963 lines (963 with data), 230.0 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 380,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2227ce8f-ce90-4ded-c782-c76bc05d9207"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695693614.2635858\n",
            "Tue Sep 26 02:00:14 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": 381,
      "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": 382,
      "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 = 5                 # 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": 383,
      "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": 384,
      "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": 385,
      "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": 386,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "0c617458-4d0f-4098-f42e-fd0e2d7a3356"
      },
      "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": 387,
      "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": 388,
      "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": 389,
      "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": 390,
      "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": 391,
      "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": 392,
      "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": 393,
      "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": 394,
      "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": "006a830d-e5d0-4b2b-fc9b-bbe0150f3edd"
      },
      "execution_count": 395,
      "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": 396,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "b2d82b43-6080-461f-c4cf-9cdc52086c6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2479 Acc: 0.1633        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.0743 Acc: 0.2322        \n",
            "Phase: train Epoch: 2/5 Loss: 2.0730 Acc: 0.2511        \n",
            "Phase: validation   Epoch: 2/5 Loss: 1.9179 Acc: 0.3782        \n",
            "Phase: train Epoch: 3/5 Loss: 1.9648 Acc: 0.3062        \n",
            "Phase: validation   Epoch: 3/5 Loss: 1.8355 Acc: 0.3468        \n",
            "Phase: train Epoch: 4/5 Loss: 1.9123 Acc: 0.2971        \n",
            "Phase: validation   Epoch: 4/5 Loss: 1.8286 Acc: 0.3201        \n",
            "Phase: train Epoch: 5/5 Loss: 1.8372 Acc: 0.3248        \n",
            "Phase: validation   Epoch: 5/5 Loss: 1.6882 Acc: 0.3927        \n",
            "Training completed in 14m 16s\n",
            "Best test loss: 1.6882 | Best test accuracy: 0.3927\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": 397,
      "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": 398,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "35b7716f-4344-42bb-f57f-30c7c116b013"
      },
      "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": "66997c50-7025-4c30-b904-6d4ec2d68dc1"
      },
      "execution_count": 399,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695694473.4356356\n",
            "Tue Sep 26 02:14:33 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 400,
      "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
}