Reviewer#1

reviewer-1.ipynb

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      "name": "Reviewer#1",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/prl900/967d8f91f2a13f57c72c424180714283/reviewer-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V47x694A8xtU",
        "colab_type": "code",
        "outputId": "14072a3f-2c2e-46c6-b960-02e134af9883",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras.backend as K\n",
        "\n",
        "tf.enable_eager_execution()\n",
        "\n",
        "tf.__version__"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.15.0'"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 1
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpcmVYtv9FxI",
        "colab_type": "code",
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      },
      "source": [
        "def get_pod_loss(threshold):\n",
        "  def pod(y_true, y_pred):\n",
        "    # Probability of detection = Hits / (Hits + Misses)\n",
        "    hits = K.sum(K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.greater(y_pred, threshold)), dtype='float32'))\n",
        "    misses = K.sum(K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.less(y_pred, threshold)), dtype='float32'))\n",
        "    return hits / (hits + misses)\n",
        "  return pod"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6No0tsGu9WLS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ! Comment out MAE, so this should be purely the differentiable POD loss\n",
        "\n",
        "def get_diff_pod_mae_loss(threshold):\n",
        "  def pod_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32'))\n",
        "    misses = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32'))\n",
        "    pod = hits / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pod #+ mae\n",
        "  return pod_mae\n",
        "\n",
        "def get_diff_pom_mae_loss(threshold):\n",
        "  def pom_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32'))\n",
        "    misses = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32'))\n",
        "    pom = misses / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pom #+ mae\n",
        "  return pom_mae\n",
        "\n",
        "def get_diff_pod_mae_loss2(threshold):\n",
        "  def pod_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32'))\n",
        "    misses = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(-1 * (y_pred - threshold)), dtype='float32'))\n",
        "    pod = hits / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pod #+ mae\n",
        "  return pod_mae\n",
        "\n",
        "def get_diff_pom_mae_loss2(threshold):\n",
        "  def pom_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32'))\n",
        "    misses = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(-1 * (y_pred - threshold)), dtype='float32'))\n",
        "    pom = misses / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pom #+ mae\n",
        "  return pom_mae\n",
        "\n",
        "def get_diff_pod(threshold):\n",
        "  def pod_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32')\n",
        "    misses = K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32')\n",
        "    pod = hits / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pod #+ mae\n",
        "  return pod_mae\n",
        "\n",
        "def get_diff_pom(threshold):\n",
        "  def pom_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32')\n",
        "    misses = K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32')\n",
        "    pom = misses / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pom #+ mae\n",
        "  return pom_mae\n",
        "\n",
        "def get_diff_pod2(threshold):\n",
        "  def pod_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32')\n",
        "    misses = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1 * (y_pred - threshold)), dtype='float32')\n",
        "    pod = hits / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pod #+ mae\n",
        "  return pod_mae\n",
        "\n",
