963 lines (963 with data), 221.0 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 233,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1f993108-6259-4da1-948e-c531b2b96085"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695682529.2749143\n",
            "Mon Sep 25 22:55:29 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": 234,
      "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": 235,
      "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 = 8                 # 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": 236,
      "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": 237,
      "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": 238,
      "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": 239,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "7d72e40f-e219-4a27-d91d-aee11217cedb"
      },
      "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": 240,
      "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": 241,
      "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": 242,
      "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": 243,
      "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": 244,
      "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": 245,
      "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": 246,
      "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": 247,
      "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": "12ace8b3-3181-4ec7-a55c-8c03c459940f"
      },
      "execution_count": 248,
      "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": 249,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "42110218-d809-4ae3-8b84-c18a93c0e8bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.7024 Acc: 0.0570        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.5662 Acc: 0.0670        \n",
            "Phase: train Epoch: 2/5 Loss: 3.5298 Acc: 0.0727        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.4433 Acc: 0.0993        \n",
            "Phase: train Epoch: 3/5 Loss: 3.4410 Acc: 0.0990        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.4010 Acc: 0.0916        \n",
            "Phase: train Epoch: 4/5 Loss: 3.3893 Acc: 0.1179        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.3210 Acc: 0.1346        \n",
            "Phase: train Epoch: 5/5 Loss: 3.3444 Acc: 0.1254        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.2856 Acc: 0.1370        \n",
            "Training completed in 25m 10s\n",
            "Best test loss: 3.2856 | Best test accuracy: 0.1370\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": 250,
      "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": 251,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "d8c77418-fdb4-4cf2-8267-6f4d211e0231"
      },
      "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": "701d1e1c-8e46-4f9f-bc35-7b768d88f97a"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695684042.3270476\n",
            "Mon Sep 25 23:20:42 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 253,
      "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
}