tutorial-BceGAN-LHCb_RICH.ipynb

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      "source": [
        "# Parameterizing the LHCb RICH system using the pidgan `BceGAN` algorithm\n",
        "\n",
        "**Author:** [mbarbetti](https://github.com/mbarbetti)\n",
        "\n",
        "**Date created:** 16/10/2023\n",
        "\n",
        "**Last modified:** 07/11/2023"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN-LHCb_RICH.ipynb)\n",
        "[![Open In Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN-LHCb_RICH.ipynb)\n",
        "[![Open in GitHub](https://img.shields.io/badge/Open_in_GitHub-blue?style=flat&logo=github&logoColor=white&labelColor=555)](https://github.com/mbarbetti/pidgan-notebooks/blob/main/tutorial-BceGAN-LHCb_RICH.ipynb)\n",
        "\n",
        "**Description:** This tutorial demonstrates how to parameterize the high-level response of the LHCb RICH system using a Generative Adversarial Network (GAN) [[1][1]]. The code is written using the [pidgan](https://github.com/mbarbetti/pidgan) package that relies on TensorFlow and Keras as backends.\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvTVEAa7i5xJ"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opu773xWi5xK"
      },
      "source": [
        "### What is LHCb?\n",
        "\n",
        "The [**LHCb experiment**](https://lhcb-outreach.web.cern.ch) has been originally designed to study rare decays of particles containing $b$ and $c$ quarks produced at the Large Hadron Collider (LHC). The LHCb detector, shown in the background of the following photo, is a single-arm forward spectrometer covering the pseudorapidity range of $2 < \\eta < 5$. The detector includes:\n",
        "\n",
        "- **Tracking system** - used for high-precision measurements of the momentum of charged particles and the position of the primary vertices\n",
        "- **Particle Identification (PID) system** - used to distiguish different species of traversing particles (i.e., muons, pions, kaons, protons)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5If4oPxki5xK"
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      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/lhcb.jpeg\" width=\"800\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTEAA_GpgXXM"
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      "source": [
        "#### The RICH detectors\n",
        "\n",
        "The LHCb PID system counts two ring-imaging Cherenkov ([RICH](https://lhcb-outreach.web.cern.ch/detector/rich-detectors/)) detectors whose response allows to separate different types of charged hadrons (e.g., pions, kaons, protons) using the [Cherenkov radiation](https://en.wikipedia.org/wiki/Cherenkov_radiation) of the traversing particles. The size of the circles produced by the Cherenkov radiation (shown on the left) within the RICH detector is related to the mass of the emitting particle. Reconstructing such circles allows to distinguish particles accordingly to their species (shown on the right)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvTtCmaBhqQ_"
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      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-response.png\" width=\"300\"/>\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/rich-performance.png\" width=\"400\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPnfIVMoi5xK"
      },
      "source": [
        "### What are GANs?\n",
        "\n",
        "Generative Adversarial Networks [[1][1]] are a powerful class of _generative models_ based on the simultaneous training of two neural networks:\n",
        "\n",
        "*  **Discriminator network** ($D$) - trained by a classification task to separate the generator output from the reference dataset\n",
        "* **Generator network** ($G$) - trained by a simulation task to reproduce the reference dataset trying to fake the discriminator\n",
        "\n",
        "The goal is that $D$ optimally discriminates on the origin of the two samples, and simultaneously the training procedure for $G$ is to maximize the _probability_ of $D$ making a mistake. This framework corresponds to a **minimax two-player game** [[1][1]].\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_Hs8F8pi5xK"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <img src=\"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/.github/images/gan-scheme.png\" width=\"800\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmUghh5Ii5xK"
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      "source": [
        "#### Mathematical details\n",
        "\n",
        "The generator $G(z)$, fed by elements $z$ sampled according to a known distribution $p_z$ (typically gaussian), maps the **latent space** $\\mathcal{Z}$ to the reference dataset $\\mathcal{X}$, inducing a distribution $p_\\rm{gen}$ trained to match with the target distribution $p_\\rm{ref}$. The discriminator $D(x)$ outputs a single scalar, readable as the **probability** that $x$ comes from the reference dataset rather than $G$. Hence, the optimization problem corresponds to train $D$ to maximize the probability of correct labelling, and simultaneously training $G$ to hinder the discriminator task.\n",
        "\n",
        "To define the discriminator **loss function** $\\mathcal{L}^{D}_{\\rm{BceGAN}}$ we can use the [binary cross-entropy](https://en.wikipedia.org/wiki/Cross-entropy) as suggested in Ref. [[2][2]]:\n",
        "\n",
        "<center>$\\mathcal{L}^{D}_{\\rm{BceGAN}} (\\theta_d, \\theta_g) = \\frac{1}{2} \\left ( {\\rm{BCE}}_{x \\sim p_\\rm{ref}} \\left ( \\mathbb{1}, D_{\\theta_d}(x) \\right ) + {\\rm{BCE}}_{z \\sim p_\\rm{z}} \\left ( \\mathbb{0}, D_{\\theta_d}(G_{\\theta_g}(z)) \\right ) \\right )$</center>\n",
        "\n",
        "where $\\mathbb{1}$ and $\\mathbb{0}$ are the labels for true and generated data respectively. The generator loss function $\\mathcal{L}^{G}_{\\rm{BceGAN}}$ follows a similar design:\n",
        "\n",
        "<center>$\\mathcal{L}^{G}_{\\rm{BceGAN}} (\\theta_d, \\theta_g) = {\\rm{BCE}}_{z \\sim p_\\rm{z}} \\left ( \\mathbb{1}, D_{\\theta_d}(G_{\\theta_g}(z)) \\right )$</center>\n",
        "\n",
        "Hence, the _minimax game_ of the **BceGAN algorithm** corresponds to minimize both the defined loss functions.\n",
        "\n",
        "Traditional GAN systems suffer from many issues, particularly during the training phase:\n",
        "\n",
        "* the generator may _collapse_ producing only a single sample or a small family of very similar samples (**mode collapse**)\n",
        "* the two players may _oscillate_ during training rather than converging to the [**Nash equilibrium**](https://en.wikipedia.org/wiki/Nash_equilibrium)\n",
        "* if _imbalance_ between the two players occurs, then the system is incapable of learning at all\n",
        "\n",
        "All these drawbacks result from the [**vanishing gradient problem**](https://en.wikipedia.org/wiki/Vanishing_gradient_problem), namely the lack of information for the update of the $G$ parameters. This is due to the saturation of the $D$ that is so good in distinguishing the origin of the two samples that no errors remain to the $G$ to improve the generated space. To fix the problem, one can add _continuous noise_ to both $D$ and $G$. This trick allows to learn thanks to a non-zero gradient [[3][3]].\n",
        "\n",
        "[2]: https://arxiv.org/abs/1511.06434\n",
        "[3]: https://arxiv.org/abs/1701.04862"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnwMxPJUi5xK"
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      "source": [
        "#### Using input conditions\n",
        "\n",
        "Feeding the generator with additional information besides the latent space sample allows to **conditionate** its output. In particular, it's sufficient to concatenate the conditional features $x$ to the random noise $z$ passed as input to $G$ to make it able to take into account this information [[4][4]]: $y_{\\rm{gen}}(x) = G(x, z)$. Obviously, to preserve the capability to learn through the minimax game, also the discriminator must be fed by the additional features $x$, simply concatenating them to either elements of the reference sample or of the generated space [[4][4]]: $D(x, y)$ with $y \\in \\{y_{\\rm{ref}}, y_{\\rm{gen}}\\}$.\n",
        "\n",
        "[4]: https://arxiv.org/abs/1606.03498"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvpIdCnSi5xL"
      },
      "source": [
        "## Tutorial\n",
        "\n",
        "The aim of this tutorial is to build a parameterization of the LHCb RICH detectors. Since we expect that the high-level response of the RICH system only depends on the kinematics of the traversing particles and the detector occupancy, the former can be parameterized training a **BceGAN model** properly conditioned [[4][4], [5][5]]. The pidgan package provides several GAN [`algorithms`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/algorithms) implementing some of the most used tricks presented in the literature to stablize their training [[3][3], [6][6]]. The pidgan [`players`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/players), namely the generator and discriminator networks, are designed to be trained taking conditions as input. In this notebook we will see how to build a model for the RICH system using the pidgan [`BceGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/BceGAN.py) algorithm.\n",
        "\n",
        "[3]: https://arxiv.org/abs/1701.04862\n",
        "[4]: https://arxiv.org/abs/1411.1784\n",
        "[5]: https://arxiv.org/abs/1905.11825\n",
        "[6]: https://arxiv.org/abs/1606.03498"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ohrh3dQi5xL"
      },
      "source": [
        "### Install\n",
        "\n",
