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{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "WNXEyZ23rdz-"
      },
      "outputs": [],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XSuLtL8rdz_"
      },
      "source": [
        "Quantum transfer learning {#quantum_transfer_learning}\n",
        "=========================\n",
        "\n",
        "::: {.meta}\n",
        ":property=\\\"og:description\\\": Combine PyTorch and PennyLane to train a\n",
        "hybrid quantum-classical image classifier using transfer learning.\n",
        ":property=\\\"og:image\\\":\n",
        "<https://pennylane.ai/qml/_images/transfer_images.png>\n",
        ":::\n",
        "\n",
        "*Author: Andrea Mari --- Posted: 19 December 2019. Last updated: 28\n",
        "January 2021.*\n",
        "\n",
        "In this tutorial we apply a machine learning method, known as *transfer\n",
        "learning*, to an image classifier based on a hybrid classical-quantum\n",
        "network.\n",
        "\n",
        "This example follows the general structure of the PyTorch [tutorial on\n",
        "transfer\n",
        "learning](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html)\n",
        "by Sasank Chilamkurthy, with the crucial difference of using a quantum\n",
        "circuit to perform the final classification task.\n",
        "\n",
        "More details on this topic can be found in the research paper \\[1\\]\n",
        "([Mari et al. (2019)](https://arxiv.org/abs/1912.08278)).\n",
        "\n",
        "Introduction\n",
        "------------\n",
        "\n",
        "Transfer learning is a well-established technique for training\n",
        "artificial neural networks (see e.g., Ref. \\[2\\]), which is based on the\n",
        "general intuition that if a pre-trained network is good at solving a\n",
        "given problem, then, with just a bit of additional training, it can be\n",
        "used to also solve a different but related problem.\n",
        "\n",
        "As discussed in Ref. \\[1\\], this idea can be formalized in terms of two\n",
        "abstract networks $A$ and $B$, independently from their quantum or\n",
        "classical physical nature.\n",
        "\n",
        "| \n",
        "\n",
        "![](../demonstrations/quantum_transfer_learning/transfer_learning_general.png){.align-center}\n",
        "\n",
        "| \n",
        "\n",
        "As sketched in the above figure, one can give the following **general\n",
        "definition of the transfer learning method**:\n",
        "\n",
        "1.  Take a network $A$ that has been pre-trained on a dataset $D_A$ and\n",
        "    for a given task $T_A$.\n",
        "2.  Remove some of the final layers. In this way, the resulting\n",
        "    truncated network $A'$ can be used as a feature extractor.\n",
        "3.  Connect a new trainable network $B$ at the end of the pre-trained\n",
        "    network $A'$.\n",
        "4.  Keep the weights of $A'$ constant, and train the final block $B$\n",
        "    with a new dataset $D_B$ and/or for a new task of interest $T_B$.\n",
        "\n",
        "When dealing with hybrid systems, depending on the physical nature\n",
        "(classical or quantum) of the networks $A$ and $B$, one can have\n",
        "different implementations of transfer learning as\n",
        "\n",
        "summarized in following table:\n",
        "\n",
        "| \n",
        "\n",
        "::: {.rst-class}\n",
        "docstable\n",
        ":::\n",
        "\n",
        "  -------------------------------------------------------------------------\n",
        "  Network A   Network B   Transfer learning scheme\n",
        "  ----------- ----------- -------------------------------------------------\n",
        "  Classical   Classical   CC - Standard classical method. See e.g., Ref.\n",
        "                          \\[2\\].\n",
        "\n",
        "  Classical   Quantum     CQ - **Hybrid model presented in this tutorial.**\n",
        "\n",
        "  Quantum     Classical   QC - Model studied in Ref. \\[1\\].\n",
        "\n",
        "  Quantum     Quantum     QQ - Model studied in Ref. \\[1\\].\n",
        "  -------------------------------------------------------------------------\n",
        "\n",
        "Classical-to-quantum transfer learning\n",
        "--------------------------------------\n",
        "\n",
        "We focus on the CQ transfer learning scheme discussed in the previous\n",
        "section and we give a specific example.\n",
        "\n",
        "1.  As pre-trained network $A$ we use **ResNet18**, a deep residual\n",
