[404218]: / Code / PennyLane / Algorithm, Learning Rate Studies / HRyERyT1 / 0.00052 lr B30e RN50 B4 97.7% kkawchak.ipynb

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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "tvTsxVFhcEu4"
      },
      "outputs": [],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !pip install pennylane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "YzU1v9emcEu6"
      },
      "outputs": [],
      "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",
        "\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": "NMuS6xLqcEu6"
      },
      "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": 20,
      "metadata": {
        "id": "mwV5eRZ2cEu7"
      },
      "outputs": [],
      "source": [
        "n_qubits = 4                # Number of qubits\n",
        "step = 0.00052              # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 30             # 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": "_1o4Rd7xcEu7"
      },
      "source": [
        "We initialize a PennyLane device with a `default.qubit` backend.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "VrhtaB9JcEu7"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=n_qubits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mV03xQP_cEu7"
      },
      "source": [
        "We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
        "used.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "8wyZgRPFcEu7"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KanGTiTWcEu7"
      },
      "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": 23,
      "metadata": {
        "id": "MTp-0NbLcEu7"
      },
      "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/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": "kUCmDIHEcEu7"
      },
      "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": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "y_nK-SxNcEu7",
        "outputId": "5dcc2a4a-7c49-4076-a639-025ee7eb1e4a"
      },
      "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": "xAyxAXAicEu8"
      },
      "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": 25,
      "metadata": {
        "id": "CRFFQeBucEu8"
      },
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFSYAOD4cEu8"
      },
      "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": 26,
      "metadata": {
        "id": "tnveQZD0cEu8"
      },
      "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": "octf05h1cEu8"
      },
      "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": 27,
      "metadata": {
        "id": "R_1TsI-zcEu8"
      },
      "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(2048, n_qubits)\n",
        "        self.q_params = nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))\n",
        "        self.post_net = nn.Linear(n_qubits, 2)\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": "Z4jMByjkcEu8"
      },
      "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": 28,
      "metadata": {
        "id": "-rTgki5ycEu8"
      },
      "outputs": [],
      "source": [
        "model_hybrid = torchvision.models.resnet50(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": "1gJgwg6GcEu8"
      },
      "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": 29,
      "metadata": {
        "id": "qPT5EdxxcEu8"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GepjtiALcEu8"
      },
      "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": 30,
      "metadata": {
        "id": "Iox9dWlOcEu8"
      },
      "outputs": [],
      "source": [
        "optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbLNf2wccEu8"
      },
      "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": 31,
      "metadata": {
        "id": "xJcZ5B3JcEu8"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gogMl00McEu8"
      },
      "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": 32,
      "metadata": {
        "id": "BCfFACd6cEu9"
      },
      "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": "XpEBNYtocEu9"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "voQ2Yb7QcEu9",
        "outputId": "78103768-e31f-4ddf-ee6b-188d065e8f76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/30 Loss: 0.7119 Acc: 0.5020        \n",
            "Phase: validation   Epoch: 1/30 Loss: 0.7058 Acc: 0.4575        \n",
            "Phase: train Epoch: 2/30 Loss: 0.6959 Acc: 0.5061        \n",
            "Phase: validation   Epoch: 2/30 Loss: 0.6991 Acc: 0.4575        \n",
            "Phase: train Epoch: 3/30 Loss: 0.6208 Acc: 0.5959        \n",
            "Phase: validation   Epoch: 3/30 Loss: 0.5588 Acc: 0.6928        \n",
            "Phase: train Epoch: 4/30 Loss: 0.5022 Acc: 0.8347        \n",
            "Phase: validation   Epoch: 4/30 Loss: 0.4310 Acc: 0.9510        \n",
            "Phase: train Epoch: 5/30 Loss: 0.4664 Acc: 0.8490        \n",
            "Phase: validation   Epoch: 5/30 Loss: 0.3903 Acc: 0.9542        \n",
            "Phase: train Epoch: 6/30 Loss: 0.4375 Acc: 0.8735        \n",
