[404218]: / Code / PennyLane / Algorithm Prototypings I / 12 YYNN 52.9% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 196,
      "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": 197,
      "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": 198,
      "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": 199,
      "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": 200,
      "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": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 202,
      "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": 203,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "u55iZYEOrd0D",
        "outputId": "3a6d8cab-757c-466a-e23c-cab9f19b9a63"
      },
      "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": 204,
      "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",
        "def circuit(f=None):\n",
        "    qml.AmplitudeEmbedding(features=f, wires=range(8), pad_with=0., normalize=True)\n",
        "    \n",
        "def RY_layer(w):\n",
        "  \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",
        "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": 205,
      "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",
        "    circuit(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": 206,
      "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": 207,
      "metadata": {
        "id": "MAh4FqBYrd0D"
      },
      "outputs": [],
      "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": 208,
      "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": 209,
      "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": 210,
      "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": 211,
      "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": 212,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "6ab56ab3-382f-440b-d4ac-58d78303c092"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/1 Loss: 1.2345 Acc: 0.4564        \n",
            "Phase: validation   Epoch: 1/1 Loss: 1.1682 Acc: 0.5290        \n",
            "Training completed in 0m 38s\n",
            "Best test loss: 1.1682 | Best test accuracy: 0.5290\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": 213,
      "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": 214,
      "metadata": {
        "id": "-RoYcZQXrd0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "0c8bbb7d-ac99-4ce1-e3e0-7972b52ac3b8"
      },
      "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
}