964 lines (963 with data), 227.1 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 485,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e0835bb0-e50b-4ba2-924b-4f697535a476"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695699782.5688365\n",
            "Tue Sep 26 03:43:02 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": 486,
      "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": 487,
      "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 = 10                # 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": 488,
      "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": 489,
      "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": 490,
      "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": 491,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "fe12ad19-ca73-4a13-aa84-110005776c28"
      },
      "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": 492,
      "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": 493,
      "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": 494,
      "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": 495,
      "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": 496,
      "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": 497,
      "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": 498,
      "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": 499,
      "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": "a95f7dc1-3d21-417f-b96d-f2ea939b245f"
      },
      "execution_count": 500,
      "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": 501,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "68951204-7c05-4dea-ae93-06e6cc34adc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2417 Acc: 0.1729        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.0541 Acc: 0.2620        \n",
            "Phase: train Epoch: 2/5 Loss: 2.0664 Acc: 0.2348        \n",
            "Phase: validation   Epoch: 2/5 Loss: 1.9575 Acc: 0.2888        \n",
            "Phase: train Epoch: 3/5 Loss: 2.0155 Acc: 0.2666        \n",
            "Phase: validation   Epoch: 3/5 Loss: 1.8787 Acc: 0.3186        \n",
            "Phase: train Epoch: 4/5 Loss: 1.9477 Acc: 0.2862        \n",
            "Phase: validation   Epoch: 4/5 Loss: 1.8390 Acc: 0.3331        \n",
            "Phase: train Epoch: 5/5 Loss: 1.8985 Acc: 0.3007        \n",
            "Phase: validation   Epoch: 5/5 Loss: 1.7823 Acc: 0.3384        \n",
            "Training completed in 22m 55s\n",
            "Best test loss: 1.7823 | Best test accuracy: 0.3384\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": 502,
      "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": 503,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "a9b5d325-05bb-4eec-b214-748464548bf7"
      },
      "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": "429b550c-4161-4604-ed1e-0bb9ffa2956f"
      },
      "execution_count": 504,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695701160.1043723\n",
            "Tue Sep 26 04:06:00 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 505,
      "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
}