APLICAÇÕES_REDES_NEURAIS.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hsantos1985/neural_network/blob/main/APLICA%C3%87%C3%95ES_REDES_NEURAIS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Redes neurais\n",
        "##Aplicações para treinamento\n",
        "##Dataset - Iris"
      ],
      "metadata": {
        "id": "_QbQnxLRmXq5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##IMPORTAÇÃO DAS BIBLIOTECAS"
      ],
      "metadata": {
        "id": "d3F_6jvhmqLO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras-visualizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EcyPSzT8m_RA",
        "outputId": "836cca8d-8f2b-44e7-c17d-4fc40b2ba9db"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting keras-visualizer\n",
            "  Downloading keras_visualizer-3.2.0-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from keras-visualizer) (0.20.3)\n",
            "Installing collected packages: keras-visualizer\n",
            "Successfully installed keras-visualizer-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "PpenmfT3mW8X"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.utils import plot_model\n",
        "\n",
        "from sklearn import datasets\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#CRIAÇÃO DE FUNÇÃO PARA PLOTAR AS IMAGENS DAS REDES\n",
        "from keras_visualizer import visualizer\n",
        "from PIL import Image\n",
        "def plot_nn(model, setting={}):\n",
        "    visualizer(model, view=True, file_name='output', file_format='png', settings=setting)\n",
        "    img = Image.open('output.png')\n",
        "    return img"
      ],
      "metadata": {
        "id": "HyuXgyGkoXz4"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##CRIAÇÃO DA ABT - ANALYTICAL BASE TABLE"
      ],
      "metadata": {
        "id": "kqxIruR3pUFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iris = datasets.load_iris()"
      ],
      "metadata": {
        "id": "VARBTgTypZm_"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#nome das colunas\n",
        "iris.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGZEKdtGpz_n",
        "outputId": "41670043-30ca-43e1-b61a-fef27d971626"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#tratamento dos espaços das colunas\n",
        "nome_colunas=[]\n",
        "for i in iris.feature_names:\n",
        "  nome_colunas.append(i.replace(' ','_').replace('(','').replace(')',''))"
      ],
      "metadata": {
        "id": "9y7xvphap0Gm"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nome_colunas"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "6s35_CfAqmMV",
        "outputId": "012397fa-27c1-4c52-a8cd-d5277af1461e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal_length_cm', 'sepal_width_cm', 'petal_length_cm', 'petal_width_cm']"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "abt=pd.DataFrame(iris.data, columns=nome_colunas)"
      ],
      "metadata": {
        "id": "0jMlWqBoqmQe"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "abt['target']=iris.target"
      ],
      "metadata": {
        "id": "I-VnRZlcrHy7"
      },
      "execution_count": 49,
      "outputs": []
    },
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        "abt.head()"
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      "outputs": [
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            "text/plain": [
              "   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  target\n",
              "0              5.1             3.5              1.4             0.2       0\n",
              "1              4.9             3.0              1.4             0.2       0\n",
              "2              4.7             3.2              1.3             0.2       0\n",
              "3              4.6             3.1              1.5             0.2       0\n",
              "4              5.0             3.6              1.4             0.2       0"
            ],
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              "summary": "{\n  \"name\": \"abt\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SEPARAÇÃO DAS VARIÁVEIS EXPLICATIVAS E TARGET\n",
        "##TRAIN TEST SPLIT"
      ],
      "metadata": {
        "id": "vDaAUGMTrZDC"
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    {
      "cell_type": "code",
      "source": [
        "#filtro da base só com o target =1\n",
        "abt_filter = abt[abt['target'] !=0 ]"
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        "id": "UNSPdTw9rRc9"
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      "execution_count": 51,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "abt_filter.head()"
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        "colab": {
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        "id": "BNplQ8sQrRfX",
        "outputId": "3e21910f-c212-4845-c10d-56ee218d2e7a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  target\n",
              "50              7.0             3.2              4.7             1.4       1\n",
              "51              6.4             3.2              4.5             1.5       1\n",
              "52              6.9             3.1              4.9             1.5       1\n",
              "53              5.5             2.3              4.0             1.3       1\n",
              "54              6.5             2.8              4.6             1.5       1"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length_cm</th>\n",
              "      <th>sepal_width_cm</th>\n",
              "      <th>petal_length_cm</th>\n",
              "      <th>petal_width_cm</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>6.5</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.6</td>\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "abt_filter",
              "summary": "{\n  \"name\": \"abt_filter\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6628344400749671,\n        \"min\": 4.9,\n        \"max\": 7.9,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          5.2,\n          7.7,\n          6.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3327510064946947,\n        \"min\": 2.0,\n        \"max\": 3.8,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          3.2,\n          3.1,\n          2.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8255784626428903,\n        \"min\": 3.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          3.8,\n          6.0,\n          5.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.42476850498628405,\n        \"min\": 1.0,\n        \"max\": 2.5,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          1.4,\n          1.5,\n          1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=abt_filter[['petal_length_cm','sepal_length_cm']]\n",
        "y=abt_filter['target']"
      ],
      "metadata": {
        "id": "HMEij7JksAO5"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3, random_state=0)"
      ],
      "metadata": {
        "id": "7YU-xcbYrRhg"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##CRIAÇÃO DOS MODELOS"
      ],
      "metadata": {
        "id": "Bpa87BbCs3yx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "mm_scaler = MinMaxScaler()\n",
        "mm_scaler.fit(X_train)\n",
        "\n",
        "X_train_transform = mm_scaler.transform(X_train)\n",
        "X_test_transform = mm_scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "2cuNXKpUzZgP"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#criação dos modelos\n",
        "model=keras.models.Sequential()\n",
        "model.add(keras.layers.Input(shape=(2,), name='camada_entrada'))\n",
        "model.add(keras.layers.Dense(1,activation='sigmoid',name='camada_saida'))\n"
      ],
      "metadata": {
        "id": "f6LJOR9WsyQ4"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "historico1 = model.fit(X_train_transform, y_train, validation_data=(X_test_transform, y_test), epochs=100, batch_size=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IjBhPeQ3Yu4",
        "outputId": "c419c3c3-d522-4f67-89cf-53727a2cb976"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -15.5721 - accuracy: 0.5000 - val_loss: -14.6106 - val_accuracy: 0.5000\n",
            "Epoch 2/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -15.8721 - accuracy: 0.5000 - val_loss: -14.9020 - val_accuracy: 0.5000\n",
            "Epoch 3/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -16.1721 - accuracy: 0.5000 - val_loss: -15.1930 - val_accuracy: 0.5000\n",
            "Epoch 4/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -16.4720 - accuracy: 0.5000 - val_loss: -15.4838 - val_accuracy: 0.5000\n",
            "Epoch 5/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -16.7720 - accuracy: 0.5000 - val_loss: -15.7745 - val_accuracy: 0.5000\n",
            "Epoch 6/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -17.0719 - accuracy: 0.5000 - val_loss: -16.0650 - val_accuracy: 0.5000\n",
            "Epoch 7/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -17.3719 - accuracy: 0.5000 - val_loss: -16.3554 - val_accuracy: 0.5000\n",
            "Epoch 8/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -17.6718 - accuracy: 0.5000 - val_loss: -16.6457 - val_accuracy: 0.5000\n",
            "Epoch 9/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -17.9718 - accuracy: 0.5000 - val_loss: -16.9360 - val_accuracy: 0.5000\n",
            "Epoch 10/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -18.2717 - accuracy: 0.5000 - val_loss: -17.2262 - val_accuracy: 0.5000\n",
            "Epoch 11/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -18.5716 - accuracy: 0.5000 - val_loss: -17.5164 - val_accuracy: 0.5000\n",
            "Epoch 12/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -18.8716 - accuracy: 0.5000 - val_loss: -17.8066 - val_accuracy: 0.5000\n",
            "Epoch 13/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -19.1715 - accuracy: 0.5000 - val_loss: -18.0968 - val_accuracy: 0.5000\n",
            "Epoch 14/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -19.4715 - accuracy: 0.5000 - val_loss: -18.3870 - val_accuracy: 0.5000\n",
            "Epoch 15/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -19.7714 - accuracy: 0.5000 - val_loss: -18.6772 - val_accuracy: 0.5000\n",
            "Epoch 16/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -20.0713 - accuracy: 0.5000 - val_loss: -18.9674 - val_accuracy: 0.5000\n",
            "Epoch 17/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -20.3713 - accuracy: 0.5000 - val_loss: -19.2575 - val_accuracy: 0.5000\n",
            "Epoch 18/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -20.6712 - accuracy: 0.5000 - val_loss: -19.5477 - val_accuracy: 0.5000\n",
            "Epoch 19/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -20.9711 - accuracy: 0.5000 - val_loss: -19.8379 - val_accuracy: 0.5000\n",
            "Epoch 20/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -21.2711 - accuracy: 0.5000 - val_loss: -20.1280 - val_accuracy: 0.5000\n",
            "Epoch 21/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -21.5710 - accuracy: 0.5000 - val_loss: -20.4182 - val_accuracy: 0.5000\n",
            "Epoch 22/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -21.8709 - accuracy: 0.5000 - val_loss: -20.7084 - val_accuracy: 0.5000\n",
            "Epoch 23/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -22.1709 - accuracy: 0.5000 - val_loss: -20.9985 - val_accuracy: 0.5000\n",
            "Epoch 24/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -22.4708 - accuracy: 0.5000 - val_loss: -21.2887 - val_accuracy: 0.5000\n",
            "Epoch 25/100\n",
            "70/70 [==============================] - 0s 5ms/step - loss: -22.7707 - accuracy: 0.5000 - val_loss: -21.5788 - val_accuracy: 0.5000\n",
            "Epoch 26/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -23.0707 - accuracy: 0.5000 - val_loss: -21.8690 - val_accuracy: 0.5000\n",
            "Epoch 27/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -23.3706 - accuracy: 0.5000 - val_loss: -22.1592 - val_accuracy: 0.5000\n",
            "Epoch 28/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -23.6705 - accuracy: 0.5000 - val_loss: -22.4493 - val_accuracy: 0.5000\n",
            "Epoch 29/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -23.9705 - accuracy: 0.5000 - val_loss: -22.7395 - val_accuracy: 0.5000\n",
            "Epoch 30/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -24.2704 - accuracy: 0.5000 - val_loss: -23.0296 - val_accuracy: 0.5000\n",
            "Epoch 31/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -24.5703 - accuracy: 0.5000 - val_loss: -23.3198 - val_accuracy: 0.5000\n",
            "Epoch 32/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -24.8703 - accuracy: 0.5000 - val_loss: -23.6100 - val_accuracy: 0.5000\n",
            "Epoch 33/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -25.1702 - accuracy: 0.5000 - val_loss: -23.9001 - val_accuracy: 0.5000\n",
            "Epoch 34/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -25.4701 - accuracy: 0.5000 - val_loss: -24.1903 - val_accuracy: 0.5000\n",
            "Epoch 35/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -25.7701 - accuracy: 0.5000 - val_loss: -24.4804 - val_accuracy: 0.5000\n",
            "Epoch 36/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -26.0700 - accuracy: 0.5000 - val_loss: -24.7706 - val_accuracy: 0.5000\n",
            "Epoch 37/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -26.3699 - accuracy: 0.5000 - val_loss: -25.0608 - val_accuracy: 0.5000\n",
            "Epoch 38/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -26.6699 - accuracy: 0.5000 - val_loss: -25.3509 - val_accuracy: 0.5000\n",
            "Epoch 39/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -26.9698 - accuracy: 0.5000 - val_loss: -25.6411 - val_accuracy: 0.5000\n",
            "Epoch 40/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -27.2697 - accuracy: 0.5000 - val_loss: -25.9313 - val_accuracy: 0.5000\n",
            "Epoch 41/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -27.5697 - accuracy: 0.5000 - val_loss: -26.2214 - val_accuracy: 0.5000\n",
            "Epoch 42/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -27.8696 - accuracy: 0.5000 - val_loss: -26.5116 - val_accuracy: 0.5000\n",
            "Epoch 43/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -28.1695 - accuracy: 0.5000 - val_loss: -26.8017 - val_accuracy: 0.5000\n",
            "Epoch 44/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -28.4695 - accuracy: 0.5000 - val_loss: -27.0919 - val_accuracy: 0.5000\n",
            "Epoch 45/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -28.7694 - accuracy: 0.5000 - val_loss: -27.3821 - val_accuracy: 0.5000\n",
            "Epoch 46/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -29.0694 - accuracy: 0.5000 - val_loss: -27.6722 - val_accuracy: 0.5000\n",
            "Epoch 47/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -29.3693 - accuracy: 0.5000 - val_loss: -27.9624 - val_accuracy: 0.5000\n",
            "Epoch 48/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -29.6692 - accuracy: 0.5000 - val_loss: -28.2526 - val_accuracy: 0.5000\n",
            "Epoch 49/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -29.9692 - accuracy: 0.5000 - val_loss: -28.5427 - val_accuracy: 0.5000\n",
            "Epoch 50/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -30.2691 - accuracy: 0.5000 - val_loss: -28.8329 - val_accuracy: 0.5000\n",
            "Epoch 51/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -30.5690 - accuracy: 0.5000 - val_loss: -29.1230 - val_accuracy: 0.5000\n",
            "Epoch 52/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -30.8690 - accuracy: 0.5000 - val_loss: -29.4132 - val_accuracy: 0.5000\n",
            "Epoch 53/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -31.1689 - accuracy: 0.5000 - val_loss: -29.7034 - val_accuracy: 0.5000\n",
            "Epoch 54/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -31.4689 - accuracy: 0.5000 - val_loss: -29.9935 - val_accuracy: 0.5000\n",
            "Epoch 55/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -31.7688 - accuracy: 0.5000 - val_loss: -30.2837 - val_accuracy: 0.5000\n",
            "Epoch 56/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -32.0687 - accuracy: 0.5000 - val_loss: -30.5739 - val_accuracy: 0.5000\n",
            "Epoch 57/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -32.3687 - accuracy: 0.5000 - val_loss: -30.8640 - val_accuracy: 0.5000\n",
            "Epoch 58/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -32.6686 - accuracy: 0.5000 - val_loss: -31.1542 - val_accuracy: 0.5000\n",
            "Epoch 59/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -32.9686 - accuracy: 0.5000 - val_loss: -31.4444 - val_accuracy: 0.5000\n",
            "Epoch 60/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -33.2685 - accuracy: 0.5000 - val_loss: -31.7345 - val_accuracy: 0.5000\n",
            "Epoch 61/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -33.5684 - accuracy: 0.5000 - val_loss: -32.0247 - val_accuracy: 0.5000\n",
            "Epoch 62/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -33.8684 - accuracy: 0.5000 - val_loss: -32.3149 - val_accuracy: 0.5000\n",