        "The first step is to install the [pidgan](https://github.com/mbarbetti/pidgan) package and some other ones that are typically needed in machine learning applications to High Energy Physics (i.e., uproot, scipy, scikit-learn, matplotlib)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "r8glZROOi5xL"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install pidgan[hep]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kmb7g7HZi5xL"
      },
      "source": [
        "Now, let's verify the correct installation of pidgan printing its version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CWg67WFhi5xL",
        "outputId": "a3585afc-76dc-439a-938c-006e548244e2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.0.7'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pidgan\n",
        "\n",
        "pidgan.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8lt6bjDi5xM"
      },
      "source": [
        "In case you're running this notebook on a machine equipped with a GPU, let's also verify the correct installation of TensorFlow:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGoxRYbGi5xM",
        "outputId": "d276d0f4-7459-4b27-d5f9-94dad14846de"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.config.list_physical_devices(\"GPU\")  # outputs a non-empty list in case of GPU equipped"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uslhx_PjvOwk"
      },
      "source": [
        "Finally, we just have to import all the modules that we will use in the following code cells:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "twM1ba1PvgVl"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from datetime import datetime\n",
        "from tensorflow import keras\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import QuantileTransformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cz9PhSN8wP2h"
      },
      "source": [
        "### Data loading\n",
        "\n",
        "The data needed for this tutorial is provided through a CSV file downloadable from the [pidgan-notebooks](https://github.com/mbarbetti/pidgan-notebooks) repository.\n",
        "\n",
        "**Disclaimer:** The data results from a toy model inspired to the LHCb RICH system combining its high-level response when traversed by a set of long-lived charged particles, namely muons, pions, kaons and protons.\n",
        "\n",
        "The CSV file contains 250000 instances, 4 input features and 4 output features. Let's import the data as a pandas [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "QfnPXl161n2f",
        "outputId": "98b7ac3b-58b9-477e-9cb4-7cc4a52fbdd6"
      },
      "outputs": [
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              "      <th>P</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-05c8392d-188d-40ba-979c-a6f4839d2a87\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-05c8392d-188d-40ba-979c-a6f4839d2a87')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-05c8392d-188d-40ba-979c-a6f4839d2a87 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   P            ETA        nTracks    trackcharge  \\\n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000   \n",
              "mean    19112.579988       3.362231     180.468471       0.021152   \n",
              "std     22610.408265       0.770202      91.626085       0.999778   \n",
              "min      3000.090800       1.800331       9.000999      -1.000000   \n",
              "25%      5970.525000       2.749856     116.000000      -1.000000   \n",
              "50%     10983.800000       3.340136     164.000000       1.000000   \n",
              "75%     22392.768000       3.949773     228.000000       1.000000   \n",
              "max    199837.420000       5.434650     739.993000       1.000000   \n",
              "\n",
              "            RichDLLe      RichDLLmu       RichDLLk       RichDLLp  \n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000  \n",
              "mean       -8.959810      -1.853031      -3.614234      -3.626358  \n",
              "std        16.090133      11.512878      28.799922      28.006328  \n",
              "min      -140.804340    -110.494200    -149.289830    -149.591670  \n",
              "25%       -12.259203      -3.784543     -16.768035     -18.402633  \n",
              "50%        -3.499332      -0.634627       1.748902       1.705883  \n",
              "75%        -0.179516       0.651420      14.155344      15.179315  \n",
              "max        58.021923     106.868935     120.245310     110.364006  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "url = \"https://raw.githubusercontent.com/mbarbetti/pidgan-notebooks/main/data/pidgan-toy-data.csv\"\n",
        "data = pd.read_csv(url)\n",
        "\n",
        "data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhaU0lWcZ-6L"
      },
      "source": [
        "We expect that the response of the RICH detectors only depends on the **kinematics** of the traversing particles and the **detector occupancy**. Hence, to build the aimed parameterization, we will conditionate the generator training using the following information:\n",
        "* `data.P` - momentum in GeV/$c$ of the traversing parrticles\n",
        "* `data.ETA` - pseudorapidity of the traversing particles\n",
        "* `data.nTracks` - total number of reconstructed tracks describing the detector occupancy\n",
        "* `data.trackcharge` - charge of the traversing particles (boolean value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "j99DTVou5KdZ",
        "outputId": "b27cb95c-9370-4743-b355-93656c1e7e5f"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 2400x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(24, 5))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.xlabel(\"Momentum [GeV/$c$]\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"P\"]/1e3, bins=np.linspace(0.0, 200.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.xlabel(\"Pseudorapidity\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"ETA\"], bins=np.linspace(1.5, 5.5, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.xlabel(\"$\\mathtt{nTracks}$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"nTracks\"], bins=np.linspace(0.0, 750.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yV9PdrDtb5gK"
      },
      "source": [
        "The high-level response of the RICH detectors describes how good the reconstructed circles match with a specific particle hypothesis in terms of **differential log-likelihood** (DLL). We aim to build a model able to reproduce the following output features:\n",
        "* `data.RichDLLe` - RICH DLL between the electron and pion tracks\n",
        "* `data.RichDLLmu` - RICH DLL between the muon and pion tracks\n",
        "* `data.RichDLLk` - RICH DLL between the kaon and pion tracks\n",
        "* `data.RichDLLp` - RICH DLL between the proton and pion tracks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "2r8zWzbI7A2V",
        "outputId": "62a6017b-a802-4e01-a309-d35cdae715ac"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 10))\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(e - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLe\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(\\mu - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLmu\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(K - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLk\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.xlabel(\"RICH differential log-likelihood $(p - \\pi)$\", fontsize=12)\n",
        "plt.ylabel(\"Candidates\", fontsize=12)\n",
        "plt.hist(data[\"RichDLLp\"], bins=np.linspace(-150.0, 150.0, 101), color=\"#3288bd\", label=\"toy data\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDfCdUkIi5xM"
      },
      "source": [
        "### Data preprocessing\n",
        "\n",
        "As usual in a machine learning application, the data sample is split into **training** and **test set**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xinGo4AJ9OFu",
        "outputId": "de6f6e05-c1bd-4a37-9810-164bfcd7d775"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train shape: (150000, 4) \t x_test shape: (100000, 4)\n",
            "y_train shape: (150000, 4) \t y_test shape: (100000, 4)\n"
          ]
        }
      ],
      "source": [
        "data = shuffle(data)\n",
        "\n",
        "x_vars = [\"P\", \"ETA\", \"nTracks\", \"trackcharge\"]\n",
        "x = data[x_vars].values\n",
        "\n",
        "y_vars = [\"RichDLLe\", \"RichDLLmu\", \"RichDLLk\", \"RichDLLp\"]\n",
        "y = data[y_vars].values\n",
        "\n",
        "x_train, x_test = x[:150000], x[150000:]\n",
        "y_train, y_test = y[:150000], y[150000:]\n",
        "\n",
        "print(f\"x_train shape: {x_train.shape} \\t x_test shape: {x_test.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape} \\t y_test shape: {y_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcGuX9oZ_HHN"
      },
      "source": [
        "To normalize the different ranges and orders of magnitude of input $x$ and output $y$ features highlighted by the previous histograms, we will use the scikit-learn [`QuantileTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html) using a Gaussian as output distribution.\n",
        "\n",
        "Since one of the $x$ features is boolean (`data.trackcharge`), the latter should be kept unchanged. Let's preprocess the rest of the conditional information and verify the results with the [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) `describe()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "BElR-YUR_0df",
        "outputId": "e385f003-6b9f-4b7d-fcc9-e0dd2b5de3ad"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P</th>\n",
              "      <th>ETA</th>\n",
              "      <th>nTracks</th>\n",
              "      <th>trackcharge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.014230</td>\n",
              "      <td>-0.016377</td>\n",
              "      <td>0.000533</td>\n",
              "      <td>0.021067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.006069</td>\n",
