12 - Video - TF Hub, Focal Loss ve Imbalanced_Classification.ipynb

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      "machine_shape": "hm"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# !pip install tensorflow-text\n",
        "# import tensorflow_text"
      ],
      "metadata": {
        "id": "C7DAMjdLiJSw"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Kütüphaneler"
      ],
      "metadata": {
        "id": "Nl1IDmxwIvIt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JisWOME-nQz5",
        "outputId": "f5c5e9fd-eecf-4745-bf89-bcafdcb25a81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Version:  2.11.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (12, 8)\n",
        "\n",
        "print(\"Version: \", tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Veri Yüklenmesi"
      ],
      "metadata": {
        "id": "ePvpl-7DIx1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://archive.org/download/fine-tune-bert-tensorflow-train.csv/train.csv.zip\"\n",
        "\n",
        "dataframe = pd.read_csv(url, compression='zip')\n",
        "dataframe.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6rVDTgKptHw",
        "outputId": "cfec12db-d3ef-4083-d7d0-5184735dc19c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1306122, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
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      "source": [
        "dataframe.head(2)"
      ],
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          "height": 112
        },
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        "outputId": "8cbec2c1-3caa-441c-ea5f-e5a238e09cf1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    qid                                      question_text  \\\n",
              "0  00002165364db923c7e6  How did Quebec nationalists see their province...   \n",
              "1  000032939017120e6e44  Do you have an adopted dog, how would you enco...   \n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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        "ax = sns.countplot(data = dataframe, x = 'target')\n",
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        "plt.xlabel('Hedef LabeL', size = 16)\n",
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        "    ax.annotate(percentage, (x, y), ha='center', va='center', fontsize=20)\n",
        "plt.tight_layout()\n",
        "plt.title('Label Dağılımı', fontsize = 18)\n",
        "plt.show()"
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      "outputs": [
        {
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              "<Figure size 864x576 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TF.Data Pipeline"
      ],
      "metadata": {
        "id": "e_qp7uncSanD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features, labels = dataframe[\"question_text\"], dataframe[\"target\"]"
      ],
      "metadata": {
        "id": "dwktiNNfrUwQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_features, X_test, train_labels, y_test = train_test_split(\n",
        "                                                            features, \n",
        "                                                            labels,\n",
        "                                                            train_size = 0.8,\n",
        "                                                            stratify=labels,\n",
        "                                                            random_state=1002\n",
        ")"
      ],
      "metadata": {
        "id": "XzT7VNl4b8_n"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_features.shape, train_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vq6RDdRndCzu",
        "outputId": "b9132360-8f44-4cb2-981d-5444f89937ab"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1044897,), (1044897,))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(\n",
        "                                    train_features, train_labels,\n",
        "                                    train_size = 0.8,\n",
        "                                    stratify=train_labels,\n",
        "                                    random_state=1002\n",
        ")"
      ],
      "metadata": {
        "id": "LxnVBJo0dGP6"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0x-48DZedYAb",
        "outputId": "c760f24e-6a85-4707-f424-bb84f1244077"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((835917,), (835917,))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxOp6CeSe98h",
        "outputId": "24d4c5e6-b9d8-421e-899a-d0115ba3c077"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10557     0\n",
              "862949    0\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
        "val_ds = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test))"
      ],
      "metadata": {
        "id": "G4ewVFaFddRX"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "next(iter(train_ds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SPfAu_NhHeE",
        "outputId": "0a41e36e-57a1-4252-dd24-1c90743c9df2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=string, numpy=b'What month will my hair grow back if I cut it back on February 25?'>,\n",
              " <tf.Tensor: shape=(), dtype=int64, numpy=0>)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "408*2048 - 835917"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJabX9Qvh7qI",