            "Epoch 63/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -34.1683 - accuracy: 0.5000 - val_loss: -32.6050 - val_accuracy: 0.5000\n",
            "Epoch 64/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -34.4683 - accuracy: 0.5000 - val_loss: -32.8952 - val_accuracy: 0.5000\n",
            "Epoch 65/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -34.7682 - accuracy: 0.5000 - val_loss: -33.1854 - val_accuracy: 0.5000\n",
            "Epoch 66/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -35.0681 - accuracy: 0.5000 - val_loss: -33.4756 - val_accuracy: 0.5000\n",
            "Epoch 67/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -35.3681 - accuracy: 0.5000 - val_loss: -33.7657 - val_accuracy: 0.5000\n",
            "Epoch 68/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -35.6680 - accuracy: 0.5000 - val_loss: -34.0559 - val_accuracy: 0.5000\n",
            "Epoch 69/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -35.9680 - accuracy: 0.5000 - val_loss: -34.3461 - val_accuracy: 0.5000\n",
            "Epoch 70/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -36.2679 - accuracy: 0.5000 - val_loss: -34.6362 - val_accuracy: 0.5000\n",
            "Epoch 71/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -36.5679 - accuracy: 0.5000 - val_loss: -34.9264 - val_accuracy: 0.5000\n",
            "Epoch 72/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -36.8678 - accuracy: 0.5000 - val_loss: -35.2166 - val_accuracy: 0.5000\n",
            "Epoch 73/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -37.1677 - accuracy: 0.5000 - val_loss: -35.5067 - val_accuracy: 0.5000\n",
            "Epoch 74/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -37.4677 - accuracy: 0.5000 - val_loss: -35.7969 - val_accuracy: 0.5000\n",
            "Epoch 75/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -37.7676 - accuracy: 0.5000 - val_loss: -36.0871 - val_accuracy: 0.5000\n",
            "Epoch 76/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -38.0676 - accuracy: 0.5000 - val_loss: -36.3772 - val_accuracy: 0.5000\n",
            "Epoch 77/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -38.3675 - accuracy: 0.5000 - val_loss: -36.6674 - val_accuracy: 0.5000\n",
            "Epoch 78/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -38.6674 - accuracy: 0.5000 - val_loss: -36.9576 - val_accuracy: 0.5000\n",
            "Epoch 79/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -38.9674 - accuracy: 0.5000 - val_loss: -37.2477 - val_accuracy: 0.5000\n",
            "Epoch 80/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -39.2673 - accuracy: 0.5000 - val_loss: -37.5379 - val_accuracy: 0.5000\n",
            "Epoch 81/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -39.5673 - accuracy: 0.5000 - val_loss: -37.8280 - val_accuracy: 0.5000\n",
            "Epoch 82/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -39.8672 - accuracy: 0.5000 - val_loss: -38.1182 - val_accuracy: 0.5000\n",
            "Epoch 83/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -40.1671 - accuracy: 0.5000 - val_loss: -38.4083 - val_accuracy: 0.5000\n",
            "Epoch 84/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -40.4670 - accuracy: 0.5000 - val_loss: -38.6984 - val_accuracy: 0.5000\n",
            "Epoch 85/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -40.7669 - accuracy: 0.5000 - val_loss: -38.9886 - val_accuracy: 0.5000\n",
            "Epoch 86/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -41.0668 - accuracy: 0.5000 - val_loss: -39.2787 - val_accuracy: 0.5000\n",
            "Epoch 87/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -41.3667 - accuracy: 0.5000 - val_loss: -39.5688 - val_accuracy: 0.5000\n",
            "Epoch 88/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -41.6666 - accuracy: 0.5000 - val_loss: -39.8590 - val_accuracy: 0.5000\n",
            "Epoch 89/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -41.9665 - accuracy: 0.5000 - val_loss: -40.1491 - val_accuracy: 0.5000\n",
            "Epoch 90/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -42.2664 - accuracy: 0.5000 - val_loss: -40.4392 - val_accuracy: 0.5000\n",
            "Epoch 91/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -42.5663 - accuracy: 0.5000 - val_loss: -40.7294 - val_accuracy: 0.5000\n",
            "Epoch 92/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -42.8662 - accuracy: 0.5000 - val_loss: -41.0195 - val_accuracy: 0.5000\n",
            "Epoch 93/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -43.1662 - accuracy: 0.5000 - val_loss: -41.3096 - val_accuracy: 0.5000\n",
            "Epoch 94/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -43.4661 - accuracy: 0.5000 - val_loss: -41.5998 - val_accuracy: 0.5000\n",
            "Epoch 95/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -43.7660 - accuracy: 0.5000 - val_loss: -41.8899 - val_accuracy: 0.5000\n",
            "Epoch 96/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -44.0659 - accuracy: 0.5000 - val_loss: -42.1800 - val_accuracy: 0.5000\n",
            "Epoch 97/100\n",
            "70/70 [==============================] - 0s 4ms/step - loss: -44.3658 - accuracy: 0.5000 - val_loss: -42.4702 - val_accuracy: 0.5000\n",
            "Epoch 98/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -44.6657 - accuracy: 0.5000 - val_loss: -42.7603 - val_accuracy: 0.5000\n",
            "Epoch 99/100\n",
            "70/70 [==============================] - 0s 3ms/step - loss: -44.9656 - accuracy: 0.5000 - val_loss: -43.0504 - val_accuracy: 0.5000\n",
            "Epoch 100/100\n",
            "70/70 [==============================] - 0s 2ms/step - loss: -45.2655 - accuracy: 0.5000 - val_loss: -43.3406 - val_accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iklsOOP-wSkO",
        "outputId": "b6ed9e26-80fe-41c6-dab9-360ac90d2423"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " camada_saida (Dense)        (None, 1)                 3         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3 (12.00 Byte)\n",
            "Trainable params: 3 (12.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='SGD', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "6lP_66bGsyTW"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=250)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JT_XYAe8syVp",
        "outputId": "eb4b7853-2608-4b87-c58d-332845fd85a0"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/250\n",
            "3/3 [==============================] - 1s 99ms/step - loss: 14.9874 - accuracy: 0.0000e+00 - val_loss: 11.2105 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 10.4394 - accuracy: 0.0000e+00 - val_loss: 6.5619 - val_accuracy: 0.0000e+00\n",
            "Epoch 3/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 5.7753 - accuracy: 0.0000e+00 - val_loss: 2.6835 - val_accuracy: 0.0000e+00\n",
            "Epoch 4/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 2.0063 - accuracy: 0.0286 - val_loss: -0.0664 - val_accuracy: 0.5000\n",
            "Epoch 5/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -0.3408 - accuracy: 0.5000 - val_loss: -1.0964 - val_accuracy: 0.5000\n",
            "Epoch 6/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.2846 - accuracy: 0.5000 - val_loss: -1.8392 - val_accuracy: 0.5000\n",
            "Epoch 7/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -2.0095 - accuracy: 0.5000 - val_loss: -2.4319 - val_accuracy: 0.5000\n",
            "Epoch 8/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.6142 - accuracy: 0.5000 - val_loss: -3.0091 - val_accuracy: 0.5000\n",
            "Epoch 9/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -3.2095 - accuracy: 0.5000 - val_loss: -3.6660 - val_accuracy: 0.5000\n",
            "Epoch 10/250\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -3.8680 - accuracy: 0.5000 - val_loss: -4.1757 - val_accuracy: 0.5000\n",
            "Epoch 11/250\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -4.3927 - accuracy: 0.5000 - val_loss: -4.6878 - val_accuracy: 0.5000\n",
            "Epoch 12/250\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -4.9134 - accuracy: 0.5000 - val_loss: -5.2146 - val_accuracy: 0.5000\n",
            "Epoch 13/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -5.4654 - accuracy: 0.5000 - val_loss: -5.8190 - val_accuracy: 0.5000\n",
            "Epoch 14/250\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -6.0800 - accuracy: 0.5000 - val_loss: -6.3625 - val_accuracy: 0.5000\n",
            "Epoch 15/250\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -6.6379 - accuracy: 0.5000 - val_loss: -6.9144 - val_accuracy: 0.5000\n",
            "Epoch 16/250\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -7.1985 - accuracy: 0.5000 - val_loss: -7.4298 - val_accuracy: 0.5000\n",
            "Epoch 17/250\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -7.7279 - accuracy: 0.5000 - val_loss: -7.9405 - val_accuracy: 0.5000\n",
            "Epoch 18/250\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -8.2591 - accuracy: 0.5000 - val_loss: -8.5403 - val_accuracy: 0.5000\n",
            "Epoch 19/250\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -8.8684 - accuracy: 0.5000 - val_loss: -9.1074 - val_accuracy: 0.5000\n",
            "Epoch 20/250\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -9.4519 - accuracy: 0.5000 - val_loss: -9.6788 - val_accuracy: 0.5000\n",
            "Epoch 21/250\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -10.0370 - accuracy: 0.5000 - val_loss: -10.2315 - val_accuracy: 0.5000\n",
            "Epoch 22/250\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -10.5981 - accuracy: 0.5000 - val_loss: -10.7346 - val_accuracy: 0.5000\n",
            "Epoch 23/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -11.1169 - accuracy: 0.5000 - val_loss: -11.3045 - val_accuracy: 0.5000\n",
            "Epoch 24/250\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -11.7057 - accuracy: 0.5000 - val_loss: -11.9048 - val_accuracy: 0.5000\n",
            "Epoch 25/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -12.3173 - accuracy: 0.5000 - val_loss: -12.4505 - val_accuracy: 0.5000\n",
            "Epoch 26/250\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -12.8748 - accuracy: 0.5000 - val_loss: -12.9727 - val_accuracy: 0.5000\n",
            "Epoch 27/250\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -13.4125 - accuracy: 0.5000 - val_loss: -13.5372 - val_accuracy: 0.5000\n",
            "Epoch 28/250\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -13.9862 - accuracy: 0.5000 - val_loss: -14.0409 - val_accuracy: 0.5000\n",
            "Epoch 29/250\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -14.5077 - accuracy: 0.5000 - val_loss: -14.6092 - val_accuracy: 0.5000\n",
            "Epoch 30/250\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -15.0857 - accuracy: 0.5000 - val_loss: -15.1163 - val_accuracy: 0.5000\n",
            "Epoch 31/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -15.6140 - accuracy: 0.5000 - val_loss: -15.7229 - val_accuracy: 0.5000\n",
            "Epoch 32/250\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -16.2265 - accuracy: 0.5000 - val_loss: -16.2237 - val_accuracy: 0.5000\n",
            "Epoch 33/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -16.7374 - accuracy: 0.5000 - val_loss: -16.7145 - val_accuracy: 0.5000\n",
            "Epoch 34/250\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -17.2384 - accuracy: 0.5000 - val_loss: -17.1754 - val_accuracy: 0.5000\n",
            "Epoch 35/250\n",
            "3/3 [==============================] - 0s 35ms/step - loss: -17.7257 - accuracy: 0.5000 - val_loss: -17.7969 - val_accuracy: 0.5000\n",
            "Epoch 36/250\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -18.3617 - accuracy: 0.5000 - val_loss: -18.4069 - val_accuracy: 0.5000\n",
            "Epoch 37/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -18.9863 - accuracy: 0.5000 - val_loss: -19.0097 - val_accuracy: 0.5000\n",
            "Epoch 38/250\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -19.5995 - accuracy: 0.5000 - val_loss: -19.5623 - val_accuracy: 0.5000\n",
            "Epoch 39/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -20.1647 - accuracy: 0.5000 - val_loss: -20.1138 - val_accuracy: 0.5000\n",
            "Epoch 40/250\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -20.7318 - accuracy: 0.5000 - val_loss: -20.6743 - val_accuracy: 0.5000\n",
            "Epoch 41/250\n",
            "3/3 [==============================] - 0s 35ms/step - loss: -21.3101 - accuracy: 0.5000 - val_loss: -21.2825 - val_accuracy: 0.5000\n",
            "Epoch 42/250\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -21.9255 - accuracy: 0.5000 - val_loss: -21.8057 - val_accuracy: 0.5000\n",
            "Epoch 43/250\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -22.4616 - accuracy: 0.5000 - val_loss: -22.4202 - val_accuracy: 0.5000\n",
            "Epoch 44/250\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -23.0918 - accuracy: 0.5000 - val_loss: -22.9738 - val_accuracy: 0.5000\n",
            "Epoch 45/250\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -23.6677 - accuracy: 0.5000 - val_loss: -23.5857 - val_accuracy: 0.5000\n",
            "Epoch 46/250\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -24.2772 - accuracy: 0.5000 - val_loss: -24.0441 - val_accuracy: 0.5000\n",
            "Epoch 47/250\n",
            "3/3 [==============================] - 0s 35ms/step - loss: -24.7520 - accuracy: 0.5000 - val_loss: -24.5617 - val_accuracy: 0.5000\n",
            "Epoch 48/250\n",
            "3/3 [==============================] - 0s 37ms/step - loss: -25.2788 - accuracy: 0.5000 - val_loss: -25.0260 - val_accuracy: 0.5000\n",
            "Epoch 49/250\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -25.7724 - accuracy: 0.5000 - val_loss: -25.7094 - val_accuracy: 0.5000\n",
            "Epoch 50/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -26.4586 - accuracy: 0.5000 - val_loss: -26.2131 - val_accuracy: 0.5000\n",
            "Epoch 51/250\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -26.9778 - accuracy: 0.5000 - val_loss: -26.7158 - val_accuracy: 0.5000\n",
            "Epoch 52/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -27.5052 - accuracy: 0.5000 - val_loss: -27.3771 - val_accuracy: 0.5000\n",
            "Epoch 53/250\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -28.1668 - accuracy: 0.5000 - val_loss: -27.8850 - val_accuracy: 0.5000\n",
            "Epoch 54/250\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -28.6967 - accuracy: 0.5000 - val_loss: -28.4443 - val_accuracy: 0.5000\n",
            "Epoch 55/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -29.2646 - accuracy: 0.5000 - val_loss: -28.9519 - val_accuracy: 0.5000\n",
            "Epoch 56/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -29.7843 - accuracy: 0.5000 - val_loss: -29.4569 - val_accuracy: 0.5000\n",
            "Epoch 57/250\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -30.3046 - accuracy: 0.5000 - val_loss: -30.0163 - val_accuracy: 0.5000\n",
            "Epoch 58/250\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -30.8879 - accuracy: 0.5000 - val_loss: -30.6739 - val_accuracy: 0.5000\n",
            "Epoch 59/250\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -31.5467 - accuracy: 0.5000 - val_loss: -31.1876 - val_accuracy: 0.5000\n",
            "Epoch 60/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -32.0759 - accuracy: 0.5000 - val_loss: -31.6982 - val_accuracy: 0.5000\n",
            "Epoch 61/250\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -32.5988 - accuracy: 0.5000 - val_loss: -32.1934 - val_accuracy: 0.5000\n",
            "Epoch 62/250\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -33.1118 - accuracy: 0.5000 - val_loss: -32.7506 - val_accuracy: 0.5000\n",
            "Epoch 63/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -33.6778 - accuracy: 0.5000 - val_loss: -33.2506 - val_accuracy: 0.5000\n",
            "Epoch 64/250\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -34.2005 - accuracy: 0.5000 - val_loss: -33.8669 - val_accuracy: 0.5000\n",
            "Epoch 65/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -34.8326 - accuracy: 0.5000 - val_loss: -34.4936 - val_accuracy: 0.5000\n",
            "Epoch 66/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -35.4733 - accuracy: 0.5000 - val_loss: -35.0942 - val_accuracy: 0.5000\n",
            "Epoch 67/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -36.0933 - accuracy: 0.5000 - val_loss: -35.7568 - val_accuracy: 0.5000\n",
            "Epoch 68/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -36.7631 - accuracy: 0.5000 - val_loss: -36.3009 - val_accuracy: 0.5000\n",
            "Epoch 69/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -37.3203 - accuracy: 0.5000 - val_loss: -36.8492 - val_accuracy: 0.5000\n",
            "Epoch 70/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -37.8831 - accuracy: 0.5000 - val_loss: -37.4193 - val_accuracy: 0.5000\n",
            "Epoch 71/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -38.4755 - accuracy: 0.5000 - val_loss: -38.0863 - val_accuracy: 0.5000\n",
            "Epoch 72/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -39.1508 - accuracy: 0.5000 - val_loss: -38.6506 - val_accuracy: 0.5000\n",