              "      <td>1.007570</td>\n",
              "      <td>1.012490</td>\n",
              "      <td>0.999781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.686893</td>\n",
              "      <td>-0.685891</td>\n",
              "      <td>-0.656486</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.020590</td>\n",
              "      <td>-0.024098</td>\n",
              "      <td>0.002509</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.654561</td>\n",
              "      <td>0.669050</td>\n",
              "      <td>0.664287</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c380299d-4078-4e09-9df7-10ab8000f52c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c380299d-4078-4e09-9df7-10ab8000f52c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c380299d-4078-4e09-9df7-10ab8000f52c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-661b45b9-1d31-4a02-803a-d9e56a61f650\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-661b45b9-1d31-4a02-803a-d9e56a61f650')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-661b45b9-1d31-4a02-803a-d9e56a61f650 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   P            ETA        nTracks    trackcharge\n",
              "count  150000.000000  150000.000000  150000.000000  150000.000000\n",
              "mean       -0.014230      -0.016377       0.000533       0.021067\n",
              "std         1.006069       1.007570       1.012490       0.999781\n",
              "min        -5.199338      -5.199338      -5.199338      -1.000000\n",
              "25%        -0.686893      -0.685891      -0.656486      -1.000000\n",
              "50%        -0.020590      -0.024098       0.002509       1.000000\n",
              "75%         0.654561       0.669050       0.664287       1.000000\n",
              "max         5.199338       5.199338       5.199338       1.000000"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_scaler = QuantileTransformer(output_distribution=\"normal\")\n",
        "x_train_prep = x_scaler.fit_transform(x_train[:, :3])\n",
        "x_train_prep = np.c_[x_train_prep, x_train[:, 3]]\n",
        "\n",
        "pd.DataFrame(x_train_prep, columns=x_vars).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY6OOsx6BhQP"
      },
      "source": [
        "Let's preprocess also the $y$ features checking what obtained:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "VvfBZUjvB4oT",
        "outputId": "a323c032-5f3c-480b-966f-5142781d50f9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0aa9ae35-2ed1-4174-89cd-96c2fc2ba115\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RichDLLe</th>\n",
              "      <th>RichDLLmu</th>\n",
              "      <th>RichDLLk</th>\n",
              "      <th>RichDLLp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.010188</td>\n",
              "      <td>-0.011044</td>\n",
              "      <td>0.003597</td>\n",
              "      <td>-0.010576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000067</td>\n",
              "      <td>0.990031</td>\n",
              "      <td>1.003903</td>\n",
              "      <td>1.008098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "      <td>-5.199338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.666851</td>\n",
              "      <td>-0.686200</td>\n",
              "      <td>-0.677827</td>\n",
              "      <td>-0.676853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.003912</td>\n",
              "      <td>-0.012541</td>\n",
              "      <td>0.012427</td>\n",
              "      <td>-0.012032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.685760</td>\n",
              "      <td>0.665781</td>\n",
              "      <td>0.675098</td>\n",
              "      <td>0.670225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "      <td>5.199338</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0aa9ae35-2ed1-4174-89cd-96c2fc2ba115')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0aa9ae35-2ed1-4174-89cd-96c2fc2ba115 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0aa9ae35-2ed1-4174-89cd-96c2fc2ba115');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d8a81498-292f-470f-ba32-8802cd3d5e83\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d8a81498-292f-470f-ba32-8802cd3d5e83')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d8a81498-292f-470f-ba32-8802cd3d5e83 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            RichDLLe      RichDLLmu       RichDLLk       RichDLLp\n",
              "count  150000.000000  150000.000000  150000.000000  150000.000000\n",
              "mean        0.010188      -0.011044       0.003597      -0.010576\n",
              "std         1.000067       0.990031       1.003903       1.008098\n",
              "min        -5.199338      -5.199338      -5.199338      -5.199338\n",
              "25%        -0.666851      -0.686200      -0.677827      -0.676853\n",
              "50%         0.003912      -0.012541       0.012427      -0.012032\n",
              "75%         0.685760       0.665781       0.675098       0.670225\n",
              "max         5.199338       5.199338       5.199338       5.199338"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_scaler = QuantileTransformer(output_distribution=\"normal\")\n",
        "y_train_prep = y_scaler.fit_transform(y_train)\n",
        "\n",
        "pd.DataFrame(y_train_prep, columns=y_vars).describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVNTdWLPi5xM"
      },
      "source": [
        "### Model definition\n",
        "\n",
        "We are finally ready to define the **BceGAN model** to be used to parameterize the RICH response. GAN algorithms rely on the competition (namely the minimax game) between the generator and discriminator networks. The latters are provided by the [pidgan](https://github.com/mbarbetti/pidgan) package through the [`players`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/players) module that implements the two networks customizing the TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvx8Dkbz5o9Q"
      },
      "source": [
        "#### The generator\n",
        "\n",
        "The **generator player** can be implemented with the high-level [`Generator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/Generator.py) class that provides the neural network model wrapping the TensorFlow [Sequential API](https://www.tensorflow.org/guide/keras/sequential_model). Before to initialize a `Generator` instance, let's have a look at its arguments:\n",
        "\n",
        "* **`output_dim`** (_int_) - Dimensionality of the generator output space.\n",
        "* **`latent_dim`** (_int_) - Dimensionality of the latent space.\n",
        "* **`num_hidden_layers`** (_int_, default=5) - Number of the hidden layers passed to the [`tf.keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model. Each hidden layer has a LeakyReLU as activation function (see [`tf.keras.layers.LeakyReLU`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU)).\n",
        "* **`mlp_hidden_units`** (_int_ or _array_like_, default=128) - Number of neuron units per each hidden layer. It can also be passed as an array of unit numbers having length equal to the number of hidden layers.\n",
        "* **`mlp_dropout_rates`** (_float_ or _array_like_, default=0.0) - Fraction of the input neurons to drop per each hidden layer. It can also be passed as an array of float numbers having length equal to the number of hidden layers.\n",
        "* **`output_activation`** (_activation_like_ or _None_, default=None) - Activation function to use in the generator output layer (see [`tf.keras.activations`](https://www.tensorflow.org/api_docs/python/tf/keras/activations)). If None, no activation is applied (i.e., \"linear\" activation: $a(x) = x$).\n",
        "* **`name`** (_str_ of _None_, default=None) - The name of the generator model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the generator layer computations and weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "0th7xpVKCGN0"
      },
      "outputs": [],
      "source": [
        "from pidgan.players.generators import Generator\n",
        "\n",
        "generator = Generator(\n",
        "    output_dim=y.shape[-1],\n",
        "    latent_dim=64,\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    output_activation=None,\n",
        "    name=\"generator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luH3v1XVEBlc",
        "outputId": "5ddacc02-9131-4e23-c95d-e909be3a27e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"generator_seq\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_0 (Dense)             (None, 128)               8832      \n",
            "                                                                 \n",
            " leaky_relu_0 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_0 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_1 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_2 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_3 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_4 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_out (Dense)           (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75396 (294.52 KB)\n",
            "Trainable params: 75396 (294.52 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "_ = generator(keras.Input(shape=x.shape[1:]))\n",
        "generator.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MkE6GA14-hz"
      },
      "source": [
        "#### The discriminator\n",
        "\n",
        "The **discriminator player** can be implemented with the high-level [`Discriminator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/Discriminator.py) class that provides the neural network model wrapping the TensorFlow [Sequential API](https://www.tensorflow.org/guide/keras/sequential_model). Again, before to initialize a `Discriminator` instance, let's have a look to its arguments:\n",
