[404218]: / Code / PennyLane / Algorithm Prototypings I / 01 NYYY 55.3% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "WNXEyZ23rdz-"
      },
      "outputs": [],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "WYVI3RMhAdap"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "sANL984ird0B"
      },
      "outputs": [],
      "source": [
        "#!pip install pennylane\n",
        "# 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 time\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": "o7eGTk_Prd0B"
      },
      "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": 5,
      "metadata": {
        "id": "Wncy61Mdrd0B"
      },
      "outputs": [],
      "source": [
        "n_qubits = 8                # Number of qubits\n",
        "step = 0.0006               # Learning rate\n",
        "batch_size = 6              # Number of samples for each training step\n",
        "num_epochs = 1              # Number of training epochs\n",
        "q_depth = 6                 # 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": "PVqjLo8Rrd0B"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "qLOa5trRrd0B"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=n_qubits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxlK7Jjtrd0C"
      },
      "source": [
        "We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
        "used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Br_YGwRDrd0C"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFHuYd5xrd0C"
      },
      "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",
      "source": [
        "#os.system(\"rm -rf /content/data/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/train/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/val/.ipynb_checkpoints\")"
      ],
      "metadata": {
        "id": "DM8SDO3Wthcc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "jQrNNVnUrd0C"
      },
      "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/4_cl_2xbraintumor_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": "piWk71nkrd0C"
      },
      "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": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "u55iZYEOrd0D",
        "outputId": "b208750a-c60b-439d-acf7-743a1db4a95a"
      },
      "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": "ds09ighxrd0D"
      },
      "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": 11,
      "metadata": {
        "id": "OIbOa-KBrd0D"
      },
      "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",
        "\n",
        "\n",
        "def entangling_layer(nqubits):\n",
        "    \"\"\"Layer of CNOTs followed by another shifted layer of CNOT.\n",
        "    \"\"\"\n",
        "    # In other words it should apply something like :\n",
        "    # CNOT  CNOT  CNOT  CNOT...  CNOT\n",
        "    #   CNOT  CNOT  CNOT...  CNOT\n",
        "    for i in range(0, nqubits - 1, 2):  # Loop over even indices: i=0,2,...N-2\n",
        "        qml.CNOT(wires=[i, i + 1])\n",
        "    for i in range(1, nqubits - 1, 2):  # Loop over odd indices:  i=1,3,...N-3\n",
        "        qml.CNOT(wires=[i, i + 1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ue__y4sQrd0D"
      },
      "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": 12,
      "metadata": {
        "id": "C-O_cK6jrd0D"
      },
      "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",
        "        entangling_layer(n_qubits)\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": "7FNHREgVrd0D"
      },
      "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": 13,
      "metadata": {
        "id": "C96kWCjWrd0D"
      },
      "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(512, n_qubits)\n",
        "        self.q_params = nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))\n",
        "        self.post_net = nn.Linear(n_qubits, 4)\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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hQlpDQdrd0D"
      },
      "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": 14,
      "metadata": {
        "id": "MAh4FqBYrd0D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d71f9b2f-b0d6-4a81-eeb4-dc18d9a769a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 225MB/s]\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = torchvision.models.resnet18(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": "ovX-Tkb0rd0E"
      },
      "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": 15,
      "metadata": {
        "id": "gkmFIK4Brd0E"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhFORuvard0E"
      },
      "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": 16,
      "metadata": {
        "id": "_K9M-VPMrd0E"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGG8pksyrd0E"
      },
      "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": 17,
      "metadata": {
        "id": "nco3IrE6rd0E"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJvpPNCRrd0E"
      },
      "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": 18,
      "metadata": {
        "id": "8uO5BrOPrd0E"
      },
      "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": "CmAl9fIQrd0E"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "fb4287bf-12ff-4c28-d497-18d0660c9543"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/1 Loss: 1.2981 Acc: 0.3976        \n",
            "Phase: validation   Epoch: 1/1 Loss: 1.2214 Acc: 0.5529        \n",
            "Training completed in 11m 43s\n",
            "Best test loss: 1.2214 | Best test accuracy: 0.5529\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": "s4cEc4mird0E"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARvjv_lbrd0E"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "CtUY4xU_rd0E"
      },
      "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": "F6I5Rk92rd0E"
      },
      "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": 21,
      "metadata": {
        "id": "-RoYcZQXrd0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "117e96f2-051a-4880-8046-c353e49a60a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_model(model_hybrid, num_images=6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBXZTnzjrd0E"
      },
      "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",
      "language": "python",
      "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": "3.9.16"
    },
    "colab": {
      "provenance": [],
      "gpuType": "V100"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}