            "Epoch 73/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -39.7244 - accuracy: 0.5000 - val_loss: -39.1690 - val_accuracy: 0.5000\n",
            "Epoch 74/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -40.2492 - accuracy: 0.5000 - val_loss: -39.6268 - val_accuracy: 0.5000\n",
            "Epoch 75/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -40.7252 - accuracy: 0.5000 - val_loss: -40.1449 - val_accuracy: 0.5000\n",
            "Epoch 76/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -41.2659 - accuracy: 0.5000 - val_loss: -40.7574 - val_accuracy: 0.5000\n",
            "Epoch 77/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -41.8940 - accuracy: 0.5000 - val_loss: -41.3726 - val_accuracy: 0.5000\n",
            "Epoch 78/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -42.5173 - accuracy: 0.5000 - val_loss: -41.9206 - val_accuracy: 0.5000\n",
            "Epoch 79/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -43.0770 - accuracy: 0.5000 - val_loss: -42.4324 - val_accuracy: 0.5000\n",
            "Epoch 80/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -43.6016 - accuracy: 0.5000 - val_loss: -42.9458 - val_accuracy: 0.5000\n",
            "Epoch 81/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -44.1401 - accuracy: 0.5000 - val_loss: -43.6055 - val_accuracy: 0.5000\n",
            "Epoch 82/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -44.8083 - accuracy: 0.5000 - val_loss: -44.2250 - val_accuracy: 0.5000\n",
            "Epoch 83/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -45.4465 - accuracy: 0.5000 - val_loss: -44.8255 - val_accuracy: 0.5000\n",
            "Epoch 84/250\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -46.0521 - accuracy: 0.5000 - val_loss: -45.3257 - val_accuracy: 0.5000\n",
            "Epoch 85/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -46.5724 - accuracy: 0.5000 - val_loss: -45.8998 - val_accuracy: 0.5000\n",
            "Epoch 86/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -47.1639 - accuracy: 0.5000 - val_loss: -46.5158 - val_accuracy: 0.5000\n",
            "Epoch 87/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -47.7973 - accuracy: 0.5000 - val_loss: -47.1674 - val_accuracy: 0.5000\n",
            "Epoch 88/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -48.4606 - accuracy: 0.5000 - val_loss: -47.7606 - val_accuracy: 0.5000\n",
            "Epoch 89/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -49.0718 - accuracy: 0.5000 - val_loss: -48.3855 - val_accuracy: 0.5000\n",
            "Epoch 90/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -49.7108 - accuracy: 0.5000 - val_loss: -48.9874 - val_accuracy: 0.5000\n",
            "Epoch 91/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -50.3189 - accuracy: 0.5000 - val_loss: -49.4909 - val_accuracy: 0.5000\n",
            "Epoch 92/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -50.8439 - accuracy: 0.5000 - val_loss: -50.1000 - val_accuracy: 0.5000\n",
            "Epoch 93/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -51.4611 - accuracy: 0.5000 - val_loss: -50.6568 - val_accuracy: 0.5000\n",
            "Epoch 94/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -52.0394 - accuracy: 0.5000 - val_loss: -51.2723 - val_accuracy: 0.5000\n",
            "Epoch 95/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -52.6636 - accuracy: 0.5000 - val_loss: -51.8291 - val_accuracy: 0.5000\n",
            "Epoch 96/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -53.2421 - accuracy: 0.5000 - val_loss: -52.4712 - val_accuracy: 0.5000\n",
            "Epoch 97/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -53.8788 - accuracy: 0.5000 - val_loss: -52.8785 - val_accuracy: 0.5000\n",
            "Epoch 98/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -54.3117 - accuracy: 0.5000 - val_loss: -53.4341 - val_accuracy: 0.5000\n",
            "Epoch 99/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -54.8759 - accuracy: 0.5000 - val_loss: -53.9370 - val_accuracy: 0.5000\n",
            "Epoch 100/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -55.3946 - accuracy: 0.5000 - val_loss: -54.4855 - val_accuracy: 0.5000\n",
            "Epoch 101/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -55.9535 - accuracy: 0.5000 - val_loss: -55.0356 - val_accuracy: 0.5000\n",
            "Epoch 102/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -56.5170 - accuracy: 0.5000 - val_loss: -55.5392 - val_accuracy: 0.5000\n",
            "Epoch 103/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -57.0387 - accuracy: 0.5000 - val_loss: -56.1121 - val_accuracy: 0.5000\n",
            "Epoch 104/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -57.6241 - accuracy: 0.5000 - val_loss: -56.6599 - val_accuracy: 0.5000\n",
            "Epoch 105/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -58.1814 - accuracy: 0.5000 - val_loss: -57.1762 - val_accuracy: 0.5000\n",
            "Epoch 106/250\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -58.7058 - accuracy: 0.5000 - val_loss: -57.6302 - val_accuracy: 0.5000\n",
            "Epoch 107/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -59.1803 - accuracy: 0.5000 - val_loss: -58.2808 - val_accuracy: 0.5000\n",
            "Epoch 108/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -59.8468 - accuracy: 0.5000 - val_loss: -58.8227 - val_accuracy: 0.5000\n",
            "Epoch 109/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -60.4031 - accuracy: 0.5000 - val_loss: -59.3751 - val_accuracy: 0.5000\n",
            "Epoch 110/250\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -60.9775 - accuracy: 0.5000 - val_loss: -60.0333 - val_accuracy: 0.5000\n",
            "Epoch 111/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -61.6520 - accuracy: 0.5000 - val_loss: -60.7010 - val_accuracy: 0.5000\n",
            "Epoch 112/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -62.3289 - accuracy: 0.5000 - val_loss: -61.2581 - val_accuracy: 0.5000\n",
            "Epoch 113/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -62.9020 - accuracy: 0.5000 - val_loss: -61.8534 - val_accuracy: 0.5000\n",
            "Epoch 114/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -63.5090 - accuracy: 0.5000 - val_loss: -62.4164 - val_accuracy: 0.5000\n",
            "Epoch 115/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -64.0816 - accuracy: 0.5000 - val_loss: -62.9182 - val_accuracy: 0.5000\n",
            "Epoch 116/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -64.6019 - accuracy: 0.5000 - val_loss: -63.4826 - val_accuracy: 0.5000\n",
            "Epoch 117/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -65.1830 - accuracy: 0.5000 - val_loss: -64.0799 - val_accuracy: 0.5000\n",
            "Epoch 118/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -65.7999 - accuracy: 0.5000 - val_loss: -64.7582 - val_accuracy: 0.5000\n",
            "Epoch 119/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -66.4775 - accuracy: 0.5000 - val_loss: -65.2609 - val_accuracy: 0.5000\n",
            "Epoch 120/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -66.9958 - accuracy: 0.5000 - val_loss: -65.7633 - val_accuracy: 0.5000\n",
            "Epoch 121/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -67.5047 - accuracy: 0.5000 - val_loss: -66.2187 - val_accuracy: 0.5000\n",
            "Epoch 122/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -67.9931 - accuracy: 0.5000 - val_loss: -66.8982 - val_accuracy: 0.5000\n",
            "Epoch 123/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -68.6671 - accuracy: 0.5000 - val_loss: -67.3546 - val_accuracy: 0.5000\n",
            "Epoch 124/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -69.1377 - accuracy: 0.5000 - val_loss: -67.9230 - val_accuracy: 0.5000\n",
            "Epoch 125/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -69.7167 - accuracy: 0.5000 - val_loss: -68.4315 - val_accuracy: 0.5000\n",
            "Epoch 126/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -70.2467 - accuracy: 0.5000 - val_loss: -68.9425 - val_accuracy: 0.5000\n",
            "Epoch 127/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -70.7735 - accuracy: 0.5000 - val_loss: -69.4907 - val_accuracy: 0.5000\n",
            "Epoch 128/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -71.3372 - accuracy: 0.5000 - val_loss: -70.0528 - val_accuracy: 0.5000\n",
            "Epoch 129/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -71.9103 - accuracy: 0.5000 - val_loss: -70.6523 - val_accuracy: 0.5000\n",
            "Epoch 130/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -72.5223 - accuracy: 0.5000 - val_loss: -71.1519 - val_accuracy: 0.5000\n",
            "Epoch 131/250\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -73.0392 - accuracy: 0.5000 - val_loss: -71.7077 - val_accuracy: 0.5000\n",
            "Epoch 132/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -73.6038 - accuracy: 0.5000 - val_loss: -72.2130 - val_accuracy: 0.5000\n",
            "Epoch 133/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -74.1222 - accuracy: 0.5000 - val_loss: -72.7177 - val_accuracy: 0.5000\n",
            "Epoch 134/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -74.6493 - accuracy: 0.5000 - val_loss: -73.3339 - val_accuracy: 0.5000\n",
            "Epoch 135/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -75.2717 - accuracy: 0.5000 - val_loss: -73.8493 - val_accuracy: 0.5000\n",
            "Epoch 136/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -75.8035 - accuracy: 0.5000 - val_loss: -74.4028 - val_accuracy: 0.5000\n",
            "Epoch 137/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -76.3596 - accuracy: 0.5000 - val_loss: -74.8612 - val_accuracy: 0.5000\n",
            "Epoch 138/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -76.8500 - accuracy: 0.5000 - val_loss: -75.5304 - val_accuracy: 0.5000\n",
            "Epoch 139/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -77.5361 - accuracy: 0.5000 - val_loss: -76.2052 - val_accuracy: 0.5000\n",
            "Epoch 140/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -78.2191 - accuracy: 0.5000 - val_loss: -76.7680 - val_accuracy: 0.5000\n",
            "Epoch 141/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -78.7964 - accuracy: 0.5000 - val_loss: -77.3463 - val_accuracy: 0.5000\n",
            "Epoch 142/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -79.3872 - accuracy: 0.5000 - val_loss: -77.9019 - val_accuracy: 0.5000\n",
            "Epoch 143/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -79.9566 - accuracy: 0.5000 - val_loss: -78.4474 - val_accuracy: 0.5000\n",
            "Epoch 144/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -80.5162 - accuracy: 0.5000 - val_loss: -79.0147 - val_accuracy: 0.5000\n",
            "Epoch 145/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -81.0974 - accuracy: 0.5000 - val_loss: -79.5774 - val_accuracy: 0.5000\n",
            "Epoch 146/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -81.6785 - accuracy: 0.5000 - val_loss: -80.1831 - val_accuracy: 0.5000\n",
            "Epoch 147/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -82.3062 - accuracy: 0.5000 - val_loss: -80.9012 - val_accuracy: 0.5000\n",
            "Epoch 148/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -83.0382 - accuracy: 0.5000 - val_loss: -81.5495 - val_accuracy: 0.5000\n",
            "Epoch 149/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -83.6952 - accuracy: 0.5000 - val_loss: -82.1184 - val_accuracy: 0.5000\n",
            "Epoch 150/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -84.2783 - accuracy: 0.5000 - val_loss: -82.7236 - val_accuracy: 0.5000\n",
            "Epoch 151/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -84.9022 - accuracy: 0.5000 - val_loss: -83.3148 - val_accuracy: 0.5000\n",
            "Epoch 152/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -85.5091 - accuracy: 0.5000 - val_loss: -83.9175 - val_accuracy: 0.5000\n",
            "Epoch 153/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -86.1259 - accuracy: 0.5000 - val_loss: -84.5116 - val_accuracy: 0.5000\n",
            "Epoch 154/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -86.7353 - accuracy: 0.5000 - val_loss: -85.1135 - val_accuracy: 0.5000\n",
            "Epoch 155/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -87.3468 - accuracy: 0.5000 - val_loss: -85.6777 - val_accuracy: 0.5000\n",
            "Epoch 156/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -87.9308 - accuracy: 0.5000 - val_loss: -86.2814 - val_accuracy: 0.5000\n",
            "Epoch 157/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -88.5306 - accuracy: 0.5000 - val_loss: -86.7938 - val_accuracy: 0.5000\n",
            "Epoch 158/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -89.0689 - accuracy: 0.5000 - val_loss: -87.3459 - val_accuracy: 0.5000\n",
            "Epoch 159/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -89.6364 - accuracy: 0.5000 - val_loss: -87.9056 - val_accuracy: 0.5000\n",
            "Epoch 160/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -90.2082 - accuracy: 0.5000 - val_loss: -88.4578 - val_accuracy: 0.5000\n",
            "Epoch 161/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -90.7694 - accuracy: 0.5000 - val_loss: -88.9517 - val_accuracy: 0.5000\n",
            "Epoch 162/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -91.2725 - accuracy: 0.5000 - val_loss: -89.4085 - val_accuracy: 0.5000\n",
            "Epoch 163/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -91.7585 - accuracy: 0.5000 - val_loss: -90.0747 - val_accuracy: 0.5000\n",
            "Epoch 164/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -92.4211 - accuracy: 0.5000 - val_loss: -90.5390 - val_accuracy: 0.5000\n",
            "Epoch 165/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -92.9041 - accuracy: 0.5000 - val_loss: -91.0530 - val_accuracy: 0.5000\n",
            "Epoch 166/250\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -93.4204 - accuracy: 0.5000 - val_loss: -91.4603 - val_accuracy: 0.5000\n",
            "Epoch 167/250\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -93.8534 - accuracy: 0.5000 - val_loss: -92.0214 - val_accuracy: 0.5000\n",
            "Epoch 168/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -94.4209 - accuracy: 0.5000 - val_loss: -92.5226 - val_accuracy: 0.5000\n",
            "Epoch 169/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -94.9394 - accuracy: 0.5000 - val_loss: -93.0650 - val_accuracy: 0.5000\n",
            "Epoch 170/250\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -95.4937 - accuracy: 0.5000 - val_loss: -93.5766 - val_accuracy: 0.5000\n",
            "Epoch 171/250\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -96.0272 - accuracy: 0.5000 - val_loss: -94.1979 - val_accuracy: 0.5000\n",
            "Epoch 172/250\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -96.6537 - accuracy: 0.5000 - val_loss: -94.7541 - val_accuracy: 0.5000\n",
            "Epoch 173/250\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -97.2295 - accuracy: 0.5000 - val_loss: -95.3152 - val_accuracy: 0.5000\n",
            "Epoch 174/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -97.8008 - accuracy: 0.5000 - val_loss: -95.8689 - val_accuracy: 0.5000\n",
            "Epoch 175/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -98.3554 - accuracy: 0.5000 - val_loss: -96.3271 - val_accuracy: 0.5000\n",
            "Epoch 176/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -98.8229 - accuracy: 0.5000 - val_loss: -96.7344 - val_accuracy: 0.5000\n",
            "Epoch 177/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -99.2626 - accuracy: 0.5000 - val_loss: -97.3390 - val_accuracy: 0.5000\n",
            "Epoch 178/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -99.8858 - accuracy: 0.5000 - val_loss: -97.9942 - val_accuracy: 0.5000\n",
            "Epoch 179/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -100.5509 - accuracy: 0.5000 - val_loss: -98.6119 - val_accuracy: 0.5000\n",
            "Epoch 180/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -101.1674 - accuracy: 0.5000 - val_loss: -99.0192 - val_accuracy: 0.5000\n",
            "Epoch 181/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -101.6030 - accuracy: 0.5000 - val_loss: -99.6438 - val_accuracy: 0.5000\n",
            "Epoch 182/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -102.2428 - accuracy: 0.5000 - val_loss: -100.2373 - val_accuracy: 0.5000\n",
            "Epoch 183/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -102.8390 - accuracy: 0.5000 - val_loss: -100.6961 - val_accuracy: 0.5000\n",
            "Epoch 184/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -103.3126 - accuracy: 0.5000 - val_loss: -101.3010 - val_accuracy: 0.5000\n",
            "Epoch 185/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -103.9414 - accuracy: 0.5000 - val_loss: -101.9100 - val_accuracy: 0.5000\n",
            "Epoch 186/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -104.5522 - accuracy: 0.5000 - val_loss: -102.3688 - val_accuracy: 0.5000\n",
            "Epoch 187/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -105.0285 - accuracy: 0.5000 - val_loss: -102.8756 - val_accuracy: 0.5000\n",
            "Epoch 188/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -105.5562 - accuracy: 0.5000 - val_loss: -103.4752 - val_accuracy: 0.5000\n",
