[404218]: / Code / PennyLane / Algorithm Prototypings I / 08 YYRYYRY 59.0% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 23,
      "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": 24,
      "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": 25,
      "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": 26,
      "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": 27,
      "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": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "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": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "u55iZYEOrd0D",
        "outputId": "4dfd103a-252d-4d29-c8f8-067e6ad26c23"
      },
      "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": 31,
      "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": 32,
      "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",
        "        RY_layer(q_weights[k])\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": 33,
      "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": 34,
      "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": 35,
      "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": 36,
      "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": 37,
      "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": 38,
      "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": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "a1b78c31-dc16-4ead-9f17-1d31762173d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/1 Loss: 1.2022 Acc: 0.5000        \n",
            "Phase: validation   Epoch: 1/1 Loss: 1.0373 Acc: 0.5904        \n",
            "Training completed in 5m 35s\n",
            "Best test loss: 1.0373 | Best test accuracy: 0.5904\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": 40,
      "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": 41,
      "metadata": {
        "id": "-RoYcZQXrd0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "a81c09e1-fbeb-4d5b-e41d-c2a8c62800fc"
      },
      "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
}