        "def get_diff_pom2(threshold):\n",
        "  def pom_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32')\n",
        "    misses = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1 * (y_pred - threshold)), dtype='float32')\n",
        "    pom = misses / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pom #+ mae\n",
        "  return pom_mae\n",
        "\n",
        "def get_diff_pod3(threshold):\n",
        "  def pod_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32')\n",
        "    misses = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1 * (K.square(y_pred) - threshold)), dtype='float32')\n",
        "    pod = hits / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pod #+ mae\n",
        "  return pod_mae\n",
        "\n",
        "def get_diff_pom3(threshold):\n",
        "  def pom_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32')\n",
        "    misses = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1 * (K.square(y_pred) - threshold)), dtype='float32')\n",
        "    pom = misses / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pom #+ mae\n",
        "  return pom_mae"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LINA9Wfe93V6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "threshold = 1.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPerahq893wK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pod_loss = get_pod_loss(threshold=threshold)\n",
        "diff_pod_loss = get_diff_pod(threshold=threshold)\n",
        "diff_pom_loss = get_diff_pom(threshold=threshold)\n",
        "diff_pod_loss2 = get_diff_pod2(threshold=threshold)\n",
        "diff_pom_loss2 = get_diff_pom2(threshold=threshold)\n",
        "diff_pod_loss3 = get_diff_pod3(threshold=threshold)\n",
        "diff_pom_loss3 = get_diff_pom3(threshold=threshold)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXSAZmnB97hh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = tf.constant(np.linspace(0,3,num=301,endpoint=True, dtype=np.float32)[:,None])\n",
        "y_true = tf.constant(np.ones((301,1), dtype=np.float32)*2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpxYdkMVjSCe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "ecd39f93-f49f-42c7-fdb0-9c9e8b01c5b5"
      },
      "source": [
        "#Hits\n",
        "plt.plot(K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.greater(y_pred, threshold)), dtype='float32').numpy().flatten())\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten())\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten())"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff825cef60>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ojh8+nntz72Vp1lJmpswkLiYy/hTV5CJeFhn/FUn3tbdC8R/hjR+dCfJbHoVp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\nEZEo8f8BsRXzA0E3/3cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjyZJaeOihru",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f2fe9c5b-fd9c-4f9c-b653-e5e7efb15944"
      },
      "source": [
        "#Misses\n",
        "plt.plot(K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.less(y_pred, threshold)), dtype='float32').numpy().flatten())\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32').numpy().flatten())\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten())"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff82790048>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJB8SiH5kQYm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "13da1e4c-9a48-41aa-e00a-e69d77993427"
      },
      "source": [
        "#Hits + Misses\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() +\n",
        "         K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff8253f5f8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6X/p1ahF0aSISYWoS9NuAxErTPcLzvsLMxgN3A2e4e9Gh+e6+Lfx9o5l9CIwA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\nYY2u3AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUWQ_jA8ktmo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "cdce0255-4fcb-40ac-8fc4-613fbf44183b"
      },
      "source": [
        "#POD Different thresholds\n",