            "Epoch 189/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -106.1558 - accuracy: 0.5000 - val_loss: -103.9275 - val_accuracy: 0.5000\n",
            "Epoch 190/250\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -106.6300 - accuracy: 0.5000 - val_loss: -104.4824 - val_accuracy: 0.5000\n",
            "Epoch 191/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -107.2031 - accuracy: 0.5000 - val_loss: -105.0840 - val_accuracy: 0.5000\n",
            "Epoch 192/250\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -107.8153 - accuracy: 0.5000 - val_loss: -105.6484 - val_accuracy: 0.5000\n",
            "Epoch 193/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -108.3897 - accuracy: 0.5000 - val_loss: -106.1550 - val_accuracy: 0.5000\n",
            "Epoch 194/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -108.9118 - accuracy: 0.5000 - val_loss: -106.7067 - val_accuracy: 0.5000\n",
            "Epoch 195/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -109.4796 - accuracy: 0.5000 - val_loss: -107.2758 - val_accuracy: 0.5000\n",
            "Epoch 196/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -110.0689 - accuracy: 0.5000 - val_loss: -107.9186 - val_accuracy: 0.5000\n",
            "Epoch 197/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -110.7207 - accuracy: 0.5000 - val_loss: -108.4821 - val_accuracy: 0.5000\n",
            "Epoch 198/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -111.2964 - accuracy: 0.5000 - val_loss: -109.0307 - val_accuracy: 0.5000\n",
            "Epoch 199/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -111.8613 - accuracy: 0.5000 - val_loss: -109.5947 - val_accuracy: 0.5000\n",
            "Epoch 200/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -112.4401 - accuracy: 0.5000 - val_loss: -110.1692 - val_accuracy: 0.5000\n",
            "Epoch 201/250\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -113.0331 - accuracy: 0.5000 - val_loss: -110.7928 - val_accuracy: 0.5000\n",
            "Epoch 202/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -113.6568 - accuracy: 0.5000 - val_loss: -111.2485 - val_accuracy: 0.5000\n",
            "Epoch 203/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -114.1329 - accuracy: 0.5000 - val_loss: -111.7974 - val_accuracy: 0.5000\n",
            "Epoch 204/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -114.7010 - accuracy: 0.5000 - val_loss: -112.4135 - val_accuracy: 0.5000\n",
            "Epoch 205/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -115.3291 - accuracy: 0.5000 - val_loss: -112.9864 - val_accuracy: 0.5000\n",
            "Epoch 206/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -115.9081 - accuracy: 0.5000 - val_loss: -113.4898 - val_accuracy: 0.5000\n",
            "Epoch 207/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -116.4390 - accuracy: 0.5000 - val_loss: -114.1594 - val_accuracy: 0.5000\n",
            "Epoch 208/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -117.1186 - accuracy: 0.5000 - val_loss: -114.7342 - val_accuracy: 0.5000\n",
            "Epoch 209/250\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -117.7043 - accuracy: 0.5000 - val_loss: -115.2874 - val_accuracy: 0.5000\n",
            "Epoch 210/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -118.2674 - accuracy: 0.5000 - val_loss: -115.7851 - val_accuracy: 0.5000\n",
            "Epoch 211/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -118.7875 - accuracy: 0.5000 - val_loss: -116.3938 - val_accuracy: 0.5000\n",
            "Epoch 212/250\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -119.4083 - accuracy: 0.5000 - val_loss: -116.9647 - val_accuracy: 0.5000\n",
            "Epoch 213/250\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -119.9856 - accuracy: 0.5000 - val_loss: -117.4588 - val_accuracy: 0.5000\n",
            "Epoch 214/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -120.4986 - accuracy: 0.5000 - val_loss: -118.0175 - val_accuracy: 0.5000\n",
            "Epoch 215/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -121.0749 - accuracy: 0.5000 - val_loss: -118.6307 - val_accuracy: 0.5000\n",
            "Epoch 216/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -121.6953 - accuracy: 0.5000 - val_loss: -119.1759 - val_accuracy: 0.5000\n",
            "Epoch 217/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -122.2632 - accuracy: 0.5000 - val_loss: -119.7932 - val_accuracy: 0.5000\n",
            "Epoch 218/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -122.8937 - accuracy: 0.5000 - val_loss: -120.3977 - val_accuracy: 0.5000\n",
            "Epoch 219/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -123.5114 - accuracy: 0.5000 - val_loss: -120.9644 - val_accuracy: 0.5000\n",
            "Epoch 220/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -124.0817 - accuracy: 0.5000 - val_loss: -121.5409 - val_accuracy: 0.5000\n",
            "Epoch 221/250\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -124.6852 - accuracy: 0.5000 - val_loss: -122.1440 - val_accuracy: 0.5000\n",
            "Epoch 222/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -125.2846 - accuracy: 0.5000 - val_loss: -122.5514 - val_accuracy: 0.5000\n",
            "Epoch 223/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -125.7188 - accuracy: 0.5000 - val_loss: -123.1504 - val_accuracy: 0.5000\n",
            "Epoch 224/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -126.3254 - accuracy: 0.5000 - val_loss: -123.6640 - val_accuracy: 0.5000\n",
            "Epoch 225/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -126.8557 - accuracy: 0.5000 - val_loss: -124.2107 - val_accuracy: 0.5000\n",
            "Epoch 226/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -127.4267 - accuracy: 0.5000 - val_loss: -124.8751 - val_accuracy: 0.5000\n",
            "Epoch 227/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -128.0940 - accuracy: 0.5000 - val_loss: -125.3780 - val_accuracy: 0.5000\n",
            "Epoch 228/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -128.6109 - accuracy: 0.5000 - val_loss: -125.8935 - val_accuracy: 0.5000\n",
            "Epoch 229/250\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -129.1385 - accuracy: 0.5000 - val_loss: -126.4071 - val_accuracy: 0.5000\n",
            "Epoch 230/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -129.6681 - accuracy: 0.5000 - val_loss: -126.9496 - val_accuracy: 0.5000\n",
            "Epoch 231/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -130.2332 - accuracy: 0.5000 - val_loss: -127.6098 - val_accuracy: 0.5000\n",
            "Epoch 232/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -130.9059 - accuracy: 0.5000 - val_loss: -128.2125 - val_accuracy: 0.5000\n",
            "Epoch 233/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -131.5119 - accuracy: 0.5000 - val_loss: -128.7205 - val_accuracy: 0.5000\n",
            "Epoch 234/250\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -132.0381 - accuracy: 0.5000 - val_loss: -129.2708 - val_accuracy: 0.5000\n",
            "Epoch 235/250\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -132.6080 - accuracy: 0.5000 - val_loss: -129.8709 - val_accuracy: 0.5000\n",
            "Epoch 236/250\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -133.2094 - accuracy: 0.5000 - val_loss: -130.3266 - val_accuracy: 0.5000\n",
            "Epoch 237/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -133.6820 - accuracy: 0.5000 - val_loss: -130.8976 - val_accuracy: 0.5000\n",
            "Epoch 238/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -134.2707 - accuracy: 0.5000 - val_loss: -131.4731 - val_accuracy: 0.5000\n",
            "Epoch 239/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -134.8511 - accuracy: 0.5000 - val_loss: -131.9287 - val_accuracy: 0.5000\n",
            "Epoch 240/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -135.3305 - accuracy: 0.5000 - val_loss: -132.5231 - val_accuracy: 0.5000\n",
            "Epoch 241/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -135.9357 - accuracy: 0.5000 - val_loss: -133.0797 - val_accuracy: 0.5000\n",
            "Epoch 242/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -136.5119 - accuracy: 0.5000 - val_loss: -133.7004 - val_accuracy: 0.5000\n",
            "Epoch 243/250\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -137.1472 - accuracy: 0.5000 - val_loss: -134.3126 - val_accuracy: 0.5000\n",
            "Epoch 244/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -137.7616 - accuracy: 0.5000 - val_loss: -134.7710 - val_accuracy: 0.5000\n",
            "Epoch 245/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -138.2456 - accuracy: 0.5000 - val_loss: -135.3800 - val_accuracy: 0.5000\n",
            "Epoch 246/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -138.8538 - accuracy: 0.5000 - val_loss: -135.8261 - val_accuracy: 0.5000\n",
            "Epoch 247/250\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -139.3177 - accuracy: 0.5000 - val_loss: -136.3309 - val_accuracy: 0.5000\n",
            "Epoch 248/250\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -139.8353 - accuracy: 0.5000 - val_loss: -136.8388 - val_accuracy: 0.5000\n",
            "Epoch 249/250\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -140.3632 - accuracy: 0.5000 - val_loss: -137.4531 - val_accuracy: 0.5000\n",
            "Epoch 250/250\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -140.9887 - accuracy: 0.5000 - val_loss: -138.0072 - val_accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1JO_C5EsyX_",
        "outputId": "2ce0f055-c241-416c-c0cc-85425d6ce526"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 4ms/step - loss: -141.4383 - accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-141.43829345703125, 0.5]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_nn(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "aZqQFZKKyY8f",
        "outputId": "ae50f0b1-435e-46a8-f97e-15a8dc3ce447"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=557x373>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#APLICANDO MAIS CAMADAS AO MODELO\n",
        "model2 = keras.models.Sequential()\n",
        "model2.add(keras.layers.Input(shape=(2, ), name='camada_entrada'))\n",
        "model2.add(keras.layers.Dense(10, activation='sigmoid'))\n",
        "model2.add(keras.layers.Dense(1, activation='sigmoid', name='camada_saida'))"
      ],
      "metadata": {
        "id": "PsBC1iQHsycl"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_nn(model2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "p4u-I93hyRKQ",
        "outputId": "c6302075-7120-4429-b0d9-1c1ccec39275"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1720x629>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_transform[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-lracwXzZrf",
        "outputId": "faa92d07-7306-41f7-e716-c32b639ba385"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.47058824, 0.53333333],\n",
              "       [0.76470588, 0.83333333],\n",
              "       [0.61764706, 0.6       ],\n",
              "       [1.        , 0.93333333],\n",
              "       [0.47058824, 0.3       ]])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z30LAiqkzaSI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#APLICANDO MAISI CAMADAS AO MODELO E OUTRAS FUNÇÕES DE ATIVAÇÃO\n",
        "model2 = keras.models.Sequential()\n",
        "model2.add(keras.layers.Input(shape=(2, ), name='camada_entrada'))\n",
        "model2.add(keras.layers.Dense(10, activation='sigmoid'))\n",
        "model2.add(keras.layers.Dense(1, activation='sigmoid', name='camada_saida'))\n",
        "\n",
        "model2.compile(optimizer='Adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "historico2 = model2.fit(X_train_transform, y_train, validation_data=(X_test_transform, y_test), epochs=500, batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdraoAqFyRR_",
        "outputId": "a782d973-7c64-4842-8f48-bf82b314bf65"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "3/3 [==============================] - 2s 92ms/step - loss: -0.0486 - accuracy: 0.5000 - val_loss: -0.0541 - val_accuracy: 0.5000\n",
            "Epoch 2/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -0.0700 - accuracy: 0.5000 - val_loss: -0.0750 - val_accuracy: 0.5000\n",
            "Epoch 3/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.0914 - accuracy: 0.5000 - val_loss: -0.0958 - val_accuracy: 0.5000\n",
            "Epoch 4/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.1125 - accuracy: 0.5000 - val_loss: -0.1168 - val_accuracy: 0.5000\n",
            "Epoch 5/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.1336 - accuracy: 0.5000 - val_loss: -0.1377 - val_accuracy: 0.5000\n",
            "Epoch 6/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.1545 - accuracy: 0.5000 - val_loss: -0.1584 - val_accuracy: 0.5000\n",
            "Epoch 7/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -0.1761 - accuracy: 0.5000 - val_loss: -0.1790 - val_accuracy: 0.5000\n",
            "Epoch 8/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.1960 - accuracy: 0.5000 - val_loss: -0.1996 - val_accuracy: 0.5000\n",
            "Epoch 9/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -0.2173 - accuracy: 0.5000 - val_loss: -0.2196 - val_accuracy: 0.5000\n",
            "Epoch 10/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.2371 - accuracy: 0.5000 - val_loss: -0.2394 - val_accuracy: 0.5000\n",
            "Epoch 11/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.2568 - accuracy: 0.5000 - val_loss: -0.2589 - val_accuracy: 0.5000\n",
            "Epoch 12/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.2767 - accuracy: 0.5000 - val_loss: -0.2781 - val_accuracy: 0.5000\n",
            "Epoch 13/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.2967 - accuracy: 0.5000 - val_loss: -0.2974 - val_accuracy: 0.5000\n",
            "Epoch 14/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -0.3159 - accuracy: 0.5000 - val_loss: -0.3171 - val_accuracy: 0.5000\n",
            "Epoch 15/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -0.3365 - accuracy: 0.5000 - val_loss: -0.3369 - val_accuracy: 0.5000\n",
            "Epoch 16/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.3557 - accuracy: 0.5000 - val_loss: -0.3566 - val_accuracy: 0.5000\n",
            "Epoch 17/500\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -0.3759 - accuracy: 0.5000 - val_loss: -0.3761 - val_accuracy: 0.5000\n",
            "Epoch 18/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.3959 - accuracy: 0.5000 - val_loss: -0.3958 - val_accuracy: 0.5000\n",
            "Epoch 19/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.4155 - accuracy: 0.5000 - val_loss: -0.4155 - val_accuracy: 0.5000\n",
            "Epoch 20/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.4346 - accuracy: 0.5000 - val_loss: -0.4344 - val_accuracy: 0.5000\n",
            "Epoch 21/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.4537 - accuracy: 0.5000 - val_loss: -0.4526 - val_accuracy: 0.5000\n",
            "Epoch 22/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.4724 - accuracy: 0.5000 - val_loss: -0.4706 - val_accuracy: 0.5000\n",
            "Epoch 23/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.4910 - accuracy: 0.5000 - val_loss: -0.4887 - val_accuracy: 0.5000\n",
            "Epoch 24/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.5089 - accuracy: 0.5000 - val_loss: -0.5067 - val_accuracy: 0.5000\n",
            "Epoch 25/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.5272 - accuracy: 0.5000 - val_loss: -0.5245 - val_accuracy: 0.5000\n",
            "Epoch 26/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.5451 - accuracy: 0.5000 - val_loss: -0.5422 - val_accuracy: 0.5000\n",
            "Epoch 27/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.5628 - accuracy: 0.5000 - val_loss: -0.5598 - val_accuracy: 0.5000\n",
            "Epoch 28/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -0.5803 - accuracy: 0.5000 - val_loss: -0.5769 - val_accuracy: 0.5000\n",
            "Epoch 29/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.5973 - accuracy: 0.5000 - val_loss: -0.5937 - val_accuracy: 0.5000\n",
            "Epoch 30/500\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -0.6140 - accuracy: 0.5000 - val_loss: -0.6102 - val_accuracy: 0.5000\n",
            "Epoch 31/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.6309 - accuracy: 0.5000 - val_loss: -0.6264 - val_accuracy: 0.5000\n",
            "Epoch 32/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.6470 - accuracy: 0.5000 - val_loss: -0.6425 - val_accuracy: 0.5000\n",
            "Epoch 33/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.6633 - accuracy: 0.5000 - val_loss: -0.6585 - val_accuracy: 0.5000\n",
            "Epoch 34/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.6793 - accuracy: 0.5000 - val_loss: -0.6742 - val_accuracy: 0.5000\n",
            "Epoch 35/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.6957 - accuracy: 0.5000 - val_loss: -0.6899 - val_accuracy: 0.5000\n",
            "Epoch 36/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.7117 - accuracy: 0.5000 - val_loss: -0.7061 - val_accuracy: 0.5000\n",