        "outputId": "d9163dda-60cd-4507-83d8-c3bf4b933068"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-333"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.shuffle(4096).batch(2048, drop_remainder=False).prefetch(tf.data.AUTOTUNE)\n",
        "val_ds = val_ds.batch(2048).prefetch(tf.data.AUTOTUNE)\n",
        "test_ds = test_ds.batch(2048).prefetch(tf.data.AUTOTUNE)"
      ],
      "metadata": {
        "id": "TM8iVXLfhSbQ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhVRcWh8huFo",
        "outputId": "1800c3e2-25be-42ab-ee9e-e9e435a3effa"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PrefetchDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.string, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modeli Oluştur"
      ],
      "metadata": {
        "id": "VMvORUb1MrlD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_link = \"https://tfhub.dev/google/tf2-preview/nnlm-en-dim128/1\"\n",
        "\n",
        "embedding = hub.KerasLayer(\n",
        "    embedding_link\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMo9DMX78uNe",
        "outputId": "984d215c-11b9-456a-a3f4-e9278a31d5b9"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample = tf.constant([\"hi guys bro\", \"hello darkness my old friend\"])\n",
        "sample"
      ],
      "metadata": {
        "id": "Uafy6RQmkY1l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1c88d12-7a2e-4e3b-8bbd-5a88359aef7a"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'hi guys bro', b'hello darkness my old friend'], dtype=object)>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding(sample)"
      ],
      "metadata": {
        "id": "KYd8YO1zjBwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_layer = tf.keras.Input(batch_input_shape = (None, ), dtype = tf.string)\n",
        "embedding_output = embedding(input_layer)\n",
        "\n",
        "# 1.branch\n",
        "x = tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1))(embedding_output)\n",
        "x = tf.keras.layers.Conv1D(filters=16, kernel_size = 5)(x)\n",
        "x = tf.nn.silu(x)\n",
        "x = tf.keras.layers.AveragePooling1D()(x)\n",
        "x = tf.keras.layers.Conv1D(filters=32, kernel_size = 5)(x)\n",
        "x = tf.nn.silu(x)\n",
        "x = tf.keras.layers.GlobalAveragePooling1D()(x)\n",
        "\n",
        "#2. branch\n",
        "y = tf.keras.layers.Dense(128)(embedding_output)\n",
        "y = tf.nn.silu(y)\n",
        "\n",
        "merged = tf.keras.layers.Concatenate()([x, y])\n",
        "\n",
        "final_layer = tf.keras.layers.Dense(1)(merged)\n",
        "final_layer = tf.nn.sigmoid(final_layer)\n",
        "\n",
        "model_normal = tf.keras.Model(input_layer, final_layer)"
      ],
      "metadata": {
        "id": "74MEY-R_jH3C"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model_normal, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ndw45cf6k_E1",
        "outputId": "8531afad-aa11-4f6d-eb05-120df15c13c4"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_normal.layers[1].trainable = True"
      ],
      "metadata": {
        "id": "XFOHoMc1maxo"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_normal.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhzAFwnZmgLr",
        "outputId": "d03b10d7-e184-4158-d609-86f3b04af2f0"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_7 (InputLayer)           [(None,)]            0           []                               \n",
            "                                                                                                  \n",
            " keras_layer (KerasLayer)       (None, 128)          124642688   ['input_7[0][0]']                \n",
            "                                                                                                  \n",
            " lambda_3 (Lambda)              (None, 128, 1)       0           ['keras_layer[5][0]']            \n",
            "                                                                                                  \n",
            " conv1d_8 (Conv1D)              (None, 124, 16)      96          ['lambda_3[0][0]']               \n",
            "                                                                                                  \n",
            " tf.nn.silu_8 (TFOpLambda)      (None, 124, 16)      0           ['conv1d_8[0][0]']               \n",
            "                                                                                                  \n",
            " average_pooling1d_3 (AveragePo  (None, 62, 16)      0           ['tf.nn.silu_8[0][0]']           \n",
            " oling1D)                                                                                         \n",
            "                                                                                                  \n",
            " conv1d_9 (Conv1D)              (None, 58, 32)       2592        ['average_pooling1d_3[0][0]']    \n",
            "                                                                                                  \n",
            " tf.nn.silu_9 (TFOpLambda)      (None, 58, 32)       0           ['conv1d_9[0][0]']               \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 128)          16512       ['keras_layer[5][0]']            \n",
            "                                                                                                  \n",
            " global_average_pooling1d_1 (Gl  (None, 32)          0           ['tf.nn.silu_9[0][0]']           \n",
            " obalAveragePooling1D)                                                                            \n",