        "threshold=.5\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() / \n",
        "         (K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() +\n",
        "         K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten()))\n",
        "\n",
        "threshold=1.\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() / \n",
        "         (K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() +\n",
        "         K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten()))\n",
        "\n",
        "threshold=1.5\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() / \n",
        "         (K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() +\n",
        "         K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten()))\n",
        "\n",
        "threshold=2.\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() / \n",
        "         (K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() +\n",
        "         K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten()))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff82375470>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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64APcBw64qIymXMPbu95mU/ImWvpWtMrda9gq1+bD5vfUNckdPeHeT6HV42B1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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7OeSKLQl-BC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "27bad24d-3a7d-4e1d-c6ee-521586001054"
      },
      "source": [
        "threshold=.5\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() /\n",
        "         (K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() +\n",
        "          K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32').numpy().flatten()))\n",
        "\n",
        "threshold=1.\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() /\n",
        "         (K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() +\n",
        "          K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32').numpy().flatten()))\n",
        "\n",
        "threshold=1.5\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() /\n",
        "         (K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() +\n",
        "          K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32').numpy().flatten()))\n",
        "\n",
        "threshold=2.\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() /\n",
        "         (K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten() +\n",
        "          K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32').numpy().flatten()))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff822c1e10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DkW2t6kJn0DH+z/F4Onkyua3ld9f6rCxS3v8Al8aNqdK3r1VzkCRLyYAulRud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\nZjXOtSve90OS7hYyoFujMBMu/Gq6C7/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dhticcdorMLm4Q/VVeK8IA1ubUXlAAOsiIiER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\n0I0xJkf8DxrrQyJL+h4uAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFjLLFLUk-90",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c39882c6-7c56-446d-9c4c-2c6fa86e733c"
      },
      "source": [
        "#POM\n",
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten() / \n",
        "         (K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32').numpy().flatten() +\n",
        "         K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1*y_pred) - threshold), dtype='float32').numpy().flatten()))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff82483940>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GntIlnKsHqNBFRA7jS7DAoZf0FPoXhPY9zjn21TUGC//T8t+zv55d++qo3N9A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\n6CIiMUKFLiISI1ToIiIx4v8A44K2tlkxzp4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjlrIVjdGn6p",
        "colab_type": "code",