        "\n",
        "* **`output_dim`** (_int_) - Dimensionality of the discriminator output space.\n",
        "* **`num_hidden_layers`** (_int_, default=5) - Number of the hidden layers passed to the [`tf.keras.Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model. Each hidden layer has a LeakyReLU as activation function (see [`tf.keras.layers.LeakyReLU`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LeakyReLU)).\n",
        "* **`mlp_hidden_units`** (_int_ or _array_like_, default=128) - Number of neuron units per each hidden layer. It can also be passed as an array of unit numbers having length equal to the number of hidden layers.\n",
        "* **`mlp_dropout_rates`** (_float_ or _array_like_, default=0.0) - Fraction of the input neurons to drop per each hidden layer. It can also be passed as an array of float numbers having length equal to the number of hidden layers.\n",
        "* **`output_activation`** (_activation_like_ or _None_, default=\"sigmoid\") - Activation function to use in the dicriminator output layer (see [`tf.keras.activations`](https://www.tensorflow.org/api_docs/python/tf/keras/activations)). If None, no activation is applied (i.e., \"linear\" activation: $a(x) = x$).\n",
        "* **`name`** (_str_ of _None_, default=None) - The name of the discriminator model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the discriminator layer computations and weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ufj8Xi4fC_Ot"
      },
      "outputs": [],
      "source": [
        "from pidgan.players.discriminators import Discriminator\n",
        "\n",
        "discriminator = Discriminator(\n",
        "    output_dim=1,\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    output_activation=\"sigmoid\",\n",
        "    name=\"discriminator\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfqi9DHvETlY",
        "outputId": "9f2cafc2-afc5-4db4-f13d-b0ae32d0799a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"discriminator_seq\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_0 (Dense)             (None, 128)               1152      \n",
            "                                                                 \n",
            " leaky_relu_0 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_0 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_1 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_2 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_3 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " leaky_relu_4 (LeakyReLU)    (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_out (Dense)           (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67329 (263.00 KB)\n",
            "Trainable params: 67329 (263.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "_ = discriminator((keras.Input(shape=x.shape[1:]), keras.Input(shape=y.shape[1:])))\n",
        "discriminator.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpQQWkPS5htN"
      },
      "source": [
        "#### The BceGAN algorithm\n",
        "\n",
        "The **BceGAN algorithm** [[2][2]] can be implemented with the high-level [`BceGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/BceGAN.py) class that defines the training procedure of the two players customizing the TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) class and, in particular, its `fit()` method. Once again, before to initialize a `BceGAN` instance, let's have a look at its arguments:\n",
        "\n",
        "* **`generator`** (_generator_like_) - The generator network. It must be passed as a pidgan [`Generator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/generators/Generator.py) instance.\n",
        "* **`discriminator`** (_discriminator_like_) - The discriminator network. It must be passed as a pidgan [`Discriminator`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/discriminators/Discriminator.py) instance.\n",
        "* **`from_logits`** (_bool_, default=False) - Whether to interpret the discriminator output as a tensor of [logit](https://en.wikipedia.org/wiki/Logit) values. By default, we assume that the discriminator outputs probabilities (i.e., values in [0, 1]).\n",
        "* **`label_smoothing`** (_float_, default=0.0) - Float in [0, 1]. When 0, no smoothing occurs. When > 0, we compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of `label_smoothing` correspond to heavier smoothing.\n",
        "* **`injected_noise_stddev`** (_float_, default=0.0) - When different from zero, it represents the standard deviation of the gaussian noise added to the discriminator input to stabilize the training procedure following what described in Ref. [[3][3]].\n",
        "* **`feature_matching_penalty`** (_float_, default=0.0) - Importance of the regularization term added to the generator training to force the hidden state of the discriminator to match between reference and generated examples. Its design follows from what proposed in Ref. [[6][6]].\n",
        "* **`referee`** (_classifier_like_ or _None_, default=None) - When passed it enables the training of a third independent player: the referee network. It can be used to assess the quality of the generator output during the training. If passed, it must be a pidgan [`Classifier`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/players/classifiers/Classifier.py) instance.\n",
        "* **`name`** (_str_ or _None_, default=\"BceGAN\") - The name of the BceGAN model.\n",
        "* **`dtype`** (_dtype_ or _None_, default=None) - The dtype of the BceGAN model computations.\n",
        "\n",
        "[2]: https://arxiv.org/abs/1511.06434\n",
        "[3]: https://arxiv.org/abs/1701.04862\n",
        "[6]: https://arxiv.org/abs/1606.03498"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgW6y0Ru1vPp"
      },
      "source": [
        "To not further complicate this tutorial, we avoid to use the referee network in this case, even if its initialization is straightforward:\n",
        "\n",
        "```python\n",
        "from pidgan.players.classifiers import Classifier\n",
        "\n",
        "referee = Classifier(\n",
        "    num_hidden_layers=5,\n",
        "    mlp_hidden_units=128,\n",
        "    mlp_dropout_rates=0.0,\n",
        "    name=\"referee\"\n",
        ")\n",
        "```\n",
        "\n",
        "It's time to initialize a [`BceGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/BceGAN.py) instance!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "SBEOKmeEDWC4"
      },
      "outputs": [],
      "source": [
        "from pidgan.algorithms import BceGAN\n",
        "\n",
        "gan = BceGAN(\n",
        "    generator=generator,\n",
        "    discriminator=discriminator,\n",
        "    from_logits=False,\n",
        "    label_smoothing=0.1,\n",
        "    injected_noise_stddev=0.05,\n",
        "    feature_matching_penalty=0.0,\n",
        "    referee=None,\n",
        "    name=\"BceGAN\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMrE8bs952KB"
      },
      "source": [
        "#### Model compilation\n",
        "\n",
        "To finalize the [`BceGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/BceGAN.py) model preparation before to run the training procedure, we need to execute the `compile()` method. Differently from the TensorFlow `compile()` method, the [pidgan](https://github.com/mbarbetti/pidgan) one doesn't require to pass the loss function since it is encoded in the customized `gan.fit()` method. The arguments of the `gan.compile()` method follows:\n",
        "\n",
        "* **`metrics`** (_list_ or _None_, default=None) - If not None, list of metrics to be evaluated by the BceGAN model during training and testing. Each of this can be a string (name of a built-in pidgan function) or a [`pidgan.metrics`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/metrics) instance.\n",
        "* **`generator_optimizer`** (_optimizer_like_, default=\"rmsprop\") - String (name of optimizer) or optimizer instance for the generator network (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`discriminator_optimizer`** (_optimizer_like_, default=\"rmsprop\") - String (name of optimizer) or optimizer instance for the discriminator network. (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`generator_upds_per_batch`** (_int_, default=1) - Number of the generator weights updates per batch of data.\n",
        "* **`discriminator_upds_per_batch`** (_int_, default=1) - Number of the discriminator weights updates per batch of data.\n",
        "* **`referee_optimizer`** (_optimizer_like_ or _None_, default=None) - If not None, string (name of optimizer) or optimizer instance for the referee network (see [`tf.keras.optimizers`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)).\n",
        "* **`referee_upds_per_batch`** (_int_ or _None_, default=None) - If not None, number of the referee weights updates per batch of data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "kHL20AxZFNOq"
      },
      "outputs": [],
      "source": [
        "gan.compile(\n",
        "    metrics=[\"accuracy\", \"bce\"],\n",
        "    generator_optimizer=keras.optimizers.RMSprop(learning_rate=0.002),\n",
        "    discriminator_optimizer=keras.optimizers.RMSprop(learning_rate=0.001),\n",
        "    generator_upds_per_batch=1,\n",
        "    discriminator_upds_per_batch=1,\n",
        "    referee_optimizer=None,\n",
        "    referee_upds_per_batch=None,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0htLzsSri5xM"
      },
      "source": [
        "### Training procedure\n",
        "\n",
        "The [pidgan](https://github.com/mbarbetti/pidgan) package provides several learning rate scheduling strategies through the [`callbacks.schedulers`](https://github.com/mbarbetti/pidgan/tree/main/src/pidgan/callbacks/schedulers) module. The pidgan schedulers are designed as [custom TensorFlow callbacks](https://www.tensorflow.org/guide/keras/writing_your_own_callbacks) that also allow to trace the learning rate value of each passed optimizer during the training. In this tutorial, we will use an exponential decay schedule both for the generator and discriminator networks:\n",