            "                                                                                                  \n",
            " tf.nn.silu_10 (TFOpLambda)     (None, 128)          0           ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " concatenate_2 (Concatenate)    (None, 160)          0           ['global_average_pooling1d_1[0][0\n",
            "                                                                 ]',                              \n",
            "                                                                  'tf.nn.silu_10[0][0]']          \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 1)            161         ['concatenate_2[0][0]']          \n",
            "                                                                                                  \n",
            " tf.math.sigmoid (TFOpLambda)   (None, 1)            0           ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 124,662,049\n",
            "Trainable params: 124,662,049\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_focal = tf.keras.models.clone_model(model_normal)"
      ],
      "metadata": {
        "id": "RQJGkRCKsA3v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_normal.save_weights(\"starting_point.h5\")"
      ],
      "metadata": {
        "id": "YzSi7OdYlUCj"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_normal.compile(optimizer = \"adam\",\n",
        "                     loss = tf.keras.losses.BinaryCrossentropy(),\n",
        "                     metrics = [tf.keras.metrics.TruePositives(),\n",
        "                                tf.keras.metrics.FalseNegatives()])"
      ],
      "metadata": {
        "id": "zaMGSn9olT5O"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n",
        "    min_lr = 1e-8, verbose=1, patience=3\n",
        ")\n",
        "\n",
        "model_normal.fit(train_ds, validation_data=val_ds, epochs = 32, callbacks = [reduce_lr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "us35cGpom_-H",
        "outputId": "09d26c16-79e7-4b3c-b031-b244220efc8d"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/32\n",
            "409/409 [==============================] - 57s 118ms/step - loss: 0.1625 - true_positives: 15314.0000 - false_negatives: 36404.0000 - val_loss: 0.1266 - val_true_positives: 5416.0000 - val_false_negatives: 7514.0000 - lr: 0.0010\n",
            "Epoch 2/32\n",
            "409/409 [==============================] - 19s 47ms/step - loss: 0.1151 - true_positives: 24245.0000 - false_negatives: 27473.0000 - val_loss: 0.1247 - val_true_positives: 5892.0000 - val_false_negatives: 7038.0000 - lr: 0.0010\n",
            "Epoch 3/32\n",
            "409/409 [==============================] - 13s 31ms/step - loss: 0.1062 - true_positives: 27089.0000 - false_negatives: 24629.0000 - val_loss: 0.1277 - val_true_positives: 6320.0000 - val_false_negatives: 6610.0000 - lr: 0.0010\n",
            "Epoch 4/32\n",
            "409/409 [==============================] - 10s 25ms/step - loss: 0.0991 - true_positives: 29034.0000 - false_negatives: 22684.0000 - val_loss: 0.1333 - val_true_positives: 6191.0000 - val_false_negatives: 6739.0000 - lr: 0.0010\n",
            "Epoch 5/32\n",
            "409/409 [==============================] - ETA: 0s - loss: 0.0926 - true_positives: 30694.0000 - false_negatives: 21024.0000\n",
            "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "409/409 [==============================] - 10s 25ms/step - loss: 0.0926 - true_positives: 30694.0000 - false_negatives: 21024.0000 - val_loss: 0.1413 - val_true_positives: 6092.0000 - val_false_negatives: 6838.0000 - lr: 0.0010\n",
            "Epoch 6/32\n",
            "409/409 [==============================] - 8s 20ms/step - loss: 0.0866 - true_positives: 32194.0000 - false_negatives: 19524.0000 - val_loss: 0.1418 - val_true_positives: 6422.0000 - val_false_negatives: 6508.0000 - lr: 1.0000e-04\n",
            "Epoch 7/32\n",
            "409/409 [==============================] - 8s 20ms/step - loss: 0.0839 - true_positives: 32991.0000 - false_negatives: 18727.0000 - val_loss: 0.1429 - val_true_positives: 6417.0000 - val_false_negatives: 6513.0000 - lr: 1.0000e-04\n",
            "Epoch 8/32\n",
            "409/409 [==============================] - ETA: 0s - loss: 0.0824 - true_positives: 33315.0000 - false_negatives: 18403.0000\n",
            "Epoch 8: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "409/409 [==============================] - 9s 21ms/step - loss: 0.0824 - true_positives: 33315.0000 - false_negatives: 18403.0000 - val_loss: 0.1443 - val_true_positives: 6429.0000 - val_false_negatives: 6501.0000 - lr: 1.0000e-04\n",
            "Epoch 9/32\n",
            "409/409 [==============================] - 8s 20ms/step - loss: 0.0807 - true_positives: 33840.0000 - false_negatives: 17878.0000 - val_loss: 0.1447 - val_true_positives: 6375.0000 - val_false_negatives: 6555.0000 - lr: 1.0000e-05\n",
            "Epoch 10/32\n",
            "409/409 [==============================] - 8s 20ms/step - loss: 0.0806 - true_positives: 33862.0000 - false_negatives: 17856.0000 - val_loss: 0.1449 - val_true_positives: 6399.0000 - val_false_negatives: 6531.0000 - lr: 1.0000e-05\n",
            "Epoch 11/32\n",
