963 lines (963 with data), 228.1 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "281d6cff-001c-4f27-fead-1c989ebd193e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695668454.7868545\n",
            "Mon Sep 25 19:00:54 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": 24,
      "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": 25,
      "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 = 1                 # 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": 26,
      "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": 27,
      "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": 28,
      "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": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "4b9e874e-23b0-4944-9440-ca62cddcaf92"
      },
      "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": 30,
      "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": 31,
      "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": 32,
      "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": 33,
      "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": 34,
      "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": 35,
      "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": 36,
      "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": 37,
      "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": "dfbaf6f4-e5d7-4be1-9524-78f1a392dd0d"
      },
      "execution_count": 38,
      "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": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "35c92f19-3865-42cd-8565-18e058af24f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 3.6888 Acc: 0.0663        \n",
            "Phase: validation   Epoch: 1/5 Loss: 3.6043 Acc: 0.0604        \n",
            "Phase: train Epoch: 2/5 Loss: 3.5442 Acc: 0.0748        \n",
            "Phase: validation   Epoch: 2/5 Loss: 3.4840 Acc: 0.0910        \n",
            "Phase: train Epoch: 3/5 Loss: 3.4677 Acc: 0.0937        \n",
            "Phase: validation   Epoch: 3/5 Loss: 3.4613 Acc: 0.1053        \n",
            "Phase: train Epoch: 4/5 Loss: 3.4257 Acc: 0.1090        \n",
            "Phase: validation   Epoch: 4/5 Loss: 3.4012 Acc: 0.1155        \n",
            "Phase: train Epoch: 5/5 Loss: 3.4138 Acc: 0.1094        \n",
            "Phase: validation   Epoch: 5/5 Loss: 3.3770 Acc: 0.1203        \n",
            "Training completed in 9m 12s\n",
            "Best test loss: 3.3770 | Best test accuracy: 0.1203\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": 40,
      "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": 41,
      "metadata": {
        "id": "mKBJn2x68PAa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "e44cb17e-0bb2-48de-f540-992fdfcc51c1"
      },
      "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": "71a99af0-7c64-4b9b-a9b6-f3d551f0a0aa"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695669009.1557386\n",
            "Mon Sep 25 19:10:09 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 43,
      "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
}