        "    neural network introduced by Microsoft in Ref. \\[3\\], which is\n",
        "    pre-trained on the *ImageNet* dataset.\n",
        "2.  After removing its final layer we obtain $A'$, a pre-processing\n",
        "    block which maps any input high-resolution image into 512 abstract\n",
        "    features.\n",
        "3.  Such features are classified by a 4-qubit \\\"dressed quantum\n",
        "    circuit\\\" $B$, i.e., a variational quantum circuit sandwiched\n",
        "    between two classical layers.\n",
        "4.  The hybrid model is trained, keeping $A'$ constant, on the\n",
        "    *Hymenoptera* dataset (a small subclass of ImageNet) containing\n",
        "    images of *ants* and *bees*.\n",
        "\n",
        "A graphical representation of the full data processing pipeline is given\n",
        "in the figure below.\n",
        "\n",
        "![](../demonstrations/quantum_transfer_learning/transfer_learning_c2q.png){.align-center}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eT6yf0-xrd0A"
      },
      "source": [
        "General setup\n",
        "=============\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "To use the PyTorch interface in PennyLane, you must first [install\n",
        "PyTorch](https://pytorch.org/get-started/locally/#start-locally).\n",
        ":::\n",
        "\n",
        "In addition to *PennyLane*, we will also need some standard *PyTorch*\n",
        "libraries and the plotting library *matplotlib*.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {
        "id": "sANL984ird0B"
      },
      "outputs": [],
      "source": [
        "# !pip install pennylane\n",
        "# Some parts of this code are based on the Python script:\n",
        "# https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py\n",
        "# License: BSD\n",
        "\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "\n",
        "# PyTorch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "# Pennylane\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "\n",
        "torch.manual_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# OpenMP: number of parallel threads.\n",
        "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7eGTk_Prd0B"
      },
      "source": [
        "Setting of the main hyper-parameters of the model\n",
        "=================================================\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "To reproduce the results of Ref. \\[1\\], `num_epochs` should be set to\n",
        "`30` which may take a long time. We suggest to first try with\n",
        "`num_epochs=1` and, if everything runs smoothly, increase it to a larger\n",
        "value.\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "id": "Wncy61Mdrd0B"
      },
      "outputs": [],
      "source": [
        "n_qubits = 12                # Number of qubits\n",
        "step = 0.0004               # Learning rate\n",
        "batch_size = 4              # Number of samples for each training step\n",
        "num_epochs = 10              # 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": 143,
      "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": 144,
      "metadata": {
        "id": "Br_YGwRDrd0C"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFHuYd5xrd0C"
      },
      "source": [
        "Dataset loading\n",
        "===============\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "The dataset containing images of *ants* and *bees* can be downloaded\n",
        "[here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) and\n",
        "should be extracted in the subfolder `../_data/hymenoptera_data`.\n",
        ":::\n",
        "\n",
        "This is a very small dataset (roughly 250 images), too small for\n",
        "training from scratch a classical or quantum model, however it is enough\n",
        "when using *transfer learning* approach.\n",
        "\n",
        "The PyTorch packages `torchvision` and `torch.utils.data` are used for\n",
        "loading the dataset and performing standard preliminary image\n",
        "operations: resize, center, crop, normalize, *etc.*\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#os.system(\"rm -rf /content/data/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/train/.ipynb_checkpoints\")\n",
        "#os.system(\"rm -rf /content/data/braintumor_data/val/.ipynb_checkpoints\")"
      ],
      "metadata": {
        "id": "DM8SDO3Wthcc"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "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/braintumor_data\"\n",
        "image_datasets = {\n",
        "    x if x == \"train\" else \"validation\": datasets.ImageFolder(\n",