            "409/409 [==============================] - ETA: 0s - loss: 0.0804 - true_positives: 33937.0000 - false_negatives: 17781.0000\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "409/409 [==============================] - 8s 20ms/step - loss: 0.0804 - true_positives: 33937.0000 - false_negatives: 17781.0000 - val_loss: 0.1451 - val_true_positives: 6398.0000 - val_false_negatives: 6532.0000 - lr: 1.0000e-05\n",
            "Epoch 12/32\n",
            "409/409 [==============================] - 8s 20ms/step - loss: 0.0802 - true_positives: 33954.0000 - false_negatives: 17764.0000 - val_loss: 0.1451 - val_true_positives: 6402.0000 - val_false_negatives: 6528.0000 - lr: 1.0000e-06\n",
            "Epoch 13/32\n",
            "409/409 [==============================] - 7s 18ms/step - loss: 0.0802 - true_positives: 33967.0000 - false_negatives: 17751.0000 - val_loss: 0.1451 - val_true_positives: 6405.0000 - val_false_negatives: 6525.0000 - lr: 1.0000e-06\n",
            "Epoch 14/32\n",
            "409/409 [==============================] - ETA: 0s - loss: 0.0802 - true_positives: 33980.0000 - false_negatives: 17738.0000\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 1.0000001111620805e-07.\n",
            "409/409 [==============================] - 7s 18ms/step - loss: 0.0802 - true_positives: 33980.0000 - false_negatives: 17738.0000 - val_loss: 0.1451 - val_true_positives: 6406.0000 - val_false_negatives: 6524.0000 - lr: 1.0000e-06\n",
            "Epoch 15/32\n",
            "409/409 [==============================] - 7s 18ms/step - loss: 0.0802 - true_positives: 33974.0000 - false_negatives: 17744.0000 - val_loss: 0.1451 - val_true_positives: 6406.0000 - val_false_negatives: 6524.0000 - lr: 1.0000e-07\n",
            "Epoch 16/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33975.0000 - false_negatives: 17743.0000 - val_loss: 0.1451 - val_true_positives: 6407.0000 - val_false_negatives: 6523.0000 - lr: 1.0000e-07\n",
            "Epoch 17/32\n",
            "409/409 [==============================] - ETA: 0s - loss: 0.0802 - true_positives: 33976.0000 - false_negatives: 17742.0000\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-08.\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33976.0000 - false_negatives: 17742.0000 - val_loss: 0.1451 - val_true_positives: 6408.0000 - val_false_negatives: 6522.0000 - lr: 1.0000e-07\n",
            "Epoch 18/32\n",
            "409/409 [==============================] - 7s 18ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 19/32\n",
            "409/409 [==============================] - 7s 18ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 20/32\n",
            "409/409 [==============================] - ETA: 0s - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 1e-08.\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 21/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 22/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 23/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 24/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 25/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33977.0000 - false_negatives: 17741.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 26/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33978.0000 - false_negatives: 17740.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 27/32\n",
            "409/409 [==============================] - 7s 16ms/step - loss: 0.0802 - true_positives: 33978.0000 - false_negatives: 17740.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 28/32\n",
            "409/409 [==============================] - 6s 16ms/step - loss: 0.0802 - true_positives: 33978.0000 - false_negatives: 17740.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 29/32\n",
            "409/409 [==============================] - 7s 17ms/step - loss: 0.0802 - true_positives: 33978.0000 - false_negatives: 17740.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 30/32\n",
            "409/409 [==============================] - 7s 18ms/step - loss: 0.0802 - true_positives: 33978.0000 - false_negatives: 17740.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 31/32\n",
            "409/409 [==============================] - 7s 16ms/step - loss: 0.0802 - true_positives: 33978.0000 - false_negatives: 17740.0000 - val_loss: 0.1451 - val_true_positives: 6409.0000 - val_false_negatives: 6521.0000 - lr: 1.0000e-08\n",
            "Epoch 32/32\n",
            "409/409 [==============================] - 7s 16ms/step - loss: 0.0802 - true_positives: 33978.0000 - false_negatives: 17740.0000 - val_loss: 0.1451 - val_true_positives: 6410.0000 - val_false_negatives: 6520.0000 - lr: 1.0000e-08\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe70041f910>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "metadata": {
        "id": "-panx3Txndae"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model,\n",
        "                   threshold,\n",
        "                   dataset):\n",
        "    predictions = []\n",
        "    true_labels = []\n",
        "\n",
        "    for x, y in dataset:\n",
        "        predictions.append(model(x).numpy())\n",
        "        true_labels.append(y.numpy())\n",
        "\n",
        "    predictions = np.concatenate(predictions, axis = 0)\n",
        "    true_labels = np.concatenate(true_labels, axis = 0)\n",
        "\n",
        "    predictions = np.where(predictions > threshold, 1, 0)\n",
        "\n",
        "    print(classification_report(true_labels, predictions, digits = 4))\n",
        "\n",
        "    plt.figure(figsize = (12, 8))\n",
        "    sns.heatmap(confusion_matrix(true_labels, predictions),\n",
