[404218]: / Code / PennyLane / Algorithm Prototypings I / 03 YNYY 28.3% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 22,
      "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": 23,
      "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": 24,
      "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": 25,
      "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": 26,
      "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": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "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": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "u55iZYEOrd0D",
        "outputId": "c570365e-4feb-40c0-edc6-63f27e8fea70"
      },
      "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": 30,
      "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": 31,
      "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": 32,
      "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": 33,
      "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": 34,
      "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": 35,
      "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": 36,
      "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": 37,
      "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": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "083b52ae-8419-4339-ffed-5c23359ecfaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/1 Loss: 1.3911 Acc: 0.2454        \n",
            "Phase: validation   Epoch: 1/1 Loss: 1.3837 Acc: 0.2833        \n",
            "Training completed in 3m 11s\n",
            "Best test loss: 1.3837 | Best test accuracy: 0.2833\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": 39,
      "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": 40,
      "metadata": {
        "id": "-RoYcZQXrd0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "3b593ef6-c66d-45aa-b9ca-5492391f250c"
      },
      "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
}