963 lines (963 with data), 223.0 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ff6d220e-ffab-43e4-caae-ed4b5cef5fd4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695674476.3472805\n",
            "Mon Sep 25 20:41:16 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": 150,
      "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": 151,
      "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 = 7                 # 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": 152,
      "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": 153,
      "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": 154,
      "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": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "53d75d74-645d-4e7e-dd7d-955652af9736"
      },
      "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": 156,
      "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": 157,
      "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": 158,
      "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": 159,
      "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": 160,
      "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": 161,
      "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": 162,
      "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": 163,
      "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": "25a1c11d-96ab-4160-d74c-27deefcad00b"
      },
      "execution_count": 164,
      "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": 165,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "4585e88a-fdd5-4fac-f6c4-2be5e17acf34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.7315 Acc: 0.0477        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.5724 Acc: 0.0592        \n",
            "Phase: train Epoch: 2/5 Loss: 3.4969 Acc: 0.0973        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.3900 Acc: 0.1233        \n",
            "Phase: train Epoch: 3/5 Loss: 3.3835 Acc: 0.1243        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.2770 Acc: 0.1676        \n",
            "Phase: train Epoch: 4/5 Loss: 3.3029 Acc: 0.1432        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.2130 Acc: 0.1664        \n",
            "Phase: train Epoch: 5/5 Loss: 3.2474 Acc: 0.1464        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.1457 Acc: 0.1718        \n",
            "Training completed in 22m 57s\n",
            "Best test loss: 3.1457 | Best test accuracy: 0.1718\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": 166,
      "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": 167,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "30fb5cb6-5ce2-4381-dc02-c611a7892647"
      },
      "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": "216807eb-71fb-4b75-8e49-72551002d302"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695675856.449247\n",
            "Mon Sep 25 21:04:16 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 169,
      "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
}