        "                annot=True, fmt = \".4f\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "Qc-dRklvqH8n"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model = model_normal, \n",
        "               threshold = 0.3,\n",
        "               dataset = test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "4g2wPhNKqxE4",
        "outputId": "1027acf0-7185-49aa-a922-6d22ded08563"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9761    0.9641    0.9701    245063\n",
            "           1     0.5414    0.6422    0.5876     16162\n",
            "\n",
            "    accuracy                         0.9442    261225\n",
            "   macro avg     0.7588    0.8032    0.7788    261225\n",
            "weighted avg     0.9492    0.9442    0.9464    261225\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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fmjVXH/qq9vZTM5/OwV87PF85aN9s8M//OM85brPFpjn/kkuTJOdfcmm23XKz7vELRo1LVVX58533ZLnlls3KA1bM5ptsmOvG35Inn5qZJ5+amevG35LNN9kwKw9YMcsuu0z+fOc9qaoqF4wal2232PTN/QCBTJw4OZsMWz9LL71UkmTbbTfPvffenyT5fx//cC6+5NK88MIL3fv37btCFl988STJZz/76VxzzY3dLfY6LrpobPbc4xNJkmHD1s+TT87M1KnTMnbsldluu63Sr1/f9OvXN9ttt1XGjr0yU6dOy1NPPZ1hw9ZPkuy5xydy4YVjXjnWnrt0je+5S/c4sOC93jWbX0kyrpRyf5KJnbF3JlkryRfbnBjtuvX2u3LhqHFZ+93vyif2+UKS5MsH7pPfXTQmf31oUso7SgYPGtjd4n73u96ZzTfZKP9vn8/nHeUd+cRHd8jaa74rt/z5zrkeZ6sPDEuSXHLpldlxu21ede5zzrswEydNyc//5+z8/H/OTpKcdOyRWal/v3z3qGOz68d2yrrv+4fsv9euOeQ7P8jvLhqdwYMG5pgjvp0k2WqzjXP19Tdlx10/m6WXWipHfPtfkyR9V1g+B37m09lt/y8nSQ7ad/f0XWH5JMm/HfKFV259tOnGVqLDAnDTTbfld7+/ODfecElmzXopt/35zvzy5K7f6U/uunOO/tEJr9r/ve9dKyf/8iepqip33/2/OfCgr3VvO/30/85WW26aAQNWzP9NGJ8jvn9MTj31V/nc/nsmSX7xyzNzyajLMmLEB3PP3dfk2Wefy+cOOCRJMmPGE/nBUcflumsvSpIc+YOfdi82+tKXD+u+9dHo0Zdn1OjLkyQ/Ovr4nH3Wz7LvZ3bLQw9Nyu57HNzuD4tFk2s2a3ndWx+VUt6RrrZ5zwVCN1VVVetCG7c+Aupy6yOgrrfDrY+eOXLvVmqcZQ87vde/twXpdVejV1U1O8kNb8FcAAAWHovgbYra4BOEAACa0EavxU3dAQBojWQTAKCJRfA2RW2QbAIA0BrJJgBAE67ZrEWxCQDQhNXotWijAwDQGskmAEAT2ui1SDYBAGiNZBMAoIHKrY9qUWwCADShjV6LNjoAAK2RbAIANCHZrEWyCQBAaySbAABNuKl7LZJNAABaI9kEAGjCNZu1KDYBABqoFJu1aKMDANAaySYAQBOSzVokmwAAtEayCQDQhM9Gr0WxCQDQhDZ6LdroAAC0RrIJANCEZLMWySYAAK2RbAIANFBVks06FJsAAE1oo9eijQ4AQGskmwAATUg2a5FsAgDQGskmAEADlWSzFskmAACtkWwCADQh2axFsQkA0MTs3p7AwkEbHQCA1kg2AQAasECoHskmAACtkWwCADQh2axFsQkA0IQFQrVoowMA0BrJJgBAAxYI1SPZBACgNZJNAIAmXLNZi2ITAKABbfR6tNEBABYipZRTSinTSil39hj791LK5FLKbZ3HTj22fauUMqGUcl8pZYce4yM6YxNKKd/sMb5GKeXGzvivSilLdMaX7Hw9obP9XXXmq9gEAGhidkuP13dqkhFzGf9JVVXrdR4XJ0kpZZ0kuyX5x85rTiil9Cml9ElyfJIdk6yT5NOdfZPkPzvHWivJjCT7dcb3SzKjM/6Tzn6vS7EJALAQqarqqiSP19x9ZJJzq6p6oaqqB5JMSDKs85hQVdVfqqr6W5Jzk4wspZQkH0zy287rT0vysR7HOq3z/LdJPtTZf74UmwAADVSz23m8CV8spdzeabP374wNSTKxxz6TOmPzGl8pyRNVVc16zfirjtXZ/mRn//lSbAIANNFSG72UckAp5eYejwNqzOZnSd6dZL0kDyc5ZkF9m2+W1egAAG8jVVWdlOSkN/iaR15+Xkr5RZKLOl9OTrJaj12HdsYyj/HHkvQrpSzWSS977v/ysSaVUhZL0rez/3xJNgEAGng7tdFLKav2+PLjSV5eqX5Bkt06K8nXSLJ2kvFJbkqydmfl+RLpWkR0QVVVVZLLk+zSef0+Sc7vcax9Os93SXJZZ//5kmwCACxESinnJNkmyYBSyqQkhyfZppSyXpIqyV+THJgkVVXdVUr5dZK7k8xK8oWqql7qHOeLSUYn6ZPklKqq7uqc4htJzi2lfD/JrUlO7oyfnOSMUsqEdC1Q2q3WfGsUpG/Ki4/+xR1PgVqWG7p1b08BWEi88PzE110F3bZHd9i6lRpnwOgre/17W5C00QEAaI02OgBAA2/yNkV/NxSbAAANKDbr0UYHAKA1kk0AgAYkm/VINgEAaI1kEwCgiWqRukNRaxSbAAANaKPXo40OAEBrJJsAAA1Us7XR65BsAgDQGskmAEADrtmsR7EJANBAZTV6LdroAAC0RrIJANCANno9kk0AAFoj2QQAaMCtj+qRbAIA0BrJJgBAA1XV2zNYOCg2AQAa0EavRxsdAIDWSDYBABqQbNYj2QQAoDWSTQCABiwQqkexCQDQgDZ6PdroAAC0RrIJANBAVUk265BsAgDQGskmAEAD1ezensHCQbEJANDAbG30WrTRAQBojWQTAKABC4TqkWwCANAaySYAQANu6l6PZBMAgNZINgEAGvDZ6PUoNgEAGtBGr0cbHQCA1kg2AQAacFP3eiSbAAC0RrIJANCAm7rXo9gEAGjAavR6tNEBAGiNZBMAoAELhOqRbAIA0BrJJgBAAxYI1aPYBABowAKherTRAQBojWQTAKABC4Tqab3YXHrwlm2fAlhELN7Hv38BFjXe2QEAGrBAqB7XbAIA0BrJJgBAA67ZrEexCQDQgDsf1aONDgBAaySbAAANaKPXI9kEAKA1kk0AgAbc+qgexSYAQAOze3sCCwltdAAAWiPZBABooIo2eh2STQAAWiPZBABoYLa7utei2AQAaGC2Nnot2ugAALRGsgkA0IAFQvVINgEAaI1kEwCgATd1r0eyCQBAaySbAAANuGazHsUmAEAD2uj1aKMDANAaySYAQAOSzXokmwAAtEayCQDQgAVC9Sg2AQAamK3WrEUbHQCA1kg2AQAamK2NXotkEwCA1kg2AQAaqHp7AgsJxSYAQAPus1mPNjoAAK2RbAIANDC7WCBUh2QTAIDWSDYBABqwQKgeySYAwEKklHJKKWVaKeXOHmMrllLGllLu7/zZvzNeSinHlVImlFJuL6Vs0OM1+3T2v7+Usk+P8Q1LKXd0XnNcKV3XC8zrHK9HsQkA0MDslh41nJpkxGvGvplkXFVVaycZ1/k6SXZMsnbncUCSnyVdhWOSw5NskmRYksN7FI8/S/K5Hq8b8TrnmC/FJgBAA7NLO4/XU1XVVUkef83wyCSndZ6fluRjPcZPr7rckKRfKWXVJDskGVtV1eNVVc1IMjbJiM62FaqquqGqqirJ6a851tzOMV+KTQCAhd8qVVU93Hk+NckqnedDkkzssd+kztj8xifNZXx+55gvC4QAABpo67PRSykHpKvl/bKTqqo6qe7rq6qqSimtrl96I+dQbAIAvI10CsvaxWXHI6WUVauqerjTCp/WGZ+cZLUe+w3tjE1Oss1rxq/ojA+dy/7zO8d8aaMDADRQtfRo6IIkL68o3yfJ+T3G9+6sSt80yZOdVvjoJMNLKf07C4OGJxnd2fZUKWXTzir0vV9zrLmdY74kmwAADdRZzNOGUso56UolB5RSJqVrVfkPk/y6lLJfkgeT7NrZ/eIkOyWZkOTZJPsmSVVVj5dSjkhyU2e/71VV9fKio4PTteJ96SSXdB6ZzznmP9+uhUbtWWyJIe55CtSyeB///gXqee65B3v9syJPH7JnKzXO3pPP7PXvbUHyzg4A0EDNe2L+3XPNJgAArZFsAgA04DrBehSbAAAN9NYCoYWNNjoAAK2RbAIANGCBUD2STQAAWiPZBABoQLJZj2QTAIDWSDYBABqorEavRbEJANCANno92ugAALRGsgkA0IBksx7JJgAArZFsAgA04LPR61FsAgA04LPR69FGBwCgNZJNAIAGLBCqR7IJAEBrJJsAAA1INutRbAIANGA1ej3a6AAAtEayCQDQgFsf1SPZBACgNZJNAIAGLBCqR7IJAEBrJJsAAA1YjV6PYhMAoIHZys1atNEBAGiNZBMAoAELhOqRbAIA0BrJJgBAA67YrEexCQDQgDZ6PdroAAC0RrIJANCAz0avR7IJAEBrJJsAAA24qXs9ik0AgAaUmvVoowMA0BrJJgBAA259VI9kEwCA1kg2AQAasECoHsUmAEADSs16tNEBAGiNZBMAoAELhOqRbAIA0BrJJgBAAxYI1SPZBACgNZJNAIAG5Jr1KDYBABqwQKgebXQAAFoj2QQAaKDSSK9FsgkAQGskmwAADbhmsx7FJgBAA+6zWY82OgAArZFsAgA0INesR7IJAEBrJJsAAA24ZrMeySZzmPC/N+TWWy7NzTeNyQ3XX5wkOfusn+Xmm8bk5pvGZML/3pCbbxqTJFlsscVyysnH5tZbLs0dt1+Rb3z9i0mSoUMH59Ixv8ntf748f77tsvzLF/eb5/l+8uPv5d67r8ktfxqb9ddbt3t8r70+mXvuuib33HVN9trrk93jG6z/T7n1lktz793