[404218]: / Code / PennyLane / 2 Class 4 Class 10 Class / 04 Class 66.2% 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 = 12                # 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": "f949acb3-9854-45db-8c17-099e18325509"
      },
      "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": "e5d2ddee-a3a9-4539-a1a3-c7c8b5149b08"
      },
      "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, 174MB/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": "99d703f0-9954-42e3-b517-2e0208720a0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/1 Loss: 1.2173 Acc: 0.5456        \n",
            "Phase: validation   Epoch: 1/1 Loss: 1.0896 Acc: 0.6621        \n",
            "Training completed in 10m 21s\n",
            "Best test loss: 1.0896 | Best test accuracy: 0.6621\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": "612bf1af-0dc5-4d10-8cde-22ebaa74861c"
      },
      "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
}