        "        os.path.join(data_dir, x), data_transforms[x]\n",
        "    )\n",
        "    for x in [\"train\", \"val\"]\n",
        "}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"validation\"]}\n",
        "class_names = image_datasets[\"train\"].classes\n",
        "\n",
        "# Initialize dataloader\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}\n",
        "\n",
        "# function to plot images\n",
        "def imshow(inp, title=None):\n",
        "    \"\"\"Display image from tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    # Inverse of the initial normalization operation.\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piWk71nkrd0C"
      },
      "source": [
        "Let us show a batch of the test data, just to have an idea of the\n",
        "classification problem.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "id": "u55iZYEOrd0D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "75afb2c2-51c5-4faf-b3cf-abd1ae9beb49"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(dataloaders[\"validation\"]))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "dataloaders = {\n",
        "    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
        "    for x in [\"train\", \"validation\"]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ds09ighxrd0D"
      },
      "source": [
        "Variational quantum circuit\n",
        "===========================\n",
        "\n",
        "We first define some quantum layers that will compose the quantum\n",
        "circuit.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "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.RX(element, wires=idx)\n",
        "        qml.RY(element, wires=idx)\n",
        "\n",
        "def entangling_layer(nqubits):\n",
        "    \"\"\"Layer of CNOTs followed by another shifted layer of CNOT.\n",
        "    \"\"\"\n",
        "    # In other words it should apply something like :\n",
        "    # CNOT  CNOT  CNOT  CNOT...  CNOT\n",
        "    #   CNOT  CNOT  CNOT...  CNOT\n",
        "    for i in range(0, nqubits - 1, 2):  # Loop over even indices: i=0,2,...N-2\n",
        "        qml.CNOT(wires=[i, i + 1])\n",
        "    for i in range(1, nqubits - 1, 2):  # Loop over odd indices:  i=1,3,...N-3\n",
        "        qml.CNOT(wires=[i, i + 1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ue__y4sQrd0D"
      },
      "source": [
        "Now we define the quantum circuit through the PennyLane\n",
        "[qnode]{.title-ref} decorator .\n",
        "\n",
        "The structure is that of a typical variational quantum circuit:\n",
        "\n",
        "-   **Embedding layer:** All qubits are first initialized in a balanced\n",
        "    superposition of *up* and *down* states, then they are rotated\n",
        "    according to the input parameters (local embedding).\n",
        "-   **Variational layers:** A sequence of trainable rotation layers and\n",
        "    constant entangling layers is applied.\n",
        "-   **Measurement layer:** For each qubit, the local expectation value\n",
        "    of the $Z$ operator is measured. This produces a classical output\n",
        "    vector, suitable for additional post-processing.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "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",
        "    # Sequence of trainable variational layers\n",
        "    for k in range(q_depth):\n",
        "        entangling_layer(n_qubits)\n",
        "        RY_layer(q_weights[k])\n",
        "\n",
        "    # 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": 150,
      "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, 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": "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": 151,
      "metadata": {
        "id": "MAh4FqBYrd0D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f2a7132-99cb-4c66-c0ea-084de9241ebe"
      },
      "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"
          ]
        }
      ],
      "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": 152,
      "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": 153,
      "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": 154,
      "metadata": {
        "id": "nco3IrE6rd0E"
      },
      "outputs": [],
      "source": [
        "exp_lr_scheduler = lr_scheduler.StepLR(\n",
        "    optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJvpPNCRrd0E"
      },
      "source": [
        "What follows is a training function that will be called later. This\n",
        "function should return a trained model that can be used to make\n",