X5Cc//l73eP/+/TLq4nNyz13XZNTF56Rfv74L9OcCJD//+Y/y4IN/ys03j+ke69+/by666MzccccVueiiM9Ov3wpJko98ZPuMHz8qN9xwca655sJ84AMbdb/myCO/lT/9aWxuvXVcjjnm37vH119/3dx00+jceeeVrxp/rWOO+ffceeeVGT9+VNbr8b6xxx6fyB13XJE77rgie+zxidc97rzmDnXMbumxqFFsMlfbbf/JbLTx8Gy62U5Jkt33+Hw22nh4Ntp4eH7/+4vzhz90FaG77PKRLLnkEll/g+0ybJMR+dz+e2b11Ydm1qxZ+drX/yP//P5ts/kWH83nP/+ZvO99a89xnh1HfDBrr7VG3rvOFvn857+R4//7qCRdheN3DvvXfGCLj2SzzT+c7xz2r93F4/H/fVQOOujree86W2TttdbIiB22TZJ84+tfyGWXX5P3/eMWuezya/KNr3/hrfhRwd+VM874TUaO3OdVY4ceenCuuOLa/NM/bZMrrrg2hx56cJLk8suvzbBhI7LppjvloIO+lhNO+M8kyaabbpjNNtsoG2+8QzbccPtsuOH7s+WWmyZJjjvuyHzhC9/MuutunXe/e40MH77NHHPYYYdt8+53r5F11906X/zit3Lccd9P0lU4HnbYV7LVViOz5ZY757DDvtJdPM7ruPOaO7DgKDZ5w3bZ5aM591fnJ0mqqsqyyy6TPn36ZOmll87fXnwxTz31dKZOnZZbb7szSfL008/k3nvvz5DBg+Y41kc/ukPOOOu3SZIbx9+Svv36ZtCggRk+fOtcOu7qzJjxRJ544slcOu7q7LDDNhk0aGCWX2H53Dj+liTJGWf9NjvvPKL7WKef8Zskyeln/KZ7HFhwrr12fB5//IlXjX3kI9vnzDPPS5KceeZ5+ehHhydJnnnm2e59ll12mVSdjmNVVVlyySWzxBKLZ8kll8hiiy2WadMe7fr9Xn65jB9/a5Lk7LNfOdZrz3f22V3nGz/+1vTtu0IGDRqY7bffOuPGXZ0ZM57ME088lXHjrs7w4dvM97jzmjvUUbX036JGsckcqqrKJRefkxtvuCT777fHq7ZtucUmeWTa9EyY8ECS5Lzz/phnnnk2kx66NQ/83/j8+Mc/z4wZr/6LaPXVh2a996+bGztv9D0NGTwokyZO6f568qSHM2TwoK7xST3GJ78yPnnSw3PsnySrDByQqVOnJUmmTp2WVQYOeJM/CaCOga/53RvY43dv5513yG23jcvvfvc/OeigryVJbrzxllx11fV54IGb8sADN+XSS6/KffdNyODBq2Ty5Kndr508+eEMnss/UgfP8f4wNYMHr9IZf/g144Pme9z5zR1YMCwQYg5bb/vxTJkyNSuvvFJGXXJu7rtvQq6+5sYkyac+9bH8qpNqJsmwjdfLSy+9lNVW3yD9+/fNFZf/PuMuuzoPPPBQkq4049e/+kW+eujhmTnz6bf0+6iqRe9fh7Aw6Pmrd8EFo3PBBaOz+ebD8t3vHpIPf3iPrLnm6nnPe0Kp1VAAAAppSURBVNbKWmt1tc7/+MezsvnmG+e5557vpRm/wtsGb8SieH1lGxonm6WUfeez7YBSys2llJtnz36m6SnoJVOmdCUA06c/lvPPvyQbb7xekqRPnz75+Md2zK9/c0H3vrvt9vGMHnNFZs2alenTH8t1192UDTd8f5KuxUO/+dUvcs45v88f/nDJXM81ecrUDF1tcPfXQ4aumslTpnaND+0xPuSV8SFDV51j/yR5pNOGS5JBgwZm2vTHFsSPA3gd017zuzd9+qNz7HPtteOzxhrvzEor9c/IkSMyfvyteeaZZ/PMM89m9OjLs8kmG2TKlEcyZMgrSeaQIat2vx/1NGWO94dBmTLlkc74qq8Znzrf49aZO/DmvJk2+n/Ma0NVVSdVVbVRVVUbveMdy76JU/BWW2aZpbPccst2P99+u61z1133JUm2+9CWue++CZk8+ZU21cSJk7PtNpt377/JJhvkvvsmJEl+cdIxuefeCTn2pyfN83wXXTQme+2xS5Jkk2Eb5Kknn8rUqdMyZsyV2X67rdKvX9/069c322+3VcaMuTJTp07LzKdmZpNhGyRJ9tpjl1x44eiuY104Jnt3Vq3vvdcnu8eBdv3xj5dmzz27Vn7vuecnctFFY5Mka665evc+6623bpZccok89tiMTJw4OVtuuUn69OmTxRZbLFtuuWnuvXdC1+/3zKczbNj6SZLdd3/lWK893+67d51v2LD189RTMzN16rSMHXtltttuq/Trt0L69Vsh2223VcaOvXK+x53X3KEO12zWM982einl9nltSrLKgp8OvW2VVVbOb39zcpJkscX65Nxz/5DRY65Ikuy668juhUEvO+Fnp+bkX/4kf77tspRSctppv8odd9yTzT+wcfbac5fcfsfd3bdJ+s53fphLRl2WAz63V5LkpF+ckYsvGZcRIz6Y++65Ns8+91z23/+rSZIZM57IkT84Njdc98ckyfeP/En3taBf/Jdv5+STf5Kll1oqo0ZfnktGXZYk+c8fHZ9zz/559v3Mp/PQQ5Oy2+4HtfvDgr9Dp512XLbccrMMGNA/EybckCOO+EmOPvqEnHnmCdlnn0/loYcmZ889u1Z0f/zjO2b33T+RF198Mc8//0L22qvrDhG/+93F2XrrD+Tmm8ekqqqMHXtlLr54XJLky1/+t5x00jFZeumlMmbMFRk9+vIkyf77d10//stfnpVRoy7LDjtsm7vuuirPPvtcDjzw0CTJjBlP5qijjss111yYJPnBD36aGTOenO9x5zV3qEMbvZ4yv+vaSimPJNkhyYzXbkpyXVVVg+d81asttsSQRa9EB1qxeB+XkQP1PPfcg6W357DPuz7RSo1z2l/P6/XvbUF6vXf2i5IsV1XVba/dUEq5opUZAQAsBGZbUVbLfIvNqqrm+bEvVVXtvuCnAwDAokTPCgCgAblmPYpNAIAGZis3a/EJQgAAtEayCQDQwKJ4T8w2SDYBAGiNZBMAoAE3da9HsQkA0IAFQvVoowMA0BrJJgBAAxYI1SPZBACgNZJNAIAGLBCqR7IJAEBrJJsAAA1UlWs261BsAgA04NZH9WijAwDQGskmAEADFgjVI9kEAFjIlFL+Wkq5o5RyWynl5s7YiqWUsaWU+zt/9u+Ml1LKcaWUCaWU20spG/Q4zj6d/e8vpezTY3zDzvEndF5bms5VsQkA0EDV0n9vwLZVVa1XVdVGna+/mWRcVVVrJxnX+TpJdkyydudxQJKfJV3FaZLDk2ySZFiSw18uUDv7fK7H60Y0/TkpNgEAGpidqpXHmzAyyWmd56cl+ViP8dOrLjck6VdKWTXJDknGVlX1eFVVM5KMTTKis22FqqpuqLqW3J/e41hvmGITAOBtpJRyQCnl5h6PA+ayW5VkTCnlTz22r1JV1cOd51OTrNJ5PiTJxB6vndQZm9/4pLmMN2KBEABAA23dZ7OqqpOSnPQ6u21RVdXkUsrAJGNLKfe+5hhVKeVtcW8mySYAwEKmqqrJnT+nJfl9uq65fKTTAk/nz2md3ScnWa3Hy4d2xuY3PnQu440oNgEAGpjd0uP1lFKWLaUs//LzJMOT3JnkgiQvryjfJ8n5necXJNm7syp90yRPdtrto5MML6X07ywMGp5kdGfbU6WUTTur0Pfucaw3TBsdAKCBN7hyfEFaJcnvO3cjWizJ2VVVjSql3JTk16WU/ZI8mGTXzv4XJ9kpyYQkzybZN0mqqnq8lHJEkps6+32vqqrHO88PTnJqkqWTXNJ5NFLa/lzPxZYY8ra4XgB4+1u8j3//AvU899yDje/7uKAMX21EKzXOmImjev17W5C8swMANOCz0etxzSYAAK2RbAIANND2pYiLCskmAACtkWwCADTgms16FJsAAA304q2PFira6AAAtEayCQDQwGwLhGqRbAIA0BrJJgBAA3LNehSbAAANWI1ejzY6AACtkWwCADQg2axHsgkAQGskmwAADfhs9HoUmwAADWij16ONDgBAaySbAAAN+Gz0eiSbAAC0RrIJANCABUL1SDYBAGiNZBMAoAGr0etRbAIANKCNXo82OgAArZFsAgA0oI1ej2QTAIDWSDYBABpwU/d6FJsAAA3MtkCoFm10AABaI9kEAGhAG70eySYAAK2RbAIANOCazXoUmwAADWij16ONDgBAaySbAAANaKPXI9kEAKA1kk0AgAZcs1mPZBMAgNZINgEAGnDNZj2KTQCABrTR69FGBwCgNZJNAIAGqmp2b09hoSDZBACgNZJNAIAGZrtmsxbFJgBAA5XV6LVoowMA0BrJJgBAA9ro9Ug2AQBojWQTAKAB12zWo9gEAGjAx1XWo40OAEBrJJsAAA34bPR6JJsAALRGsgkA0IAFQvVINgEAaI1kEwCgATd1r0exCQDQgDZ6PdroAAC0RrIJANCAm7rXI9kEAKA1kk0AgAZcs1mPYhMAoAGr0evRRgcAoDWSTQCABrTR65FsAgDQGskmAEADbn1Uj2ITAKCBygKhWrTRAQBojWQTAKABbfR6JJsAALRGsgkA0IBbH9Uj2QQAoDWSTQCABqxGr0exCQDQgDZ6PdroAAC0RrIJANCAZLMeySYAAK2RbAIANCDXrKeIgOkNpZQDqqo6qbfnAbz9eb+AhZs2Or3lgN6eALDQ8H4BCzHFJgAArVFsAgDQGsUmvcX1V0Bd3i9gIWaBEAAArZFsAgDQGsUmb7lSyohSyn2llAmllG/29nyAt6dSyimllGmllDt7ey5Ac4pN3lKllD5Jjk+yY5J1kny6lLJO784KeJs6NcmI3p4E8OYoNnmrDUsyoaqqv1RV9bck5yYZ2ctzAt6Gqqq6KsnjvT0P4M1RbPJWG5JkYo+vJ3XGAIBFkGITAIDWKDZ5q01OslqPr4d2xgCARZBik7faTUnWLqWsUUpZIsluSS7o5TkBAC1RbPKWqqpqVpIvJhmd5J4kv66q6q7enRXwdlRKOSfJ9UneU0qZVErZr7fnBLxxPkEIAIDWSDYBAGiNYhMAgNYoNgEAaI1iEwCA1ig2AQBojWITAIDWKDYBAGiNYhMAgNb8f2tB/Pb+oSwHAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.Model"
      ],
      "metadata": {
        "id": "XXXwCmZyr59_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_focal.load_weights(\"/content/starting_point.h5\")"
      ],
      "metadata": {
        "id": "XhHMN06MqxCY"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_focal.compile(optimizer = \"adam\",\n",