        "outputId": "3daa0847-bb63-48e8-c1db-2d531ab88127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(diff_pod_loss3(y_true, y_pred).numpy().flatten())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff82b00080>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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89bg7l0zIY9rnhtCjo26mJSL7p0JvRZZu2MX0t9bw0pJNpBicd0wfrj01X5fs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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcyL-DTXJdlL",
        "colab_type": "code",
        "outputId": "5d4a7429-f884-4035-c563-c0b9635ae675",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "threshold = 1.5\n",
        "y_pred = tf.constant([[1.], [2.]])\n",
        "y_true = tf.constant([[2.], [2.]])\n",
        "\n",
        "diff_pods = []\n",
        "pods = []\n",
        "for threshold in np.arange(-.1, 2.2, 0.1):\n",
        "  pod_loss = get_pod_loss(threshold=threshold)\n",
        "  diff_pod_loss = get_diff_pod_mae_loss(threshold=threshold)\n",
        "  diff_pods.append(diff_pod_loss(y_true, y_pred).numpy())\n",
        "  pods.append(pod_loss(y_true, y_pred).numpy())\n",
        "\n",
        "plt.plot(np.arange(-.1, 2.2, 0.1), diff_pods, label='differentiable')\n",
        "plt.plot(np.arange(-.1, 2.2, 0.1), pods, label='non-differentiable')\n",
        "\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('POD')\n",
        "plt.title('y_pred = [[1.], [2.]], y_true = [[2.], [2.]]')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f75a1832978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tKGV9YSnvFngr/0q/eSeUnMS4Qf24LH8I4wf14+S8TE7IzaB3oq70W9NOx3Mi\nQaIgkM+stLqedwrLeKeglHcLS1lfUMb+Su8q3OQkY+zAflx4ymAm5mUyaUgWo3Izuv+tDBUEkkAU\nBNIptQ0H2bCnnHW7Sni3sIx3CkvZWVwNeLfnHZWbwawxOUweksXEvCzGDuxLn94J0Kbf1RQEkkAU\nBNIm5xyFJTWsKyjl7Z0lrCsoZeOesuarcQdlpjJpSBaXTxvKpCGZnDw4k76p6jYZUBBIQlEQSLPq\n+kbeLSxj3a5S3t5Vwrpdpc1NPKHkJCbmZfLNM0YyZWgWk4dmMaBvapwrDrA+/cCSFASSEBQEPZRz\njp3F1azdWcK6ghLe3lnKB59UNF+VOzInnVljcjhlaJgpQ7M48bi+3etgbrSZQWqmgkASgoKgh6hv\nbOL9PWWs3VHCmp0HWLvz0639vn16M3loFt8aO4pThoaZPCSLcLougvrMdHWxJAgFQTdVUlXP2p0l\nrNlZwtqdB3insIz6xiYAhvZPY9boHKYOD5M/rD8nDOgBZ/HEg4JAEoSCoBtwzvHR/ipvpe9v8W8r\nqgK80zfHD8rk6zOGkT88zJRhYbXtx0ooDNX7412FSLsUBAnoYJNj88flrProAKt3HGDVRyXNzTyZ\noWSmDgtz0ZQ88oeFmTQkKzG6ZOiOQmEo3hLvKkTapSBIAPWNTby3u5RVH5Ww6qNi1uwsocLvX39w\nVojPjc5h2vD+TBseZlRuRvx63JTDhcJQUxrvKkTapSAIoOr6Rt7eWcqqHQdY9VEx63aVUue374/K\nTef8iYOYPiLMtOH9yQunxblaaVMoDLVl0HQQemmvTIJLQRAAlXWNrNlxgLe2H+Ct7cW8v7uMxiZH\nL4Nxg/pxxanDmD4iTP7w/uQErbtlaVsoDDgvDNL6x7sakTYpCOKg5Yr/vd1lHGxyJCcZE/OyuPHM\nkUwb3p+pw8K6UjeRRV5drCCQAFMQxEBVXSNrdpbw1vZi3tpezLuF3oq/dy9j8pAs/unMUcwYmc2U\nYVmkpegr6Taag0DHCSTYtNaJgur6RtbsKGGlVvw9m/obkgShtVAXqG9sYn1BKW9u28+bW4tZV1BC\nw0FvxT9pSBY3nTmSGSOzmTosrBV/TxLK8v5VEEjAaa10DA42OTbsKePNbcW8ua2Y1R8doKbhIGZw\n8uBMrj1jBKePymHacK34ezTtEUiC0FqqA5xzbN1XyRtb9/PmNq+5p9w/j3/0gAwuy8/j9BNymDEi\nm8w0HdwVX6r2CCQxRDUIzGwu8HMgCXjSOfeTFq8PBZ4BsvxxbnfOLYlmTR21t6yG17fs5/Wt+3lj\na3Hzlbt54RBfnDCQ00/I5ubU8K0AABBlSURBVLRR2equQdqW1NvrjlpBIAEXtSAwsyTgceAcoBBY\nbWaLnXMbI0a7E/i9c+4XZjYOWAIMj1ZNR1NR28Bb2w/wxtb9rNhS1NxXT05GCqePymHmCdmcPiqH\nIf11AZd0QihLQSCBF809gunAVufcdgAzex64AIgMAgf08x9nAnuiWM9hGg56B3gPbfWvLyjlYJMj\nNbkXp47I5vJpQzljdA4nHtdXXTbIsVMPpJIAohkEg4GCiOeFwKktxrkHWGpmtwDpwNmtTcjMbgBu\nABg6dOgxFeOcY1tRJSu27OeNrft5a/sBKusaMYOJgzO56cyRzDwhh6nDwj3zHrsSHaEw1Oo6Agm2\neB8s/irwtHPuP83sNOA3ZjbBOdcUOZJzbgGwACA/P98dy4we+etWHlr2IQDDstO4YPIgzjghh9NG\nZZOVppuwSJSEwlAesx1dkWMSzSDYDQyJeJ7nD4v0TWAugHNupZmlAjnAvq4u5pxxxzGgXx/OOEHt\n/BJDahqSBBDNIFgNjDazEXgBcDnwtRbj7AK+ADxtZmOBVKAoGsWMG9SPcYP6tT+iSFc6FATOefcx\nFgmgqN2N3DnXCNwMvApswjs7aIOZ3Wdm8/zR/i9wvZm9AzwHXO2cO6amH5FACoWhqRHqK+NdiUib\nonqMwL8mYEmLYXdFPN4IzIxmDSJxFXl1cZ++8a1FpA1R2yMQEXR1sSQEBYFINKm/IUkACgKRaFIQ\nSAJQEIhEk4JAEoCCQCSadE8CSQAKApFoSg5B75BuVymBpiAQiTZdXSwB124QmNlZZvZHM9vg/y0y\ns9kxqE2ke1AQSMAdNQjM7P8AC4E/4XUPcQXeBWILzey86Jcn0g2EwmoakkBr78ri24AvO+feiRi2\n3szWAI/S4qphEWlFKAsOfBTvKkTa1F7T0PEtQgAA59y7wHHRKUmkm1HTkARce0FQdYyvicghCgIJ\nuPaahkaZ2eJWhhswMgr1iHQ/oSxorIGGGu90UpGAaS8ILjjKaw90ZSEi3Vbz1cWlCgIJpKMGgXPu\nfwH8O4ed4A/e6pyrjXZhIt1GZDcT/QbGtxaRVrR3+mhvM/sZ3o3nnwF+DRSY2c/MLDkWBYokPPU3\nJAHX3sHi+4H+wAjn3FTn3BRgFJCFmoZEOkZBIAHXXhCcD1zvnKs4NMA5Vw78E6ALykQ64lAQ1Oqi\nMgmm9oLAtXYPYefcQUD3FhbpCO0RSMC1FwQbzezrLQea2ZXA5uiUJNLNpGRAr94KAgms9k4f/Rbw\nRzO7FljrD8sHQsCF0SxMpNsw00VlEmjtnT66GzjVzD4PjPcHL3HO/TXqlYl0JwoCCbCjBoF//cBN\neNcQvAf8yjnXGIvCRLoVBYEEWHvHCJ7Bawp6D/giOmVU5NgoCCTA2jtGMM45dzKAmf0KWBX9kkS6\nodQs2Lcx3lWItKq9PYKGQw/UJCTyGejmNBJg7e0RTDKzcv+xASH/ueFdY9AvqtWJdBehMNSVw8EG\nSFLvLBIs7Z01lBSrQkS6teari8sgPSe+tYi00O7N60WkC+jqYgkwBYFILETek0AkYBQEIrGgPQIJ\nsKgGgZnNNbMPzGyrmd3eyusPmdl6/+9DM9PmknRPoSzvXwWBBFB7Zw0dMzNLAh4HzsG7sc1qM1vs\nnGs+mdo5952I8W8BTolWPSJxpT0CCbBo7hFMx7ut5XbnXD3wPEe/B/JXgeeiWI9I/KRmAqYgkECK\nZhAMBgoinhf6w45gZsOAEcDf2nj9BjNbY2ZrioqKurxQkajrleSFgYJAAigoB4svBxb5N7w5gnNu\ngXMu3zmXn5ubG+PSRLqI+huSgIpmEOwGhkQ8z/OHteZy1Cwk3V0oS0EggRTNIFgNjDazEWaWgrey\nX9xyJDM7CQgDK6NYi0j8aY9AAipqQeB3Uncz8CqwCfi9c26Dmd1nZvMiRr0ceL61eyOLdCsKAgmo\nqJ0+CuCcWwIsaTHsrhbP74lmDSKBoSCQgArKwWKR7i8UhtpSaGqKdyUih1EQiMRKKAyuCeor4l2J\nyGEUBCKxoquLJaAUBCKxoiCQgFIQiMSKgkACSkEgEisKAgkoBYFIrCgIJKAUBCKxkqp7EkgwKQhE\nYqV3CiSn63aVEjgKApFY0tXFEkAKApFYUhBIACkIRGJJXVFLACkIRGJJewQSQAoCkVgKhXWwWAJH\nQSASS4f2CHT7DQkQBYFILIXCcLAOGmriXYlIMwWBSCzp6mIJIAWBSCwpCCSAFAQisaQgkABSEIjE\nkoJAAkhBIBJLIXU8J8GjIBCJJe0RSAApCERiKTkNklIUBBIoCgKRWDJTNxMSOAoCkVhTEEjAKAhE\nYi0Uhlr1NyTBoSAQiTXtEUjAKAhEYk09kErAKAhEYk17BBIwCgKRWAtlQX0lNNbHuxIRIMpBYGZz\nzewDM9tqZre3Mc5lZrbRzDaY2bPRrEckEA5dVKYDxhIQvaM1YTNLAh4HzgEKgdVmttg5tzFinNHA\nHcBM51yJmQ2IVj0igRF5dXGGfvISf9HcI5gObHXObXfO1QPPAxe0GOd64HHnXAmAc25fFOsRCYZU\n9TckwRLNIBgMFEQ8L/SHRRoDjDGzN8zsLTOb29qEzOwGM1tjZmuKioqiVK5IjKi/IQmYeB8s7g2M\nBmYDXwWeMLOsliM55xY45/Kdc/m5ubkxLlGkiykIJGCiGQS7gSERz/P8YZEKgcXOuQbn3EfAh3jB\nINJ9KQgkYKIZBKuB0WY2wsxSgMuBxS3GeQlvbwAzy8FrKtoexZpE4q9PP7BeCgIJjKgFgXOuEbgZ\neBXYBPzeObfBzO4zs3n+aK8CxWa2Efg7cJtzrjhaNYkEQq9e3gFjXV0sARG100cBnHNLgCUtht0V\n8dgB/+r/ifQcurpYAiTeB4tFeiYFgQSIgkAkHhQEEiAKApF4UBBIgCgIROJBQSABoiAQiYdQGGrL\noOlgvCsRURCIxEUoC3BeGIjEmYJAJB50dbEEiIJAJB6ag0AXlUn8KQhE4kF7BBIgCgKReFAQSIAo\nCETiQberlABREIjEg+5SJgGiIBCJh6TeXnfUCgIJAAWBSLyEshQEEggKApF4UTcTEhAKApF4URBI\nQCgIROIlVU1DEgwKApF40R6BBISCQCReDgWBc/GuRHo4BYFIvITC0NQI9ZXxrkR6OAWBSLyomwkJ\nCAWBSLwoCCQgFAQi8aKuqCUgFAQi8aI9AgkIBYFIvCgIJCAUBCLxElIPpBIMCgKReEkOQe+QgkDi\nTkEgEk+6ulgCQEEgEk/qiloCQEEgEk+hsE4flbhTEIjEk5qGJACiGgRmNtfMPjCzrWZ2eyuvX21m\nRWa23v+7Lpr1iASOmoYkAHpHa8JmlgQ8DpwDFAKrzWyxc25ji1FfcM7dHK06RAJNewQSAFELAmA6\nsNU5tx3AzJ4HLgBaBoFIzxUKQ2MNPDYdzOJdjXTW4Hz48uPxruIzi2YQDAYKIp4XAqe2Mt7FZjYL\n+BD4jnOuoOUIZnYDcAPA0KFDo1CqSJyc9CX4ZIPXHbUknqwh8a6gS0QzCDriT8Bzzrk6M7sReAb4\nfMuRnHMLgAUA+fn5uouHdB+5Y+CShfGuQnq4aB4s3g1ExmWeP6yZc67YOVfnP30SmBrFekREpBXR\nDILVwGgzG2FmKcDlwOLIEcxsYMTTecCmKNYjIiKtiFrTkHOu0cxuBl4FkoCFzrkNZnYfsMY5txi4\n1czmAY3AAeDqaNUjIiKtM5dgN87Oz893a9asiXcZIiIJxczWOufyW3tNVxaLiPRwCgIRkR5OQSAi\n0sMpCEREeriEO1hsZkXAzhjPNgfYH+N5Bp2WyZG0TFqn5XKkeCyTYc653NZeSLggiAczW9PW0fae\nSsvkSFomrdNyOVLQlomahkREejgFgYhID6cg6JgF8S4ggLRMjqRl0jotlyMFapnoGIGISA+nPQIR\nkR5OQSAi0sMpCCKY2Vwz+8DMtprZ7a283sfMXvBf/4eZDY99lbHVgWVytZkVmdl6/++6eNQZS2a2\n0Mz2mdn7bbxuZvaIv8zeNbMpsa4x1jqwTGabWVnE7+SuWNcYa2Y2xMz+bmYbzWyDmf1LK+ME47fi\nnNOfd5wkCdgGjARSgHeAcS3G+Wdgvv/4cuCFeNcdgGVyNfBYvGuN8XKZBUwB3m/j9fOAVwADZgD/\niHfNAVgms4E/x7vOGC+TgcAU/3FfvNvxtvz/E4jfivYIPjUd2Oqc2+6cqweeBy5oMc4FeLfTBFgE\nfMGsW99xvCPLpMdxzr2Gd/+MtlwA/Np53gKyWtyEqdvpwDLpcZxze51zb/uPK/BuvDW4xWiB+K0o\nCD41GCiIeF7IkV9a8zjOuUagDMiOSXXx0ZFlAnCxv1u7yMy6x928P5uOLree5jQze8fMXjGz8fEu\nJpb8ZuRTgH+0eCkQvxUFgXxWfwKGO+cmAn/h0z0mkUhv4/V1Mwl4FHgpzvXEjJllAC8C33bOlce7\nntYoCD61G4jcms3zh7U6jpn1BjKB4phUFx/tLhPnXLFzrs5/+iQwNUa1BVlHfks9inOu3DlX6T9e\nAiSbWU6cy4o6M0vGC4HfOef+2MoogfitKAg+tRoYbWYjzCwF72Dw4hbjLAa+4T++BPib84/4dFPt\nLpMW7Znz8NpBe7rFwNf9M0JmAGXOub3xLiqezOz4Q8fTzGw63rqnO29E4X/eXwGbnHMPtjFaIH4r\nUbt5faJxzjWa2c3Aq3hnyyx0zm0ws/uANc65xXhf6m/MbCvegbHL41dx9HVwmdxqZvOARrxlcnXc\nCo4RM3sO7yyYHDMrBO4GkgGcc/OBJXhng2wFqoFr4lNp7HRgmVwC/JOZNQI1wOXdfCMKYCZwFfCe\nma33h30fGArB+q2oiwkRkR5OTUMiIj2cgkBEpIdTEIiI9HAKAhGRHk5BICLSwykIpMcws+yI3i8/\nNrPd/uNSM9sYhfnNNrM/d/I9y83siJua+728PtZ11Yl8SkEgPYZ/FfRk59xkYD7wkP94MtDU3vv9\nq8lFuh0FgYgnycye8PuNX2pmIWjeQn/YzNYA/2JmuWb2opmt9v9m+uOdGbG3sc7M+vrTzfA749ts\nZr+LuLr2C/547/l9+fdpWZCZXWNmH5rZKryLk0SiQkEg4hkNPO6cGw+UAhdHvJbinMt3zv0n8HO8\nPYlp/jhP+uN8F/iWv4fxObyrZ8HrcfLbwDi8+zrMNLNU4GngK865k/Gu8P+nyGL8rjvuxQuAM/z3\ni0SFgkDE85Fz7lA3AGuB4RGvvRDx+GzgMb/LgMVAP793yTeAB83sViDL76YcYJVzrtA51wSs96d7\noj+/D/1xnsG7sUukU4Hlzrki/14QLyASJWrzFPHURTw+CIQinldFPO4FzHDO1bZ4/0/M7P/h9Rvz\nhpnNaWO6+j8ngaM9ApHOWQrccuiJmU32/x3lnHvPOfdTvF5bTzrKND4AhpvZCf7zq4D/bTHOP4Az\n/TOdkoFLu+oDiLSkIBDpnFuBfP+ObBuBm/zh3zaz983sXaAB7z60rfL3Jq4B/mBm7+GdsTS/xTh7\ngXuAlXjNTureW6JGvY+KiPRw2iMQEenhFAQiIj2cgkBEpIdTEIiI9HAKAhGRHk5BICLSwykIRER6\nuP8PrJotpU2Y+pEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efLZXd7j9-Wa",
