ANN.ipynb

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    "colab": {
      "name": "ANN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "start = time.time()"
      ],
      "metadata": {
        "id": "od-cG9AKcx_Y"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Kütüphanelerin import edilmesi\n",
        "%pylab inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3U3In83c0fh",
        "outputId": "dedb465a-d5ea-4c5b-e053-e2d1e2c8a265"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Sklearn kütüphanelerinin import edilmesi\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "from sklearn import preprocessing"
      ],
      "metadata": {
        "id": "mxvFonB0c46m"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Yapay Sinir Ağları için Keras kütüphanelerinin import edilmesi\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import merge\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.advanced_activations import PReLU\n",
        "from keras.layers import Convolution2D, Convolution1D, MaxPooling2D\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "metadata": {
        "id": "HlLwia2EdALL"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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        "outputId": "fc64ff01-dd18-4a89-8dc8-c2eb93711e6b"
      },
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        {
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              "\n",
              "     <input type=\"file\" id=\"files-65ba276a-aa8c-4874-83a9-f414e2243a59\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-65ba276a-aa8c-4874-83a9-f414e2243a59\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving sample_submission.csv to sample_submission.csv\n",
            "Saving submission.csv to submission.csv\n",
            "Saving test.csv to test.csv\n",
            "Saving train.csv to train.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Train Veri Seti\n",
        "parent_data = pd.read_csv('train.csv')\n",
        "data = parent_data.copy()   \n",
        "data.pop('id')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgG1DJgfdKI8",
        "outputId": "fc2e714a-ba09-4b26-d776-6d99807a91e5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1\n",
              "1         2\n",
              "2         3\n",
              "3         5\n",
              "4         6\n",
              "       ... \n",
              "985    1575\n",
              "986    1578\n",
              "987    1581\n",
              "988    1582\n",
              "989    1584\n",
              "Name: id, Length: 990, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Test Veri Seti\n",
        "test = pd.read_csv('test.csv')\n",
        "testId = test.pop('id')"
      ],
      "metadata": {
        "id": "H5-8SYfrdPA6"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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        "colab": {
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          "height": 300
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        "id": "TU2N4UfadTWS",
        "outputId": "cb278c14-95e4-41f8-e5d1-d16f1ccbb999"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 species   margin1   margin2   margin3   margin4   margin5  \\\n",
              "0            Acer_Opalus  0.007812  0.023438  0.023438  0.003906  0.011719   \n",
              "1  Pterocarya_Stenoptera  0.005859  0.000000  0.031250  0.015625  0.025391   \n",
              "2   Quercus_Hartwissiana  0.005859  0.009766  0.019531  0.007812  0.003906   \n",
              "3        Tilia_Tomentosa  0.000000  0.003906  0.023438  0.005859  0.021484   \n",
              "4     Quercus_Variabilis  0.005859  0.003906  0.048828  0.009766  0.013672   \n",
              "\n",
              "    margin6   margin7  margin8   margin9  ...  texture55  texture56  \\\n",
              "0  0.009766  0.027344      0.0  0.001953  ...   0.007812   0.000000   \n",
              "1  0.001953  0.019531      0.0  0.000000  ...   0.000977   0.000000   \n",
              "2  0.005859  0.068359      0.0  0.000000  ...   0.154300   0.000000   \n",
              "3  0.019531  0.023438      0.0  0.013672  ...   0.000000   0.000977   \n",
              "4  0.015625  0.005859      0.0  0.000000  ...   0.096680   0.000000   \n",
              "\n",
              "   texture57  texture58  texture59  texture60  texture61  texture62  \\\n",
              "0   0.002930   0.002930   0.035156        0.0        0.0   0.004883   \n",
              "1   0.000000   0.000977   0.023438        0.0        0.0   0.000977   \n",
              "2   0.005859   0.000977   0.007812        0.0        0.0   0.000000   \n",
              "3   0.000000   0.000000   0.020508        0.0        0.0   0.017578   \n",
              "4   0.021484   0.000000   0.000000        0.0        0.0   0.000000   \n",
              "\n",
              "   texture63  texture64  \n",
              "0   0.000000   0.025391  \n",
              "1   0.039062   0.022461  \n",
              "2   0.020508   0.002930  \n",
              "3   0.000000   0.047852  \n",
              "4   0.000000   0.031250  \n",
              "\n",
              "[5 rows x 193 columns]"
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>species</th>\n",
              "      <th>margin1</th>\n",
              "      <th>margin2</th>\n",
              "      <th>margin3</th>\n",
              "      <th>margin4</th>\n",
              "      <th>margin5</th>\n",
              "      <th>margin6</th>\n",
              "      <th>margin7</th>\n",
              "      <th>margin8</th>\n",
              "      <th>margin9</th>\n",
              "      <th>...</th>\n",
              "      <th>texture55</th>\n",
              "      <th>texture56</th>\n",
              "      <th>texture57</th>\n",
              "      <th>texture58</th>\n",
              "      <th>texture59</th>\n",
              "      <th>texture60</th>\n",
              "      <th>texture61</th>\n",
              "      <th>texture62</th>\n",
              "      <th>texture63</th>\n",
              "      <th>texture64</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acer_Opalus</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.023438</td>\n",
              "      <td>0.023438</td>\n",
              "      <td>0.003906</td>\n",
              "      <td>0.011719</td>\n",
              "      <td>0.009766</td>\n",
              "      <td>0.027344</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001953</td>\n",
              "      <td>...</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002930</td>\n",
              "      <td>0.002930</td>\n",
              "      <td>0.035156</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004883</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.025391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pterocarya_Stenoptera</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.031250</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.025391</td>\n",
              "      <td>0.001953</td>\n",
              "      <td>0.019531</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>0.023438</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>0.039062</td>\n",
              "      <td>0.022461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Quercus_Hartwissiana</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.009766</td>\n",
              "      <td>0.019531</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.003906</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.068359</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.154300</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020508</td>\n",
              "      <td>0.002930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tilia_Tomentosa</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003906</td>\n",
              "      <td>0.023438</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.021484</td>\n",
              "      <td>0.019531</td>\n",
              "      <td>0.023438</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.013672</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000977</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020508</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017578</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.047852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Quercus_Variabilis</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.003906</td>\n",
              "      <td>0.048828</td>\n",
              "      <td>0.009766</td>\n",
              "      <td>0.013672</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.005859</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.096680</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.021484</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.031250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 193 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2907eab3-13b2-4181-95e6-769ab7c25d44')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2907eab3-13b2-4181-95e6-769ab7c25d44 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2907eab3-13b2-4181-95e6-769ab7c25d44');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Sayısal hale dönüştürme işlemi yapıldı\n",
        "species_label = data.pop('species')\n",
        "species_label = LabelEncoder().fit(species_label).transform(species_label)\n",
        "print(species_label.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zvjd7_24dcKF",
        "outputId": "750ead37-4d38-4797-c439-f199082713e6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(990,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot = to_categorical(species_label)\n",
        "print(one_hot.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pm0BZ6pCdgEW",
        "outputId": "7759e70b-db41-4c21-b622-921e2669642c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(990, 99)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_train_data = preprocessing.MinMaxScaler().fit(data).transform(data)\n",
        "preprocessed_train_data = StandardScaler().fit(data).transform(data)\n",
        "\n",
        "print(preprocessed_train_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XpdtK6dRdkPy",
        "outputId": "128754fd-3bfa-4359-cddb-96c71b305609"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(990, 192)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Eğitim setinden Test setine aynı dönüşümler yapıldı\n",
