963 lines (963 with data), 230.1 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7492bb43-f09d-43fd-9f95-93c2a826c956"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695669164.762607\n",
            "Mon Sep 25 19:12:44 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": 45,
      "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": 46,
      "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 = 2                 # 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": 47,
      "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": 48,
      "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": 49,
      "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": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "f664eeb9-9625-423c-cd6e-795b54124187"
      },
      "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": 51,
      "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": 52,
      "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": 53,
      "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": 54,
      "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": 55,
      "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": 56,
      "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": 57,
      "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": 58,
      "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": "b59323be-bd74-4177-b5c1-986e020d7584"
      },
      "execution_count": 59,
      "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": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "fed91092-b8ca-4ad1-c1d7-7bc9ccfc70bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.7489 Acc: 0.0328        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.5680 Acc: 0.0958        \n",
            "Phase: train Epoch: 2/5 Loss: 3.5026 Acc: 0.1222        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.3538 Acc: 0.1694        \n",
            "Phase: train Epoch: 3/5 Loss: 3.3693 Acc: 0.1482        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.2727 Acc: 0.1514        \n",
            "Phase: train Epoch: 4/5 Loss: 3.2861 Acc: 0.1535        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.1486 Acc: 0.1915        \n",
            "Phase: train Epoch: 5/5 Loss: 3.2011 Acc: 0.1682        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.1057 Acc: 0.1735        \n",
            "Training completed in 11m 26s\n",
            "Best test loss: 3.1057 | Best test accuracy: 0.1915\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": 61,
      "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": 62,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "35a5b0d2-63dc-47d7-c59f-389b9ae7ddf7"
      },
      "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": "7631dbf8-6b63-4078-92f0-61364d3df0cd"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695669853.8297062\n",
            "Mon Sep 25 19:24:13 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 64,
      "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
}