        "                     loss = tf.keras.losses.BinaryFocalCrossentropy(),\n",
        "                     metrics = [tf.keras.metrics.TruePositives(),\n",
        "                                tf.keras.metrics.FalseNegatives()])"
      ],
      "metadata": {
        "id": "aGBmPPMoqxAG"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n",
        "    min_lr = 1e-8, verbose=1, patience=3\n",
        ")\n",
        "\n",
        "model_focal.fit(train_ds, validation_data=val_ds, epochs = 32, callbacks = [reduce_lr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-SUS50osGRL",
        "outputId": "f222a951-9097-466e-cb05-036fc4e0ebfe"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/32\n",
            "409/409 [==============================] - 6s 11ms/step - loss: 0.0478 - true_positives_1: 9373.0000 - false_negatives_1: 42345.0000 - val_loss: 0.0407 - val_true_positives_1: 3505.0000 - val_false_negatives_1: 9425.0000 - lr: 0.0010\n",
            "Epoch 2/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0404 - true_positives_1: 14076.0000 - false_negatives_1: 37642.0000 - val_loss: 0.0403 - val_true_positives_1: 3450.0000 - val_false_negatives_1: 9480.0000 - lr: 0.0010\n",
            "Epoch 3/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0400 - true_positives_1: 14313.0000 - false_negatives_1: 37405.0000 - val_loss: 0.0400 - val_true_positives_1: 3596.0000 - val_false_negatives_1: 9334.0000 - lr: 0.0010\n",
            "Epoch 4/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0397 - true_positives_1: 14523.0000 - false_negatives_1: 37195.0000 - val_loss: 0.0398 - val_true_positives_1: 3786.0000 - val_false_negatives_1: 9144.0000 - lr: 0.0010\n",
            "Epoch 5/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0394 - true_positives_1: 14734.0000 - false_negatives_1: 36984.0000 - val_loss: 0.0396 - val_true_positives_1: 3609.0000 - val_false_negatives_1: 9321.0000 - lr: 0.0010\n",
            "Epoch 6/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0393 - true_positives_1: 14836.0000 - false_negatives_1: 36882.0000 - val_loss: 0.0394 - val_true_positives_1: 3710.0000 - val_false_negatives_1: 9220.0000 - lr: 0.0010\n",
            "Epoch 7/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0391 - true_positives_1: 14921.0000 - false_negatives_1: 36797.0000 - val_loss: 0.0393 - val_true_positives_1: 3531.0000 - val_false_negatives_1: 9399.0000 - lr: 0.0010\n",
            "Epoch 8/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0389 - true_positives_1: 14982.0000 - false_negatives_1: 36736.0000 - val_loss: 0.0391 - val_true_positives_1: 3823.0000 - val_false_negatives_1: 9107.0000 - lr: 0.0010\n",
            "Epoch 9/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0387 - true_positives_1: 15165.0000 - false_negatives_1: 36553.0000 - val_loss: 0.0389 - val_true_positives_1: 3698.0000 - val_false_negatives_1: 9232.0000 - lr: 0.0010\n",
            "Epoch 10/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0385 - true_positives_1: 15333.0000 - false_negatives_1: 36385.0000 - val_loss: 0.0387 - val_true_positives_1: 3397.0000 - val_false_negatives_1: 9533.0000 - lr: 0.0010\n",
            "Epoch 11/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0383 - true_positives_1: 15511.0000 - false_negatives_1: 36207.0000 - val_loss: 0.0385 - val_true_positives_1: 3937.0000 - val_false_negatives_1: 8993.0000 - lr: 0.0010\n",
            "Epoch 12/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0381 - true_positives_1: 15763.0000 - false_negatives_1: 35955.0000 - val_loss: 0.0384 - val_true_positives_1: 3823.0000 - val_false_negatives_1: 9107.0000 - lr: 0.0010\n",
            "Epoch 13/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0379 - true_positives_1: 15964.0000 - false_negatives_1: 35754.0000 - val_loss: 0.0382 - val_true_positives_1: 3926.0000 - val_false_negatives_1: 9004.0000 - lr: 0.0010\n",
            "Epoch 14/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0378 - true_positives_1: 16109.0000 - false_negatives_1: 35609.0000 - val_loss: 0.0384 - val_true_positives_1: 4719.0000 - val_false_negatives_1: 8211.0000 - lr: 0.0010\n",
            "Epoch 15/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0376 - true_positives_1: 16280.0000 - false_negatives_1: 35438.0000 - val_loss: 0.0380 - val_true_positives_1: 3756.0000 - val_false_negatives_1: 9174.0000 - lr: 0.0010\n",
            "Epoch 16/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0375 - true_positives_1: 16422.0000 - false_negatives_1: 35296.0000 - val_loss: 0.0379 - val_true_positives_1: 4001.0000 - val_false_negatives_1: 8929.0000 - lr: 0.0010\n",
            "Epoch 17/32\n",
            "409/409 [==============================] - 4s 11ms/step - loss: 0.0374 - true_positives_1: 16585.0000 - false_negatives_1: 35133.0000 - val_loss: 0.0378 - val_true_positives_1: 3882.0000 - val_false_negatives_1: 9048.0000 - lr: 0.0010\n",
            "Epoch 18/32\n",
            "409/409 [==============================] - 4s 11ms/step - loss: 0.0373 - true_positives_1: 16707.0000 - false_negatives_1: 35011.0000 - val_loss: 0.0377 - val_true_positives_1: 3896.0000 - val_false_negatives_1: 9034.0000 - lr: 0.0010\n",
            "Epoch 19/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0372 - true_positives_1: 16812.0000 - false_negatives_1: 34906.0000 - val_loss: 0.0376 - val_true_positives_1: 4100.0000 - val_false_negatives_1: 8830.0000 - lr: 0.0010\n",