        "colab_type": "code",
        "outputId": "f5b1e165-9066-43d2-a19f-1d56311b965a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "pod_loss(y_true, y_pred)\n",
        "diff_pod_loss(y_true, y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=6416, shape=(), dtype=float32, numpy=0.55521077>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zLvYJ8Xt-Dby",
        "colab_type": "code",
        "outputId": "95d1b8ad-1df4-4ca5-d072-a17b4af99bfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#hits = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32'))\n",
        "#misses = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32'))\n",
        "\n",
        "hits = K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32')\n",
        "misses = K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(-1 * (y_pred - threshold)), dtype='float32')\n",
        "\n",
        "hits, misses"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: id=6423, shape=(301, 1), dtype=float32, numpy=\n",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
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      "metadata": {
        "id": "x-tA86i-l6-k",
        "colab_type": "code",
        "outputId": "26b17c9b-6c69-4ee4-b224-52d5fc55cf6d",
        "colab": {
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          "height": 282
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      },
      "source": [
        "plt.plot(K.sigmoid(K.square(y_pred) - threshold).numpy().flatten())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffb1a99b630>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
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VwCcH+J9nA9VBjOMlLUvoiZTlAC1LqDqYZRnqnMvZ0wueFfrBMLNi51yR1zmC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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmDy4EU8meJX",
        "colab_type": "code",
        "outputId": "c9aa2889-bf5f-4040-dfa5-e6284cf95fe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "# Misses Non-diff vs Diff\n",
        "\n",
        "plt.plot(K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.less(y_pred, threshold)), dtype='float32').numpy().flatten(), label='non-diff')\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32').numpy().flatten(), label='diff')\n",
        "\n",
        "plt.xlabel('y_pred [/10]')\n",
        "plt.ylabel('Misses')\n",
        "plt.title('Non-diff vs Diff Misses (threshold=1.5)')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ffb1a88f320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oMO5onKuyyiaC24GngTaSRgPTgYqes+8LLDSzxWa2GRgPDCul3O+Am4nONpxL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\n54nAOecynCcC55zLcP8PNoDMZE1YR6kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6HMZ3yHwyCQ",
        "colab_type": "code",
        "outputId": "fc4be03d-6fe9-485f-cd98-9255412b5996",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "# Hits Non-diff vs Diff\n",
        "\n",
        "plt.plot(K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.greater(y_pred, threshold)), dtype='float32').numpy().flatten(), label='non-diff')\n",
        "plt.plot(K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32').numpy().flatten(), label='diff')\n",
        "plt.xlabel('y_pred [/10]')\n",
        "plt.ylabel('Misses')\n",
        "plt.title('Non-diff vs Diff Misses (threshold=1.5)')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ffb292ceda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Asz+FXX4Ig/8cWw+vis5JWlX3a1scjS9wEnCXu/8C2DF7YYlkycKPYOww6NQb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\nEYiI5Lj/D1H9cKmi2iGYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cupRhVS81Yr0",
        "colab_type": "code",