        "\n",
        "<center>$\\eta(t) = \\eta_0 \\cdot e^{- \\alpha \\cdot t / \\tau}$</center>\n",
        "\n",
        "where $\\eta(t)$ indicates the learning rate value at a specific training step $t$, $\\eta_0$ the initial learning rate, $\\alpha$ the decay rate and $\\tau$ the decay step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "C0-PqvTDGd1J"
      },
      "outputs": [],
      "source": [
        "from pidgan.callbacks.schedulers import LearnRateExpDecay\n",
        "\n",
        "schedules = list()\n",
        "\n",
        "g_lr_sched = LearnRateExpDecay(\n",
        "    gan.generator_optimizer,\n",
        "    decay_rate=0.1,\n",
        "    decay_steps=50000,\n",
        "    min_learning_rate=1e-6,\n",
        "    verbose=True,\n",
        "    key=\"g_lr\",\n",
        ")\n",
        "schedules.append(g_lr_sched)\n",
        "\n",
        "d_lr_sched = LearnRateExpDecay(\n",
        "    gan.discriminator_optimizer,\n",
        "    decay_rate=0.1,\n",
        "    decay_steps=75000,\n",
        "    min_learning_rate=1e-6,\n",
        "    verbose=True,\n",
        "    key=\"d_lr\",\n",
        ")\n",
        "schedules.append(d_lr_sched)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hDNGvV_FI0S"
      },
      "source": [
        "Calling `gan.fit()` allows to perform the **minimax two-player game**, training the generator to reproduce the reference space $y$ on the basis of the conditions $x$ thanks to the discriminator feedbacks. The arguments taken by the pidgan `fit()` method are the same ones traditionally passed to a TensorFlow [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) instance. The schedulers are passed through the `callbacks` argument and, since their verbosity has been enabled (`verbose` and `key` passed), the learning rates of both the generator and discriminator are printed during the training procedure.\n",
        "\n",
        "**Note:** Training the [`BceGAN`](https://github.com/mbarbetti/pidgan/blob/main/src/pidgan/algorithms/BceGAN.py) model on Colab using the hyperparameters chosen for this tutorial (i.e., batch-size, epochs, updates per batch) takes about 20 minutes with no-GPU equipped or 10 minutes enabling the T4-powered runtime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1mGZ3_FHMNg",
        "outputId": "4dac804a-08c3-400b-ef98-8914cb739de4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "  4/411 [..............................] - ETA: 9s - g_loss: 0.6663 - d_loss: 0.6852 - accuracy: 0.9775 - bce: 0.6578 - g_lr: 9.9993e-04 - d_lr: 4.9998e-04 "
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Callback method `on_train_batch_begin` is slow compared to the batch time (batch time: 0.0137s vs `on_train_batch_begin` time: 0.5286s). Check your callbacks.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "411/411 [==============================] - 29s 20ms/step - g_loss: 0.7623 - d_loss: 0.6820 - accuracy: 0.3985 - bce: 0.7272 - g_lr: 9.9060e-04 - d_lr: 4.9686e-04 - val_g_loss: 0.7000 - val_d_loss: 0.6880 - val_accuracy: 0.5176 - val_bce: 0.7010\n",
            "Epoch 2/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.7033 - d_loss: 0.6931 - accuracy: 0.5030 - bce: 0.6950 - g_lr: 9.7202e-04 - d_lr: 4.9063e-04 - val_g_loss: 0.6819 - val_d_loss: 0.6961 - val_accuracy: 0.6658 - val_bce: 0.6806\n",
            "Epoch 3/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6966 - d_loss: 0.6932 - accuracy: 0.5167 - bce: 0.6934 - g_lr: 9.5380e-04 - d_lr: 4.8448e-04 - val_g_loss: 0.6838 - val_d_loss: 0.6949 - val_accuracy: 0.6254 - val_bce: 0.6824\n",
            "Epoch 4/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6966 - d_loss: 0.6929 - accuracy: 0.4945 - bce: 0.6938 - g_lr: 9.3592e-04 - d_lr: 4.7840e-04 - val_g_loss: 0.6884 - val_d_loss: 0.6927 - val_accuracy: 0.5924 - val_bce: 0.6876\n",
            "Epoch 5/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6960 - d_loss: 0.6929 - accuracy: 0.4861 - bce: 0.6938 - g_lr: 9.1837e-04 - d_lr: 4.7240e-04 - val_g_loss: 0.6930 - val_d_loss: 0.6912 - val_accuracy: 0.4946 - val_bce: 0.6928\n",
            "Epoch 6/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6959 - d_loss: 0.6929 - accuracy: 0.4804 - bce: 0.6939 - g_lr: 9.0115e-04 - d_lr: 4.6648e-04 - val_g_loss: 0.6965 - val_d_loss: 0.6927 - val_accuracy: 0.4130 - val_bce: 0.6969\n",
            "Epoch 7/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6952 - d_loss: 0.6931 - accuracy: 0.4975 - bce: 0.6935 - g_lr: 8.8425e-04 - d_lr: 4.6063e-04 - val_g_loss: 0.6944 - val_d_loss: 0.6933 - val_accuracy: 0.4871 - val_bce: 0.6944\n",
            "Epoch 8/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6954 - d_loss: 0.6930 - accuracy: 0.4899 - bce: 0.6936 - g_lr: 8.6767e-04 - d_lr: 4.5485e-04 - val_g_loss: 0.6952 - val_d_loss: 0.6928 - val_accuracy: 0.4740 - val_bce: 0.6952\n",
            "Epoch 9/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6947 - d_loss: 0.6930 - accuracy: 0.5054 - bce: 0.6931 - g_lr: 8.5141e-04 - d_lr: 4.4915e-04 - val_g_loss: 0.6975 - val_d_loss: 0.6929 - val_accuracy: 0.3841 - val_bce: 0.6977\n",
            "Epoch 10/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6952 - d_loss: 0.6930 - accuracy: 0.5007 - bce: 0.6937 - g_lr: 8.3544e-04 - d_lr: 4.4352e-04 - val_g_loss: 0.6953 - val_d_loss: 0.6930 - val_accuracy: 0.4798 - val_bce: 0.6952\n",
            "Epoch 11/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6948 - d_loss: 0.6930 - accuracy: 0.5245 - bce: 0.6934 - g_lr: 8.1978e-04 - d_lr: 4.3796e-04 - val_g_loss: 0.6874 - val_d_loss: 0.6932 - val_accuracy: 0.6915 - val_bce: 0.6867\n",
            "Epoch 12/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6948 - d_loss: 0.6930 - accuracy: 0.5091 - bce: 0.6933 - g_lr: 8.0441e-04 - d_lr: 4.3247e-04 - val_g_loss: 0.6942 - val_d_loss: 0.6931 - val_accuracy: 0.4802 - val_bce: 0.6939\n",
            "Epoch 13/100\n",
            "411/411 [==============================] - 9s 21ms/step - g_loss: 0.6948 - d_loss: 0.6930 - accuracy: 0.5170 - bce: 0.6935 - g_lr: 7.8933e-04 - d_lr: 4.2704e-04 - val_g_loss: 0.6929 - val_d_loss: 0.6930 - val_accuracy: 0.5236 - val_bce: 0.6926\n",
            "Epoch 14/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6947 - d_loss: 0.6929 - accuracy: 0.5039 - bce: 0.6934 - g_lr: 7.7453e-04 - d_lr: 4.2169e-04 - val_g_loss: 0.6927 - val_d_loss: 0.6941 - val_accuracy: 0.5346 - val_bce: 0.6927\n",
            "Epoch 15/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6949 - d_loss: 0.6930 - accuracy: 0.4991 - bce: 0.6933 - g_lr: 7.6001e-04 - d_lr: 4.1640e-04 - val_g_loss: 0.6949 - val_d_loss: 0.6926 - val_accuracy: 0.4498 - val_bce: 0.6949\n",
            "Epoch 16/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6949 - d_loss: 0.6930 - accuracy: 0.4946 - bce: 0.6933 - g_lr: 7.4576e-04 - d_lr: 4.1118e-04 - val_g_loss: 0.6991 - val_d_loss: 0.6929 - val_accuracy: 0.3939 - val_bce: 0.6994\n",
            "Epoch 17/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6948 - d_loss: 0.6930 - accuracy: 0.5052 - bce: 0.6933 - g_lr: 7.3177e-04 - d_lr: 4.0603e-04 - val_g_loss: 0.6951 - val_d_loss: 0.6930 - val_accuracy: 0.4869 - val_bce: 0.6948\n",
            "Epoch 18/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6948 - d_loss: 0.6930 - accuracy: 0.5055 - bce: 0.6932 - g_lr: 7.1805e-04 - d_lr: 4.0093e-04 - val_g_loss: 0.6927 - val_d_loss: 0.6926 - val_accuracy: 0.5334 - val_bce: 0.6924\n",
            "Epoch 19/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6951 - d_loss: 0.6930 - accuracy: 0.4975 - bce: 0.6933 - g_lr: 7.0459e-04 - d_lr: 3.9591e-04 - val_g_loss: 0.6975 - val_d_loss: 0.6930 - val_accuracy: 0.4318 - val_bce: 0.6975\n",
            "Epoch 20/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6948 - d_loss: 0.6929 - accuracy: 0.4920 - bce: 0.6932 - g_lr: 6.9138e-04 - d_lr: 3.9094e-04 - val_g_loss: 0.6962 - val_d_loss: 0.6932 - val_accuracy: 0.3978 - val_bce: 0.6961\n",
            "Epoch 21/100\n",
            "411/411 [==============================] - 8s 20ms/step - g_loss: 0.6950 - d_loss: 0.6930 - accuracy: 0.5026 - bce: 0.6934 - g_lr: 6.7842e-04 - d_lr: 3.8604e-04 - val_g_loss: 0.6941 - val_d_loss: 0.6932 - val_accuracy: 0.4787 - val_bce: 0.6938\n",
            "Epoch 22/100\n",
            "411/411 [==============================] - 8s 20ms/step - g_loss: 0.6948 - d_loss: 0.6930 - accuracy: 0.5130 - bce: 0.6932 - g_lr: 6.6570e-04 - d_lr: 3.8120e-04 - val_g_loss: 0.6967 - val_d_loss: 0.6928 - val_accuracy: 0.4249 - val_bce: 0.6964\n",
            "Epoch 23/100\n",
            "411/411 [==============================] - 11s 26ms/step - g_loss: 0.6950 - d_loss: 0.6930 - accuracy: 0.4966 - bce: 0.6935 - g_lr: 6.5322e-04 - d_lr: 3.7642e-04 - val_g_loss: 0.6945 - val_d_loss: 0.6932 - val_accuracy: 0.5538 - val_bce: 0.6942\n",
            "Epoch 24/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6945 - d_loss: 0.6930 - accuracy: 0.5173 - bce: 0.6929 - g_lr: 6.4097e-04 - d_lr: 3.7170e-04 - val_g_loss: 0.6924 - val_d_loss: 0.6940 - val_accuracy: 0.5250 - val_bce: 0.6921\n",
            "Epoch 25/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6951 - d_loss: 0.6929 - accuracy: 0.4904 - bce: 0.6935 - g_lr: 6.2895e-04 - d_lr: 3.6704e-04 - val_g_loss: 0.7010 - val_d_loss: 0.6927 - val_accuracy: 0.3188 - val_bce: 0.7012\n",
            "Epoch 26/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6949 - d_loss: 0.6929 - accuracy: 0.5133 - bce: 0.6933 - g_lr: 6.1716e-04 - d_lr: 3.6244e-04 - val_g_loss: 0.6943 - val_d_loss: 0.6929 - val_accuracy: 0.5473 - val_bce: 0.6937\n",