            "Epoch 37/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.7281 - accuracy: 0.5000 - val_loss: -0.7225 - val_accuracy: 0.5000\n",
            "Epoch 38/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.7446 - accuracy: 0.5000 - val_loss: -0.7391 - val_accuracy: 0.5000\n",
            "Epoch 39/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.7619 - accuracy: 0.5000 - val_loss: -0.7557 - val_accuracy: 0.5000\n",
            "Epoch 40/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -0.7785 - accuracy: 0.5000 - val_loss: -0.7727 - val_accuracy: 0.5000\n",
            "Epoch 41/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.7955 - accuracy: 0.5000 - val_loss: -0.7899 - val_accuracy: 0.5000\n",
            "Epoch 42/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.8130 - accuracy: 0.5000 - val_loss: -0.8070 - val_accuracy: 0.5000\n",
            "Epoch 43/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.8300 - accuracy: 0.5000 - val_loss: -0.8239 - val_accuracy: 0.5000\n",
            "Epoch 44/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.8470 - accuracy: 0.5000 - val_loss: -0.8407 - val_accuracy: 0.5000\n",
            "Epoch 45/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.8636 - accuracy: 0.5000 - val_loss: -0.8571 - val_accuracy: 0.5000\n",
            "Epoch 46/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -0.8802 - accuracy: 0.5000 - val_loss: -0.8734 - val_accuracy: 0.5000\n",
            "Epoch 47/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.8968 - accuracy: 0.5000 - val_loss: -0.8890 - val_accuracy: 0.5000\n",
            "Epoch 48/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -0.9122 - accuracy: 0.5000 - val_loss: -0.9045 - val_accuracy: 0.5000\n",
            "Epoch 49/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -0.9281 - accuracy: 0.5000 - val_loss: -0.9195 - val_accuracy: 0.5000\n",
            "Epoch 50/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.9432 - accuracy: 0.5000 - val_loss: -0.9345 - val_accuracy: 0.5000\n",
            "Epoch 51/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.9585 - accuracy: 0.5000 - val_loss: -0.9494 - val_accuracy: 0.5000\n",
            "Epoch 52/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -0.9737 - accuracy: 0.5000 - val_loss: -0.9643 - val_accuracy: 0.5000\n",
            "Epoch 53/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -0.9890 - accuracy: 0.5000 - val_loss: -0.9796 - val_accuracy: 0.5000\n",
            "Epoch 54/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.0039 - accuracy: 0.5000 - val_loss: -0.9951 - val_accuracy: 0.5000\n",
            "Epoch 55/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -1.0198 - accuracy: 0.5000 - val_loss: -1.0104 - val_accuracy: 0.5000\n",
            "Epoch 56/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -1.0352 - accuracy: 0.5000 - val_loss: -1.0256 - val_accuracy: 0.5000\n",
            "Epoch 57/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.0500 - accuracy: 0.5000 - val_loss: -1.0406 - val_accuracy: 0.5000\n",
            "Epoch 58/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.0652 - accuracy: 0.5000 - val_loss: -1.0547 - val_accuracy: 0.5000\n",
            "Epoch 59/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.0790 - accuracy: 0.5000 - val_loss: -1.0685 - val_accuracy: 0.5000\n",
            "Epoch 60/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.0935 - accuracy: 0.5000 - val_loss: -1.0821 - val_accuracy: 0.5000\n",
            "Epoch 61/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -1.1078 - accuracy: 0.5000 - val_loss: -1.0962 - val_accuracy: 0.5000\n",
            "Epoch 62/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -1.1222 - accuracy: 0.5000 - val_loss: -1.1108 - val_accuracy: 0.5000\n",
            "Epoch 63/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -1.1362 - accuracy: 0.5000 - val_loss: -1.1259 - val_accuracy: 0.5000\n",
            "Epoch 64/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -1.1521 - accuracy: 0.5000 - val_loss: -1.1404 - val_accuracy: 0.5000\n",
            "Epoch 65/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -1.1665 - accuracy: 0.5000 - val_loss: -1.1554 - val_accuracy: 0.5000\n",
            "Epoch 66/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -1.1819 - accuracy: 0.5000 - val_loss: -1.1704 - val_accuracy: 0.5000\n",
            "Epoch 67/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -1.1963 - accuracy: 0.5000 - val_loss: -1.1854 - val_accuracy: 0.5000\n",
            "Epoch 68/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: -1.2119 - accuracy: 0.5000 - val_loss: -1.1999 - val_accuracy: 0.5000\n",
            "Epoch 69/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -1.2260 - accuracy: 0.5000 - val_loss: -1.2142 - val_accuracy: 0.5000\n",
            "Epoch 70/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.2400 - accuracy: 0.5000 - val_loss: -1.2284 - val_accuracy: 0.5000\n",
            "Epoch 71/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -1.2558 - accuracy: 0.5000 - val_loss: -1.2428 - val_accuracy: 0.5000\n",
            "Epoch 72/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: -1.2700 - accuracy: 0.5000 - val_loss: -1.2578 - val_accuracy: 0.5000\n",
            "Epoch 73/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: -1.2857 - accuracy: 0.5000 - val_loss: -1.2728 - val_accuracy: 0.5000\n",
            "Epoch 74/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -1.3003 - accuracy: 0.5000 - val_loss: -1.2880 - val_accuracy: 0.5000\n",
            "Epoch 75/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -1.3161 - accuracy: 0.5000 - val_loss: -1.3029 - val_accuracy: 0.5000\n",
            "Epoch 76/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -1.3309 - accuracy: 0.5000 - val_loss: -1.3177 - val_accuracy: 0.5000\n",
            "Epoch 77/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.3455 - accuracy: 0.5000 - val_loss: -1.3324 - val_accuracy: 0.5000\n",
            "Epoch 78/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.3604 - accuracy: 0.5000 - val_loss: -1.3471 - val_accuracy: 0.5000\n",
            "Epoch 79/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -1.3749 - accuracy: 0.5000 - val_loss: -1.3621 - val_accuracy: 0.5000\n",
            "Epoch 80/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.3909 - accuracy: 0.5000 - val_loss: -1.3769 - val_accuracy: 0.5000\n",
            "Epoch 81/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -1.4056 - accuracy: 0.5000 - val_loss: -1.3923 - val_accuracy: 0.5000\n",
            "Epoch 82/500\n",
            "3/3 [==============================] - 0s 44ms/step - loss: -1.4213 - accuracy: 0.5000 - val_loss: -1.4073 - val_accuracy: 0.5000\n",
            "Epoch 83/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -1.4364 - accuracy: 0.5000 - val_loss: -1.4224 - val_accuracy: 0.5000\n",
            "Epoch 84/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -1.4514 - accuracy: 0.5000 - val_loss: -1.4374 - val_accuracy: 0.5000\n",
            "Epoch 85/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.4669 - accuracy: 0.5000 - val_loss: -1.4520 - val_accuracy: 0.5000\n",
            "Epoch 86/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -1.4815 - accuracy: 0.5000 - val_loss: -1.4667 - val_accuracy: 0.5000\n",
            "Epoch 87/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -1.4961 - accuracy: 0.5000 - val_loss: -1.4816 - val_accuracy: 0.5000\n",
            "Epoch 88/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -1.5111 - accuracy: 0.5000 - val_loss: -1.4961 - val_accuracy: 0.5000\n",
            "Epoch 89/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: -1.5255 - accuracy: 0.5000 - val_loss: -1.5104 - val_accuracy: 0.5000\n",
            "Epoch 90/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -1.5405 - accuracy: 0.5000 - val_loss: -1.5250 - val_accuracy: 0.5000\n",
            "Epoch 91/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -1.5549 - accuracy: 0.5000 - val_loss: -1.5401 - val_accuracy: 0.5000\n",
            "Epoch 92/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.5699 - accuracy: 0.5000 - val_loss: -1.5549 - val_accuracy: 0.5000\n",
            "Epoch 93/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: -1.5851 - accuracy: 0.5000 - val_loss: -1.5693 - val_accuracy: 0.5000\n",
            "Epoch 94/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -1.5997 - accuracy: 0.5000 - val_loss: -1.5839 - val_accuracy: 0.5000\n",
            "Epoch 95/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -1.6142 - accuracy: 0.5000 - val_loss: -1.5981 - val_accuracy: 0.5000\n",
            "Epoch 96/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: -1.6289 - accuracy: 0.5000 - val_loss: -1.6122 - val_accuracy: 0.5000\n",
            "Epoch 97/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -1.6427 - accuracy: 0.5000 - val_loss: -1.6264 - val_accuracy: 0.5000\n",
            "Epoch 98/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -1.6570 - accuracy: 0.5000 - val_loss: -1.6406 - val_accuracy: 0.5000\n",
            "Epoch 99/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -1.6714 - accuracy: 0.5000 - val_loss: -1.6548 - val_accuracy: 0.5000\n",
            "Epoch 100/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -1.6856 - accuracy: 0.5000 - val_loss: -1.6690 - val_accuracy: 0.5000\n",
            "Epoch 101/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -1.6999 - accuracy: 0.5000 - val_loss: -1.6828 - val_accuracy: 0.5000\n",
            "Epoch 102/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.7138 - accuracy: 0.5000 - val_loss: -1.6966 - val_accuracy: 0.5000\n",
            "Epoch 103/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -1.7278 - accuracy: 0.5000 - val_loss: -1.7106 - val_accuracy: 0.5000\n",
            "Epoch 104/500\n",
            "3/3 [==============================] - 0s 43ms/step - loss: -1.7417 - accuracy: 0.5000 - val_loss: -1.7244 - val_accuracy: 0.5000\n",
            "Epoch 105/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -1.7556 - accuracy: 0.5000 - val_loss: -1.7377 - val_accuracy: 0.5000\n",
            "Epoch 106/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -1.7696 - accuracy: 0.5000 - val_loss: -1.7510 - val_accuracy: 0.5000\n",
            "Epoch 107/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -1.7826 - accuracy: 0.5000 - val_loss: -1.7647 - val_accuracy: 0.5000\n",
            "Epoch 108/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -1.7966 - accuracy: 0.5000 - val_loss: -1.7782 - val_accuracy: 0.5000\n",
            "Epoch 109/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -1.8104 - accuracy: 0.5000 - val_loss: -1.7917 - val_accuracy: 0.5000\n",
            "Epoch 110/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: -1.8241 - accuracy: 0.5000 - val_loss: -1.8053 - val_accuracy: 0.5000\n",
            "Epoch 111/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.8373 - accuracy: 0.5000 - val_loss: -1.8196 - val_accuracy: 0.5000\n",
            "Epoch 112/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -1.8517 - accuracy: 0.5000 - val_loss: -1.8335 - val_accuracy: 0.5000\n",
            "Epoch 113/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.8659 - accuracy: 0.5000 - val_loss: -1.8470 - val_accuracy: 0.5000\n",
            "Epoch 114/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -1.8797 - accuracy: 0.5000 - val_loss: -1.8608 - val_accuracy: 0.5000\n",
            "Epoch 115/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -1.8942 - accuracy: 0.5000 - val_loss: -1.8746 - val_accuracy: 0.5000\n",
            "Epoch 116/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -1.9075 - accuracy: 0.5000 - val_loss: -1.8885 - val_accuracy: 0.5000\n",
            "Epoch 117/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.9217 - accuracy: 0.5000 - val_loss: -1.9026 - val_accuracy: 0.5000\n",
            "Epoch 118/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -1.9359 - accuracy: 0.5000 - val_loss: -1.9165 - val_accuracy: 0.5000\n",
            "Epoch 119/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -1.9498 - accuracy: 0.5000 - val_loss: -1.9308 - val_accuracy: 0.5000\n",
            "Epoch 120/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.9645 - accuracy: 0.5000 - val_loss: -1.9454 - val_accuracy: 0.5000\n",
            "Epoch 121/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -1.9796 - accuracy: 0.5000 - val_loss: -1.9599 - val_accuracy: 0.5000\n",
            "Epoch 122/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -1.9938 - accuracy: 0.5000 - val_loss: -1.9745 - val_accuracy: 0.5000\n",
            "Epoch 123/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.0087 - accuracy: 0.5000 - val_loss: -1.9890 - val_accuracy: 0.5000\n",
            "Epoch 124/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.0233 - accuracy: 0.5000 - val_loss: -2.0035 - val_accuracy: 0.5000\n",
            "Epoch 125/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.0378 - accuracy: 0.5000 - val_loss: -2.0178 - val_accuracy: 0.5000\n",
            "Epoch 126/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.0518 - accuracy: 0.5000 - val_loss: -2.0318 - val_accuracy: 0.5000\n",
            "Epoch 127/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.0665 - accuracy: 0.5000 - val_loss: -2.0458 - val_accuracy: 0.5000\n",
            "Epoch 128/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.0806 - accuracy: 0.5000 - val_loss: -2.0599 - val_accuracy: 0.5000\n",
            "Epoch 129/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.0949 - accuracy: 0.5000 - val_loss: -2.0738 - val_accuracy: 0.5000\n",
            "Epoch 130/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -2.1088 - accuracy: 0.5000 - val_loss: -2.0882 - val_accuracy: 0.5000\n",
            "Epoch 131/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.1233 - accuracy: 0.5000 - val_loss: -2.1030 - val_accuracy: 0.5000\n",
            "Epoch 132/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.1380 - accuracy: 0.5000 - val_loss: -2.1173 - val_accuracy: 0.5000\n",
            "Epoch 133/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.1524 - accuracy: 0.5000 - val_loss: -2.1313 - val_accuracy: 0.5000\n",
            "Epoch 134/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.1669 - accuracy: 0.5000 - val_loss: -2.1451 - val_accuracy: 0.5000\n",
            "Epoch 135/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.1808 - accuracy: 0.5000 - val_loss: -2.1590 - val_accuracy: 0.5000\n",
            "Epoch 136/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.1937 - accuracy: 0.5000 - val_loss: -2.1726 - val_accuracy: 0.5000\n",
            "Epoch 137/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.2072 - accuracy: 0.5000 - val_loss: -2.1856 - val_accuracy: 0.5000\n",
            "Epoch 138/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.2212 - accuracy: 0.5000 - val_loss: -2.1982 - val_accuracy: 0.5000\n",
            "Epoch 139/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.2331 - accuracy: 0.5000 - val_loss: -2.2116 - val_accuracy: 0.5000\n",
            "Epoch 140/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.2470 - accuracy: 0.5000 - val_loss: -2.2250 - val_accuracy: 0.5000\n",
            "Epoch 141/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.2602 - accuracy: 0.5000 - val_loss: -2.2382 - val_accuracy: 0.5000\n",
            "Epoch 142/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -2.2738 - accuracy: 0.5000 - val_loss: -2.2511 - val_accuracy: 0.5000\n",
            "Epoch 143/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.2865 - accuracy: 0.5000 - val_loss: -2.2639 - val_accuracy: 0.5000\n",
            "Epoch 144/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -2.2993 - accuracy: 0.5000 - val_loss: -2.2765 - val_accuracy: 0.5000\n",
            "Epoch 145/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.3129 - accuracy: 0.5000 - val_loss: -2.2893 - val_accuracy: 0.5000\n",
            "Epoch 146/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.3258 - accuracy: 0.5000 - val_loss: -2.3030 - val_accuracy: 0.5000\n",
            "Epoch 147/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.3386 - accuracy: 0.5000 - val_loss: -2.3166 - val_accuracy: 0.5000\n",
            "Epoch 148/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.3530 - accuracy: 0.5000 - val_loss: -2.3294 - val_accuracy: 0.5000\n",
            "Epoch 149/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.3657 - accuracy: 0.5000 - val_loss: -2.3424 - val_accuracy: 0.5000\n",
            "Epoch 150/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.3786 - accuracy: 0.5000 - val_loss: -2.3555 - val_accuracy: 0.5000\n",
            "Epoch 151/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -2.3917 - accuracy: 0.5000 - val_loss: -2.3684 - val_accuracy: 0.5000\n",
            "Epoch 152/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.4048 - accuracy: 0.5000 - val_loss: -2.3815 - val_accuracy: 0.5000\n",
            "Epoch 153/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.4188 - accuracy: 0.5000 - val_loss: -2.3951 - val_accuracy: 0.5000\n",
            "Epoch 154/500\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -2.4319 - accuracy: 0.5000 - val_loss: -2.4091 - val_accuracy: 0.5000\n",