            "Epoch 20/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0371 - true_positives_1: 16922.0000 - false_negatives_1: 34796.0000 - val_loss: 0.0376 - val_true_positives_1: 3839.0000 - val_false_negatives_1: 9091.0000 - lr: 0.0010\n",
            "Epoch 21/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0370 - true_positives_1: 17055.0000 - false_negatives_1: 34663.0000 - val_loss: 0.0375 - val_true_positives_1: 4425.0000 - val_false_negatives_1: 8505.0000 - lr: 0.0010\n",
            "Epoch 22/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0369 - true_positives_1: 17181.0000 - false_negatives_1: 34537.0000 - val_loss: 0.0374 - val_true_positives_1: 4117.0000 - val_false_negatives_1: 8813.0000 - lr: 0.0010\n",
            "Epoch 23/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0368 - true_positives_1: 17219.0000 - false_negatives_1: 34499.0000 - val_loss: 0.0374 - val_true_positives_1: 4182.0000 - val_false_negatives_1: 8748.0000 - lr: 0.0010\n",
            "Epoch 24/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0367 - true_positives_1: 17346.0000 - false_negatives_1: 34372.0000 - val_loss: 0.0373 - val_true_positives_1: 4283.0000 - val_false_negatives_1: 8647.0000 - lr: 0.0010\n",
            "Epoch 25/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0366 - true_positives_1: 17401.0000 - false_negatives_1: 34317.0000 - val_loss: 0.0373 - val_true_positives_1: 4179.0000 - val_false_negatives_1: 8751.0000 - lr: 0.0010\n",
            "Epoch 26/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0366 - true_positives_1: 17502.0000 - false_negatives_1: 34216.0000 - val_loss: 0.0372 - val_true_positives_1: 4358.0000 - val_false_negatives_1: 8572.0000 - lr: 0.0010\n",
            "Epoch 27/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0365 - true_positives_1: 17567.0000 - false_negatives_1: 34151.0000 - val_loss: 0.0372 - val_true_positives_1: 4666.0000 - val_false_negatives_1: 8264.0000 - lr: 0.0010\n",
            "Epoch 28/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0364 - true_positives_1: 17663.0000 - false_negatives_1: 34055.0000 - val_loss: 0.0372 - val_true_positives_1: 4586.0000 - val_false_negatives_1: 8344.0000 - lr: 0.0010\n",
            "Epoch 29/32\n",
            "408/409 [============================>.] - ETA: 0s - loss: 0.0364 - true_positives_1: 17695.0000 - false_negatives_1: 34002.0000\n",
            "Epoch 29: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0364 - true_positives_1: 17701.0000 - false_negatives_1: 34017.0000 - val_loss: 0.0371 - val_true_positives_1: 4556.0000 - val_false_negatives_1: 8374.0000 - lr: 0.0010\n",
            "Epoch 30/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0362 - true_positives_1: 17693.0000 - false_negatives_1: 34025.0000 - val_loss: 0.0370 - val_true_positives_1: 4564.0000 - val_false_negatives_1: 8366.0000 - lr: 1.0000e-04\n",
            "Epoch 31/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0362 - true_positives_1: 17755.0000 - false_negatives_1: 33963.0000 - val_loss: 0.0370 - val_true_positives_1: 4532.0000 - val_false_negatives_1: 8398.0000 - lr: 1.0000e-04\n",
            "Epoch 32/32\n",
            "409/409 [==============================] - 4s 10ms/step - loss: 0.0362 - true_positives_1: 17765.0000 - false_negatives_1: 33953.0000 - val_loss: 0.0370 - val_true_positives_1: 4554.0000 - val_false_negatives_1: 8376.0000 - lr: 1.0000e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe6c7556340>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model = model_focal, \n",
        "               threshold = 0.3,\n",
        "               dataset = test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "ajMRbrlwsLsQ",
        "outputId": "b3e206e6-43e9-41a8-d827-dc512250f45c"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9864    0.8873    0.9342    245063\n",
            "           1     0.3227    0.8143    0.4623     16162\n",
            "\n",
            "    accuracy                         0.8828    261225\n",
            "   macro avg     0.6546    0.8508    0.6983    261225\n",
            "weighted avg     0.9453    0.8828    0.9050    261225\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loss Karşılaştırması"
      ],
      "metadata": {
        "id": "mc5YZpE2ttO1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bce_loss = tf.keras.losses.BinaryCrossentropy(reduction=\"none\")\n",
        "bfce_loss = tf.keras.losses.BinaryFocalCrossentropy(reduction=\"none\")"
      ],
      "metadata": {
        "id": "LWM_uhTHsLKy"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = np.array([[0.1], [0.9], [0.2], [0.3]])\n",
        "labels = np.array([[0.0], [1.0], [1.0], [0.0]])"
      ],
      "metadata": {
        "id": "vcR-sjdptwka"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bce_loss(predictions, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxH40J35uJW_",
        "outputId": "39d7a3d3-4a60-4fbc-b77e-876954e262ac"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(4,), dtype=float64, numpy=array([ 1.54249485,  1.54249485, 12.33995878,  4.62748454])>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bfce_loss(predictions, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxwoV8sEuPcz",
        "outputId": "b581bb27-82f7-49a6-ba75-2ab16475c30e"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(4,), dtype=float64, numpy=array([0.01542495, 0.01542495, 7.89757362, 0.41647361])>"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p-4QD8abvO4Q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}