963 lines (963 with data), 224.4 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fb16ae25-85d5-40bc-b43b-a048d23eb79e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695673141.9990485\n",
            "Mon Sep 25 20:19:01 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": 129,
      "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": 130,
      "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 = 6                 # 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": 131,
      "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": 132,
      "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": 133,
      "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": 134,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "e0a788f6-f460-4296-d803-5478388db0dd"
      },
      "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": 135,
      "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": 136,
      "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": 137,
      "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": 138,
      "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": 139,
      "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": 140,
      "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": 141,
      "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": 142,
      "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": "eb7d9c51-38ad-4fbe-d0fc-b581534276a5"
      },
      "execution_count": 143,
      "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": 144,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "9619225f-799d-4313-a214-fc60fdbbfafd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.7385 Acc: 0.0588        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.6093 Acc: 0.0922        \n",
            "Phase: train Epoch: 2/5 Loss: 3.6316 Acc: 0.0794        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.5438 Acc: 0.0993        \n",
            "Phase: train Epoch: 3/5 Loss: 3.5113 Acc: 0.0926        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.4780 Acc: 0.0904        \n",
            "Phase: train Epoch: 4/5 Loss: 3.4638 Acc: 0.1047        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.4252 Acc: 0.1059        \n",
            "Phase: train Epoch: 5/5 Loss: 3.4307 Acc: 0.1040        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.3979 Acc: 0.1197        \n",
            "Training completed in 20m 33s\n",
            "Best test loss: 3.3979 | Best test accuracy: 0.1197\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": 145,
      "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": 146,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "fc85e06f-88a7-48fa-8a8a-d1b1512d4f91"
      },
      "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": "784a5558-6838-4bcf-a233-cb4da0a7d0d9"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695674378.3652003\n",
            "Mon Sep 25 20:39:38 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 148,
      "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
}