        "predictions (classifications).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "id": "8uO5BrOPrd0E"
      },
      "outputs": [],
      "source": [
        "def train_model(model, criterion, optimizer, scheduler, num_epochs):\n",
        "    since = time.time()\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "    best_loss = 10000.0  # Large arbitrary number\n",
        "    best_acc_train = 0.0\n",
        "    best_loss_train = 10000.0  # Large arbitrary number\n",
        "    print(\"Training started:\")\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in [\"train\", \"validation\"]:\n",
        "            if phase == \"train\":\n",
        "                # Set model to training mode\n",
        "                model.train()\n",
        "            else:\n",
        "                # Set model to evaluate mode\n",
        "                model.eval()\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            # Iterate over data.\n",
        "            n_batches = dataset_sizes[phase] // batch_size\n",
        "            it = 0\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                since_batch = time.time()\n",
        "                batch_size_ = len(inputs)\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                # Track/compute gradient and make an optimization step only when training\n",
        "                with torch.set_grad_enabled(phase == \"train\"):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "                    if phase == \"train\":\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                # Print iteration results\n",
        "                running_loss += loss.item() * batch_size_\n",
        "                batch_corrects = torch.sum(preds == labels.data).item()\n",
        "                running_corrects += batch_corrects\n",
        "                print(\n",
        "                    \"Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}\".format(\n",
        "                        phase,\n",
        "                        epoch + 1,\n",
        "                        num_epochs,\n",
        "                        it + 1,\n",
        "                        n_batches + 1,\n",
        "                        time.time() - since_batch,\n",
        "                    ),\n",
        "                    end=\"\\r\",\n",
        "                    flush=True,\n",
        "                )\n",
        "                it += 1\n",
        "\n",
        "            # Print epoch results\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects / dataset_sizes[phase]\n",
        "            print(\n",
        "                \"Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f}        \".format(\n",
        "                    \"train\" if phase == \"train\" else \"validation  \",\n",
        "                    epoch + 1,\n",
        "                    num_epochs,\n",
        "                    epoch_loss,\n",
        "                    epoch_acc,\n",
        "                )\n",
        "            )\n",
        "\n",
        "            # Check if this is the best model wrt previous epochs\n",
        "            if phase == \"validation\" and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "            if phase == \"validation\" and epoch_loss < best_loss:\n",
        "                best_loss = epoch_loss\n",
        "            if phase == \"train\" and epoch_acc > best_acc_train:\n",
        "                best_acc_train = epoch_acc\n",
        "            if phase == \"train\" and epoch_loss < best_loss_train:\n",
        "                best_loss_train = epoch_loss\n",
        "      \n",
        "            # Update learning rate\n",
        "            if phase == \"train\":\n",
        "                scheduler.step()\n",
        "\n",
        "    # Print final results\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    time_elapsed = time.time() - since\n",
        "    print(\n",
        "        \"Training completed in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60)\n",
        "    )\n",
        "    print(\"Best test loss: {:.4f} | Best test accuracy: {:.4f}\".format(best_loss, best_acc))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmAl9fIQrd0E"
      },
      "source": [
        "We are ready to perform the actual training process.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "rAQBdDA_rd0E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e076dd8-6d8d-471e-fda5-58e695cb9489"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training started:\n",