        "test = preprocessing.MinMaxScaler().fit(test).transform(test)\n",
        "test = StandardScaler().fit(test).transform(test)"
      ],
      "metadata": {
        "id": "1I1MNxJmdmzV"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sss = StratifiedShuffleSplit(n_splits=10, test_size=0.3,random_state=12345)\n",
        "train_index, val_index = next(iter(sss.split(preprocessed_train_data, one_hot)))\n",
        "\n",
        "x_train, x_val = preprocessed_train_data[train_index], preprocessed_train_data[val_index]\n",
        "y_train, y_val = one_hot[train_index], one_hot[val_index]\n",
        "\n",
        "print(\"x_train dim: \",x_train.shape)\n",
        "print(\"x_val dim:   \",x_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lUSOwbpdn6b",
        "outputId": "abb3d692-801b-455c-a04f-c4440d300542"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train dim:  (693, 192)\n",
            "x_val dim:    (297, 192)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(768,input_dim=192,  kernel_initializer='glorot_normal', activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(768, activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Dense(99, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FmV-tDUudrKg",
        "outputId": "c6395770-5a53-4cce-faa4-b8933d529aba"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 768)               148224    \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 768)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 768)               590592    \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 768)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 99)                76131     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 814,947\n",
            "Trainable params: 814,947\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Adagrad, rmsprop, SGD, Adadelta, Adam, Adamax, Nadam\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',optimizer='adam', metrics = [\"accuracy\"])"
      ],
      "metadata": {
        "id": "Gwb9z_ayduO2"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=300)\n",
        "\n",
        "history = model.fit(x_train, y_train,batch_size=192,epochs=2500 ,verbose=1,\n",
        "                    validation_data=(x_val, y_val),callbacks=[early_stopping])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iU2BVd7Fdywd",
        "outputId": "73e049aa-2e1e-432a-ba68-9aacc946cfc4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2500\n",
            "4/4 [==============================] - 1s 96ms/step - loss: 4.5128 - accuracy: 0.0491 - val_loss: 3.5733 - val_accuracy: 0.3535\n",
            "Epoch 2/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 3.3927 - accuracy: 0.3175 - val_loss: 2.6512 - val_accuracy: 0.6734\n",
            "Epoch 3/2500\n",
            "4/4 [==============================] - 0s 54ms/step - loss: 2.4168 - accuracy: 0.6003 - val_loss: 1.7217 - val_accuracy: 0.8384\n",
            "Epoch 4/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 1.5672 - accuracy: 0.7605 - val_loss: 0.9806 - val_accuracy: 0.9327\n",
            "Epoch 5/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.9599 - accuracy: 0.8456 - val_loss: 0.5370 - val_accuracy: 0.9495\n",
            "Epoch 6/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5649 - accuracy: 0.9105 - val_loss: 0.3429 - val_accuracy: 0.9731\n",
            "Epoch 7/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.4234 - accuracy: 0.9192 - val_loss: 0.2249 - val_accuracy: 0.9832\n",
            "Epoch 8/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3034 - accuracy: 0.9380 - val_loss: 0.1604 - val_accuracy: 0.9798\n",
            "Epoch 9/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.2115 - accuracy: 0.9538 - val_loss: 0.1448 - val_accuracy: 0.9663\n",
            "Epoch 10/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.1726 - accuracy: 0.9567 - val_loss: 0.1142 - val_accuracy: 0.9798\n",
            "Epoch 11/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.1329 - accuracy: 0.9697 - val_loss: 0.0932 - val_accuracy: 0.9933\n",
            "Epoch 12/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.1034 - accuracy: 0.9856 - val_loss: 0.0725 - val_accuracy: 0.9899\n",
            "Epoch 13/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.1062 - accuracy: 0.9683 - val_loss: 0.0587 - val_accuracy: 0.9933\n",
            "Epoch 14/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0886 - accuracy: 0.9812 - val_loss: 0.0568 - val_accuracy: 0.9933\n",
            "Epoch 15/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0746 - accuracy: 0.9885 - val_loss: 0.0579 - val_accuracy: 0.9899\n",
            "Epoch 16/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0567 - accuracy: 0.9899 - val_loss: 0.0533 - val_accuracy: 0.9899\n",
            "Epoch 17/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0530 - accuracy: 0.9913 - val_loss: 0.0503 - val_accuracy: 0.9933\n",
            "Epoch 18/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0573 - accuracy: 0.9885 - val_loss: 0.0464 - val_accuracy: 0.9933\n",
            "Epoch 19/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0494 - accuracy: 0.9899 - val_loss: 0.0457 - val_accuracy: 0.9933\n",
            "Epoch 20/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0388 - accuracy: 0.9942 - val_loss: 0.0471 - val_accuracy: 0.9933\n",
            "Epoch 21/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0437 - accuracy: 0.9899 - val_loss: 0.0452 - val_accuracy: 0.9899\n",
            "Epoch 22/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0339 - accuracy: 0.9928 - val_loss: 0.0421 - val_accuracy: 0.9933\n",
            "Epoch 23/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0395 - accuracy: 0.9928 - val_loss: 0.0379 - val_accuracy: 0.9899\n",
            "Epoch 24/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0225 - accuracy: 0.9986 - val_loss: 0.0363 - val_accuracy: 0.9899\n",
            "Epoch 25/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0261 - accuracy: 0.9986 - val_loss: 0.0332 - val_accuracy: 0.9933\n",
            "Epoch 26/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0251 - accuracy: 0.9971 - val_loss: 0.0292 - val_accuracy: 0.9933\n",
            "Epoch 27/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0263 - accuracy: 0.9957 - val_loss: 0.0265 - val_accuracy: 0.9966\n",
            "Epoch 28/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0194 - accuracy: 0.9971 - val_loss: 0.0268 - val_accuracy: 0.9966\n",
            "Epoch 29/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0229 - accuracy: 0.9942 - val_loss: 0.0263 - val_accuracy: 0.9966\n",
            "Epoch 30/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0185 - accuracy: 0.9986 - val_loss: 0.0338 - val_accuracy: 0.9966\n",
            "Epoch 31/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0224 - accuracy: 0.9957 - val_loss: 0.0340 - val_accuracy: 0.9966\n",
            "Epoch 32/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0189 - accuracy: 0.9986 - val_loss: 0.0339 - val_accuracy: 0.9966\n",
            "Epoch 33/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0140 - accuracy: 0.9986 - val_loss: 0.0339 - val_accuracy: 0.9933\n",
            "Epoch 34/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 0.0329 - val_accuracy: 0.9966\n",
            "Epoch 35/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0180 - accuracy: 0.9971 - val_loss: 0.0317 - val_accuracy: 0.9933\n",
            "Epoch 36/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0139 - accuracy: 0.9986 - val_loss: 0.0292 - val_accuracy: 0.9966\n",
            "Epoch 37/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.0248 - val_accuracy: 0.9966\n",
            "Epoch 38/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.0231 - val_accuracy: 0.9966\n",
            "Epoch 39/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.0222 - val_accuracy: 0.9966\n",
            "Epoch 40/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0214 - val_accuracy: 0.9966\n",
            "Epoch 41/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0104 - accuracy: 0.9986 - val_loss: 0.0204 - val_accuracy: 0.9966\n",
            "Epoch 42/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.0204 - val_accuracy: 0.9966\n",
            "Epoch 43/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.0206 - val_accuracy: 0.9966\n",
            "Epoch 44/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.0207 - val_accuracy: 1.0000\n",
            "Epoch 45/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0128 - accuracy: 0.9986 - val_loss: 0.0203 - val_accuracy: 0.9966\n",
            "Epoch 46/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0127 - accuracy: 0.9971 - val_loss: 0.0247 - val_accuracy: 0.9966\n",
            "Epoch 47/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0263 - accuracy: 0.9913 - val_loss: 0.0243 - val_accuracy: 0.9966\n",
            "Epoch 48/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0125 - accuracy: 0.9957 - val_loss: 0.0304 - val_accuracy: 0.9933\n",
            "Epoch 49/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.0409 - val_accuracy: 0.9899\n",
            "Epoch 50/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0095 - accuracy: 0.9986 - val_loss: 0.0387 - val_accuracy: 0.9899\n",
            "Epoch 51/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0119 - accuracy: 0.9986 - val_loss: 0.0355 - val_accuracy: 0.9865\n",
            "Epoch 52/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.0314 - val_accuracy: 0.9899\n",
            "Epoch 53/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0125 - accuracy: 0.9971 - val_loss: 0.0288 - val_accuracy: 0.9899\n",
            "Epoch 54/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0268 - val_accuracy: 0.9933\n",
            "Epoch 55/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0089 - accuracy: 0.9986 - val_loss: 0.0252 - val_accuracy: 0.9899\n",
            "Epoch 56/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0076 - accuracy: 0.9986 - val_loss: 0.0241 - val_accuracy: 0.9899\n",
            "Epoch 57/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.0229 - val_accuracy: 0.9899\n",
            "Epoch 58/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0208 - val_accuracy: 0.9933\n",
            "Epoch 59/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0066 - accuracy: 0.9986 - val_loss: 0.0187 - val_accuracy: 0.9933\n",
