963 lines (963 with data), 241.1 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 317,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2299dd49-546c-42ea-ef3e-8632a2256032"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695690589.1260583\n",
            "Tue Sep 26 01:09:49 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": 318,
      "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": 319,
      "metadata": {
        "id": "5LRcEYZg8PAR"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 5              # Number of training epochs\n",
        "q_depth = 2                 # Depth of the quantum circuit (number of variational layers)\n",
        "gamma_lr_scheduler = 0.1    # Learning rate reduction applied every 10 epochs.\n",
        "q_delta = 0.01              # Initial spread of random quantum weights\n",
        "start_time = time.time()    # Start of the computation timer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlU2Q7zd8PAR"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 320,
      "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": 321,
      "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": 322,
      "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": 323,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "e7ddf32d-584f-400a-df46-e47802b61a1c"
      },
      "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": 324,
      "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": 325,
      "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": 326,
      "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": 327,
      "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": 328,
      "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": 329,
      "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": 330,
      "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": 331,
      "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": "491ebf2d-c85d-44b6-8cfb-9ae5780e286e"
      },
      "execution_count": 332,
      "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": 333,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "0479d8a2-6954-4d20-caf2-505ecf56b999"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2702 Acc: 0.1228        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.1462 Acc: 0.1276        \n",
            "Phase: train Epoch: 2/5 Loss: 2.1092 Acc: 0.1442        \n",
            "Phase: validation   Epoch: 2/5 Loss: 2.0117 Acc: 0.1826        \n",
            "Phase: train Epoch: 3/5 Loss: 2.0240 Acc: 0.2125        \n",
            "Phase: validation   Epoch: 3/5 Loss: 1.9472 Acc: 0.2704        \n",
            "Phase: train Epoch: 4/5 Loss: 1.9806 Acc: 0.2493        \n",
            "Phase: validation   Epoch: 4/5 Loss: 1.8617 Acc: 0.2704        \n",
            "Phase: train Epoch: 5/5 Loss: 1.9205 Acc: 0.2934        \n",
            "Phase: validation   Epoch: 5/5 Loss: 1.8080 Acc: 0.3392        \n",
            "Training completed in 8m 58s\n",
            "Best test loss: 1.8080 | 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": 334,
      "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": 335,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "8b670298-f89b-47e9-9e32-2a8373f08e64"
      },
      "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": "c08303fa-0a08-4691-b281-5253b46ca039"
      },
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695691129.2879126\n",
            "Tue Sep 26 01:18:49 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 337,
      "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
}