            "Phase: validation   Epoch: 6/30 Loss: 0.3545 Acc: 0.9641        \n",
            "Phase: train Epoch: 7/30 Loss: 0.4288 Acc: 0.8673        \n",
            "Phase: validation   Epoch: 7/30 Loss: 0.3325 Acc: 0.9608        \n",
            "Phase: train Epoch: 8/30 Loss: 0.4011 Acc: 0.8857        \n",
            "Phase: validation   Epoch: 8/30 Loss: 0.3125 Acc: 0.9641        \n",
            "Phase: train Epoch: 9/30 Loss: 0.4039 Acc: 0.8551        \n",
            "Phase: validation   Epoch: 9/30 Loss: 0.3265 Acc: 0.9379        \n",
            "Phase: train Epoch: 10/30 Loss: 0.4329 Acc: 0.8306        \n",
            "Phase: validation   Epoch: 10/30 Loss: 0.3129 Acc: 0.9281        \n",
            "Phase: train Epoch: 11/30 Loss: 0.3320 Acc: 0.9204        \n",
            "Phase: validation   Epoch: 11/30 Loss: 0.2739 Acc: 0.9706        \n",
            "Phase: train Epoch: 12/30 Loss: 0.3589 Acc: 0.8857        \n",
            "Phase: validation   Epoch: 12/30 Loss: 0.2759 Acc: 0.9739        \n",
            "Phase: train Epoch: 13/30 Loss: 0.3633 Acc: 0.8959        \n",
            "Phase: validation   Epoch: 13/30 Loss: 0.2804 Acc: 0.9673        \n",
            "Phase: train Epoch: 14/30 Loss: 0.3395 Acc: 0.9041        \n",
            "Phase: validation   Epoch: 14/30 Loss: 0.2723 Acc: 0.9706        \n",
            "Phase: train Epoch: 15/30 Loss: 0.3599 Acc: 0.8918        \n",
            "Phase: validation   Epoch: 15/30 Loss: 0.2656 Acc: 0.9739        \n",
            "Phase: train Epoch: 16/30 Loss: 0.3274 Acc: 0.9122        \n",
            "Phase: validation   Epoch: 16/30 Loss: 0.2652 Acc: 0.9706        \n",
            "Phase: train Epoch: 17/30 Loss: 0.3314 Acc: 0.9143        \n",
            "Phase: validation   Epoch: 17/30 Loss: 0.2665 Acc: 0.9641        \n",
            "Phase: train Epoch: 18/30 Loss: 0.3299 Acc: 0.9184        \n",
            "Phase: validation   Epoch: 18/30 Loss: 0.2645 Acc: 0.9706        \n",
            "Phase: train Epoch: 19/30 Loss: 0.3133 Acc: 0.9245        \n",
            "Phase: validation   Epoch: 19/30 Loss: 0.2587 Acc: 0.9739        \n",
            "Phase: train Epoch: 20/30 Loss: 0.3145 Acc: 0.9204        \n",
            "Phase: validation   Epoch: 20/30 Loss: 0.2597 Acc: 0.9673        \n",
            "Phase: train Epoch: 21/30 Loss: 0.3176 Acc: 0.9245        \n",
            "Phase: validation   Epoch: 21/30 Loss: 0.2564 Acc: 0.9673        \n",
            "Phase: train Epoch: 22/30 Loss: 0.3157 Acc: 0.9204        \n",
            "Phase: validation   Epoch: 22/30 Loss: 0.2596 Acc: 0.9706        \n",
            "Phase: train Epoch: 23/30 Loss: 0.3071 Acc: 0.9367        \n",
            "Phase: validation   Epoch: 23/30 Loss: 0.2578 Acc: 0.9706        \n",
            "Phase: train Epoch: 24/30 Loss: 0.3180 Acc: 0.9224        \n",
            "Phase: validation   Epoch: 24/30 Loss: 0.2689 Acc: 0.9608        \n",
            "Phase: train Epoch: 25/30 Loss: 0.3364 Acc: 0.9122        \n",
            "Phase: validation   Epoch: 25/30 Loss: 0.2563 Acc: 0.9771        \n",
            "Phase: train Epoch: 26/30 Loss: 0.3288 Acc: 0.9224        \n",
            "Phase: validation   Epoch: 26/30 Loss: 0.2531 Acc: 0.9739        \n",
            "Phase: train Epoch: 27/30 Loss: 0.3038 Acc: 0.9306        \n",
            "Phase: validation   Epoch: 27/30 Loss: 0.2586 Acc: 0.9673        \n",
            "Phase: train Epoch: 28/30 Loss: 0.3167 Acc: 0.9265        \n",
            "Phase: validation   Epoch: 28/30 Loss: 0.2631 Acc: 0.9673        \n",
            "Phase: train Epoch: 29/30 Loss: 0.3199 Acc: 0.9204        \n",
            "Phase: validation   Epoch: 29/30 Loss: 0.2585 Acc: 0.9739        \n",
            "Phase: train Epoch: 30/30 Loss: 0.3360 Acc: 0.9122        \n",
            "Phase: validation   Epoch: 30/30 Loss: 0.2545 Acc: 0.9771        \n",
            "Training completed in 11m 41s\n",
            "Best test loss: 0.2531 | Best test accuracy: 0.9771\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": "OitpTjhacEu9"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGIiHKWHcEu9"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "lW9ytOLBcEu9"
      },
      "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": "837ToUo2cEu9"
      },
      "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": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "dMVtD1GScEu9",
        "outputId": "1313a220-58fa-4db3-96c8-a6ebbbc2ba56"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_model(model_hybrid, num_images=batch_size)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import runtime\n",
        "runtime.unassign()"
      ],
      "metadata": {
        "id": "C0U5dvrYM7LX"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HywUAsp3cEu9"
      },
      "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": "3.9.17"
    },
    "colab": {
      "provenance": [],
      "gpuType": "V100"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}