LLMs-Pharmaceutical / Git / [404218] /Code/PennyLane/QML Algorithm Search/Quantum transfer learning/10C 20Q HRYe1RYt 76.6% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "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')\n",
        "# !pip install pennylane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "sANL984ird0B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "0dc86835-e988-4ad8-f009-0e91797ef6e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1706031809.805226\n",
            "Tue Jan 23 17:43:29 2024\n"
          ]
        }
      ],
      "source": [
        "# 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",
        "import sklearn\n",
        "from sklearn.metrics import confusion_matrix\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": 4,
      "metadata": {
        "id": "Wncy61Mdrd0B"
      },
      "outputs": [],
      "source": [
        "n_qubits = 20               # Number of qubits\n",
        "step = 0.0006               # Learning rate\n",
        "batch_size = 17             # Number of samples for each training step\n",
        "num_epochs = 10             # Number of training epochs\n",
        "q_depth = 1                 # 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": 5,
      "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": 6,
      "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": 7,
      "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/Shuffle Split 10 of 17 Classes Big Brain Tumor MRI Images\"\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": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "u55iZYEOrd0D",
        "outputId": "075b589f-835d-47b6-c2b5-7435ed98a970"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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      ],
      "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": 9,
      "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)"
      ]
    },
    {
      "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": 10,
      "metadata": {
        "id": "C-O_cK6jrd0D"
      },
      "outputs": [],
      "source": [
        "@qml.qnode(dev, interface=\"torch\")\n",
        "def quantum_net(q_input_features, q_weights_flat):\n",
        "    \"\"\"\n",
        "    The variational quantum circuit.\n",
        "    \"\"\"\n",
        "\n",
        "    # Reshape weights\n",
        "    q_weights = q_weights_flat.reshape(q_depth, n_qubits)\n",
        "\n",
        "    # Start from state |+> , unbiased w.r.t. |0> and |1>\n",
        "    H_layer(n_qubits)\n",
        "\n",
        "    # Embed features in the quantum node\n",
        "    RY_layer(q_input_features)\n",
        "\n",
        "    # Sequence of trainable variational layers\n",
        "    for k in range(q_depth):\n",
        "        RY_layer(q_weights[k])\n",
        "\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": 11,
      "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, 10)\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": 12,
      "metadata": {
        "id": "MAh4FqBYrd0D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "44efefe7-8fdb-4a4b-f6a2-49bf29bbeeac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 127MB/s]\n"
          ]
        }
      ],
      "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": 13,
      "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": 14,
      "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": 15,
      "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": 16,
      "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": [
        "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",
        "            # 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": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmAl9fIQrd0E"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "rAQBdDA_rd0E",
        "outputId": "26fbd18d-0f5a-4787-cfa1-5294a79f15c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/10 Loss: 1.8876 Acc: 0.3285        \n",
            "Phase: validation   Epoch: 1/10 Loss: 1.5722 Acc: 0.5019        \n",