            "Epoch 60/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0078 - accuracy: 0.9986 - val_loss: 0.0169 - val_accuracy: 0.9966\n",
            "Epoch 61/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0161 - val_accuracy: 1.0000\n",
            "Epoch 62/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0159 - val_accuracy: 1.0000\n",
            "Epoch 63/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.0151 - val_accuracy: 1.0000\n",
            "Epoch 64/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0149 - val_accuracy: 1.0000\n",
            "Epoch 65/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0061 - accuracy: 0.9986 - val_loss: 0.0136 - val_accuracy: 1.0000\n",
            "Epoch 66/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.0131 - val_accuracy: 0.9966\n",
            "Epoch 67/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.0134 - val_accuracy: 0.9966\n",
            "Epoch 68/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0118 - accuracy: 0.9986 - val_loss: 0.0139 - val_accuracy: 0.9966\n",
            "Epoch 69/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.0138 - val_accuracy: 0.9966\n",
            "Epoch 70/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0081 - accuracy: 0.9986 - val_loss: 0.0146 - val_accuracy: 0.9966\n",
            "Epoch 71/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0118 - accuracy: 0.9971 - val_loss: 0.0210 - val_accuracy: 0.9899\n",
            "Epoch 72/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0066 - accuracy: 0.9986 - val_loss: 0.0268 - val_accuracy: 0.9933\n",
            "Epoch 73/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0065 - accuracy: 0.9986 - val_loss: 0.0306 - val_accuracy: 0.9933\n",
            "Epoch 74/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0102 - accuracy: 0.9986 - val_loss: 0.0212 - val_accuracy: 0.9933\n",
            "Epoch 75/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.0145 - val_accuracy: 1.0000\n",
            "Epoch 76/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.0140 - val_accuracy: 1.0000\n",
            "Epoch 77/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0063 - accuracy: 0.9986 - val_loss: 0.0110 - val_accuracy: 1.0000\n",
            "Epoch 78/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
            "Epoch 79/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.0130 - val_accuracy: 0.9966\n",
            "Epoch 80/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0161 - val_accuracy: 0.9966\n",
            "Epoch 81/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.0187 - val_accuracy: 0.9966\n",
            "Epoch 82/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0204 - val_accuracy: 0.9966\n",
            "Epoch 83/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0213 - val_accuracy: 0.9966\n",
            "Epoch 84/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0248 - val_accuracy: 0.9933\n",
            "Epoch 85/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.0288 - val_accuracy: 0.9933\n",
            "Epoch 86/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0083 - accuracy: 0.9986 - val_loss: 0.0276 - val_accuracy: 0.9933\n",
            "Epoch 87/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0249 - val_accuracy: 0.9966\n",
            "Epoch 88/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 0.0202 - val_accuracy: 0.9966\n",
            "Epoch 89/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0062 - accuracy: 0.9986 - val_loss: 0.0178 - val_accuracy: 0.9966\n",
            "Epoch 90/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0056 - accuracy: 0.9986 - val_loss: 0.0167 - val_accuracy: 0.9966\n",
            "Epoch 91/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0067 - accuracy: 0.9986 - val_loss: 0.0109 - val_accuracy: 1.0000\n",
            "Epoch 92/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0100 - val_accuracy: 1.0000\n",
            "Epoch 93/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 0.0103 - val_accuracy: 1.0000\n",
            "Epoch 94/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0103 - val_accuracy: 1.0000\n",
            "Epoch 95/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0050 - accuracy: 0.9986 - val_loss: 0.0102 - val_accuracy: 1.0000\n",
            "Epoch 96/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0104 - val_accuracy: 1.0000\n",
            "Epoch 97/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0110 - val_accuracy: 1.0000\n",
            "Epoch 98/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0120 - val_accuracy: 1.0000\n",
            "Epoch 99/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0057 - accuracy: 0.9986 - val_loss: 0.0119 - val_accuracy: 1.0000\n",
            "Epoch 100/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0121 - val_accuracy: 1.0000\n",
            "Epoch 101/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0121 - val_accuracy: 1.0000\n",
            "Epoch 102/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0119 - val_accuracy: 1.0000\n",
            "Epoch 103/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0118 - val_accuracy: 1.0000\n",
            "Epoch 104/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0115 - val_accuracy: 1.0000\n",
            "Epoch 105/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0029 - accuracy: 0.9986 - val_loss: 0.0120 - val_accuracy: 1.0000\n",
            "Epoch 106/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0055 - accuracy: 0.9971 - val_loss: 0.0127 - val_accuracy: 1.0000\n",
            "Epoch 107/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0132 - val_accuracy: 1.0000\n",
            "Epoch 108/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0066 - accuracy: 0.9971 - val_loss: 0.0134 - val_accuracy: 1.0000\n",
            "Epoch 109/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0063 - accuracy: 0.9986 - val_loss: 0.0130 - val_accuracy: 0.9966\n",
            "Epoch 110/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0142 - val_accuracy: 0.9966\n",
            "Epoch 111/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.0144 - val_accuracy: 0.9966\n",
            "Epoch 112/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0137 - val_accuracy: 0.9966\n",
            "Epoch 113/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0132 - val_accuracy: 0.9966\n",
            "Epoch 114/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0131 - val_accuracy: 1.0000\n",
            "Epoch 115/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0127 - val_accuracy: 1.0000\n",
            "Epoch 116/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0120 - val_accuracy: 1.0000\n",
            "Epoch 117/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0123 - val_accuracy: 1.0000\n",
            "Epoch 118/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0137 - val_accuracy: 1.0000\n",
            "Epoch 119/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0150 - val_accuracy: 1.0000\n",
            "Epoch 120/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0154 - val_accuracy: 1.0000\n",
            "Epoch 121/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0157 - val_accuracy: 1.0000\n",
            "Epoch 122/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0160 - val_accuracy: 0.9966\n",
            "Epoch 123/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0160 - val_accuracy: 0.9966\n",
            "Epoch 124/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0152 - val_accuracy: 0.9966\n",
            "Epoch 125/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0141 - val_accuracy: 0.9966\n",
            "Epoch 126/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0135 - val_accuracy: 1.0000\n",
            "Epoch 127/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0110 - val_accuracy: 1.0000\n",
            "Epoch 128/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0105 - val_accuracy: 1.0000\n",
            "Epoch 129/2500\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
            "Epoch 130/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0027 - accuracy: 0.9986 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
            "Epoch 131/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0105 - val_accuracy: 1.0000\n",
            "Epoch 132/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0102 - val_accuracy: 1.0000\n",
            "Epoch 133/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0030 - accuracy: 0.9986 - val_loss: 0.0103 - val_accuracy: 1.0000\n",
            "Epoch 134/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0104 - val_accuracy: 1.0000\n",
            "Epoch 135/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0038 - accuracy: 0.9986 - val_loss: 0.0108 - val_accuracy: 1.0000\n",
            "Epoch 136/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 1.0000\n",
            "Epoch 137/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0115 - val_accuracy: 1.0000\n",
            "Epoch 138/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0115 - val_accuracy: 1.0000\n",
            "Epoch 139/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 1.0000\n",
            "Epoch 140/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.0104 - val_accuracy: 1.0000\n",
            "Epoch 141/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0101 - val_accuracy: 1.0000\n",
            "Epoch 142/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0104 - val_accuracy: 1.0000\n",
            "Epoch 143/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0107 - val_accuracy: 1.0000\n",
            "Epoch 144/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0065 - accuracy: 0.9971 - val_loss: 0.0104 - val_accuracy: 1.0000\n",
            "Epoch 145/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0038 - accuracy: 0.9986 - val_loss: 0.0115 - val_accuracy: 1.0000\n",
            "Epoch 146/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0066 - accuracy: 0.9971 - val_loss: 0.0132 - val_accuracy: 0.9966\n",
            "Epoch 147/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0135 - val_accuracy: 0.9966\n",
            "Epoch 148/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0143 - val_accuracy: 0.9966\n",
