963 lines (963 with data), 237.2 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 422,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "43dc6677-f03f-4933-b741-7161f4170da4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695695838.2537808\n",
            "Tue Sep 26 02:37:18 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": 423,
      "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": 424,
      "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 = 7                 # 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": 425,
      "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": 426,
      "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": 427,
      "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": 428,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "a55562c3-85dd-4513-ebcf-c0f19830b457"
      },
      "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": 429,
      "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": 430,
      "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": 431,
      "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": 432,
      "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": 433,
      "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": 434,
      "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": 435,
      "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": 436,
      "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": "4c2574a1-8a0c-4a31-d9cf-c12dafaea25a"
      },
      "execution_count": 437,
      "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": 438,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "c15ef881-aade-499d-84b0-8e49a1a26566"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.1232 Acc: 0.1961        \n",
            "Phase: validation   Epoch: 1/5 Loss: 1.9124 Acc: 0.2712        \n",
            "Phase: train Epoch: 2/5 Loss: 1.9651 Acc: 0.2580        \n",
            "Phase: validation   Epoch: 2/5 Loss: 1.8046 Acc: 0.3392        \n",
            "Phase: train Epoch: 3/5 Loss: 1.8650 Acc: 0.2953        \n",
            "Phase: validation   Epoch: 3/5 Loss: 1.7602 Acc: 0.3713        \n",
            "Phase: train Epoch: 4/5 Loss: 1.7965 Acc: 0.3248        \n",
            "Phase: validation   Epoch: 4/5 Loss: 1.6473 Acc: 0.3850        \n",
            "Phase: train Epoch: 5/5 Loss: 1.7631 Acc: 0.3180        \n",
            "Phase: validation   Epoch: 5/5 Loss: 1.6022 Acc: 0.3934        \n",
            "Training completed in 17m 36s\n",
            "Best test loss: 1.6022 | Best test accuracy: 0.3934\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": 439,
      "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": 440,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "62b5c3d2-6516-496d-dfa8-cf1f374659b4"
      },
      "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": "6c20b36d-3c31-4bc2-9035-f0a33aa78ccb"
      },
      "execution_count": 441,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695696897.469152\n",
            "Tue Sep 26 02:54:57 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 442,
      "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
      },
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