            "Epoch 27/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6949 - d_loss: 0.6930 - accuracy: 0.5121 - bce: 0.6932 - g_lr: 6.0559e-04 - d_lr: 3.5789e-04 - val_g_loss: 0.6907 - val_d_loss: 0.6928 - val_accuracy: 0.6278 - val_bce: 0.6900\n",
            "Epoch 28/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6948 - d_loss: 0.6929 - accuracy: 0.5181 - bce: 0.6931 - g_lr: 5.9423e-04 - d_lr: 3.5341e-04 - val_g_loss: 0.6931 - val_d_loss: 0.6928 - val_accuracy: 0.5108 - val_bce: 0.6927\n",
            "Epoch 29/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6953 - d_loss: 0.6929 - accuracy: 0.5093 - bce: 0.6935 - g_lr: 5.8309e-04 - d_lr: 3.4897e-04 - val_g_loss: 0.6992 - val_d_loss: 0.6930 - val_accuracy: 0.3788 - val_bce: 0.6993\n",
            "Epoch 30/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6948 - d_loss: 0.6930 - accuracy: 0.5062 - bce: 0.6932 - g_lr: 5.7216e-04 - d_lr: 3.4460e-04 - val_g_loss: 0.6937 - val_d_loss: 0.6930 - val_accuracy: 0.4558 - val_bce: 0.6933\n",
            "Epoch 31/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6951 - d_loss: 0.6929 - accuracy: 0.5036 - bce: 0.6934 - g_lr: 5.6143e-04 - d_lr: 3.4028e-04 - val_g_loss: 0.6954 - val_d_loss: 0.6931 - val_accuracy: 0.4623 - val_bce: 0.6952\n",
            "Epoch 32/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6945 - d_loss: 0.6930 - accuracy: 0.5106 - bce: 0.6930 - g_lr: 5.5090e-04 - d_lr: 3.3601e-04 - val_g_loss: 0.6936 - val_d_loss: 0.6933 - val_accuracy: 0.4900 - val_bce: 0.6933\n",
            "Epoch 33/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6951 - d_loss: 0.6929 - accuracy: 0.4985 - bce: 0.6935 - g_lr: 5.4058e-04 - d_lr: 3.3180e-04 - val_g_loss: 0.6948 - val_d_loss: 0.6932 - val_accuracy: 0.4786 - val_bce: 0.6945\n",
            "Epoch 34/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6946 - d_loss: 0.6929 - accuracy: 0.5206 - bce: 0.6932 - g_lr: 5.3044e-04 - d_lr: 3.2764e-04 - val_g_loss: 0.6939 - val_d_loss: 0.6930 - val_accuracy: 0.4740 - val_bce: 0.6936\n",
            "Epoch 35/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6951 - d_loss: 0.6929 - accuracy: 0.4927 - bce: 0.6936 - g_lr: 5.2050e-04 - d_lr: 3.2353e-04 - val_g_loss: 0.6910 - val_d_loss: 0.6931 - val_accuracy: 0.5645 - val_bce: 0.6905\n",
            "Epoch 36/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6948 - d_loss: 0.6929 - accuracy: 0.5226 - bce: 0.6933 - g_lr: 5.1074e-04 - d_lr: 3.1947e-04 - val_g_loss: 0.6960 - val_d_loss: 0.6928 - val_accuracy: 0.4887 - val_bce: 0.6958\n",
            "Epoch 37/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6952 - d_loss: 0.6929 - accuracy: 0.5077 - bce: 0.6935 - g_lr: 5.0116e-04 - d_lr: 3.1547e-04 - val_g_loss: 0.6937 - val_d_loss: 0.6933 - val_accuracy: 0.5034 - val_bce: 0.6935\n",
            "Epoch 38/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6952 - d_loss: 0.6928 - accuracy: 0.4928 - bce: 0.6935 - g_lr: 4.9176e-04 - d_lr: 3.1151e-04 - val_g_loss: 0.6946 - val_d_loss: 0.6932 - val_accuracy: 0.4973 - val_bce: 0.6942\n",
            "Epoch 39/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6949 - d_loss: 0.6928 - accuracy: 0.5007 - bce: 0.6931 - g_lr: 4.8254e-04 - d_lr: 3.0760e-04 - val_g_loss: 0.6915 - val_d_loss: 0.6931 - val_accuracy: 0.5352 - val_bce: 0.6909\n",
            "Epoch 40/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6952 - d_loss: 0.6928 - accuracy: 0.4923 - bce: 0.6936 - g_lr: 4.7350e-04 - d_lr: 3.0375e-04 - val_g_loss: 0.6941 - val_d_loss: 0.6933 - val_accuracy: 0.5198 - val_bce: 0.6937\n",
            "Epoch 41/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6953 - d_loss: 0.6928 - accuracy: 0.5062 - bce: 0.6937 - g_lr: 4.6462e-04 - d_lr: 2.9994e-04 - val_g_loss: 0.6934 - val_d_loss: 0.6929 - val_accuracy: 0.5487 - val_bce: 0.6929\n",
            "Epoch 42/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6951 - d_loss: 0.6928 - accuracy: 0.5086 - bce: 0.6932 - g_lr: 4.5591e-04 - d_lr: 2.9618e-04 - val_g_loss: 0.6953 - val_d_loss: 0.6935 - val_accuracy: 0.4660 - val_bce: 0.6948\n",
            "Epoch 43/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6952 - d_loss: 0.6927 - accuracy: 0.4963 - bce: 0.6934 - g_lr: 4.4736e-04 - d_lr: 2.9246e-04 - val_g_loss: 0.6958 - val_d_loss: 0.6927 - val_accuracy: 0.4416 - val_bce: 0.6956\n",
            "Epoch 44/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6954 - d_loss: 0.6927 - accuracy: 0.4915 - bce: 0.6935 - g_lr: 4.3897e-04 - d_lr: 2.8880e-04 - val_g_loss: 0.6938 - val_d_loss: 0.6929 - val_accuracy: 0.5007 - val_bce: 0.6932\n",
            "Epoch 45/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6951 - d_loss: 0.6927 - accuracy: 0.4957 - bce: 0.6933 - g_lr: 4.3074e-04 - d_lr: 2.8518e-04 - val_g_loss: 0.6946 - val_d_loss: 0.6936 - val_accuracy: 0.4610 - val_bce: 0.6940\n",
            "Epoch 46/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6956 - d_loss: 0.6927 - accuracy: 0.5020 - bce: 0.6938 - g_lr: 4.2267e-04 - d_lr: 2.8160e-04 - val_g_loss: 0.6899 - val_d_loss: 0.6925 - val_accuracy: 0.5970 - val_bce: 0.6889\n",
            "Epoch 47/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6955 - d_loss: 0.6927 - accuracy: 0.5025 - bce: 0.6936 - g_lr: 4.1474e-04 - d_lr: 2.7807e-04 - val_g_loss: 0.6916 - val_d_loss: 0.6936 - val_accuracy: 0.5581 - val_bce: 0.6906\n",
            "Epoch 48/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6956 - d_loss: 0.6928 - accuracy: 0.5017 - bce: 0.6938 - g_lr: 4.0696e-04 - d_lr: 2.7458e-04 - val_g_loss: 0.6980 - val_d_loss: 0.6927 - val_accuracy: 0.4274 - val_bce: 0.6978\n",
            "Epoch 49/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6956 - d_loss: 0.6925 - accuracy: 0.5011 - bce: 0.6936 - g_lr: 3.9933e-04 - d_lr: 2.7114e-04 - val_g_loss: 0.6944 - val_d_loss: 0.6933 - val_accuracy: 0.5257 - val_bce: 0.6937\n",
            "Epoch 50/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6960 - d_loss: 0.6926 - accuracy: 0.5011 - bce: 0.6940 - g_lr: 3.9185e-04 - d_lr: 2.6774e-04 - val_g_loss: 0.6975 - val_d_loss: 0.6930 - val_accuracy: 0.4659 - val_bce: 0.6976\n",
            "Epoch 51/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6951 - d_loss: 0.6927 - accuracy: 0.5223 - bce: 0.6931 - g_lr: 3.8450e-04 - d_lr: 2.6438e-04 - val_g_loss: 0.6886 - val_d_loss: 0.6931 - val_accuracy: 0.6152 - val_bce: 0.6873\n",
            "Epoch 52/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6951 - d_loss: 0.6926 - accuracy: 0.5072 - bce: 0.6933 - g_lr: 3.7729e-04 - d_lr: 2.6107e-04 - val_g_loss: 0.6956 - val_d_loss: 0.6931 - val_accuracy: 0.4784 - val_bce: 0.6954\n",
            "Epoch 53/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6954 - d_loss: 0.6927 - accuracy: 0.5044 - bce: 0.6934 - g_lr: 3.7022e-04 - d_lr: 2.5779e-04 - val_g_loss: 0.6933 - val_d_loss: 0.6936 - val_accuracy: 0.5150 - val_bce: 0.6926\n",
            "Epoch 54/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6954 - d_loss: 0.6927 - accuracy: 0.5052 - bce: 0.6935 - g_lr: 3.6328e-04 - d_lr: 2.5456e-04 - val_g_loss: 0.6976 - val_d_loss: 0.6931 - val_accuracy: 0.4798 - val_bce: 0.6971\n",
            "Epoch 55/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6960 - d_loss: 0.6927 - accuracy: 0.4901 - bce: 0.6940 - g_lr: 3.5646e-04 - d_lr: 2.5137e-04 - val_g_loss: 0.6948 - val_d_loss: 0.6935 - val_accuracy: 0.5478 - val_bce: 0.6939\n",
            "Epoch 56/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6956 - d_loss: 0.6926 - accuracy: 0.5003 - bce: 0.6936 - g_lr: 3.4978e-04 - d_lr: 2.4822e-04 - val_g_loss: 0.6902 - val_d_loss: 0.6936 - val_accuracy: 0.5337 - val_bce: 0.6898\n",
            "Epoch 57/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6958 - d_loss: 0.6926 - accuracy: 0.4915 - bce: 0.6939 - g_lr: 3.4322e-04 - d_lr: 2.4511e-04 - val_g_loss: 0.6946 - val_d_loss: 0.6932 - val_accuracy: 0.4836 - val_bce: 0.6940\n",
            "Epoch 58/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6957 - d_loss: 0.6926 - accuracy: 0.5157 - bce: 0.6935 - g_lr: 3.3679e-04 - d_lr: 2.4203e-04 - val_g_loss: 0.6947 - val_d_loss: 0.6930 - val_accuracy: 0.4861 - val_bce: 0.6941\n",
            "Epoch 59/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6954 - d_loss: 0.6926 - accuracy: 0.5044 - bce: 0.6930 - g_lr: 3.3047e-04 - d_lr: 2.3900e-04 - val_g_loss: 0.6929 - val_d_loss: 0.6931 - val_accuracy: 0.5055 - val_bce: 0.6918\n",
            "Epoch 60/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6959 - d_loss: 0.6924 - accuracy: 0.4896 - bce: 0.6936 - g_lr: 3.2428e-04 - d_lr: 2.3600e-04 - val_g_loss: 0.6971 - val_d_loss: 0.6932 - val_accuracy: 0.4483 - val_bce: 0.6963\n",
            "Epoch 61/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6963 - d_loss: 0.6925 - accuracy: 0.4958 - bce: 0.6940 - g_lr: 3.1820e-04 - d_lr: 2.3304e-04 - val_g_loss: 0.6945 - val_d_loss: 0.6932 - val_accuracy: 0.4898 - val_bce: 0.6931\n",
            "Epoch 62/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6955 - d_loss: 0.6925 - accuracy: 0.5059 - bce: 0.6934 - g_lr: 3.1223e-04 - d_lr: 2.3012e-04 - val_g_loss: 0.6998 - val_d_loss: 0.6932 - val_accuracy: 0.4053 - val_bce: 0.6997\n",
            "Epoch 63/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6961 - d_loss: 0.6926 - accuracy: 0.4964 - bce: 0.6940 - g_lr: 3.0638e-04 - d_lr: 2.2723e-04 - val_g_loss: 0.7023 - val_d_loss: 0.6932 - val_accuracy: 0.4104 - val_bce: 0.7023\n",
            "Epoch 64/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6959 - d_loss: 0.6925 - accuracy: 0.5016 - bce: 0.6938 - g_lr: 3.0063e-04 - d_lr: 2.2438e-04 - val_g_loss: 0.6951 - val_d_loss: 0.6933 - val_accuracy: 0.5026 - val_bce: 0.6946\n",
            "Epoch 65/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6963 - d_loss: 0.6925 - accuracy: 0.5109 - bce: 0.6941 - g_lr: 2.9500e-04 - d_lr: 2.2157e-04 - val_g_loss: 0.6881 - val_d_loss: 0.6933 - val_accuracy: 0.6057 - val_bce: 0.6864\n",
            "Epoch 66/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6961 - d_loss: 0.6925 - accuracy: 0.5148 - bce: 0.6942 - g_lr: 2.8947e-04 - d_lr: 2.1879e-04 - val_g_loss: 0.6907 - val_d_loss: 0.6941 - val_accuracy: 0.5435 - val_bce: 0.6891\n",
