963 lines (963 with data), 241.1 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 359,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cc562622-f094-4c68-863a-5510c7f09b51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695692202.95691\n",
            "Tue Sep 26 01:36:42 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": 360,
      "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": 361,
      "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 = 4                 # 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": 362,
      "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": 363,
      "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": 364,
      "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": 365,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "db51744b-0c7a-4792-d4d8-9cc866b4947d"
      },
      "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": 366,
      "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": 367,
      "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": 368,
      "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": 369,
      "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": 370,
      "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": 371,
      "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": 372,
      "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": 373,
      "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": "5bcc7c37-1e8a-4afd-ff94-7550ea331554"
      },
      "execution_count": 374,
      "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": 375,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "a03481e6-efe2-4c44-b2f3-3e0e9f57c90f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2595 Acc: 0.1233        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.1459 Acc: 0.1276        \n",
            "Phase: train Epoch: 2/5 Loss: 2.1145 Acc: 0.1510        \n",
            "Phase: validation   Epoch: 2/5 Loss: 2.0210 Acc: 0.3117        \n",
            "Phase: train Epoch: 3/5 Loss: 2.0233 Acc: 0.2839        \n",
            "Phase: validation   Epoch: 3/5 Loss: 1.9313 Acc: 0.3308        \n",
            "Phase: train Epoch: 4/5 Loss: 1.9684 Acc: 0.2953        \n",
            "Phase: validation   Epoch: 4/5 Loss: 1.8616 Acc: 0.3270        \n",
            "Phase: train Epoch: 5/5 Loss: 1.9024 Acc: 0.3103        \n",
            "Phase: validation   Epoch: 5/5 Loss: 1.7901 Acc: 0.3392        \n",
            "Training completed in 12m 26s\n",
            "Best test loss: 1.7901 | Best test accuracy: 0.3392\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": 376,
      "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": 377,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "53e0ce3f-6e3f-4282-e705-8f9283fdbdf1"
      },
      "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": "108a104e-c0dc-47f8-cd56-081f4c156816"
      },
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695692952.0926077\n",
            "Tue Sep 26 01:49:12 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 379,
      "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
}