            "Epoch 149/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0146 - val_accuracy: 0.9966\n",
            "Epoch 150/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0150 - val_accuracy: 0.9966\n",
            "Epoch 151/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0156 - val_accuracy: 0.9966\n",
            "Epoch 152/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0165 - val_accuracy: 0.9966\n",
            "Epoch 153/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0057 - accuracy: 0.9986 - val_loss: 0.0167 - val_accuracy: 0.9966\n",
            "Epoch 154/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0031 - accuracy: 0.9986 - val_loss: 0.0174 - val_accuracy: 0.9966\n",
            "Epoch 155/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0169 - val_accuracy: 0.9966\n",
            "Epoch 156/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0153 - val_accuracy: 0.9966\n",
            "Epoch 157/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0139 - val_accuracy: 0.9966\n",
            "Epoch 158/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0130 - val_accuracy: 0.9966\n",
            "Epoch 159/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0117 - val_accuracy: 0.9966\n",
            "Epoch 160/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0103 - val_accuracy: 1.0000\n",
            "Epoch 161/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0094 - val_accuracy: 1.0000\n",
            "Epoch 162/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 9.3213e-04 - accuracy: 1.0000 - val_loss: 0.0091 - val_accuracy: 1.0000\n",
            "Epoch 163/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0096 - val_accuracy: 1.0000\n",
            "Epoch 164/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 8.9696e-04 - accuracy: 1.0000 - val_loss: 0.0102 - val_accuracy: 1.0000\n",
            "Epoch 165/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
            "Epoch 166/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 7.6126e-04 - accuracy: 1.0000 - val_loss: 0.0107 - val_accuracy: 1.0000\n",
            "Epoch 167/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 9.7029e-04 - accuracy: 1.0000 - val_loss: 0.0108 - val_accuracy: 1.0000\n",
            "Epoch 168/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0109 - val_accuracy: 0.9966\n",
            "Epoch 169/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0110 - val_accuracy: 0.9966\n",
            "Epoch 170/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0109 - val_accuracy: 0.9966\n",
            "Epoch 171/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0108 - val_accuracy: 0.9966\n",
            "Epoch 172/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9933\n",
            "Epoch 173/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 8.0754e-04 - accuracy: 1.0000 - val_loss: 0.0115 - val_accuracy: 0.9933\n",
            "Epoch 174/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 8.3635e-04 - accuracy: 1.0000 - val_loss: 0.0117 - val_accuracy: 0.9933\n",
            "Epoch 175/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0118 - val_accuracy: 0.9933\n",
            "Epoch 176/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0123 - val_accuracy: 0.9933\n",
            "Epoch 177/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 8.3077e-04 - accuracy: 1.0000 - val_loss: 0.0190 - val_accuracy: 0.9933\n",
            "Epoch 178/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0127 - accuracy: 0.9971 - val_loss: 0.0131 - val_accuracy: 0.9966\n",
            "Epoch 179/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0109 - val_accuracy: 0.9966\n",
            "Epoch 180/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0132 - val_accuracy: 1.0000\n",
            "Epoch 181/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0061 - accuracy: 0.9971 - val_loss: 0.0113 - val_accuracy: 1.0000\n",
            "Epoch 182/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0099 - val_accuracy: 1.0000\n",
            "Epoch 183/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0100 - val_accuracy: 1.0000\n",
            "Epoch 184/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0100 - val_accuracy: 1.0000\n",
            "Epoch 185/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0099 - val_accuracy: 1.0000\n",
            "Epoch 186/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0025 - accuracy: 0.9986 - val_loss: 0.0094 - val_accuracy: 1.0000\n",
            "Epoch 187/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0074 - accuracy: 0.9986 - val_loss: 0.0089 - val_accuracy: 1.0000\n",
            "Epoch 188/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 6.9404e-04 - accuracy: 1.0000 - val_loss: 0.0094 - val_accuracy: 1.0000\n",
            "Epoch 189/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0107 - val_accuracy: 1.0000\n",
            "Epoch 190/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0073 - accuracy: 0.9957 - val_loss: 0.0084 - val_accuracy: 1.0000\n",
            "Epoch 191/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0081 - val_accuracy: 1.0000\n",
            "Epoch 192/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0089 - val_accuracy: 1.0000\n",
            "Epoch 193/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0041 - accuracy: 0.9986 - val_loss: 0.0094 - val_accuracy: 1.0000\n",
            "Epoch 194/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0101 - val_accuracy: 1.0000\n",
            "Epoch 195/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0029 - accuracy: 0.9986 - val_loss: 0.0099 - val_accuracy: 1.0000\n",
            "Epoch 196/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0104 - val_accuracy: 0.9966\n",
            "Epoch 197/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0110 - val_accuracy: 0.9966\n",
            "Epoch 198/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9966\n",
            "Epoch 199/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0036 - accuracy: 0.9986 - val_loss: 0.0110 - val_accuracy: 0.9966\n",
            "Epoch 200/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0086 - val_accuracy: 0.9966\n",
            "Epoch 201/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 9.2857e-04 - accuracy: 1.0000 - val_loss: 0.0071 - val_accuracy: 1.0000\n",
            "Epoch 202/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 9.1150e-04 - accuracy: 1.0000 - val_loss: 0.0084 - val_accuracy: 1.0000\n",
            "Epoch 203/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0092 - val_accuracy: 1.0000\n",
            "Epoch 204/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 7.8144e-04 - accuracy: 1.0000 - val_loss: 0.0101 - val_accuracy: 0.9966\n",
            "Epoch 205/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0109 - val_accuracy: 0.9966\n",
            "Epoch 206/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 9.0823e-04 - accuracy: 1.0000 - val_loss: 0.0118 - val_accuracy: 0.9966\n",
            "Epoch 207/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0121 - val_accuracy: 0.9933\n",
            "Epoch 208/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 9.9204e-04 - accuracy: 1.0000 - val_loss: 0.0121 - val_accuracy: 0.9933\n",
            "Epoch 209/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 6.6848e-04 - accuracy: 1.0000 - val_loss: 0.0117 - val_accuracy: 0.9966\n",
            "Epoch 210/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 6.5317e-04 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 0.9966\n",
            "Epoch 211/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0017 - accuracy: 0.9986 - val_loss: 0.0076 - val_accuracy: 1.0000\n",
            "Epoch 212/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0264 - val_accuracy: 0.9933\n",
            "Epoch 213/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0112 - accuracy: 0.9986 - val_loss: 0.0078 - val_accuracy: 1.0000\n",
            "Epoch 214/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 9.1965e-04 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 1.0000\n",
            "Epoch 215/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 6.0081e-04 - accuracy: 1.0000 - val_loss: 0.0230 - val_accuracy: 0.9966\n",
            "Epoch 216/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 7.4759e-04 - accuracy: 1.0000 - val_loss: 0.0377 - val_accuracy: 0.9966\n",
            "Epoch 217/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0406 - val_accuracy: 0.9966\n",
            "Epoch 218/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0270 - val_accuracy: 0.9933\n",
            "Epoch 219/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0140 - val_accuracy: 0.9966\n",
            "Epoch 220/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 9.4585e-04 - accuracy: 1.0000 - val_loss: 0.0147 - val_accuracy: 0.9966\n",
            "Epoch 221/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 9.8857e-04 - accuracy: 1.0000 - val_loss: 0.0129 - val_accuracy: 0.9966\n",
            "Epoch 222/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 7.2822e-04 - accuracy: 1.0000 - val_loss: 0.0099 - val_accuracy: 0.9966\n",
            "Epoch 223/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 1.0000\n",
            "Epoch 224/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 1.0000\n",
            "Epoch 225/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 8.1713e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 1.0000\n",
            "Epoch 226/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 1.0000\n",
            "Epoch 227/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 8.7963e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 1.0000\n",
            "Epoch 228/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 1.0000\n",
            "Epoch 229/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "Epoch 230/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 5.9853e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "Epoch 231/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 4.9337e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 1.0000\n",
            "Epoch 232/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "Epoch 233/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "Epoch 234/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 8.2620e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "Epoch 235/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "Epoch 236/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 7.5122e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 1.0000\n",
