[404218]: / Code / PennyLane / Quantum Parameters / 02 Class 4 Depth kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "UJOq3mdA8PAH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a14be6a9-579b-4f64-94cb-44b2d5a02d0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1695627145.895063\n",
            "Mon Sep 25 07:32:25 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": 122,
      "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": 123,
      "metadata": {
        "id": "5LRcEYZg8PAR"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 5              # Number of training epochs\n",
        "q_depth = 4                 # Depth of the quantum circuit (number of variational layers)\n",
        "gamma_lr_scheduler = 0.1    # Learning rate reduction applied every 10 epochs.\n",
        "q_delta = 0.01              # Initial spread of random quantum weights\n",
        "start_time = time.time()    # Start of the computation timer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlU2Q7zd8PAR"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "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": 125,
      "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": 126,
      "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": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "QzIKQxS78PAU",
        "outputId": "8e870c0a-bf6a-4156-f84c-f02fff0ebc86"
      },
      "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": 128,
      "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": 129,
      "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": 130,
      "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": 131,
      "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": 132,
      "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": 133,
      "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": 134,
      "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": 135,
      "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": "d70e50ee-c1c1-4ff5-dc7c-da6a30e676e4"
      },
      "execution_count": 136,
      "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": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5VgfdD3-8PAZ",
        "outputId": "195a467d-0214-49d8-cd5b-529c44ca8a9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/5 Loss: 0.5980 Acc: 0.6803        \n",
            "Phase: validation   Epoch: 1/5 Loss: 0.4144 Acc: 0.9281        \n",
            "Phase: train Epoch: 2/5 Loss: 0.5057 Acc: 0.7746        \n",
            "Phase: validation   Epoch: 2/5 Loss: 0.3590 Acc: 0.9346        \n",
            "Phase: train Epoch: 3/5 Loss: 0.4202 Acc: 0.8607        \n",
            "Phase: validation   Epoch: 3/5 Loss: 0.3125 Acc: 0.9412        \n",
            "Phase: train Epoch: 4/5 Loss: 0.4591 Acc: 0.8156        \n",
            "Phase: validation   Epoch: 4/5 Loss: 0.3321 Acc: 0.9412        \n",
            "Phase: train Epoch: 5/5 Loss: 0.4519 Acc: 0.8033        \n",
            "Phase: validation   Epoch: 5/5 Loss: 0.2821 Acc: 0.9477        \n",
            "Training completed in 1m 35s\n",
            "Best test loss: 0.2821 | Best test accuracy: 0.9477\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": 138,
      "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": 139,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "mKBJn2x68PAa",
        "outputId": "630ceed3-4d5e-46ae-9355-09e190e4f949"
      },
      "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": "340e07d7-d81c-4281-ef62-4c8252e914fd"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1695627243.6547954\n",
            "Mon Sep 25 07:34:03 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import runtime\n",
        "# runtime.unassign()"
      ],
      "metadata": {
        "id": "fALJ8tZXA0to"
      },
      "execution_count": 141,
      "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"
      ]
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