            "Confusion Matrix:\n",
            "[[ 64  18   2  22  30   7  11   1   0   2]\n",
            " [  5 106   3   1  45   2   3   2   0   0]\n",
            " [  0   2  65   0   5  31   3  15   0   2]\n",
            " [ 29   9   0  52  27   2   6   3   0   1]\n",
            " [  6  30   3   8 172   4   5   2   0   3]\n",
            " [  0   1  59   0   4  42   3  14   0   0]\n",
            " [ 17   2   0   0  18   1  63   0   0   0]\n",
            " [  0   0  31   0   0   2   0  76   0   0]\n",
            " [  0  37   0   2  49   0   0   0   7   0]\n",
            " [  4   2   1   0  51   2   1   1   0  10]]\n",
            "Phase: train Epoch: 2/10 Loss: 1.4566 Acc: 0.5400        \n",
            "Phase: validation   Epoch: 2/10 Loss: 1.3188 Acc: 0.6073        \n",
            "Confusion Matrix:\n",
            "[[105   9   1  32   1   1   7   0   1   0]\n",
            " [  8 109   0  11  33   3   2   1   0   0]\n",
            " [  3   0  36   0   2  71   0  10   0   1]\n",
            " [ 33   4   0  74  11   1   5   0   0   1]\n",
            " [  7  23   2  22 168   5   4   0   0   2]\n",
            " [  1   1  25   1   2  88   1   4   0   0]\n",
            " [ 17   2   0   3   7   0  72   0   0   0]\n",
            " [  0   0  26   0   0  17   0  66   0   0]\n",
            " [  0  18   0   3  12   0   0   0  62   0]\n",
            " [ 10   3   1   3  34   5   0   0   1  15]]\n",
            "Phase: train Epoch: 3/10 Loss: 1.2397 Acc: 0.6278        \n",
            "Phase: validation   Epoch: 3/10 Loss: 1.1287 Acc: 0.6494        \n",
            "Confusion Matrix:\n",
            "[[104  18   0  13   6   0  12   3   1   0]\n",
            " [  2 125   0   1  32   0   1   4   0   2]\n",
            " [  0   1  55   0   1  15   1  47   0   3]\n",
            " [ 38   7   1  47  25   1   8   1   0   1]\n",
            " [  2  24   1   4 186   2   4   2   0   8]\n",
            " [  0   0  35   0   2  47   0  39   0   0]\n",
            " [  9   4   0   0   5   0  80   0   0   3]\n",
            " [  0   0   5   0   0   0   0 104   0   0]\n",
            " [  0  11   0   2   9   0   0   0  73   0]\n",
            " [  4   8   1   0  26   0   1   3   0  29]]\n",
            "Phase: train Epoch: 4/10 Loss: 1.0839 Acc: 0.6747        \n",
            "Phase: validation   Epoch: 4/10 Loss: 1.0194 Acc: 0.6730        \n",
            "Confusion Matrix:\n",
            "[[120   6   0   4   1   0  17   2   1   6]\n",
            " [  5 113   0   1  29   0   8   3   2   6]\n",
            " [  2   0  50   0   0  24   1  37   0   9]\n",
            " [ 61   5   0  33  16   2   8   1   0   3]\n",
            " [  4  17   0   4 172   2   5   3   5  21]\n",
            " [  0   0  28   0   1  57   0  36   0   1]\n",
            " [  8   1   1   0   0   0  88   0   0   3]\n",
            " [  0   0   5   0   0   0   0 104   0   0]\n",
            " [  0   1   0   2   5   0   0   0  87   0]\n",
            " [  6   1   0   0   6   0   1   1   0  57]]\n",
            "Phase: train Epoch: 5/10 Loss: 0.9720 Acc: 0.7047        \n",
            "Phase: validation   Epoch: 5/10 Loss: 0.9159 Acc: 0.7257        \n",
            "Confusion Matrix:\n",
            "[[119   8   0  15   1   0  11   1   1   1]\n",
            " [  3 137   0   3  18   0   2   2   0   2]\n",
            " [  1   0  86   0   0  19   0  17   0   0]\n",
            " [ 32   6   0  67  15   2   6   0   0   1]\n",
            " [  2  24   0  13 178   1   3   2   0  10]\n",
            " [  0   0  39   0   1  70   0  13   0   0]\n",
            " [  8   2   1   1   0   0  89   0   0   0]\n",
            " [  0   0  13   0   0   2   1  93   0   0]\n",
            " [  0  10   0   2   9   0   0   0  74   0]\n",
            " [  9   3   5   2  14   0   1   1   0  37]]\n",
            "Phase: train Epoch: 6/10 Loss: 0.8894 Acc: 0.7216        \n",
            "Phase: validation   Epoch: 6/10 Loss: 0.8470 Acc: 0.7426        \n",
            "Confusion Matrix:\n",
            "[[109  12   0  17   3   0  14   0   1   1]\n",
            " [  2 144   0   2  12   0   3   1   0   3]\n",