            "Epoch 237/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 5.2602e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 1.0000\n",
            "Epoch 238/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 8.0871e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 1.0000\n",
            "Epoch 239/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 6.2588e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 1.0000\n",
            "Epoch 240/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "Epoch 241/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 4.4264e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 1.0000\n",
            "Epoch 242/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 4.4461e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 1.0000\n",
            "Epoch 243/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 8.4041e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 1.0000\n",
            "Epoch 244/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 1.0000\n",
            "Epoch 245/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 7.2759e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 1.0000\n",
            "Epoch 246/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 8.1419e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 1.0000\n",
            "Epoch 247/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 6.4743e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 1.0000\n",
            "Epoch 248/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 3.9357e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 1.0000\n",
            "Epoch 249/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 4.8454e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 1.0000\n",
            "Epoch 250/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 4.5736e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "Epoch 251/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0022 - accuracy: 0.9986 - val_loss: 0.0056 - val_accuracy: 1.0000\n",
            "Epoch 252/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 6.4053e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 1.0000\n",
            "Epoch 253/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0036 - accuracy: 0.9971 - val_loss: 0.0055 - val_accuracy: 1.0000\n",
            "Epoch 254/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 4.4218e-04 - accuracy: 1.0000 - val_loss: 0.0084 - val_accuracy: 0.9966\n",
            "Epoch 255/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0187 - accuracy: 0.9957 - val_loss: 0.0060 - val_accuracy: 1.0000\n",
            "Epoch 256/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 3.6245e-04 - accuracy: 1.0000 - val_loss: 0.0536 - val_accuracy: 0.9899\n",
            "Epoch 257/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0041 - accuracy: 0.9986 - val_loss: 0.0340 - val_accuracy: 0.9899\n",
            "Epoch 258/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0119 - accuracy: 0.9957 - val_loss: 0.0097 - val_accuracy: 0.9966\n",
            "Epoch 259/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 4.9861e-04 - accuracy: 1.0000 - val_loss: 0.0096 - val_accuracy: 1.0000\n",
            "Epoch 260/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0205 - val_accuracy: 0.9933\n",
            "Epoch 261/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0036 - accuracy: 0.9986 - val_loss: 0.0208 - val_accuracy: 0.9933\n",
            "Epoch 262/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0144 - val_accuracy: 0.9899\n",
            "Epoch 263/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 6.8078e-04 - accuracy: 1.0000 - val_loss: 0.0115 - val_accuracy: 0.9933\n",
            "Epoch 264/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 1.0000\n",
            "Epoch 265/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 1.0000\n",
            "Epoch 266/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0019 - accuracy: 0.9986 - val_loss: 0.0046 - val_accuracy: 1.0000\n",
            "Epoch 267/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 8.6268e-04 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000\n",
            "Epoch 268/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 1.0000\n",
            "Epoch 269/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0022 - accuracy: 0.9986 - val_loss: 0.0062 - val_accuracy: 1.0000\n",
            "Epoch 270/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 9.0240e-04 - accuracy: 1.0000 - val_loss: 0.0080 - val_accuracy: 0.9966\n",
            "Epoch 271/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 7.1477e-04 - accuracy: 1.0000 - val_loss: 0.0100 - val_accuracy: 0.9966\n",
            "Epoch 272/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 8.6006e-04 - accuracy: 1.0000 - val_loss: 0.0116 - val_accuracy: 0.9933\n",
            "Epoch 273/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0122 - val_accuracy: 0.9933\n",
            "Epoch 274/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0118 - val_accuracy: 0.9933\n",
            "Epoch 275/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0110 - val_accuracy: 0.9933\n",
            "Epoch 276/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 6.8450e-04 - accuracy: 1.0000 - val_loss: 0.0101 - val_accuracy: 0.9933\n",
            "Epoch 277/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0026 - accuracy: 0.9986 - val_loss: 0.0116 - val_accuracy: 0.9933\n",
            "Epoch 278/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0255 - val_accuracy: 0.9899\n",
            "Epoch 279/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0244 - val_accuracy: 0.9899\n",
            "Epoch 280/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 4.1363e-04 - accuracy: 1.0000 - val_loss: 0.0106 - val_accuracy: 0.9966\n",
            "Epoch 281/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0020 - accuracy: 0.9986 - val_loss: 0.0083 - val_accuracy: 1.0000\n",
            "Epoch 282/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 7.4575e-04 - accuracy: 1.0000 - val_loss: 0.0144 - val_accuracy: 0.9966\n",
            "Epoch 283/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0015 - accuracy: 0.9986 - val_loss: 0.0107 - val_accuracy: 0.9966\n",
            "Epoch 284/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 1.0000\n",
            "Epoch 285/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 4.5267e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 1.0000\n",
            "Epoch 286/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 8.8571e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "Epoch 287/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 5.2368e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "Epoch 288/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 1.0000\n",
            "Epoch 289/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 1.0000\n",
            "Epoch 290/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 1.0000\n",
            "Epoch 291/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 7.0959e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 1.0000\n",
            "Epoch 292/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0022 - accuracy: 0.9986 - val_loss: 0.0068 - val_accuracy: 1.0000\n",
            "Epoch 293/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 5.8885e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 1.0000\n",
            "Epoch 294/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 1.0000\n",
            "Epoch 295/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 9.7135e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 1.0000\n",
            "Epoch 296/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 4.6506e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 1.0000\n",
            "Epoch 297/2500\n",
            "4/4 [==============================] - 0s 47ms/step - loss: 7.9150e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 1.0000\n",
            "Epoch 298/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 6.4685e-04 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000\n",
            "Epoch 299/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 4.4939e-04 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000\n",
            "Epoch 300/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 3.2635e-04 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000\n",
            "Epoch 301/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 5.7939e-04 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000\n",
            "Epoch 302/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 3.3319e-04 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000\n",
            "Epoch 303/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 8.3170e-04 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000\n",
            "Epoch 304/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 3.0101e-04 - accuracy: 1.0000 - val_loss: 0.0041 - val_accuracy: 1.0000\n",
            "Epoch 305/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0038 - accuracy: 0.9986 - val_loss: 0.0042 - val_accuracy: 1.0000\n",
            "Epoch 306/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 6.2110e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 1.0000\n",
            "Epoch 307/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 8.6678e-04 - accuracy: 1.0000 - val_loss: 0.0254 - val_accuracy: 0.9899\n",
            "Epoch 308/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0060 - accuracy: 0.9986 - val_loss: 0.0086 - val_accuracy: 0.9966\n",
            "Epoch 309/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0015 - accuracy: 0.9986 - val_loss: 0.0057 - val_accuracy: 1.0000\n",
            "Epoch 310/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0030 - accuracy: 0.9986 - val_loss: 0.0054 - val_accuracy: 1.0000\n",
            "Epoch 311/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 1.0000\n",
            "Epoch 312/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0029 - accuracy: 0.9986 - val_loss: 0.0065 - val_accuracy: 1.0000\n",
            "Epoch 313/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 1.0000\n",
