964 lines (963 with data), 238.5 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 443,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cfc91fa1-9c4e-44cc-a600-6f4d149dd0e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695696940.1295893\n",
            "Tue Sep 26 02:55:40 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": 444,
      "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": 445,
      "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 = 8                 # 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": 446,
      "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": 447,
      "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": 448,
      "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": 449,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "55823e0b-d4f1-400c-d977-7d95c0e18b18"
      },
      "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": 450,
      "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": 451,
      "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": 452,
      "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": 453,
      "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": 454,
      "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": 455,
      "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": 456,
      "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": 457,
      "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": "66ebfb1d-5449-408d-a61f-f2264f3953e9"
      },
      "execution_count": 458,
      "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": 459,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "90f8608e-aa55-4ce0-969e-4caff873f465"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 2.2026 Acc: 0.2093        \n",
            "Phase: validation   Epoch: 1/5 Loss: 2.0288 Acc: 0.2659        \n",
            "Phase: train Epoch: 2/5 Loss: 2.0414 Acc: 0.2716        \n",
            "Phase: validation   Epoch: 2/5 Loss: 1.9228 Acc: 0.3117        \n",
            "Phase: train Epoch: 3/5 Loss: 1.9595 Acc: 0.2944        \n",
            "Phase: validation   Epoch: 3/5 Loss: 1.8117 Acc: 0.3728        \n",
            "Phase: train Epoch: 4/5 Loss: 1.9000 Acc: 0.3280        \n",
            "Phase: validation   Epoch: 4/5 Loss: 1.7902 Acc: 0.3728        \n",
            "Phase: train Epoch: 5/5 Loss: 1.8263 Acc: 0.3435        \n",
            "Phase: validation   Epoch: 5/5 Loss: 1.6910 Acc: 0.4156        \n",
            "Training completed in 19m 24s\n",
            "Best test loss: 1.6910 | Best test accuracy: 0.4156\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": 460,
      "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": 461,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "93116c97-5cd0-4cbb-d747-90037371c4a5"
      },
      "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": "f982c3c4-768d-408b-9ecb-efa2c87d1b39"
      },
      "execution_count": 462,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695698106.8667762\n",
            "Tue Sep 26 03:15:06 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 463,
      "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
}