963 lines (963 with data), 224.2 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cbc45d3a-f87a-4ff7-86c3-b1298083a26a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695671999.802994\n",
            "Mon Sep 25 19:59:59 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": 108,
      "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": 109,
      "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 = 5                 # 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": 110,
      "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": 111,
      "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": 112,
      "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": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "9a359e1e-0c33-4ae0-ccc0-6259cae6e036"
      },
      "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": 114,
      "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": 115,
      "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": 116,
      "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": 117,
      "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": 118,
      "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": 119,
      "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": 120,
      "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": 121,
      "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": "f1a130f6-8e1a-4f13-c566-539ea3f9aa62"
      },
      "execution_count": 122,
      "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": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "31803065-a77e-4c8d-8ceb-85099b275edc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.6920 Acc: 0.0745        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.4843 Acc: 0.1676        \n",
            "Phase: train Epoch: 2/5 Loss: 3.4981 Acc: 0.1229        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.3537 Acc: 0.2011        \n",
            "Phase: train Epoch: 3/5 Loss: 3.3785 Acc: 0.1400        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.2703 Acc: 0.1747        \n",
            "Phase: train Epoch: 4/5 Loss: 3.2893 Acc: 0.1603        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.1961 Acc: 0.1652        \n",
            "Phase: train Epoch: 5/5 Loss: 3.2454 Acc: 0.1731        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.1531 Acc: 0.1897        \n",
            "Training completed in 18m 14s\n",
            "Best test loss: 3.1531 | Best test accuracy: 0.2011\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": 124,
      "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": 125,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "edba3e39-708a-41d2-8bde-fc65ca31e5d5"
      },
      "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": "d1b70450-71e5-46e7-8ea6-4b1f3113cbeb"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695673096.7279348\n",
            "Mon Sep 25 20:18:16 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 127,
      "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
}