            "Epoch 314/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 1.0000\n",
            "Epoch 315/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 4.6698e-04 - accuracy: 1.0000 - val_loss: 0.0086 - val_accuracy: 1.0000\n",
            "Epoch 316/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0091 - val_accuracy: 1.0000\n",
            "Epoch 317/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 4.9610e-04 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 1.0000\n",
            "Epoch 318/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0090 - val_accuracy: 1.0000\n",
            "Epoch 319/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0077 - val_accuracy: 1.0000\n",
            "Epoch 320/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0037 - accuracy: 0.9986 - val_loss: 0.0077 - val_accuracy: 1.0000\n",
            "Epoch 321/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 4.7323e-04 - accuracy: 1.0000 - val_loss: 0.0178 - val_accuracy: 0.9933\n",
            "Epoch 322/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0292 - val_accuracy: 0.9933\n",
            "Epoch 323/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 8.8558e-04 - accuracy: 1.0000 - val_loss: 0.0301 - val_accuracy: 0.9933\n",
            "Epoch 324/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0085 - val_accuracy: 0.9966\n",
            "Epoch 325/2500\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
            "Epoch 326/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 6.0214e-04 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000\n",
            "Epoch 327/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "Epoch 328/2500\n",
            "4/4 [==============================] - 0s 107ms/step - loss: 0.0025 - accuracy: 0.9986 - val_loss: 0.0085 - val_accuracy: 1.0000\n",
            "Epoch 329/2500\n",
            "4/4 [==============================] - 0s 97ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0123 - val_accuracy: 1.0000\n",
            "Epoch 330/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0026 - accuracy: 0.9986 - val_loss: 0.0122 - val_accuracy: 1.0000\n",
            "Epoch 331/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 7.8645e-04 - accuracy: 1.0000 - val_loss: 0.0295 - val_accuracy: 0.9865\n",
            "Epoch 332/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0073 - accuracy: 0.9957 - val_loss: 0.0165 - val_accuracy: 0.9899\n",
            "Epoch 333/2500\n",
            "4/4 [==============================] - 0s 112ms/step - loss: 5.0953e-04 - accuracy: 1.0000 - val_loss: 0.0366 - val_accuracy: 0.9865\n",
            "Epoch 334/2500\n",
            "4/4 [==============================] - 0s 98ms/step - loss: 0.0056 - accuracy: 0.9971 - val_loss: 0.0221 - val_accuracy: 0.9899\n",
            "Epoch 335/2500\n",
            "4/4 [==============================] - 0s 52ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0394 - val_accuracy: 0.9832\n",
            "Epoch 336/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0037 - accuracy: 0.9986 - val_loss: 0.0353 - val_accuracy: 0.9865\n",
            "Epoch 337/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0126 - val_accuracy: 0.9966\n",
            "Epoch 338/2500\n",
            "4/4 [==============================] - 0s 57ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0300 - val_accuracy: 0.9933\n",
            "Epoch 339/2500\n",
            "4/4 [==============================] - 0s 104ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0376 - val_accuracy: 0.9933\n",
            "Epoch 340/2500\n",
            "4/4 [==============================] - 0s 83ms/step - loss: 0.0075 - accuracy: 0.9971 - val_loss: 0.0229 - val_accuracy: 0.9899\n",
            "Epoch 341/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 5.9924e-04 - accuracy: 1.0000 - val_loss: 0.0665 - val_accuracy: 0.9832\n",
            "Epoch 342/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0943 - val_accuracy: 0.9798\n",
            "Epoch 343/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0102 - accuracy: 0.9986 - val_loss: 0.0279 - val_accuracy: 0.9899\n",
            "Epoch 344/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0065 - accuracy: 0.9986 - val_loss: 0.0615 - val_accuracy: 0.9832\n",
            "Epoch 345/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0084 - accuracy: 0.9971 - val_loss: 0.0138 - val_accuracy: 0.9933\n",
            "Epoch 346/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0098 - accuracy: 0.9986 - val_loss: 0.0334 - val_accuracy: 0.9933\n",
            "Epoch 347/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0028 - accuracy: 0.9986 - val_loss: 0.0082 - val_accuracy: 1.0000\n",
            "Epoch 348/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0077 - val_accuracy: 1.0000\n",
            "Epoch 349/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0157 - val_accuracy: 0.9966\n",
            "Epoch 350/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0322 - val_accuracy: 0.9966\n",
            "Epoch 351/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0165 - val_accuracy: 0.9966\n",
            "Epoch 352/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 5.0589e-04 - accuracy: 1.0000 - val_loss: 0.0090 - val_accuracy: 1.0000\n",
            "Epoch 353/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 0.0091 - val_accuracy: 0.9966\n",
            "Epoch 354/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0026 - accuracy: 0.9986 - val_loss: 0.0321 - val_accuracy: 0.9933\n",
            "Epoch 355/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0052 - accuracy: 0.9971 - val_loss: 0.0373 - val_accuracy: 0.9966\n",
            "Epoch 356/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0082 - accuracy: 0.9971 - val_loss: 0.0130 - val_accuracy: 0.9966\n",
            "Epoch 357/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0063 - accuracy: 0.9971 - val_loss: 0.0232 - val_accuracy: 0.9933\n",
            "Epoch 358/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0108 - accuracy: 0.9957 - val_loss: 0.0105 - val_accuracy: 0.9966\n",
            "Epoch 359/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9966\n",
            "Epoch 360/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0081 - accuracy: 0.9971 - val_loss: 0.0301 - val_accuracy: 0.9899\n",
            "Epoch 361/2500\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0069 - accuracy: 0.9971 - val_loss: 0.0433 - val_accuracy: 0.9899\n",
            "Epoch 362/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0025 - accuracy: 0.9986 - val_loss: 0.0330 - val_accuracy: 0.9899\n",
            "Epoch 363/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0472 - val_accuracy: 0.9798\n",
            "Epoch 364/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0076 - accuracy: 0.9986 - val_loss: 0.0377 - val_accuracy: 0.9865\n",
            "Epoch 365/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0027 - accuracy: 0.9986 - val_loss: 0.0385 - val_accuracy: 0.9832\n",
            "Epoch 366/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0212 - val_accuracy: 0.9966\n",
            "Epoch 367/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0070 - accuracy: 0.9971 - val_loss: 0.0151 - val_accuracy: 0.9966\n",
            "Epoch 368/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0063 - accuracy: 0.9986 - val_loss: 0.0109 - val_accuracy: 0.9966\n",
            "Epoch 369/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.0101 - val_accuracy: 0.9966\n",
            "Epoch 370/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0201 - val_accuracy: 0.9933\n",
            "Epoch 371/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0102 - accuracy: 0.9957 - val_loss: 0.0169 - val_accuracy: 0.9933\n",
            "Epoch 372/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0080 - accuracy: 0.9986 - val_loss: 0.0153 - val_accuracy: 0.9966\n",
            "Epoch 373/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0039 - accuracy: 0.9986 - val_loss: 0.0351 - val_accuracy: 0.9899\n",
            "Epoch 374/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 0.0265 - val_accuracy: 0.9865\n",
            "Epoch 375/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0275 - accuracy: 0.9928 - val_loss: 0.0111 - val_accuracy: 0.9966\n",
            "Epoch 376/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0060 - accuracy: 0.9971 - val_loss: 0.0934 - val_accuracy: 0.9731\n",
            "Epoch 377/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0145 - accuracy: 0.9942 - val_loss: 0.0215 - val_accuracy: 0.9966\n",
            "Epoch 378/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0061 - accuracy: 0.9986 - val_loss: 0.0139 - val_accuracy: 0.9933\n",
            "Epoch 379/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0089 - accuracy: 0.9957 - val_loss: 0.0188 - val_accuracy: 0.9933\n",
            "Epoch 380/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0078 - accuracy: 0.9971 - val_loss: 0.0174 - val_accuracy: 0.9933\n",
            "Epoch 381/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0277 - accuracy: 0.9913 - val_loss: 0.0797 - val_accuracy: 0.9798\n",
            "Epoch 382/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0415 - accuracy: 0.9942 - val_loss: 0.0328 - val_accuracy: 0.9899\n",
            "Epoch 383/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0702 - val_accuracy: 0.9764\n",
            "Epoch 384/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0198 - accuracy: 0.9928 - val_loss: 0.0495 - val_accuracy: 0.9865\n",
            "Epoch 385/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0324 - accuracy: 0.9971 - val_loss: 0.0529 - val_accuracy: 0.9865\n",
            "Epoch 386/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0186 - accuracy: 0.9942 - val_loss: 0.0501 - val_accuracy: 0.9832\n",
            "Epoch 387/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0337 - accuracy: 0.9942 - val_loss: 0.0501 - val_accuracy: 0.9865\n",
            "Epoch 388/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0072 - accuracy: 0.9971 - val_loss: 0.0622 - val_accuracy: 0.9764\n",
            "Epoch 389/2500\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.0091 - accuracy: 0.9971 - val_loss: 0.1040 - val_accuracy: 0.9764\n",