            "Epoch 155/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.4461 - accuracy: 0.5000 - val_loss: -2.4228 - val_accuracy: 0.5000\n",
            "Epoch 156/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.4603 - accuracy: 0.5000 - val_loss: -2.4362 - val_accuracy: 0.5000\n",
            "Epoch 157/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.4733 - accuracy: 0.5000 - val_loss: -2.4500 - val_accuracy: 0.5000\n",
            "Epoch 158/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.4871 - accuracy: 0.5000 - val_loss: -2.4639 - val_accuracy: 0.5000\n",
            "Epoch 159/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.5014 - accuracy: 0.5000 - val_loss: -2.4779 - val_accuracy: 0.5000\n",
            "Epoch 160/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -2.5157 - accuracy: 0.5000 - val_loss: -2.4918 - val_accuracy: 0.5000\n",
            "Epoch 161/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.5294 - accuracy: 0.5000 - val_loss: -2.5059 - val_accuracy: 0.5000\n",
            "Epoch 162/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.5438 - accuracy: 0.5000 - val_loss: -2.5201 - val_accuracy: 0.5000\n",
            "Epoch 163/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -2.5579 - accuracy: 0.5000 - val_loss: -2.5344 - val_accuracy: 0.5000\n",
            "Epoch 164/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.5721 - accuracy: 0.5000 - val_loss: -2.5486 - val_accuracy: 0.5000\n",
            "Epoch 165/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -2.5868 - accuracy: 0.5000 - val_loss: -2.5626 - val_accuracy: 0.5000\n",
            "Epoch 166/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.6008 - accuracy: 0.5000 - val_loss: -2.5769 - val_accuracy: 0.5000\n",
            "Epoch 167/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.6149 - accuracy: 0.5000 - val_loss: -2.5914 - val_accuracy: 0.5000\n",
            "Epoch 168/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.6293 - accuracy: 0.5000 - val_loss: -2.6053 - val_accuracy: 0.5000\n",
            "Epoch 169/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.6436 - accuracy: 0.5000 - val_loss: -2.6187 - val_accuracy: 0.5000\n",
            "Epoch 170/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.6568 - accuracy: 0.5000 - val_loss: -2.6321 - val_accuracy: 0.5000\n",
            "Epoch 171/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.6705 - accuracy: 0.5000 - val_loss: -2.6455 - val_accuracy: 0.5000\n",
            "Epoch 172/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -2.6839 - accuracy: 0.5000 - val_loss: -2.6590 - val_accuracy: 0.5000\n",
            "Epoch 173/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.6978 - accuracy: 0.5000 - val_loss: -2.6725 - val_accuracy: 0.5000\n",
            "Epoch 174/500\n",
            "3/3 [==============================] - 0s 16ms/step - loss: -2.7115 - accuracy: 0.5000 - val_loss: -2.6865 - val_accuracy: 0.5000\n",
            "Epoch 175/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.7257 - accuracy: 0.5000 - val_loss: -2.7013 - val_accuracy: 0.5000\n",
            "Epoch 176/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.7405 - accuracy: 0.5000 - val_loss: -2.7166 - val_accuracy: 0.5000\n",
            "Epoch 177/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.7559 - accuracy: 0.5000 - val_loss: -2.7318 - val_accuracy: 0.5000\n",
            "Epoch 178/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.7709 - accuracy: 0.5000 - val_loss: -2.7469 - val_accuracy: 0.5000\n",
            "Epoch 179/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.7856 - accuracy: 0.5000 - val_loss: -2.7619 - val_accuracy: 0.5000\n",
            "Epoch 180/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -2.8016 - accuracy: 0.5000 - val_loss: -2.7767 - val_accuracy: 0.5000\n",
            "Epoch 181/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.8157 - accuracy: 0.5000 - val_loss: -2.7917 - val_accuracy: 0.5000\n",
            "Epoch 182/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.8309 - accuracy: 0.5000 - val_loss: -2.8064 - val_accuracy: 0.5000\n",
            "Epoch 183/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.8460 - accuracy: 0.5000 - val_loss: -2.8209 - val_accuracy: 0.5000\n",
            "Epoch 184/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.8594 - accuracy: 0.5000 - val_loss: -2.8352 - val_accuracy: 0.5000\n",
            "Epoch 185/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.8747 - accuracy: 0.5000 - val_loss: -2.8487 - val_accuracy: 0.5000\n",
            "Epoch 186/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.8881 - accuracy: 0.5000 - val_loss: -2.8625 - val_accuracy: 0.5000\n",
            "Epoch 187/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.9019 - accuracy: 0.5000 - val_loss: -2.8765 - val_accuracy: 0.5000\n",
            "Epoch 188/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -2.9162 - accuracy: 0.5000 - val_loss: -2.8906 - val_accuracy: 0.5000\n",
            "Epoch 189/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.9303 - accuracy: 0.5000 - val_loss: -2.9051 - val_accuracy: 0.5000\n",
            "Epoch 190/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.9446 - accuracy: 0.5000 - val_loss: -2.9203 - val_accuracy: 0.5000\n",
            "Epoch 191/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -2.9600 - accuracy: 0.5000 - val_loss: -2.9350 - val_accuracy: 0.5000\n",
            "Epoch 192/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -2.9754 - accuracy: 0.5000 - val_loss: -2.9496 - val_accuracy: 0.5000\n",
            "Epoch 193/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -2.9896 - accuracy: 0.5000 - val_loss: -2.9643 - val_accuracy: 0.5000\n",
            "Epoch 194/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.0038 - accuracy: 0.5000 - val_loss: -2.9792 - val_accuracy: 0.5000\n",
            "Epoch 195/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.0192 - accuracy: 0.5000 - val_loss: -2.9938 - val_accuracy: 0.5000\n",
            "Epoch 196/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.0341 - accuracy: 0.5000 - val_loss: -3.0086 - val_accuracy: 0.5000\n",
            "Epoch 197/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.0486 - accuracy: 0.5000 - val_loss: -3.0233 - val_accuracy: 0.5000\n",
            "Epoch 198/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.0631 - accuracy: 0.5000 - val_loss: -3.0376 - val_accuracy: 0.5000\n",
            "Epoch 199/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.0779 - accuracy: 0.5000 - val_loss: -3.0519 - val_accuracy: 0.5000\n",
            "Epoch 200/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.0918 - accuracy: 0.5000 - val_loss: -3.0668 - val_accuracy: 0.5000\n",
            "Epoch 201/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.1073 - accuracy: 0.5000 - val_loss: -3.0816 - val_accuracy: 0.5000\n",
            "Epoch 202/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.1221 - accuracy: 0.5000 - val_loss: -3.0963 - val_accuracy: 0.5000\n",
            "Epoch 203/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -3.1366 - accuracy: 0.5000 - val_loss: -3.1110 - val_accuracy: 0.5000\n",
            "Epoch 204/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -3.1515 - accuracy: 0.5000 - val_loss: -3.1257 - val_accuracy: 0.5000\n",
            "Epoch 205/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.1656 - accuracy: 0.5000 - val_loss: -3.1402 - val_accuracy: 0.5000\n",
            "Epoch 206/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -3.1803 - accuracy: 0.5000 - val_loss: -3.1544 - val_accuracy: 0.5000\n",
            "Epoch 207/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.1945 - accuracy: 0.5000 - val_loss: -3.1687 - val_accuracy: 0.5000\n",
            "Epoch 208/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.2085 - accuracy: 0.5000 - val_loss: -3.1827 - val_accuracy: 0.5000\n",
            "Epoch 209/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.2229 - accuracy: 0.5000 - val_loss: -3.1967 - val_accuracy: 0.5000\n",
            "Epoch 210/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.2377 - accuracy: 0.5000 - val_loss: -3.2106 - val_accuracy: 0.5000\n",
            "Epoch 211/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.2514 - accuracy: 0.5000 - val_loss: -3.2252 - val_accuracy: 0.5000\n",
            "Epoch 212/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.2657 - accuracy: 0.5000 - val_loss: -3.2399 - val_accuracy: 0.5000\n",
            "Epoch 213/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.2813 - accuracy: 0.5000 - val_loss: -3.2541 - val_accuracy: 0.5000\n",
            "Epoch 214/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -3.2958 - accuracy: 0.5000 - val_loss: -3.2692 - val_accuracy: 0.5000\n",
            "Epoch 215/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.3100 - accuracy: 0.5000 - val_loss: -3.2849 - val_accuracy: 0.5000\n",
            "Epoch 216/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.3266 - accuracy: 0.5000 - val_loss: -3.3005 - val_accuracy: 0.5000\n",
            "Epoch 217/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.3416 - accuracy: 0.5000 - val_loss: -3.3167 - val_accuracy: 0.5000\n",
            "Epoch 218/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.3582 - accuracy: 0.5000 - val_loss: -3.3325 - val_accuracy: 0.5000\n",
            "Epoch 219/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.3733 - accuracy: 0.5000 - val_loss: -3.3484 - val_accuracy: 0.5000\n",
            "Epoch 220/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.3894 - accuracy: 0.5000 - val_loss: -3.3639 - val_accuracy: 0.5000\n",
            "Epoch 221/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.4049 - accuracy: 0.5000 - val_loss: -3.3792 - val_accuracy: 0.5000\n",
            "Epoch 222/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.4203 - accuracy: 0.5000 - val_loss: -3.3944 - val_accuracy: 0.5000\n",
            "Epoch 223/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.4361 - accuracy: 0.5000 - val_loss: -3.4094 - val_accuracy: 0.5000\n",
            "Epoch 224/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -3.4506 - accuracy: 0.5000 - val_loss: -3.4243 - val_accuracy: 0.5000\n",
            "Epoch 225/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.4648 - accuracy: 0.5000 - val_loss: -3.4388 - val_accuracy: 0.5000\n",
            "Epoch 226/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -3.4801 - accuracy: 0.5000 - val_loss: -3.4529 - val_accuracy: 0.5000\n",
            "Epoch 227/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.4942 - accuracy: 0.5000 - val_loss: -3.4675 - val_accuracy: 0.5000\n",
            "Epoch 228/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.5088 - accuracy: 0.5000 - val_loss: -3.4822 - val_accuracy: 0.5000\n",
            "Epoch 229/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.5235 - accuracy: 0.5000 - val_loss: -3.4970 - val_accuracy: 0.5000\n",
            "Epoch 230/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.5384 - accuracy: 0.5000 - val_loss: -3.5122 - val_accuracy: 0.5000\n",
            "Epoch 231/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.5532 - accuracy: 0.5000 - val_loss: -3.5275 - val_accuracy: 0.5000\n",
            "Epoch 232/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -3.5691 - accuracy: 0.5000 - val_loss: -3.5424 - val_accuracy: 0.5000\n",
            "Epoch 233/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.5832 - accuracy: 0.5000 - val_loss: -3.5573 - val_accuracy: 0.5000\n",
            "Epoch 234/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -3.5992 - accuracy: 0.5000 - val_loss: -3.5720 - val_accuracy: 0.5000\n",
            "Epoch 235/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.6132 - accuracy: 0.5000 - val_loss: -3.5874 - val_accuracy: 0.5000\n",
            "Epoch 236/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.6283 - accuracy: 0.5000 - val_loss: -3.6026 - val_accuracy: 0.5000\n",
            "Epoch 237/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.6440 - accuracy: 0.5000 - val_loss: -3.6176 - val_accuracy: 0.5000\n",
            "Epoch 238/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.6589 - accuracy: 0.5000 - val_loss: -3.6329 - val_accuracy: 0.5000\n",
            "Epoch 239/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.6748 - accuracy: 0.5000 - val_loss: -3.6483 - val_accuracy: 0.5000\n",
            "Epoch 240/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.6896 - accuracy: 0.5000 - val_loss: -3.6636 - val_accuracy: 0.5000\n",
            "Epoch 241/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.7044 - accuracy: 0.5000 - val_loss: -3.6784 - val_accuracy: 0.5000\n",
            "Epoch 242/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.7201 - accuracy: 0.5000 - val_loss: -3.6928 - val_accuracy: 0.5000\n",
            "Epoch 243/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -3.7346 - accuracy: 0.5000 - val_loss: -3.7077 - val_accuracy: 0.5000\n",
            "Epoch 244/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.7487 - accuracy: 0.5000 - val_loss: -3.7230 - val_accuracy: 0.5000\n",
            "Epoch 245/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -3.7646 - accuracy: 0.5000 - val_loss: -3.7383 - val_accuracy: 0.5000\n",
            "Epoch 246/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.7801 - accuracy: 0.5000 - val_loss: -3.7537 - val_accuracy: 0.5000\n",
            "Epoch 247/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.7951 - accuracy: 0.5000 - val_loss: -3.7689 - val_accuracy: 0.5000\n",
            "Epoch 248/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.8105 - accuracy: 0.5000 - val_loss: -3.7834 - val_accuracy: 0.5000\n",
            "Epoch 249/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.8243 - accuracy: 0.5000 - val_loss: -3.7978 - val_accuracy: 0.5000\n",
            "Epoch 250/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.8391 - accuracy: 0.5000 - val_loss: -3.8120 - val_accuracy: 0.5000\n",
            "Epoch 251/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -3.8535 - accuracy: 0.5000 - val_loss: -3.8264 - val_accuracy: 0.5000\n",
            "Epoch 252/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.8678 - accuracy: 0.5000 - val_loss: -3.8405 - val_accuracy: 0.5000\n",
            "Epoch 253/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -3.8823 - accuracy: 0.5000 - val_loss: -3.8543 - val_accuracy: 0.5000\n",
            "Epoch 254/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -3.8962 - accuracy: 0.5000 - val_loss: -3.8687 - val_accuracy: 0.5000\n",
            "Epoch 255/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.9108 - accuracy: 0.5000 - val_loss: -3.8838 - val_accuracy: 0.5000\n",
            "Epoch 256/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -3.9252 - accuracy: 0.5000 - val_loss: -3.8996 - val_accuracy: 0.5000\n",
            "Epoch 257/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -3.9417 - accuracy: 0.5000 - val_loss: -3.9150 - val_accuracy: 0.5000\n",
            "Epoch 258/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.9564 - accuracy: 0.5000 - val_loss: -3.9306 - val_accuracy: 0.5000\n",
            "Epoch 259/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -3.9724 - accuracy: 0.5000 - val_loss: -3.9461 - val_accuracy: 0.5000\n",
            "Epoch 260/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -3.9875 - accuracy: 0.5000 - val_loss: -3.9614 - val_accuracy: 0.5000\n",
            "Epoch 261/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -4.0030 - accuracy: 0.5000 - val_loss: -3.9763 - val_accuracy: 0.5000\n",
            "Epoch 262/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.0180 - accuracy: 0.5000 - val_loss: -3.9910 - val_accuracy: 0.5000\n",
            "Epoch 263/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -4.0328 - accuracy: 0.5000 - val_loss: -4.0055 - val_accuracy: 0.5000\n",
            "Epoch 264/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.0468 - accuracy: 0.5000 - val_loss: -4.0203 - val_accuracy: 0.5000\n",
            "Epoch 265/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.0619 - accuracy: 0.5000 - val_loss: -4.0348 - val_accuracy: 0.5000\n",
            "Epoch 266/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.0762 - accuracy: 0.5000 - val_loss: -4.0496 - val_accuracy: 0.5000\n",
            "Epoch 267/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -4.0911 - accuracy: 0.5000 - val_loss: -4.0647 - val_accuracy: 0.5000\n",
            "Epoch 268/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -4.1072 - accuracy: 0.5000 - val_loss: -4.0800 - val_accuracy: 0.5000\n",
            "Epoch 269/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.1213 - accuracy: 0.5000 - val_loss: -4.0959 - val_accuracy: 0.5000\n",
            "Epoch 270/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.1375 - accuracy: 0.5000 - val_loss: -4.1115 - val_accuracy: 0.5000\n",
            "Epoch 271/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.1535 - accuracy: 0.5000 - val_loss: -4.1270 - val_accuracy: 0.5000\n",
            "Epoch 272/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.1690 - accuracy: 0.5000 - val_loss: -4.1430 - val_accuracy: 0.5000\n",
