985 lines (984 with data), 229.2 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 54,
      "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": 55,
      "metadata": {
        "id": "sANL984ird0B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a318de89-c5d4-4ee2-ad70-4193e1d00536"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1689912643.4020953\n",
            "Fri Jul 21 04:10:43 2023\n"
          ]
        }
      ],
      "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\"] = \"16\"\n",
        "\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "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": 56,
      "metadata": {
        "id": "Wncy61Mdrd0B"
      },
      "outputs": [],
      "source": [
        "n_qubits = 17               # Number of qubits\n",
        "step = 0.0006               # Learning rate\n",
        "batch_size = 17             # 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": 57,
      "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": 58,
      "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",
      "execution_count": 59,
      "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",
        "# data_dir = \"/content/drive/MyDrive/Colab Notebooks/data/1image44classConfusionMatrixCheck\"\n",
        "data_dir = \"/content/drive/MyDrive/Colab Notebooks/data/44 Class 4478 Brain Tumor Images Split 0.627 Shuffle Rename\"\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": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "u55iZYEOrd0D",
        "outputId": "965a0601-b5ff-46d5-9a4e-a42425ef1e24"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "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": 61,
      "metadata": {
        "id": "OIbOa-KBrd0D"
      },
      "outputs": [],
      "source": [
        "def H_layer(nqubits):\n",
        "    \"\"\"Layer of single-qubit Hadamard gates.\n",
        "    \"\"\"\n",
        "    for idx in range(nqubits):\n",
        "        qml.Hadamard(wires=idx)\n",
        "\n",
        "def 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"
      ]
    },
    {
      "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": 62,
      "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",
        "    # 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": 63,
      "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, 44)\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": 64,
      "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": 65,
      "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": 66,
      "metadata": {
        "id": "_K9M-VPMrd0E"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step, weight_decay = 0.01)"
      ]
    },
    {
      "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": 67,
      "metadata": {
        "id": "nco3IrE6rd0E"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=1000, 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": 68,
      "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": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import copy\n",
        "import time\n",
        "import sklearn\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "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",
        "            all_labels = []\n",
        "            all_preds = []\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",
        "\n",
        "                all_labels.extend(labels.cpu().numpy())\n",
        "                all_preds.extend(preds.cpu().numpy())\n",
        "\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",
        "            np.set_printoptions(threshold=44*44, linewidth=1000)  # Set the linewidth to a higher value\n",
        "\n",
        "            # Calculate and print confusion matrix\n",
        "            if phase == \"validation\":\n",
        "                cm = confusion_matrix(all_labels, all_preds)\n",
        "                print(\"Confusion Matrix:\")\n",
        "                print(cm)\n",
        "\n",
        "    # Print final results"
      ],
      "metadata": {
        "id": "tMGx77J_zSLE"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmAl9fIQrd0E"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "18a9df99-6a65-4d86-99c2-60d43ade7e04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/1 Loss: 3.4209 Acc: 0.1478        \n",
            "Phase: validation   Epoch: 1/1 Loss: 3.1637 Acc: 0.2370        \n",
            "Confusion Matrix:\n",
            "[[ 0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 48  1  0 13  2  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0]\n",
            " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  9 46  0  6  1  0  0  0  0  0  0  0  0  0  2  2  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  9  9  2 35  0  0  0  0  0  0  0  0  0  0  9  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 24  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20 14  0  4  0  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 18  1  1  4  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 10  2  0  3  1  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2  6  0  3  2  0  0  0  0  0  0  0  0  0  1  4  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  5  2  0  7  0  0  0  0  0  0  0  0  0  1  6  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2  4  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  3  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  8  0  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2  7  0  2  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2  1  0  9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  1  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  4 17  0  5  1  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  2  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2  1  0  1  2  0  0  0  0  0  0  0  0  0  2  3  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  8  0  0  0  0  0  0  0  0  0  0  0  0  2  1  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  2  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  3 13  0  1  3  0  1  0  0  0  0  0  0  0  0  4  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0  8  0  0  0  0  0  0  0  0  0  0  3  1  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 94  2  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 16  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 28 76  0 11  1  0  4  0  0  0  0  0  0  0  0  2  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2 13 11  2 58  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0]\n",
            " [ 0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 66  5  0 18  0  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  6  1  1 92  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 46  1  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0 24  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  8 24  0  1  0  0 26  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 10 16  1 10  0  0  0  0  0  0  0  0  0  0  2  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 30  1  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2 16  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 25  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 13  0  0  2  0  0  0  0  0  0  0  0  0  0  2  5  0  0  0  0]\n",
            " [ 0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  3 16  0  4  1  0  7  0  0  0  0  0  0  0  2  6  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2  2  1  2  8  0  0  0  0  0  0  0  0  0  0  7  1  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 15 13  0  6  2  0  0  0  0  0  0  0  0  0 17  2  0  0  0  0]\n",
            " [ 0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  3 19  0  3  0  0  0  0  0  0  0  0  0  0  8 37  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  8  1  3 13  0  0  0  0  0  0  0  0  0  2 15  4  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  1  0  4  0  0  0  0  0  0  0  0  0  0  0  2  0  0  0  0]\n",
            " [ 0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 13  0  4  1  0  0  0  0  0  0  0  0  0  1  8  0  0  0  0]\n",
            " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  2  9  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]]\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = train_model(\n",
        "    model_hybrid, criterion, optimizer_hybrid, exp_lr_scheduler, num_epochs=num_epochs\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "id": "-qU71GfkJOnf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7822367e-6c7b-4e27-c025-b378462f0c0a"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1689913245.5120208\n",
            "Fri Jul 21 04:20:45 2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import runtime\n",
        "runtime.unassign()"
      ],
      "metadata": {
        "id": "AICv9u8UJLkK"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "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",
      "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"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}