            "Epoch 390/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0192 - accuracy: 0.9928 - val_loss: 0.0873 - val_accuracy: 0.9764\n",
            "Epoch 391/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0220 - accuracy: 0.9928 - val_loss: 0.0959 - val_accuracy: 0.9663\n",
            "Epoch 392/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0232 - accuracy: 0.9957 - val_loss: 0.1114 - val_accuracy: 0.9596\n",
            "Epoch 393/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0243 - accuracy: 0.9957 - val_loss: 0.0812 - val_accuracy: 0.9697\n",
            "Epoch 394/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0153 - accuracy: 0.9942 - val_loss: 0.0873 - val_accuracy: 0.9663\n",
            "Epoch 395/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0271 - accuracy: 0.9928 - val_loss: 0.0394 - val_accuracy: 0.9798\n",
            "Epoch 396/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0387 - val_accuracy: 0.9865\n",
            "Epoch 397/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.0481 - val_accuracy: 0.9865\n",
            "Epoch 398/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0178 - accuracy: 0.9957 - val_loss: 0.0693 - val_accuracy: 0.9798\n",
            "Epoch 399/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0239 - accuracy: 0.9899 - val_loss: 0.0452 - val_accuracy: 0.9865\n",
            "Epoch 400/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0106 - accuracy: 0.9957 - val_loss: 0.0595 - val_accuracy: 0.9832\n",
            "Epoch 401/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0109 - accuracy: 0.9971 - val_loss: 0.1547 - val_accuracy: 0.9663\n",
            "Epoch 402/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0178 - accuracy: 0.9971 - val_loss: 0.0854 - val_accuracy: 0.9764\n",
            "Epoch 403/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0070 - accuracy: 0.9986 - val_loss: 0.0826 - val_accuracy: 0.9731\n",
            "Epoch 404/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0102 - accuracy: 0.9957 - val_loss: 0.0717 - val_accuracy: 0.9798\n",
            "Epoch 405/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.0575 - val_accuracy: 0.9832\n",
            "Epoch 406/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0102 - accuracy: 0.9971 - val_loss: 0.0386 - val_accuracy: 0.9865\n",
            "Epoch 407/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0061 - accuracy: 0.9971 - val_loss: 0.0418 - val_accuracy: 0.9832\n",
            "Epoch 408/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0088 - accuracy: 0.9986 - val_loss: 0.0591 - val_accuracy: 0.9798\n",
            "Epoch 409/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0153 - accuracy: 0.9957 - val_loss: 0.0433 - val_accuracy: 0.9798\n",
            "Epoch 410/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0070 - accuracy: 0.9971 - val_loss: 0.0612 - val_accuracy: 0.9832\n",
            "Epoch 411/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0059 - accuracy: 0.9971 - val_loss: 0.0670 - val_accuracy: 0.9832\n",
            "Epoch 412/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0070 - accuracy: 0.9971 - val_loss: 0.0534 - val_accuracy: 0.9832\n",
            "Epoch 413/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0053 - accuracy: 0.9971 - val_loss: 0.0497 - val_accuracy: 0.9832\n",
            "Epoch 414/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0066 - accuracy: 0.9971 - val_loss: 0.0263 - val_accuracy: 0.9865\n",
            "Epoch 415/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0054 - accuracy: 0.9971 - val_loss: 0.0379 - val_accuracy: 0.9832\n",
            "Epoch 416/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0135 - accuracy: 0.9971 - val_loss: 0.0502 - val_accuracy: 0.9832\n",
            "Epoch 417/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.0689 - val_accuracy: 0.9798\n",
            "Epoch 418/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0090 - accuracy: 0.9971 - val_loss: 0.0989 - val_accuracy: 0.9697\n",
            "Epoch 419/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0135 - accuracy: 0.9942 - val_loss: 0.0980 - val_accuracy: 0.9731\n",
            "Epoch 420/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.1157 - val_accuracy: 0.9764\n",
            "Epoch 421/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.1064 - val_accuracy: 0.9798\n",
            "Epoch 422/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9832\n",
            "Epoch 423/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0053 - accuracy: 0.9986 - val_loss: 0.0819 - val_accuracy: 0.9832\n",
            "Epoch 424/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0019 - accuracy: 0.9986 - val_loss: 0.0387 - val_accuracy: 0.9899\n",
            "Epoch 425/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0258 - val_accuracy: 0.9899\n",
            "Epoch 426/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0044 - accuracy: 0.9986 - val_loss: 0.0223 - val_accuracy: 0.9865\n",
            "Epoch 427/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0157 - accuracy: 0.9971 - val_loss: 0.0205 - val_accuracy: 0.9933\n",
            "Epoch 428/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0350 - val_accuracy: 0.9933\n",
            "Epoch 429/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0081 - accuracy: 0.9957 - val_loss: 0.0486 - val_accuracy: 0.9865\n",
            "Epoch 430/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0033 - accuracy: 0.9986 - val_loss: 0.0545 - val_accuracy: 0.9865\n",
            "Epoch 431/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.0531 - val_accuracy: 0.9865\n",
            "Epoch 432/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0455 - val_accuracy: 0.9865\n",
            "Epoch 433/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 6.7270e-04 - accuracy: 1.0000 - val_loss: 0.0435 - val_accuracy: 0.9899\n",
            "Epoch 434/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0079 - accuracy: 0.9986 - val_loss: 0.0401 - val_accuracy: 0.9933\n",
            "Epoch 435/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 8.8552e-04 - accuracy: 1.0000 - val_loss: 0.0333 - val_accuracy: 0.9933\n",
            "Epoch 436/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0243 - val_accuracy: 0.9933\n",
            "Epoch 437/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0229 - accuracy: 0.9971 - val_loss: 0.0242 - val_accuracy: 0.9966\n",
            "Epoch 438/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0045 - accuracy: 0.9971 - val_loss: 0.0265 - val_accuracy: 0.9933\n",
            "Epoch 439/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0033 - accuracy: 0.9986 - val_loss: 0.0288 - val_accuracy: 0.9933\n",
            "Epoch 440/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0039 - accuracy: 0.9986 - val_loss: 0.0442 - val_accuracy: 0.9865\n",
            "Epoch 441/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0083 - accuracy: 0.9986 - val_loss: 0.0430 - val_accuracy: 0.9899\n",
            "Epoch 442/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0448 - val_accuracy: 0.9832\n",
            "Epoch 443/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0113 - accuracy: 0.9971 - val_loss: 0.0326 - val_accuracy: 0.9899\n",
            "Epoch 444/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0057 - accuracy: 0.9971 - val_loss: 0.0374 - val_accuracy: 0.9899\n",
            "Epoch 445/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0274 - val_accuracy: 0.9865\n",
            "Epoch 446/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0090 - accuracy: 0.9986 - val_loss: 0.0169 - val_accuracy: 0.9899\n",
            "Epoch 447/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0136 - val_accuracy: 0.9899\n",
            "Epoch 448/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 9.4353e-04 - accuracy: 1.0000 - val_loss: 0.0158 - val_accuracy: 0.9933\n",
            "Epoch 449/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0059 - accuracy: 0.9986 - val_loss: 0.0224 - val_accuracy: 0.9899\n",
            "Epoch 450/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0047 - accuracy: 0.9986 - val_loss: 0.0128 - val_accuracy: 0.9966\n",
            "Epoch 451/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0067 - accuracy: 0.9971 - val_loss: 0.0180 - val_accuracy: 0.9933\n",
            "Epoch 452/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0183 - val_accuracy: 0.9933\n",
            "Epoch 453/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0118 - accuracy: 0.9957 - val_loss: 0.0174 - val_accuracy: 0.9966\n",
            "Epoch 454/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 0.0344 - val_accuracy: 0.9966\n",
            "Epoch 455/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0022 - accuracy: 0.9986 - val_loss: 0.0225 - val_accuracy: 0.9966\n",
            "Epoch 456/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0163 - accuracy: 0.9971 - val_loss: 0.0395 - val_accuracy: 0.9899\n",
            "Epoch 457/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0020 - accuracy: 0.9986 - val_loss: 0.0826 - val_accuracy: 0.9832\n",
            "Epoch 458/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0181 - accuracy: 0.9971 - val_loss: 0.0374 - val_accuracy: 0.9832\n",
            "Epoch 459/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0031 - accuracy: 0.9986 - val_loss: 0.0438 - val_accuracy: 0.9865\n",
            "Epoch 460/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0062 - accuracy: 0.9986 - val_loss: 0.0707 - val_accuracy: 0.9832\n",
            "Epoch 461/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0143 - accuracy: 0.9942 - val_loss: 0.0717 - val_accuracy: 0.9764\n",
            "Epoch 462/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0053 - accuracy: 0.9971 - val_loss: 0.0640 - val_accuracy: 0.9764\n",
            "Epoch 463/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0085 - accuracy: 0.9986 - val_loss: 0.0488 - val_accuracy: 0.9764\n",
            "Epoch 464/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0086 - accuracy: 0.9971 - val_loss: 0.0400 - val_accuracy: 0.9865\n",
            "Epoch 465/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0323 - accuracy: 0.9957 - val_loss: 0.0417 - val_accuracy: 0.9798\n",
            "Epoch 466/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0332 - accuracy: 0.9971 - val_loss: 0.1233 - val_accuracy: 0.9697\n",
