963 lines (963 with data), 230.8 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "48390074-09e6-4832-9e91-2cb5bea38e45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695670907.4286964\n",
            "Mon Sep 25 19:41:47 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": 87,
      "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": 88,
      "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 = 4                 # 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": 89,
      "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": 90,
      "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": 91,
      "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": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "3f2547ff-0af6-4f0b-9e5b-935cc0e7b89e"
      },
      "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": 93,
      "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": 94,
      "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": 95,
      "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": 96,
      "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": 97,
      "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": 98,
      "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": 99,
      "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": 100,
      "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": "549ceb96-4c78-4a24-c7b6-bb677bbf05a3"
      },
      "execution_count": 101,
      "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": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "cabdd391-d8fb-44ce-c0f7-7a6d84f6c931"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.7345 Acc: 0.0317        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.5530 Acc: 0.0898        \n",
            "Phase: train Epoch: 2/5 Loss: 3.4885 Acc: 0.1044        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.3518 Acc: 0.1526        \n",
            "Phase: train Epoch: 3/5 Loss: 3.3553 Acc: 0.1457        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.2684 Acc: 0.1490        \n",
            "Phase: train Epoch: 4/5 Loss: 3.2689 Acc: 0.1721        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.1182 Acc: 0.2370        \n",
            "Phase: train Epoch: 5/5 Loss: 3.1718 Acc: 0.1838        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.0580 Acc: 0.2184        \n",
            "Training completed in 15m 58s\n",
            "Best test loss: 3.0580 | Best test accuracy: 0.2370\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": 103,
      "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": 104,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "2cca0efc-1bec-4374-9322-6b4158572476"
      },
      "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": "548676fb-3805-4c0b-95b2-458ed99d8e17"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695671867.7294097\n",
            "Mon Sep 25 19:57:47 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 106,
      "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
}