            "Epoch 67/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6958 - d_loss: 0.6925 - accuracy: 0.5036 - bce: 0.6935 - g_lr: 2.8404e-04 - d_lr: 2.1605e-04 - val_g_loss: 0.6931 - val_d_loss: 0.6926 - val_accuracy: 0.4882 - val_bce: 0.6921\n",
            "Epoch 68/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6958 - d_loss: 0.6924 - accuracy: 0.4968 - bce: 0.6934 - g_lr: 2.7871e-04 - d_lr: 2.1334e-04 - val_g_loss: 0.6962 - val_d_loss: 0.6931 - val_accuracy: 0.4892 - val_bce: 0.6959\n",
            "Epoch 69/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6964 - d_loss: 0.6924 - accuracy: 0.5010 - bce: 0.6939 - g_lr: 2.7349e-04 - d_lr: 2.1067e-04 - val_g_loss: 0.6972 - val_d_loss: 0.6921 - val_accuracy: 0.4833 - val_bce: 0.6967\n",
            "Epoch 70/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6961 - d_loss: 0.6925 - accuracy: 0.4994 - bce: 0.6938 - g_lr: 2.6836e-04 - d_lr: 2.0802e-04 - val_g_loss: 0.7007 - val_d_loss: 0.6931 - val_accuracy: 0.4403 - val_bce: 0.7008\n",
            "Epoch 71/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6962 - d_loss: 0.6924 - accuracy: 0.4953 - bce: 0.6939 - g_lr: 2.6333e-04 - d_lr: 2.0542e-04 - val_g_loss: 0.6877 - val_d_loss: 0.6937 - val_accuracy: 0.5890 - val_bce: 0.6859\n",
            "Epoch 72/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6959 - d_loss: 0.6923 - accuracy: 0.5007 - bce: 0.6940 - g_lr: 2.5839e-04 - d_lr: 2.0284e-04 - val_g_loss: 0.6986 - val_d_loss: 0.6934 - val_accuracy: 0.4946 - val_bce: 0.6972\n",
            "Epoch 73/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6963 - d_loss: 0.6924 - accuracy: 0.5091 - bce: 0.6943 - g_lr: 2.5355e-04 - d_lr: 2.0030e-04 - val_g_loss: 0.6953 - val_d_loss: 0.6928 - val_accuracy: 0.5263 - val_bce: 0.6946\n",
            "Epoch 74/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6963 - d_loss: 0.6922 - accuracy: 0.5110 - bce: 0.6938 - g_lr: 2.4879e-04 - d_lr: 1.9778e-04 - val_g_loss: 0.6982 - val_d_loss: 0.6929 - val_accuracy: 0.4623 - val_bce: 0.6979\n",
            "Epoch 75/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6958 - d_loss: 0.6925 - accuracy: 0.5040 - bce: 0.6937 - g_lr: 2.4413e-04 - d_lr: 1.9530e-04 - val_g_loss: 0.6968 - val_d_loss: 0.6933 - val_accuracy: 0.5135 - val_bce: 0.6955\n",
            "Epoch 76/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6967 - d_loss: 0.6922 - accuracy: 0.4865 - bce: 0.6943 - g_lr: 2.3955e-04 - d_lr: 1.9286e-04 - val_g_loss: 0.6972 - val_d_loss: 0.6929 - val_accuracy: 0.4820 - val_bce: 0.6962\n",
            "Epoch 77/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6966 - d_loss: 0.6923 - accuracy: 0.4864 - bce: 0.6938 - g_lr: 2.3506e-04 - d_lr: 1.9044e-04 - val_g_loss: 0.7012 - val_d_loss: 0.6927 - val_accuracy: 0.4143 - val_bce: 0.7006\n",
            "Epoch 78/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6968 - d_loss: 0.6922 - accuracy: 0.4939 - bce: 0.6944 - g_lr: 2.3065e-04 - d_lr: 1.8805e-04 - val_g_loss: 0.6973 - val_d_loss: 0.6929 - val_accuracy: 0.4678 - val_bce: 0.6967\n",
            "Epoch 79/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6962 - d_loss: 0.6923 - accuracy: 0.5015 - bce: 0.6938 - g_lr: 2.2633e-04 - d_lr: 1.8569e-04 - val_g_loss: 0.6947 - val_d_loss: 0.6931 - val_accuracy: 0.5051 - val_bce: 0.6935\n",
            "Epoch 80/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6967 - d_loss: 0.6921 - accuracy: 0.4937 - bce: 0.6942 - g_lr: 2.2208e-04 - d_lr: 1.8336e-04 - val_g_loss: 0.7039 - val_d_loss: 0.6930 - val_accuracy: 0.3902 - val_bce: 0.7036\n",
            "Epoch 81/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6968 - d_loss: 0.6921 - accuracy: 0.4940 - bce: 0.6943 - g_lr: 2.1792e-04 - d_lr: 1.8106e-04 - val_g_loss: 0.6900 - val_d_loss: 0.6933 - val_accuracy: 0.5675 - val_bce: 0.6882\n",
            "Epoch 82/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6966 - d_loss: 0.6922 - accuracy: 0.4931 - bce: 0.6940 - g_lr: 2.1383e-04 - d_lr: 1.7879e-04 - val_g_loss: 0.7001 - val_d_loss: 0.6931 - val_accuracy: 0.4464 - val_bce: 0.6994\n",
            "Epoch 83/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6964 - d_loss: 0.6923 - accuracy: 0.4956 - bce: 0.6940 - g_lr: 2.0982e-04 - d_lr: 1.7655e-04 - val_g_loss: 0.6992 - val_d_loss: 0.6936 - val_accuracy: 0.4610 - val_bce: 0.6983\n",
            "Epoch 84/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6969 - d_loss: 0.6921 - accuracy: 0.5009 - bce: 0.6942 - g_lr: 2.0589e-04 - d_lr: 1.7434e-04 - val_g_loss: 0.6944 - val_d_loss: 0.6929 - val_accuracy: 0.5220 - val_bce: 0.6929\n",
            "Epoch 85/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6968 - d_loss: 0.6921 - accuracy: 0.4975 - bce: 0.6942 - g_lr: 2.0203e-04 - d_lr: 1.7215e-04 - val_g_loss: 0.6943 - val_d_loss: 0.6932 - val_accuracy: 0.5042 - val_bce: 0.6927\n",
            "Epoch 86/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6963 - d_loss: 0.6922 - accuracy: 0.5008 - bce: 0.6939 - g_lr: 1.9824e-04 - d_lr: 1.6999e-04 - val_g_loss: 0.6967 - val_d_loss: 0.6934 - val_accuracy: 0.5021 - val_bce: 0.6954\n",
            "Epoch 87/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6965 - d_loss: 0.6921 - accuracy: 0.4988 - bce: 0.6942 - g_lr: 1.9453e-04 - d_lr: 1.6786e-04 - val_g_loss: 0.6985 - val_d_loss: 0.6924 - val_accuracy: 0.4841 - val_bce: 0.6978\n",
            "Epoch 88/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6964 - d_loss: 0.6920 - accuracy: 0.4970 - bce: 0.6937 - g_lr: 1.9088e-04 - d_lr: 1.6576e-04 - val_g_loss: 0.7006 - val_d_loss: 0.6929 - val_accuracy: 0.4351 - val_bce: 0.6999\n",
            "Epoch 89/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6969 - d_loss: 0.6920 - accuracy: 0.4893 - bce: 0.6945 - g_lr: 1.8730e-04 - d_lr: 1.6368e-04 - val_g_loss: 0.6974 - val_d_loss: 0.6927 - val_accuracy: 0.4480 - val_bce: 0.6965\n",
            "Epoch 90/100\n",
            "411/411 [==============================] - 9s 23ms/step - g_loss: 0.6968 - d_loss: 0.6920 - accuracy: 0.4964 - bce: 0.6943 - g_lr: 1.8379e-04 - d_lr: 1.6163e-04 - val_g_loss: 0.6938 - val_d_loss: 0.6928 - val_accuracy: 0.5120 - val_bce: 0.6920\n",
            "Epoch 91/100\n",
            "411/411 [==============================] - 7s 16ms/step - g_loss: 0.6970 - d_loss: 0.6921 - accuracy: 0.5020 - bce: 0.6944 - g_lr: 1.8034e-04 - d_lr: 1.5960e-04 - val_g_loss: 0.6987 - val_d_loss: 0.6935 - val_accuracy: 0.4646 - val_bce: 0.6984\n",
            "Epoch 92/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6967 - d_loss: 0.6920 - accuracy: 0.4969 - bce: 0.6942 - g_lr: 1.7696e-04 - d_lr: 1.5760e-04 - val_g_loss: 0.6938 - val_d_loss: 0.6942 - val_accuracy: 0.5022 - val_bce: 0.6920\n",
            "Epoch 93/100\n",
            "411/411 [==============================] - 8s 18ms/step - g_loss: 0.6966 - d_loss: 0.6920 - accuracy: 0.5068 - bce: 0.6940 - g_lr: 1.7364e-04 - d_lr: 1.5562e-04 - val_g_loss: 0.7003 - val_d_loss: 0.6941 - val_accuracy: 0.4606 - val_bce: 0.6992\n",
            "Epoch 94/100\n",
            "411/411 [==============================] - 6s 15ms/step - g_loss: 0.6971 - d_loss: 0.6920 - accuracy: 0.4874 - bce: 0.6945 - g_lr: 1.7039e-04 - d_lr: 1.5367e-04 - val_g_loss: 0.6922 - val_d_loss: 0.6940 - val_accuracy: 0.5507 - val_bce: 0.6900\n",
            "Epoch 95/100\n",
            "411/411 [==============================] - 7s 17ms/step - g_loss: 0.6966 - d_loss: 0.6921 - accuracy: 0.4996 - bce: 0.6942 - g_lr: 1.6719e-04 - d_lr: 1.5174e-04 - val_g_loss: 0.6934 - val_d_loss: 0.6938 - val_accuracy: 0.5088 - val_bce: 0.6918\n",
            "Epoch 96/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6969 - d_loss: 0.6919 - accuracy: 0.4985 - bce: 0.6947 - g_lr: 1.6406e-04 - d_lr: 1.4984e-04 - val_g_loss: 0.6943 - val_d_loss: 0.6930 - val_accuracy: 0.5377 - val_bce: 0.6928\n",
            "Epoch 97/100\n",
            "411/411 [==============================] - 7s 18ms/step - g_loss: 0.6972 - d_loss: 0.6918 - accuracy: 0.5105 - bce: 0.6945 - g_lr: 1.6098e-04 - d_lr: 1.4796e-04 - val_g_loss: 0.6899 - val_d_loss: 0.6939 - val_accuracy: 0.5764 - val_bce: 0.6881\n",
            "Epoch 98/100\n",
            "411/411 [==============================] - 6s 16ms/step - g_loss: 0.6969 - d_loss: 0.6920 - accuracy: 0.5050 - bce: 0.6946 - g_lr: 1.5796e-04 - d_lr: 1.4611e-04 - val_g_loss: 0.6989 - val_d_loss: 0.6934 - val_accuracy: 0.4629 - val_bce: 0.6981\n",
            "Epoch 99/100\n",
            "411/411 [==============================] - 8s 20ms/step - g_loss: 0.6968 - d_loss: 0.6919 - accuracy: 0.5036 - bce: 0.6945 - g_lr: 1.5500e-04 - d_lr: 1.4428e-04 - val_g_loss: 0.6983 - val_d_loss: 0.6930 - val_accuracy: 0.4735 - val_bce: 0.6964\n",
            "Epoch 100/100\n",
            "411/411 [==============================] - 6s 14ms/step - g_loss: 0.6971 - d_loss: 0.6918 - accuracy: 0.4975 - bce: 0.6946 - g_lr: 1.5210e-04 - d_lr: 1.4247e-04 - val_g_loss: 0.6973 - val_d_loss: 0.6933 - val_accuracy: 0.4590 - val_bce: 0.6960\n",
            "Training procedure completed in 0:11:29.709238\n"
          ]
        }
      ],
      "source": [
        "start = datetime.now()\n",
        "\n",
        "train = gan.fit(\n",
        "    x=x_train_prep,\n",
        "    y=y_train_prep,\n",
        "    batch_size=256,\n",
        "    epochs=100,\n",
        "    validation_split=0.3,\n",
        "    callbacks=schedules,\n",
        "    verbose=1,\n",
        ")\n",
        "\n",
        "stop = datetime.now()\n",
        "\n",
        "print(f\"Training procedure completed in {stop - start}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GomKgX8QLeQM"
      },
      "source": [
        "The following code cells report the **learning** and **metric curves** for the training just finished."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z97AGJog5-jN"
      },
      "source": [