            "Epoch 273/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.1843 - accuracy: 0.5000 - val_loss: -4.1592 - val_accuracy: 0.5000\n",
            "Epoch 274/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -4.2016 - accuracy: 0.5000 - val_loss: -4.1751 - val_accuracy: 0.5000\n",
            "Epoch 275/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -4.2168 - accuracy: 0.5000 - val_loss: -4.1912 - val_accuracy: 0.5000\n",
            "Epoch 276/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -4.2333 - accuracy: 0.5000 - val_loss: -4.2068 - val_accuracy: 0.5000\n",
            "Epoch 277/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.2494 - accuracy: 0.5000 - val_loss: -4.2225 - val_accuracy: 0.5000\n",
            "Epoch 278/500\n",
            "3/3 [==============================] - 0s 17ms/step - loss: -4.2646 - accuracy: 0.5000 - val_loss: -4.2385 - val_accuracy: 0.5000\n",
            "Epoch 279/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.2802 - accuracy: 0.5000 - val_loss: -4.2547 - val_accuracy: 0.5000\n",
            "Epoch 280/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -4.2971 - accuracy: 0.5000 - val_loss: -4.2706 - val_accuracy: 0.5000\n",
            "Epoch 281/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.3123 - accuracy: 0.5000 - val_loss: -4.2868 - val_accuracy: 0.5000\n",
            "Epoch 282/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.3282 - accuracy: 0.5000 - val_loss: -4.3021 - val_accuracy: 0.5000\n",
            "Epoch 283/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.3437 - accuracy: 0.5000 - val_loss: -4.3170 - val_accuracy: 0.5000\n",
            "Epoch 284/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.3592 - accuracy: 0.5000 - val_loss: -4.3322 - val_accuracy: 0.5000\n",
            "Epoch 285/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.3745 - accuracy: 0.5000 - val_loss: -4.3484 - val_accuracy: 0.5000\n",
            "Epoch 286/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.3910 - accuracy: 0.5000 - val_loss: -4.3650 - val_accuracy: 0.5000\n",
            "Epoch 287/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.4075 - accuracy: 0.5000 - val_loss: -4.3819 - val_accuracy: 0.5000\n",
            "Epoch 288/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.4233 - accuracy: 0.5000 - val_loss: -4.3991 - val_accuracy: 0.5000\n",
            "Epoch 289/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.4416 - accuracy: 0.5000 - val_loss: -4.4160 - val_accuracy: 0.5000\n",
            "Epoch 290/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.4581 - accuracy: 0.5000 - val_loss: -4.4333 - val_accuracy: 0.5000\n",
            "Epoch 291/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -4.4749 - accuracy: 0.5000 - val_loss: -4.4502 - val_accuracy: 0.5000\n",
            "Epoch 292/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.4928 - accuracy: 0.5000 - val_loss: -4.4664 - val_accuracy: 0.5000\n",
            "Epoch 293/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.5081 - accuracy: 0.5000 - val_loss: -4.4831 - val_accuracy: 0.5000\n",
            "Epoch 294/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.5246 - accuracy: 0.5000 - val_loss: -4.4993 - val_accuracy: 0.5000\n",
            "Epoch 295/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -4.5409 - accuracy: 0.5000 - val_loss: -4.5147 - val_accuracy: 0.5000\n",
            "Epoch 296/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.5560 - accuracy: 0.5000 - val_loss: -4.5300 - val_accuracy: 0.5000\n",
            "Epoch 297/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.5720 - accuracy: 0.5000 - val_loss: -4.5456 - val_accuracy: 0.5000\n",
            "Epoch 298/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.5878 - accuracy: 0.5000 - val_loss: -4.5617 - val_accuracy: 0.5000\n",
            "Epoch 299/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.6036 - accuracy: 0.5000 - val_loss: -4.5779 - val_accuracy: 0.5000\n",
            "Epoch 300/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.6200 - accuracy: 0.5000 - val_loss: -4.5941 - val_accuracy: 0.5000\n",
            "Epoch 301/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -4.6360 - accuracy: 0.5000 - val_loss: -4.6101 - val_accuracy: 0.5000\n",
            "Epoch 302/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.6516 - accuracy: 0.5000 - val_loss: -4.6262 - val_accuracy: 0.5000\n",
            "Epoch 303/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.6683 - accuracy: 0.5000 - val_loss: -4.6418 - val_accuracy: 0.5000\n",
            "Epoch 304/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -4.6833 - accuracy: 0.5000 - val_loss: -4.6581 - val_accuracy: 0.5000\n",
            "Epoch 305/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.7002 - accuracy: 0.5000 - val_loss: -4.6742 - val_accuracy: 0.5000\n",
            "Epoch 306/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.7162 - accuracy: 0.5000 - val_loss: -4.6903 - val_accuracy: 0.5000\n",
            "Epoch 307/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.7324 - accuracy: 0.5000 - val_loss: -4.7067 - val_accuracy: 0.5000\n",
            "Epoch 308/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -4.7481 - accuracy: 0.5000 - val_loss: -4.7229 - val_accuracy: 0.5000\n",
            "Epoch 309/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -4.7645 - accuracy: 0.5000 - val_loss: -4.7387 - val_accuracy: 0.5000\n",
            "Epoch 310/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -4.7803 - accuracy: 0.5000 - val_loss: -4.7544 - val_accuracy: 0.5000\n",
            "Epoch 311/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -4.7959 - accuracy: 0.5000 - val_loss: -4.7700 - val_accuracy: 0.5000\n",
            "Epoch 312/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -4.8116 - accuracy: 0.5000 - val_loss: -4.7858 - val_accuracy: 0.5000\n",
            "Epoch 313/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: -4.8270 - accuracy: 0.5000 - val_loss: -4.8019 - val_accuracy: 0.5000\n",
            "Epoch 314/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -4.8446 - accuracy: 0.5000 - val_loss: -4.8179 - val_accuracy: 0.5000\n",
            "Epoch 315/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -4.8593 - accuracy: 0.5000 - val_loss: -4.8342 - val_accuracy: 0.5000\n",
            "Epoch 316/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: -4.8758 - accuracy: 0.5000 - val_loss: -4.8500 - val_accuracy: 0.5000\n",
            "Epoch 317/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -4.8911 - accuracy: 0.5000 - val_loss: -4.8656 - val_accuracy: 0.5000\n",
            "Epoch 318/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -4.9069 - accuracy: 0.5000 - val_loss: -4.8807 - val_accuracy: 0.5000\n",
            "Epoch 319/500\n",
            "3/3 [==============================] - 0s 43ms/step - loss: -4.9225 - accuracy: 0.5000 - val_loss: -4.8954 - val_accuracy: 0.5000\n",
            "Epoch 320/500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: -4.9363 - accuracy: 0.5000 - val_loss: -4.9106 - val_accuracy: 0.5000\n",
            "Epoch 321/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -4.9523 - accuracy: 0.5000 - val_loss: -4.9256 - val_accuracy: 0.5000\n",
            "Epoch 322/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -4.9669 - accuracy: 0.5000 - val_loss: -4.9409 - val_accuracy: 0.5000\n",
            "Epoch 323/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: -4.9824 - accuracy: 0.5000 - val_loss: -4.9560 - val_accuracy: 0.5000\n",
            "Epoch 324/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -4.9979 - accuracy: 0.5000 - val_loss: -4.9716 - val_accuracy: 0.5000\n",
            "Epoch 325/500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: -5.0128 - accuracy: 0.5000 - val_loss: -4.9875 - val_accuracy: 0.5000\n",
            "Epoch 326/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -5.0291 - accuracy: 0.5000 - val_loss: -5.0033 - val_accuracy: 0.5000\n",
            "Epoch 327/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -5.0451 - accuracy: 0.5000 - val_loss: -5.0195 - val_accuracy: 0.5000\n",
            "Epoch 328/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -5.0617 - accuracy: 0.5000 - val_loss: -5.0361 - val_accuracy: 0.5000\n",
            "Epoch 329/500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: -5.0775 - accuracy: 0.5000 - val_loss: -5.0530 - val_accuracy: 0.5000\n",
            "Epoch 330/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -5.0950 - accuracy: 0.5000 - val_loss: -5.0697 - val_accuracy: 0.5000\n",
            "Epoch 331/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -5.1115 - accuracy: 0.5000 - val_loss: -5.0865 - val_accuracy: 0.5000\n",
            "Epoch 332/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -5.1278 - accuracy: 0.5000 - val_loss: -5.1032 - val_accuracy: 0.5000\n",
            "Epoch 333/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -5.1443 - accuracy: 0.5000 - val_loss: -5.1191 - val_accuracy: 0.5000\n",
            "Epoch 334/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -5.1605 - accuracy: 0.5000 - val_loss: -5.1343 - val_accuracy: 0.5000\n",
            "Epoch 335/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -5.1757 - accuracy: 0.5000 - val_loss: -5.1492 - val_accuracy: 0.5000\n",
            "Epoch 336/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -5.1903 - accuracy: 0.5000 - val_loss: -5.1645 - val_accuracy: 0.5000\n",
            "Epoch 337/500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: -5.2059 - accuracy: 0.5000 - val_loss: -5.1795 - val_accuracy: 0.5000\n",
            "Epoch 338/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -5.2208 - accuracy: 0.5000 - val_loss: -5.1947 - val_accuracy: 0.5000\n",
            "Epoch 339/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -5.2359 - accuracy: 0.5000 - val_loss: -5.2100 - val_accuracy: 0.5000\n",
            "Epoch 340/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -5.2516 - accuracy: 0.5000 - val_loss: -5.2256 - val_accuracy: 0.5000\n",
            "Epoch 341/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -5.2672 - accuracy: 0.5000 - val_loss: -5.2420 - val_accuracy: 0.5000\n",
            "Epoch 342/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -5.2833 - accuracy: 0.5000 - val_loss: -5.2587 - val_accuracy: 0.5000\n",
            "Epoch 343/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -5.3010 - accuracy: 0.5000 - val_loss: -5.2754 - val_accuracy: 0.5000\n",
            "Epoch 344/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -5.3169 - accuracy: 0.5000 - val_loss: -5.2925 - val_accuracy: 0.5000\n",
            "Epoch 345/500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: -5.3342 - accuracy: 0.5000 - val_loss: -5.3093 - val_accuracy: 0.5000\n",
            "Epoch 346/500\n",
            "3/3 [==============================] - 0s 133ms/step - loss: -5.3506 - accuracy: 0.5000 - val_loss: -5.3264 - val_accuracy: 0.5000\n",
            "Epoch 347/500\n",
            "3/3 [==============================] - 0s 161ms/step - loss: -5.3675 - accuracy: 0.5000 - val_loss: -5.3435 - val_accuracy: 0.5000\n",
            "Epoch 348/500\n",
            "3/3 [==============================] - 0s 139ms/step - loss: -5.3847 - accuracy: 0.5000 - val_loss: -5.3603 - val_accuracy: 0.5000\n",
            "Epoch 349/500\n",
            "3/3 [==============================] - 0s 140ms/step - loss: -5.4015 - accuracy: 0.5000 - val_loss: -5.3765 - val_accuracy: 0.5000\n",
            "Epoch 350/500\n",
            "3/3 [==============================] - 0s 53ms/step - loss: -5.4182 - accuracy: 0.5000 - val_loss: -5.3926 - val_accuracy: 0.5000\n",
            "Epoch 351/500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: -5.4334 - accuracy: 0.5000 - val_loss: -5.4091 - val_accuracy: 0.5000\n",
            "Epoch 352/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: -5.4507 - accuracy: 0.5000 - val_loss: -5.4253 - val_accuracy: 0.5000\n",
            "Epoch 353/500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: -5.4669 - accuracy: 0.5000 - val_loss: -5.4415 - val_accuracy: 0.5000\n",
            "Epoch 354/500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: -5.4824 - accuracy: 0.5000 - val_loss: -5.4581 - val_accuracy: 0.5000\n",
            "Epoch 355/500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: -5.4996 - accuracy: 0.5000 - val_loss: -5.4746 - val_accuracy: 0.5000\n",
            "Epoch 356/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -5.5162 - accuracy: 0.5000 - val_loss: -5.4915 - val_accuracy: 0.5000\n",
            "Epoch 357/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.5341 - accuracy: 0.5000 - val_loss: -5.5085 - val_accuracy: 0.5000\n",
            "Epoch 358/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -5.5498 - accuracy: 0.5000 - val_loss: -5.5259 - val_accuracy: 0.5000\n",
            "Epoch 359/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -5.5676 - accuracy: 0.5000 - val_loss: -5.5426 - val_accuracy: 0.5000\n",
            "Epoch 360/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.5845 - accuracy: 0.5000 - val_loss: -5.5597 - val_accuracy: 0.5000\n",
            "Epoch 361/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.6004 - accuracy: 0.5000 - val_loss: -5.5766 - val_accuracy: 0.5000\n",
            "Epoch 362/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -5.6176 - accuracy: 0.5000 - val_loss: -5.5926 - val_accuracy: 0.5000\n",
            "Epoch 363/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -5.6337 - accuracy: 0.5000 - val_loss: -5.6086 - val_accuracy: 0.5000\n",
            "Epoch 364/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -5.6497 - accuracy: 0.5000 - val_loss: -5.6253 - val_accuracy: 0.5000\n",
            "Epoch 365/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.6659 - accuracy: 0.5000 - val_loss: -5.6420 - val_accuracy: 0.5000\n",
            "Epoch 366/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.6827 - accuracy: 0.5000 - val_loss: -5.6586 - val_accuracy: 0.5000\n",
            "Epoch 367/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.6991 - accuracy: 0.5000 - val_loss: -5.6752 - val_accuracy: 0.5000\n",
            "Epoch 368/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.7158 - accuracy: 0.5000 - val_loss: -5.6915 - val_accuracy: 0.5000\n",
            "Epoch 369/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -5.7319 - accuracy: 0.5000 - val_loss: -5.7078 - val_accuracy: 0.5000\n",
            "Epoch 370/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -5.7481 - accuracy: 0.5000 - val_loss: -5.7241 - val_accuracy: 0.5000\n",
            "Epoch 371/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -5.7644 - accuracy: 0.5000 - val_loss: -5.7398 - val_accuracy: 0.5000\n",
            "Epoch 372/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -5.7809 - accuracy: 0.5000 - val_loss: -5.7555 - val_accuracy: 0.5000\n",
            "Epoch 373/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -5.7963 - accuracy: 0.5000 - val_loss: -5.7721 - val_accuracy: 0.5000\n",
            "Epoch 374/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -5.8133 - accuracy: 0.5000 - val_loss: -5.7892 - val_accuracy: 0.5000\n",
            "Epoch 375/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.8303 - accuracy: 0.5000 - val_loss: -5.8067 - val_accuracy: 0.5000\n",
            "Epoch 376/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -5.8481 - accuracy: 0.5000 - val_loss: -5.8242 - val_accuracy: 0.5000\n",
            "Epoch 377/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -5.8650 - accuracy: 0.5000 - val_loss: -5.8414 - val_accuracy: 0.5000\n",
            "Epoch 378/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -5.8826 - accuracy: 0.5000 - val_loss: -5.8584 - val_accuracy: 0.5000\n",
            "Epoch 379/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.8993 - accuracy: 0.5000 - val_loss: -5.8756 - val_accuracy: 0.5000\n",
            "Epoch 380/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -5.9157 - accuracy: 0.5000 - val_loss: -5.8926 - val_accuracy: 0.5000\n",
            "Epoch 381/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.9337 - accuracy: 0.5000 - val_loss: -5.9090 - val_accuracy: 0.5000\n",
            "Epoch 382/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -5.9497 - accuracy: 0.5000 - val_loss: -5.9251 - val_accuracy: 0.5000\n",
            "Epoch 383/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -5.9654 - accuracy: 0.5000 - val_loss: -5.9409 - val_accuracy: 0.5000\n",
            "Epoch 384/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -5.9809 - accuracy: 0.5000 - val_loss: -5.9570 - val_accuracy: 0.5000\n",
            "Epoch 385/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -5.9974 - accuracy: 0.5000 - val_loss: -5.9734 - val_accuracy: 0.5000\n",
            "Epoch 386/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -6.0138 - accuracy: 0.5000 - val_loss: -5.9899 - val_accuracy: 0.5000\n",
            "Epoch 387/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.0303 - accuracy: 0.5000 - val_loss: -6.0064 - val_accuracy: 0.5000\n",
            "Epoch 388/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.0469 - accuracy: 0.5000 - val_loss: -6.0232 - val_accuracy: 0.5000\n",