            "Phase: train Epoch: 1/10 Loss: 0.6261 Acc: 0.6857        \n",
            "Phase: validation   Epoch: 1/10 Loss: 0.5526 Acc: 0.7843        \n",
            "Phase: train Epoch: 2/10 Loss: 0.5020 Acc: 0.8000        \n",
            "Phase: validation   Epoch: 2/10 Loss: 0.3525 Acc: 0.9281        \n",
            "Phase: train Epoch: 3/10 Loss: 0.4192 Acc: 0.8449        \n",
            "Phase: validation   Epoch: 3/10 Loss: 0.2672 Acc: 0.9542        \n",
            "Phase: train Epoch: 4/10 Loss: 0.3512 Acc: 0.8857        \n",
            "Phase: validation   Epoch: 4/10 Loss: 0.2676 Acc: 0.9085        \n",
            "Phase: train Epoch: 5/10 Loss: 0.4057 Acc: 0.8082        \n",
            "Phase: validation   Epoch: 5/10 Loss: 0.2056 Acc: 0.9542        \n",
            "Phase: train Epoch: 6/10 Loss: 0.3478 Acc: 0.8449        \n",
            "Phase: validation   Epoch: 6/10 Loss: 0.1781 Acc: 0.9673        \n",
            "Phase: train Epoch: 7/10 Loss: 0.3383 Acc: 0.8531        \n",
            "Phase: validation   Epoch: 7/10 Loss: 0.1732 Acc: 0.9608        \n",
            "Phase: train Epoch: 8/10 Loss: 0.2743 Acc: 0.9061        \n",
            "Phase: validation   Epoch: 8/10 Loss: 0.1405 Acc: 0.9673        \n",
            "Phase: train Epoch: 9/10 Loss: 0.2951 Acc: 0.8735        \n",
            "Phase: validation   Epoch: 9/10 Loss: 0.1782 Acc: 0.9477        \n",
            "Phase: train Epoch: 10/10 Loss: 0.3054 Acc: 0.8653        \n",
            "Phase: validation   Epoch: 10/10 Loss: 0.2188 Acc: 0.9216        \n",
            "Training completed in 22m 33s\n",
            "Best test loss: 0.1405 | Best test accuracy: 0.9673\n"
          ]
        }
      ],
      "source": [
        "model_hybrid = train_model(\n",
        "    model_hybrid, criterion, optimizer_hybrid, exp_lr_scheduler, num_epochs=num_epochs\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4cEc4mird0E"
      },
      "source": [
        "Visualizing the model predictions\n",
        "=================================\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARvjv_lbrd0E"
      },
      "source": [
        "We first define a visualization function for a batch of test data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "id": "CtUY4xU_rd0E"
      },
      "outputs": [],
      "source": [
        "def visualize_model(model, num_images=6, fig_name=\"Predictions\"):\n",
        "    images_so_far = 0\n",
        "    _fig = plt.figure(fig_name)\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for _i, (inputs, labels) in enumerate(dataloaders[\"validation\"]):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images // 2, 2, images_so_far)\n",
        "                ax.axis(\"off\")\n",
        "                ax.set_title(\"[{}]\".format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "                if images_so_far == num_images:\n",
        "                    return"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6I5Rk92rd0E"
      },
      "source": [
        "Finally, we can run the previous function to see a batch of images with\n",
        "the corresponding predictions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "id": "-RoYcZQXrd0E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "71d8b633-61df-4149-c426-c26b14a60eb1"
      },
      "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": "markdown",
      "metadata": {
        "id": "IBXZTnzjrd0E"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "\\[1\\] Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, and\n",
        "Nathan Killoran. *Transfer learning in hybrid classical-quantum neural\n",
        "networks*. arXiv:1912.08278 (2019).\n",
        "\n",
        "\\[2\\] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and\n",
        "Andrew Y Ng. *Self-taught learning: transfer learning from unlabeled\n",
        "data*. Proceedings of the 24th International Conference on Machine\n",
        "Learning\\*, 759--766 (2007).\n",
        "\n",
        "\\[3\\] Kaiming He, Xiangyu Zhang, Shaoqing ren and Jian Sun. *Deep\n",
        "residual learning for image recognition*. Proceedings of the IEEE\n",
        "Conference on Computer Vision and Pattern Recognition, 770-778 (2016).\n",
        "\n",
        "\\[4\\] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin,\n",
        "Carsten Blank, Keri McKiernan, and Nathan Killoran. *PennyLane:\n",
        "Automatic differentiation of hybrid quantum-classical computations*.\n",
        "arXiv:1811.04968 (2018).\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.16"
    },
    "colab": {
      "provenance": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}