        "#### Learning curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "-jdGN4aFIhdn",
        "outputId": "6a6f2364-68ce-47f5-b53b-cedef8c47884"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Loss\", fontsize=12)\n",
        "plt.plot(train.history[\"g_loss\"], color=\"#3288bd\", label=\"generator\")\n",
        "plt.plot(train.history[\"d_loss\"], color=\"#fc8d59\", label=\"discriminator\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Learning rate scheduling\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Learning rate\", fontsize=12)\n",
        "plt.plot(train.history[\"g_lr\"], color=\"#3288bd\", label=\"generator\")\n",
        "plt.plot(train.history[\"d_lr\"], color=\"#fc8d59\", label=\"discriminator\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "plt.yscale(\"log\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "2id4-AK5J-WW",
        "outputId": "aa39d867-ebce-4c97-dfff-d1a030284b66"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Generator learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(gan.loss_name, fontsize=12)\n",
        "plt.plot(train.history[\"g_loss\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_g_loss\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Discriminator learning curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(gan.loss_name, fontsize=12)\n",
        "plt.plot(train.history[\"d_loss\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_d_loss\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"lower right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5wv91XH6BDC"
      },
      "source": [
        "#### Metric curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "P8rgOZgpKjPj",
        "outputId": "afc80683-22a3-439c-9a14-fb56e63da575"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Accuracy\", fontsize=12)\n",
        "plt.plot(train.history[\"accuracy\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_accuracy\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"lower right\", fontsize=10)\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Metric curves\", fontsize=14)\n",
        "plt.xlabel(\"Training epochs\", fontsize=12)\n",
        "plt.ylabel(\"Binary cross-entropy\", fontsize=12)\n",
        "plt.plot(train.history[\"bce\"], color=\"#d01c8b\", label=\"training set\")\n",
        "plt.plot(train.history[\"val_bce\"], color=\"#4dac26\", label=\"validation set\")\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hdn_BRai5xM"
      },
      "source": [
        "### Validation plots\n",
        "\n",
        "Once trained the GAN system, the **generator model** can be used to parameterize the high-level response of the LHCb RICH detectors, namely the RICH differential log-likelihoods for different particle hypotheses. Let's have a look at the achieved performance on the **test set**!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "HVid9HKfLzPW"
      },
      "outputs": [],
      "source": [
        "x_test_prep = x_scaler.transform(x_test[:, :3])  # input pre-processing\n",
        "x_test_prep = np.c_[x_test_prep, x_test[:, -1]]\n",
        "\n",
        "out_test = gan.generate(x_test_prep, seed=None)\n",
        "out_test_post = y_scaler.inverse_transform(out_test)  # output post-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfMNEUEs6EHn"
      },
      "source": [
        "#### RICH differential log-likelihood $(e - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "XM985aPPNPR3",
        "outputId": "f8972d7e-71ae-44e0-d244-87620be04a86"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-100.0, 50.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(e - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 0], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 0], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"BceGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df-bcHAW6lGH"
      },
      "source": [
        "#### RICH differential log-likelihood $(\\mu - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "KeRJbgTMPl6g",
        "outputId": "dfdf33ba-81de-455d-8d8e-2f8860a55e02"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-75.0, 75.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(\\mu - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 1], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 1], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"BceGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "yNgeWILAY5NC"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36gOX6UI6nsL"
      },
      "source": [
        "#### RICH differential log-likelihood $(K - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "rMh4nOK3PyEI",
        "outputId": "83623737-a169-4ae5-913f-739fe20759ce"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-150.0, 100.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(K - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 2], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 2], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"BceGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7EW3FlfL6qJX"
      },
      "source": [
        "#### RICH differential log-likelihood $(p - \\pi)$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "z3WQRODrQE7H",
        "outputId": "3984bcf6-c994-4514-c4b0-e667f1f2e852"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 5), dpi=100)\n",
        "\n",
        "bins = np.linspace(-150.0, 100.0, 101)\n",
        "\n",
        "for i, scale in enumerate([\"linear\", \"log\"]):\n",
        "    plt.subplot(1, 2, i + 1)\n",
        "    plt.xlabel(\"RICH differential log-likelihood $(p - \\pi)$\", fontsize=12)\n",
        "    plt.ylabel(\"Candidates\", fontsize=12)\n",
        "    plt.hist(y_test[:, 3], bins=bins, color=\"#3288bd\", label=\"toy data\")\n",
        "    plt.hist(out_test_post[:, 3], bins=bins, histtype=\"step\", lw=2, color=\"#fc8d59\", label=\"BceGAN model\")\n",
        "    plt.legend(loc=\"upper left\", fontsize=10)\n",
        "    plt.yscale(scale)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOYaaPWMnJXD"
      },
      "source": [
        "### Model export\n",
        "\n",
        "The [pidgan](https://github.com/mbarbetti/pidgan) package provides the **generator model** with the `export_model()` to export it as a plain [`Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model to ease the **deployment** of the RICH parameterization through tools like [scikinC](https://github.com/landerlini/scikinC) or [keras2c](https://github.com/f0uriest/keras2c) [[7][7], [8][8]].\n",
        "\n",
        "[7]: https://pos.sissa.it/409/034\n",
        "[8]: https://doi.org/10.1016/j.engappai.2021.104182"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxbF1xSaPlhf",
        "outputId": "ff5bd505-433a-43a1-ea0e-b1d048d91982"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ],
      "source": [
        "keras.models.save_model(\n",
        "    gan.generator.export_model,  # TF Sequential model\n",
        "    filepath=\"saved_generator\",\n",
        "    save_format=\"tf\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUflfVY7bm5z"
      },
      "source": [
        "Exporting the generator as a [TensorFlow SavedModel format](https://www.tensorflow.org/guide/saved_model) file allows to use **scikinC** to transpile the [`Sequential`](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model to a C file as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "MxsMQAkhU2_S"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!scikinC saved_generator > generator.C"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZ9f1NZAafov",
        "outputId": "b665a5ad-254e-42c2-b071-f9d99a1d1f10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 1.8M Nov  7 15:52 generator.C\n"
          ]
        }
      ],
      "source": [
        "!ls -lrth generator.C"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKqBzg-Fi5xM"
      },
      "source": [
        "## References\n",
        "\n",
        "1. I.J. Goodfellow _et al._, \"Generative Adversarial Networks\", [arXiv:1406.2661][1]\n",
        "2. A. Radford, L. Metz, S. Chintala, \"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks\", [arXiv:1511.06434][2]\n",
        "3. M. Arjovsky, L. Bottou, \"Towards Principled Methods for Training Generative Adversarial Networks\", [arXiv:1701.04862][3]\n",
        "4. M. Mirza, S. Osindero, \"Conditional Generative Adversarial Nets\", [arXiv:1411.1784][4]\n",
        "5. A. Maevskiy _et al._, \"Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks\", [arXiv:1905.11825][5]\n",
        "6. T. Salimans _et al._, \"Improved Techniques for Training GANs\", [arXiv:1606.03498][6]\n",
        "7. L. Anderlini, M. Barbetti, \"scikinC: a tool for deploying machine learning as binaries\", [PoS **CompTools2021** (2022) 034][7]\n",
        "8. R. Conlin _et al._, \"Keras2c: A library for converting Keras neural networks to real-time compatible C\", [Eng Appl Artif Intell **100** (2021) 104182][8]\n",
        "\n",
        "[1]: https://arxiv.org/abs/1406.2661\n",
        "[2]: https://arxiv.org/abs/1511.06434\n",
        "[3]: https://arxiv.org/abs/1701.04862\n",
        "[4]: https://arxiv.org/abs/1411.1784\n",
        "[5]: https://arxiv.org/abs/1905.11825\n",
        "[6]: https://arxiv.org/abs/1606.03498\n",
        "[7]: https://pos.sissa.it/409/034\n",
        "[8]: https://doi.org/10.1016/j.engappai.2021.104182"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1f26wCo4i5xN"
      },
      "source": [
        "## Credits\n",
        "This tutorial is based on the notebooks provided by the [mbarbetti/tf-gen-models](https://github.com/mbarbetti/tf-gen-models) repository."
      ]
    }
  ],
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    "accelerator": "GPU",
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      "provenance": [],
      "toc_visible": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.16"
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