        "outputId": "34ebc78a-4675-4027-fedf-f3b239d7955a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "#POM\n",
        "hits = K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.greater(y_pred, threshold)), dtype='float32')\n",
        "misses = K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.less(y_pred, threshold)), dtype='float32')\n",
        "plt.plot(misses.numpy().flatten() / (hits + misses).numpy().flatten())\n",
        "\n",
        "dhits = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32')\n",
        "dmisses = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32')\n",
        "plt.plot(dmisses.numpy().flatten() / (dhits + dmisses).numpy().flatten())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffb2f535d30>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SYNlXzGwA0NM599TOfpGZjTOzUjMrXblyZb2LFamX/EIYPck/k/TRc/2rSIzt8klRM0sA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\nCnQRkZhQoIuIxMT/B9XAtCPSf0/yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgCUAKAw-BF-",
        "colab_type": "code",
        "outputId": "f3503a4f-1501-4053-f88b-cbe3a0ef8929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "#POD\n",
        "hits = K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.greater(y_pred, threshold)), dtype='float32')\n",
        "misses = K.cast(tf.math.logical_and(K.greater(y_true, threshold), K.less(y_pred, threshold)), dtype='float32')\n",
        "plt.plot(hits.numpy().flatten() / (hits + misses).numpy().flatten())\n",
        "\n",
        "dhits = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(K.square(y_pred) - threshold), dtype='float32')\n",
        "dmisses = K.cast(K.sigmoid(K.square(y_true) - threshold) * K.sigmoid(-1*(K.square(y_pred) - threshold)), dtype='float32')\n",
        "plt.plot(dhits.numpy().flatten() / (dhits + dmisses).numpy().flatten())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffb29482630>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RmHxSfh1_49",
        "colab_type": "code",
        "outputId": "d005764a-196f-483c-efaf-48d75f7a50e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "#POFD\n",
        "\n",
        "f_alarms = K.cast(tf.math.logical_and(K.less(y_true, threshold), K.greater(y_pred, threshold)), dtype='float32')\n",
        "true_neg = K.cast(tf.math.logical_and(K.less(y_true, threshold), K.less(y_pred, threshold)), dtype='float32')\n",
        "#plt.plot(f_alarms.numpy().flatten() / (f_alarms + true_neg).numpy().flatten())\n",
        "#plt.plot(f_alarms)\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "dtrue_neg = K.cast(K.sigmoid((-1 * y_true) - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32')\n",
        "df_alarms = K.cast(K.sigmoid((-1 * y_true) - threshold) * K.sigmoid(y_pred - threshold), dtype='float32')\n",
        "plt.plot(df_alarms.numpy().flatten() / (df_alarms + dtrue_neg).numpy().flatten())\n",
        "\"\"\"\n",
        "plt.plot(true_neg.numpy().flatten())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffb29164e80>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tfTOjqva2r08Bn2X4A/Lkgdvz9vWp6VW4YkvVPnNrVVVPtj+8vwD+nv/fZjim55LkRQz/\n4vzHqvqX1j2T67LYXGZ1XQ6oqh8D9wFvYLgVt7qdGq33l3Np508Gnl7ptXoJggeADe3J+wkMH6rs\nmHJNy5bkt5K89MAxcAnwMMM5bG7DNgOfm06Fh2Wp2ncA72yfUnk98OzIVsUxacFe+R8xXBsYzuWq\n9smO9cAG4CtHu77FtH3kW4BvVdXHRk7N3LosNZcZXZc1SU5pxy8BLmb4zOM+4Mo2bOG6HFivK4F7\n253cykz7KfnRejH81MO3Ge63XTvtelZY+3kMP+XwNWD3gfoZ7gXeA+wBvgicNu1al6j/Mwxvzf+X\n4f7m1UvVzvBTEze3dfoGMJh2/cuYy22t1q+3P5hnjYy/ts3lEeDyadc/UtcbGW77fB14qL3eNovr\ncpC5zOK6/B7w1Vbzw8D7W/95DMNqHvgn4MTW/+LWnm/nzzuc6/q/mJCkzvWyNSRJWoJBIEmdMwgk\nqXMGgSR1ziCQpM4ZBJLUOYNAkjr3f0vYX5hZMYSWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yr4qFXLCqk1i",
        "colab_type": "code",
        "outputId": "8055b2d4-84f3-4533-9c0f-96b63db33864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32').numpy().flatten())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe4c2ffa080>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4BmV2ZFBmPH1SOxARptsoNtbRzl4pItIkosJDGdY9gWHdEwCoqqll0cYy5q4r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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sh8jcg3rrhQP",
