-4917-b961-34924fffed45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695694672.7314703\n",
            "Tue Sep 26 02:17:52 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": 402,
      "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": 403,
      "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 = 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": 404,
      "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": 405,
      "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": 406,
      "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": 407,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "169ecf98-8431-41da-c6cd-666653a20008"
      },
      "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": 408,
      "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": 409,
      "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": 410,
      "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": 411,
      "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": 412,
      "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": 413,
      "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": 414,
      "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": 415,
      "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": "9e1228de-9427-4884-be1a-596f1dbc1b43"
      },
      "execution_count": 416,
      "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": 417,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "540d685f-2acf-4200-a307-323b40599292"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2724 Acc: 0.1747        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.2612 Acc: 0.2131        \n",
            "Phase: train Epoch: 2/5 Loss: 2.2173 Acc: 0.2116        \n",
            "Phase: validation   Epoch: 2/5 Loss: 2.0995 Acc: 0.2796        \n",
            "Phase: train Epoch: 3/5 Loss: 2.1419 Acc: 0.2439        \n",
            "Phase: validation   Epoch: 3/5 Loss: 2.0887 Acc: 0.2804        \n",
            "Phase: train Epoch: 4/5 Loss: 2.1050 Acc: 0.2493        \n",
            "Phase: validation   Epoch: 4/5 Loss: 2.0009 Acc: 0.2865        \n",
            "Phase: train Epoch: 5/5 Loss: 2.0865 Acc: 0.2511        \n",
            "Phase: validation   Epoch: 5/5 Loss: 2.0355 Acc: 0.2704        \n",
            "Training completed in 15m 56s\n",
            "Best test loss: 2.0009 | Best test accuracy: 0.2865\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": 418,
      "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": 419,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "68891bf8-b7e6-4279-8a49-a6859c11d20d"
      },
      "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": "6c82d4c4-12af-48f0-bcdd-4edba372db99"
      },
      "execution_count": 420,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695695632.0883715\n",
            "Tue Sep 26 02:33:52 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 421,
      "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
}