[404218]: / Code / PennyLane / Algorithm Prototypings I / 02 YYNN 52.7% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 71,
      "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": 72,
      "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": 73,
      "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": 74,
      "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": 75,
      "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": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "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": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "u55iZYEOrd0D",
        "outputId": "aa553cff-82bc-4533-c13d-74ed871da0fe"
      },
      "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": 79,
      "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": 80,
      "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": 81,
      "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": 82,
      "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": 83,
      "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": 84,
      "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": 85,
      "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": 86,
      "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": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "9811a34a-a94b-454e-84e3-ab02081ea88f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/1 Loss: 1.2518 Acc: 0.4513        \n",
            "Phase: validation   Epoch: 1/1 Loss: 1.0856 Acc: 0.5273        \n",
            "Training completed in 1m 50s\n",
            "Best test loss: 1.0856 | Best test accuracy: 0.5273\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": 88,
      "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": 89,
      "metadata": {
        "id": "-RoYcZQXrd0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "95681cc3-0d5d-4c76-d5a8-17a474a16586"
      },
      "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
}