            "Epoch 389/500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: -6.0627 - accuracy: 0.5000 - val_loss: -6.0401 - val_accuracy: 0.5000\n",
            "Epoch 390/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.0805 - accuracy: 0.5000 - val_loss: -6.0566 - val_accuracy: 0.5000\n",
            "Epoch 391/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -6.0962 - accuracy: 0.5000 - val_loss: -6.0729 - val_accuracy: 0.5000\n",
            "Epoch 392/500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: -6.1125 - accuracy: 0.5000 - val_loss: -6.0891 - val_accuracy: 0.5000\n",
            "Epoch 393/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.1289 - accuracy: 0.5000 - val_loss: -6.1049 - val_accuracy: 0.5000\n",
            "Epoch 394/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.1445 - accuracy: 0.5000 - val_loss: -6.1207 - val_accuracy: 0.5000\n",
            "Epoch 395/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -6.1600 - accuracy: 0.5000 - val_loss: -6.1364 - val_accuracy: 0.5000\n",
            "Epoch 396/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -6.1757 - accuracy: 0.5000 - val_loss: -6.1518 - val_accuracy: 0.5000\n",
            "Epoch 397/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -6.1918 - accuracy: 0.5000 - val_loss: -6.1674 - val_accuracy: 0.5000\n",
            "Epoch 398/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -6.2062 - accuracy: 0.5000 - val_loss: -6.1832 - val_accuracy: 0.5000\n",
            "Epoch 399/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.2227 - accuracy: 0.5000 - val_loss: -6.1983 - val_accuracy: 0.5000\n",
            "Epoch 400/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -6.2378 - accuracy: 0.5000 - val_loss: -6.2135 - val_accuracy: 0.5000\n",
            "Epoch 401/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.2527 - accuracy: 0.5000 - val_loss: -6.2289 - val_accuracy: 0.5000\n",
            "Epoch 402/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.2678 - accuracy: 0.5000 - val_loss: -6.2442 - val_accuracy: 0.5000\n",
            "Epoch 403/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.2834 - accuracy: 0.5000 - val_loss: -6.2594 - val_accuracy: 0.5000\n",
            "Epoch 404/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.2982 - accuracy: 0.5000 - val_loss: -6.2745 - val_accuracy: 0.5000\n",
            "Epoch 405/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.3138 - accuracy: 0.5000 - val_loss: -6.2898 - val_accuracy: 0.5000\n",
            "Epoch 406/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -6.3291 - accuracy: 0.5000 - val_loss: -6.3050 - val_accuracy: 0.5000\n",
            "Epoch 407/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -6.3443 - accuracy: 0.5000 - val_loss: -6.3205 - val_accuracy: 0.5000\n",
            "Epoch 408/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.3599 - accuracy: 0.5000 - val_loss: -6.3364 - val_accuracy: 0.5000\n",
            "Epoch 409/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.3758 - accuracy: 0.5000 - val_loss: -6.3521 - val_accuracy: 0.5000\n",
            "Epoch 410/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -6.3913 - accuracy: 0.5000 - val_loss: -6.3678 - val_accuracy: 0.5000\n",
            "Epoch 411/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.4069 - accuracy: 0.5000 - val_loss: -6.3839 - val_accuracy: 0.5000\n",
            "Epoch 412/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.4230 - accuracy: 0.5000 - val_loss: -6.4001 - val_accuracy: 0.5000\n",
            "Epoch 413/500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: -6.4396 - accuracy: 0.5000 - val_loss: -6.4160 - val_accuracy: 0.5000\n",
            "Epoch 414/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.4555 - accuracy: 0.5000 - val_loss: -6.4326 - val_accuracy: 0.5000\n",
            "Epoch 415/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.4718 - accuracy: 0.5000 - val_loss: -6.4498 - val_accuracy: 0.5000\n",
            "Epoch 416/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -6.4895 - accuracy: 0.5000 - val_loss: -6.4668 - val_accuracy: 0.5000\n",
            "Epoch 417/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -6.5050 - accuracy: 0.5000 - val_loss: -6.4834 - val_accuracy: 0.5000\n",
            "Epoch 418/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -6.5220 - accuracy: 0.5000 - val_loss: -6.4989 - val_accuracy: 0.5000\n",
            "Epoch 419/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.5372 - accuracy: 0.5000 - val_loss: -6.5139 - val_accuracy: 0.5000\n",
            "Epoch 420/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.5523 - accuracy: 0.5000 - val_loss: -6.5287 - val_accuracy: 0.5000\n",
            "Epoch 421/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.5676 - accuracy: 0.5000 - val_loss: -6.5437 - val_accuracy: 0.5000\n",
            "Epoch 422/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.5823 - accuracy: 0.5000 - val_loss: -6.5596 - val_accuracy: 0.5000\n",
            "Epoch 423/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.5984 - accuracy: 0.5000 - val_loss: -6.5757 - val_accuracy: 0.5000\n",
            "Epoch 424/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.6147 - accuracy: 0.5000 - val_loss: -6.5925 - val_accuracy: 0.5000\n",
            "Epoch 425/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.6311 - accuracy: 0.5000 - val_loss: -6.6096 - val_accuracy: 0.5000\n",
            "Epoch 426/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -6.6475 - accuracy: 0.5000 - val_loss: -6.6268 - val_accuracy: 0.5000\n",
            "Epoch 427/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.6652 - accuracy: 0.5000 - val_loss: -6.6431 - val_accuracy: 0.5000\n",
            "Epoch 428/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -6.6812 - accuracy: 0.5000 - val_loss: -6.6596 - val_accuracy: 0.5000\n",
            "Epoch 429/500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: -6.6981 - accuracy: 0.5000 - val_loss: -6.6761 - val_accuracy: 0.5000\n",
            "Epoch 430/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.7148 - accuracy: 0.5000 - val_loss: -6.6924 - val_accuracy: 0.5000\n",
            "Epoch 431/500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: -6.7306 - accuracy: 0.5000 - val_loss: -6.7087 - val_accuracy: 0.5000\n",
            "Epoch 432/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.7469 - accuracy: 0.5000 - val_loss: -6.7252 - val_accuracy: 0.5000\n",
            "Epoch 433/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -6.7639 - accuracy: 0.5000 - val_loss: -6.7423 - val_accuracy: 0.5000\n",
            "Epoch 434/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -6.7805 - accuracy: 0.5000 - val_loss: -6.7598 - val_accuracy: 0.5000\n",
            "Epoch 435/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.7983 - accuracy: 0.5000 - val_loss: -6.7769 - val_accuracy: 0.5000\n",
            "Epoch 436/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -6.8148 - accuracy: 0.5000 - val_loss: -6.7945 - val_accuracy: 0.5000\n",
            "Epoch 437/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -6.8326 - accuracy: 0.5000 - val_loss: -6.8115 - val_accuracy: 0.5000\n",
            "Epoch 438/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -6.8494 - accuracy: 0.5000 - val_loss: -6.8283 - val_accuracy: 0.5000\n",
            "Epoch 439/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -6.8664 - accuracy: 0.5000 - val_loss: -6.8447 - val_accuracy: 0.5000\n",
            "Epoch 440/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.8830 - accuracy: 0.5000 - val_loss: -6.8612 - val_accuracy: 0.5000\n",
            "Epoch 441/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.8997 - accuracy: 0.5000 - val_loss: -6.8781 - val_accuracy: 0.5000\n",
            "Epoch 442/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.9163 - accuracy: 0.5000 - val_loss: -6.8954 - val_accuracy: 0.5000\n",
            "Epoch 443/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -6.9333 - accuracy: 0.5000 - val_loss: -6.9128 - val_accuracy: 0.5000\n",
            "Epoch 444/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.9507 - accuracy: 0.5000 - val_loss: -6.9303 - val_accuracy: 0.5000\n",
            "Epoch 445/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -6.9682 - accuracy: 0.5000 - val_loss: -6.9475 - val_accuracy: 0.5000\n",
            "Epoch 446/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -6.9849 - accuracy: 0.5000 - val_loss: -6.9646 - val_accuracy: 0.5000\n",
            "Epoch 447/500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: -7.0023 - accuracy: 0.5000 - val_loss: -6.9814 - val_accuracy: 0.5000\n",
            "Epoch 448/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.0191 - accuracy: 0.5000 - val_loss: -6.9987 - val_accuracy: 0.5000\n",
            "Epoch 449/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.0355 - accuracy: 0.5000 - val_loss: -7.0165 - val_accuracy: 0.5000\n",
            "Epoch 450/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.0541 - accuracy: 0.5000 - val_loss: -7.0337 - val_accuracy: 0.5000\n",
            "Epoch 451/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -7.0710 - accuracy: 0.5000 - val_loss: -7.0509 - val_accuracy: 0.5000\n",
            "Epoch 452/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.0879 - accuracy: 0.5000 - val_loss: -7.0677 - val_accuracy: 0.5000\n",
            "Epoch 453/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.1053 - accuracy: 0.5000 - val_loss: -7.0844 - val_accuracy: 0.5000\n",
            "Epoch 454/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.1224 - accuracy: 0.5000 - val_loss: -7.1018 - val_accuracy: 0.5000\n",
            "Epoch 455/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.1394 - accuracy: 0.5000 - val_loss: -7.1202 - val_accuracy: 0.5000\n",
            "Epoch 456/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -7.1577 - accuracy: 0.5000 - val_loss: -7.1389 - val_accuracy: 0.5000\n",
            "Epoch 457/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.1768 - accuracy: 0.5000 - val_loss: -7.1574 - val_accuracy: 0.5000\n",
            "Epoch 458/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -7.1949 - accuracy: 0.5000 - val_loss: -7.1759 - val_accuracy: 0.5000\n",
            "Epoch 459/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.2133 - accuracy: 0.5000 - val_loss: -7.1942 - val_accuracy: 0.5000\n",
            "Epoch 460/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.2317 - accuracy: 0.5000 - val_loss: -7.2123 - val_accuracy: 0.5000\n",
            "Epoch 461/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -7.2492 - accuracy: 0.5000 - val_loss: -7.2303 - val_accuracy: 0.5000\n",
            "Epoch 462/500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: -7.2673 - accuracy: 0.5000 - val_loss: -7.2480 - val_accuracy: 0.5000\n",
            "Epoch 463/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.2848 - accuracy: 0.5000 - val_loss: -7.2653 - val_accuracy: 0.5000\n",
            "Epoch 464/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.3023 - accuracy: 0.5000 - val_loss: -7.2825 - val_accuracy: 0.5000\n",
            "Epoch 465/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -7.3194 - accuracy: 0.5000 - val_loss: -7.3000 - val_accuracy: 0.5000\n",
            "Epoch 466/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -7.3368 - accuracy: 0.5000 - val_loss: -7.3174 - val_accuracy: 0.5000\n",
            "Epoch 467/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -7.3542 - accuracy: 0.5000 - val_loss: -7.3351 - val_accuracy: 0.5000\n",
            "Epoch 468/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.3720 - accuracy: 0.5000 - val_loss: -7.3527 - val_accuracy: 0.5000\n",
            "Epoch 469/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.3885 - accuracy: 0.5000 - val_loss: -7.3697 - val_accuracy: 0.5000\n",
            "Epoch 470/500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: -7.4062 - accuracy: 0.5000 - val_loss: -7.3863 - val_accuracy: 0.5000\n",
            "Epoch 471/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.4219 - accuracy: 0.5000 - val_loss: -7.4034 - val_accuracy: 0.5000\n",
            "Epoch 472/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.4388 - accuracy: 0.5000 - val_loss: -7.4198 - val_accuracy: 0.5000\n",
            "Epoch 473/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -7.4557 - accuracy: 0.5000 - val_loss: -7.4358 - val_accuracy: 0.5000\n",
            "Epoch 474/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.4723 - accuracy: 0.5000 - val_loss: -7.4517 - val_accuracy: 0.5000\n",
            "Epoch 475/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.4879 - accuracy: 0.5000 - val_loss: -7.4680 - val_accuracy: 0.5000\n",
            "Epoch 476/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.5038 - accuracy: 0.5000 - val_loss: -7.4845 - val_accuracy: 0.5000\n",
            "Epoch 477/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.5206 - accuracy: 0.5000 - val_loss: -7.5013 - val_accuracy: 0.5000\n",
            "Epoch 478/500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: -7.5372 - accuracy: 0.5000 - val_loss: -7.5185 - val_accuracy: 0.5000\n",
            "Epoch 479/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.5545 - accuracy: 0.5000 - val_loss: -7.5356 - val_accuracy: 0.5000\n",
            "Epoch 480/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -7.5718 - accuracy: 0.5000 - val_loss: -7.5528 - val_accuracy: 0.5000\n",
            "Epoch 481/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.5886 - accuracy: 0.5000 - val_loss: -7.5704 - val_accuracy: 0.5000\n",
            "Epoch 482/500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: -7.6065 - accuracy: 0.5000 - val_loss: -7.5877 - val_accuracy: 0.5000\n",
            "Epoch 483/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -7.6238 - accuracy: 0.5000 - val_loss: -7.6049 - val_accuracy: 0.5000\n",
            "Epoch 484/500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: -7.6404 - accuracy: 0.5000 - val_loss: -7.6217 - val_accuracy: 0.5000\n",
            "Epoch 485/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.6568 - accuracy: 0.5000 - val_loss: -7.6383 - val_accuracy: 0.5000\n",
            "Epoch 486/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.6739 - accuracy: 0.5000 - val_loss: -7.6548 - val_accuracy: 0.5000\n",
            "Epoch 487/500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: -7.6905 - accuracy: 0.5000 - val_loss: -7.6716 - val_accuracy: 0.5000\n",
            "Epoch 488/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.7067 - accuracy: 0.5000 - val_loss: -7.6884 - val_accuracy: 0.5000\n",
            "Epoch 489/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.7234 - accuracy: 0.5000 - val_loss: -7.7052 - val_accuracy: 0.5000\n",
            "Epoch 490/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.7411 - accuracy: 0.5000 - val_loss: -7.7221 - val_accuracy: 0.5000\n",
            "Epoch 491/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.7576 - accuracy: 0.5000 - val_loss: -7.7402 - val_accuracy: 0.5000\n",
            "Epoch 492/500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: -7.7761 - accuracy: 0.5000 - val_loss: -7.7584 - val_accuracy: 0.5000\n",
            "Epoch 493/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.7937 - accuracy: 0.5000 - val_loss: -7.7769 - val_accuracy: 0.5000\n",
            "Epoch 494/500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: -7.8121 - accuracy: 0.5000 - val_loss: -7.7952 - val_accuracy: 0.5000\n",
            "Epoch 495/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.8309 - accuracy: 0.5000 - val_loss: -7.8131 - val_accuracy: 0.5000\n",
            "Epoch 496/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.8486 - accuracy: 0.5000 - val_loss: -7.8315 - val_accuracy: 0.5000\n",
            "Epoch 497/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.8674 - accuracy: 0.5000 - val_loss: -7.8497 - val_accuracy: 0.5000\n",
            "Epoch 498/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.8850 - accuracy: 0.5000 - val_loss: -7.8682 - val_accuracy: 0.5000\n",
            "Epoch 499/500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: -7.9036 - accuracy: 0.5000 - val_loss: -7.8866 - val_accuracy: 0.5000\n",
            "Epoch 500/500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: -7.9221 - accuracy: 0.5000 - val_loss: -7.9048 - val_accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_nn(model2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "La7ld9B9syiW",
        "outputId": "3a7703a6-254b-4941-aef2-6b3a7ce0ffc9"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1720x629>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "smxRh95f23yH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#comprarando as perdas entre 2 modelos\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(historico1.history['loss'], 'r')\n",
        "plt.plot(historico2.history['loss'], 'b')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "tiq80n5I239g",
        "outputId": "633864b8-3ebf-4546-ecf5-b3582480a0cf"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x79980ddef2e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}