            " [  1   1  69   0   1  19   1  25   0   6]\n",
            " [ 19   7   1  84  10   0   6   0   0   2]\n",
            " [  0  27   0   8 181   1   4   1   3   8]\n",
            " [  0   1  34   1   2  66   0  19   0   0]\n",
            " [  7   2   0   1   0   0  90   0   0   1]\n",
            " [  0   0   9   0   0   0   1  99   0   0]\n",
            " [  0   3   0   2   5   0   0   0  85   0]\n",
            " [  2  10   1   3  10   0   1   0   0  45]]\n",
            "Phase: train Epoch: 7/10 Loss: 0.8368 Acc: 0.7466        \n",
            "Phase: validation   Epoch: 7/10 Loss: 0.8258 Acc: 0.7219        \n",
            "Confusion Matrix:\n",
            "[[137   2   0   7   0   0   7   2   1   1]\n",
            " [ 12 131   1   3  12   0   3   3   0   2]\n",
            " [  1   0  76   0   0  27   0  19   0   0]\n",
            " [ 71   3   0  43   3   2   6   0   0   1]\n",
            " [  8  18   1  11 166   2   5   7   2  13]\n",
            " [  0   0  29   0   1  77   0  16   0   0]\n",
            " [ 13   1   2   0   0   0  84   0   0   1]\n",
            " [  0   0   8   0   0   3   0  98   0   0]\n",
            " [  0   2   0   3   5   0   0   0  85   0]\n",
            " [ 11   3   5   0   4   0   0   1   0  48]]\n",
            "Phase: train Epoch: 8/10 Loss: 0.7939 Acc: 0.7498        \n",
            "Phase: validation   Epoch: 8/10 Loss: 0.7575 Acc: 0.7685        \n",
            "Confusion Matrix:\n",
            "[[128   3   0  13   2   0   9   0   1   1]\n",
            " [  4 126   0   2  25   0   3   1   2   4]\n",
            " [  1   0  71   0   1  30   0  20   0   0]\n",
            " [ 22   2   0  85  13   1   4   0   1   1]\n",
            " [  3  10   0   6 192   1   3   2   5  11]\n",
            " [  0   0  23   0   1  82   0  17   0   0]\n",
            " [  8   0   1   1   1   0  90   0   0   0]\n",
            " [  0   0   5   0   0   5   1  98   0   0]\n",
            " [  0   1   0   2   5   0   0   0  87   0]\n",
            " [  9   1   1   0  13   0   1   0   0  47]]\n",
            "Phase: train Epoch: 9/10 Loss: 0.7384 Acc: 0.7693        \n",
            "Phase: validation   Epoch: 9/10 Loss: 0.7225 Acc: 0.7746        \n",
            "Confusion Matrix:\n",
            "[[115   4   0  20   3   0  12   1   1   1]\n",
            " [  2 130   0   3  22   0   3   1   2   4]\n",
            " [  1   0  69   0   1  25   1  25   0   1]\n",
            " [  9   2   0  99  12   1   5   0   1   0]\n",
            " [  0  12   0   6 201   1   3   2   3   5]\n",
            " [  0   0  24   0   2  78   0  19   0   0]\n",
            " [  5   1   0   1   1   0  91   0   0   2]\n",
            " [  0   0   4   0   0   4   1 100   0   0]\n",
            " [  0   1   0   2   6   0   0   0  86   0]\n",
            " [  2   9   1   3  10   0   1   1   0  45]]\n",
            "Phase: train Epoch: 10/10 Loss: 0.6887 Acc: 0.7857        \n",
            "Phase: validation   Epoch: 10/10 Loss: 0.7228 Acc: 0.7662        \n",
            "Confusion Matrix:\n",
            "[[132   0   0  11   0   0  11   1   1   1]\n",
            " [  8 127   0   4   9   0   8   2   6   3]\n",
            " [  1   0  74   0   1  17   0  30   0   0]\n",
            " [ 25   1   1  88   7   0   4   0   2   1]\n",
            " [  3  13   0  13 178   1   4   5   5  11]\n",
            " [  0   0  26   0   1  68   0  28   0   0]\n",
            " [  4   1   0   1   0   0  94   0   0   1]\n",
            " [  0   0   2   0   0   4   1 102   0   0]\n",
            " [  0   1   0   3   2   0   0   0  89   0]\n",
            " [  8   3   0   2   4   0   2   2   0  51]]\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": "99a0cf09-88cb-42f5-aae2-30cfca777be0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1706049433.3510358\n",
            "Tue Jan 23 22:37:13 2024\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": "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
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.16"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
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