        "colab_type": "code",
        "outputId": "83082f31-f0ac-44a9-cff3-441fe01d4fd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(K.cast(K.less(y_pred, threshold), dtype='float32').numpy().flatten())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e6fe0a550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LukfSMUljkh5e5PVPSBqV9BVJfynpLc2ParZ2laTE6QtXef3SdNpRzJpu2UKX1A48BtwL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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71jn7kANmqX9",
        "colab_type": "code",
        "outputId": "eed7ded8-3f1d-4697-8d88-5882f6816533",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "plt.plot(np.square(K.cast(K.sigmoid(y_true - threshold), dtype='float32').numpy().flatten()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7e7039fba8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aGEMy23Pvldlcmh0aB2+WonFwETkzKvQYK95X9mmBv1+0h9IjoXHw7M6tGTs0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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAplCQ622UUi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ! Comment out MAE, so this should be purely the differentiable POD loss\n",
        "def get_diff_pod_mae_loss_old(threshold):\n",
        "  def pod_mae(y_true, y_pred):\n",
        "    # Probability of detection = Misses / (Hits + Misses)\n",
        "    hits = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32'))\n",
        "    misses = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32'))\n",
        "    pod = hits / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pod #+ mae\n",
        "  return pod_mae\n",
        "\n",
        "def get_diff_pom_mae_loss_old(threshold):\n",
        "  def pom_mae(y_true, y_pred):\n",
        "    # Probability of missing = Misses / (Hits + Misses)\n",
        "    hits = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid(y_pred - threshold), dtype='float32'))\n",
        "    misses = K.sum(K.cast(K.sigmoid(y_true - threshold) * K.sigmoid((-1 * y_pred) - threshold), dtype='float32'))\n",
        "    pom = misses / (hits + misses)\n",
        "    #mae = K.mean(K.abs(y_pred - y_true), axis=-1)\n",
        "    return pom #+ mae\n",
        "  return pom_mae"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRjI13hZ-v3g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "threshold = 1.5\n",
        "y_pred = tf.constant([[1.], [2.]])\n",
        "y_true = tf.constant([[2.], [2.]])\n",
        "\n",
        "diff_pods = []\n",
        "diff_poms = []\n",
        "pods = []\n",
        "for threshold in np.arange(-.1, 2.2, 0.1):\n",
        "  pod_loss = get_pod_loss(threshold=threshold)\n",
        "  diff_pod_loss = get_diff_pod_mae_loss_old(threshold=threshold)\n",
        "  diff_pom_loss = get_diff_pom_mae_loss_old(threshold=threshold)\n",
        "  diff_pods.append(diff_pod_loss(y_true, y_pred).numpy())\n",
        "  diff_poms.append(diff_pom_loss(y_true, y_pred).numpy())\n",
        "  pods.append(pod_loss(y_true, y_pred).numpy())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSCxk5KU-_7T",
        "colab_type": "code",
        "outputId": "78329ff3-afe4-4687-f05d-3fee98df5e1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.plot(np.arange(-.1, 2.2, 0.1), diff_pods, label='pod differentiable')\n",
        "plt.plot(np.arange(-.1, 2.2, 0.1), diff_poms, label='pom differentiable')\n",
        "plt.plot(np.arange(-.1, 2.2, 0.1), pods, label='non-differentiable')\n",
        "\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('POD')\n",
        "plt.title('y_pred = [[1.], [2.]], y_true = [[2.], [2.]]')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ffb1a824e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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02bJlC/n5+QP/JXqpuLiYM844g02bNoUsD9Es1L9/f8x7cR5Ts6fyu+N/F+qs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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5Zse6fa_G6T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}