963 lines (963 with data), 220.7 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 254,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "39f96a65-91bf-46ed-8992-1de914e8a078"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695684207.3565943\n",
            "Mon Sep 25 23:23:27 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": 255,
      "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": 256,
      "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 = 9                 # 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": 257,
      "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": 258,
      "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": 259,
      "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": 260,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "c20af4da-3d00-481e-d09d-4bdcba7f1783"
      },
      "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": 261,
      "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": 262,
      "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": 263,
      "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": 264,
      "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": 265,
      "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": 266,
      "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": 267,
      "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": 268,
      "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": "363c218b-1d6d-45c5-d28e-096865bbdc06"
      },
      "execution_count": 269,
      "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": 270,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "a558c8c7-7f49-46db-8424-b0bbc6e097db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.6942 Acc: 0.0656        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.5836 Acc: 0.0652        \n",
            "Phase: train Epoch: 2/5 Loss: 3.5333 Acc: 0.1233        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.4668 Acc: 0.1376        \n",
            "Phase: train Epoch: 3/5 Loss: 3.4530 Acc: 0.1279        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.3955 Acc: 0.1376        \n",
            "Phase: train Epoch: 4/5 Loss: 3.4022 Acc: 0.1350        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.3465 Acc: 0.1382        \n",
            "Phase: train Epoch: 5/5 Loss: 3.3657 Acc: 0.1336        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.3161 Acc: 0.1412        \n",
            "Training completed in 27m 14s\n",
            "Best test loss: 3.3161 | Best test accuracy: 0.1412\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": 271,
      "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": 272,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "f7eab1f4-b78f-469a-cdcf-913c1b1e00ab"
      },
      "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": "7fba4dd7-9afb-4ddd-af08-fb2ba2045d16"
      },
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695685844.7978876\n",
            "Mon Sep 25 23:50:44 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 274,
      "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
}