[404218]: / Code / PennyLane / Quantum Parameters / 02 Class 7 Depth kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "50107e29-64f9-43ce-8fce-19aff8a129fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695627666.1410704\n",
            "Mon Sep 25 07:41:06 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": 185,
      "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": 186,
      "metadata": {
        "id": "5LRcEYZg8PAR"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 5              # Number of training epochs\n",
        "q_depth = 7                 # Depth of the quantum circuit (number of variational layers)\n",
        "gamma_lr_scheduler = 0.1    # Learning rate reduction applied every 10 epochs.\n",
        "q_delta = 0.01              # Initial spread of random quantum weights\n",
        "start_time = time.time()    # Start of the computation timer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlU2Q7zd8PAR"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 187,
      "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": 188,
      "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": 189,
      "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/hymenoptera_data\"\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": 190,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "22b3f22c-ec06-4efa-f58f-90c3fb923d4a"
      },
      "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": 191,
      "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": 192,
      "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": 193,
      "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, 2)\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": 194,
      "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": 195,
      "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": 196,
      "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": 197,
      "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": 198,
      "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": "1bec425b-a7c3-4d48-bb26-bd0137f3b0e9"
      },
      "execution_count": 199,
      "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": 200,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "5cd2fd87-f35b-47a9-a14b-be91b271dee2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 0.5983 Acc: 0.7049        \n",
            "Phase: validation   Epoch: 1/5 Loss: 0.4870 Acc: 0.7124        \n",
            "Phase: train Epoch: 2/5 Loss: 0.4607 Acc: 0.7869        \n",
            "Phase: validation   Epoch: 2/5 Loss: 0.3907 Acc: 0.8693        \n",
            "Phase: train Epoch: 3/5 Loss: 0.3941 Acc: 0.8566        \n",
            "Phase: validation   Epoch: 3/5 Loss: 0.3574 Acc: 0.8693        \n",
            "Phase: train Epoch: 4/5 Loss: 0.4065 Acc: 0.8361        \n",
            "Phase: validation   Epoch: 4/5 Loss: 0.2953 Acc: 0.9216        \n",
            "Phase: train Epoch: 5/5 Loss: 0.3668 Acc: 0.8689        \n",
            "Phase: validation   Epoch: 5/5 Loss: 0.2494 Acc: 0.9281        \n",
            "Training completed in 2m 9s\n",
            "Best test loss: 0.2494 | Best test accuracy: 0.9281\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": 201,
      "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": 202,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "mKBJn2x68PAa",
        "outputId": "52f7a3f3-cb2a-4266-fa36-52640d00539b"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "D3AaQc2xMk-G",
        "outputId": "c2dc3ddb-f6ab-4108-9e7f-6a2ab9bcdc95"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695627797.8043034\n",
            "Mon Sep 25 07:43:17 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 204,
      "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
}