            "Epoch 467/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0545 - accuracy: 0.9870 - val_loss: 0.1327 - val_accuracy: 0.9697\n",
            "Epoch 468/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0300 - accuracy: 0.9942 - val_loss: 0.1034 - val_accuracy: 0.9663\n",
            "Epoch 469/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0119 - accuracy: 0.9942 - val_loss: 0.1531 - val_accuracy: 0.9663\n",
            "Epoch 470/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0632 - accuracy: 0.9841 - val_loss: 0.0568 - val_accuracy: 0.9899\n",
            "Epoch 471/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0158 - accuracy: 0.9928 - val_loss: 0.0928 - val_accuracy: 0.9798\n",
            "Epoch 472/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0186 - accuracy: 0.9957 - val_loss: 0.1090 - val_accuracy: 0.9764\n",
            "Epoch 473/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0482 - accuracy: 0.9913 - val_loss: 0.0476 - val_accuracy: 0.9865\n",
            "Epoch 474/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0188 - accuracy: 0.9942 - val_loss: 0.0136 - val_accuracy: 0.9966\n",
            "Epoch 475/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0155 - accuracy: 0.9971 - val_loss: 0.0774 - val_accuracy: 0.9798\n",
            "Epoch 476/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0161 - accuracy: 0.9942 - val_loss: 0.0451 - val_accuracy: 0.9899\n",
            "Epoch 477/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0066 - accuracy: 0.9971 - val_loss: 0.0554 - val_accuracy: 0.9764\n",
            "Epoch 478/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0109 - accuracy: 0.9986 - val_loss: 0.0906 - val_accuracy: 0.9731\n",
            "Epoch 479/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0197 - accuracy: 0.9957 - val_loss: 0.0757 - val_accuracy: 0.9731\n",
            "Epoch 480/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0091 - accuracy: 0.9957 - val_loss: 0.0720 - val_accuracy: 0.9731\n",
            "Epoch 481/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0092 - accuracy: 0.9957 - val_loss: 0.1141 - val_accuracy: 0.9663\n",
            "Epoch 482/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0161 - accuracy: 0.9957 - val_loss: 0.0693 - val_accuracy: 0.9764\n",
            "Epoch 483/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0114 - accuracy: 0.9928 - val_loss: 0.0625 - val_accuracy: 0.9798\n",
            "Epoch 484/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0306 - accuracy: 0.9957 - val_loss: 0.0836 - val_accuracy: 0.9731\n",
            "Epoch 485/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0079 - accuracy: 0.9971 - val_loss: 0.1532 - val_accuracy: 0.9630\n",
            "Epoch 486/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0434 - accuracy: 0.9942 - val_loss: 0.0625 - val_accuracy: 0.9764\n",
            "Epoch 487/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0156 - accuracy: 0.9942 - val_loss: 0.0314 - val_accuracy: 0.9865\n",
            "Epoch 488/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0223 - accuracy: 0.9899 - val_loss: 0.0215 - val_accuracy: 0.9933\n",
            "Epoch 489/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0393 - accuracy: 0.9899 - val_loss: 0.0230 - val_accuracy: 0.9933\n",
            "Epoch 490/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0156 - accuracy: 0.9971 - val_loss: 0.0495 - val_accuracy: 0.9832\n",
            "Epoch 491/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0256 - accuracy: 0.9942 - val_loss: 0.0412 - val_accuracy: 0.9865\n",
            "Epoch 492/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0199 - accuracy: 0.9986 - val_loss: 0.0518 - val_accuracy: 0.9832\n",
            "Epoch 493/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0351 - accuracy: 0.9971 - val_loss: 0.0514 - val_accuracy: 0.9764\n",
            "Epoch 494/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 0.9697\n",
            "Epoch 495/2500\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 0.0205 - accuracy: 0.9957 - val_loss: 0.1157 - val_accuracy: 0.9731\n",
            "Epoch 496/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0095 - accuracy: 0.9986 - val_loss: 0.0749 - val_accuracy: 0.9798\n",
            "Epoch 497/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0114 - accuracy: 0.9957 - val_loss: 0.0620 - val_accuracy: 0.9832\n",
            "Epoch 498/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 4.3941e-04 - accuracy: 1.0000 - val_loss: 0.0639 - val_accuracy: 0.9832\n",
            "Epoch 499/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0137 - accuracy: 0.9971 - val_loss: 0.0693 - val_accuracy: 0.9832\n",
            "Epoch 500/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0161 - accuracy: 0.9957 - val_loss: 0.0815 - val_accuracy: 0.9731\n",
            "Epoch 501/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0892 - val_accuracy: 0.9731\n",
            "Epoch 502/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9731\n",
            "Epoch 503/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0039 - accuracy: 0.9986 - val_loss: 0.0878 - val_accuracy: 0.9731\n",
            "Epoch 504/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0105 - accuracy: 0.9971 - val_loss: 0.0766 - val_accuracy: 0.9798\n",
            "Epoch 505/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 9.8292e-04 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 0.9764\n",
            "Epoch 506/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0018 - accuracy: 0.9986 - val_loss: 0.0798 - val_accuracy: 0.9798\n",
            "Epoch 507/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0024 - accuracy: 0.9986 - val_loss: 0.1050 - val_accuracy: 0.9764\n",
            "Epoch 508/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.1353 - val_accuracy: 0.9697\n",
            "Epoch 509/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0041 - accuracy: 0.9986 - val_loss: 0.1499 - val_accuracy: 0.9731\n",
            "Epoch 510/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0141 - accuracy: 0.9957 - val_loss: 0.0982 - val_accuracy: 0.9764\n",
            "Epoch 511/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 7.0875e-04 - accuracy: 1.0000 - val_loss: 0.0834 - val_accuracy: 0.9798\n",
            "Epoch 512/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0753 - val_accuracy: 0.9798\n",
            "Epoch 513/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0102 - accuracy: 0.9986 - val_loss: 0.0569 - val_accuracy: 0.9865\n",
            "Epoch 514/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0029 - accuracy: 0.9986 - val_loss: 0.0505 - val_accuracy: 0.9865\n",
            "Epoch 515/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0022 - accuracy: 0.9986 - val_loss: 0.0475 - val_accuracy: 0.9865\n",
            "Epoch 516/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 1.8146e-04 - accuracy: 1.0000 - val_loss: 0.0667 - val_accuracy: 0.9832\n",
            "Epoch 517/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 8.7968e-04 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 0.9832\n",
            "Epoch 518/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0189 - accuracy: 0.9971 - val_loss: 0.0372 - val_accuracy: 0.9865\n",
            "Epoch 519/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 1.1560e-04 - accuracy: 1.0000 - val_loss: 0.0322 - val_accuracy: 0.9933\n",
            "Epoch 520/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 3.0219e-04 - accuracy: 1.0000 - val_loss: 0.0317 - val_accuracy: 0.9933\n",
            "Epoch 521/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0042 - accuracy: 0.9986 - val_loss: 0.0306 - val_accuracy: 0.9933\n",
            "Epoch 522/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0038 - accuracy: 0.9971 - val_loss: 0.0548 - val_accuracy: 0.9899\n",
            "Epoch 523/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0177 - accuracy: 0.9971 - val_loss: 0.0806 - val_accuracy: 0.9865\n",
            "Epoch 524/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 3.0365e-04 - accuracy: 1.0000 - val_loss: 0.0767 - val_accuracy: 0.9832\n",
            "Epoch 525/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0031 - accuracy: 0.9986 - val_loss: 0.0710 - val_accuracy: 0.9832\n",
            "Epoch 526/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0018 - accuracy: 0.9986 - val_loss: 0.0602 - val_accuracy: 0.9832\n",
            "Epoch 527/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0478 - val_accuracy: 0.9865\n",
            "Epoch 528/2500\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.0024 - accuracy: 0.9986 - val_loss: 0.0365 - val_accuracy: 0.9865\n",
            "Epoch 529/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 2.9896e-04 - accuracy: 1.0000 - val_loss: 0.0382 - val_accuracy: 0.9832\n",
            "Epoch 530/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 6.1969e-04 - accuracy: 1.0000 - val_loss: 0.0584 - val_accuracy: 0.9832\n",
            "Epoch 531/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 1.6793e-04 - accuracy: 1.0000 - val_loss: 0.0733 - val_accuracy: 0.9832\n",
            "Epoch 532/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 3.0297e-04 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 0.9865\n",
            "Epoch 533/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0027 - accuracy: 0.9986 - val_loss: 0.0796 - val_accuracy: 0.9865\n",
            "Epoch 534/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 3.3117e-04 - accuracy: 1.0000 - val_loss: 0.0794 - val_accuracy: 0.9865\n",
            "Epoch 535/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 7.5410e-04 - accuracy: 1.0000 - val_loss: 0.0735 - val_accuracy: 0.9865\n",
            "Epoch 536/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 3.1024e-04 - accuracy: 1.0000 - val_loss: 0.0553 - val_accuracy: 0.9865\n",
            "Epoch 537/2500\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 4.1297e-04 - accuracy: 1.0000 - val_loss: 0.0425 - val_accuracy: 0.9899\n",
            "Epoch 538/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 2.7912e-04 - accuracy: 1.0000 - val_loss: 0.0347 - val_accuracy: 0.9933\n",
            "Epoch 539/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 5.1048e-04 - accuracy: 1.0000 - val_loss: 0.0304 - val_accuracy: 0.9933\n",
            "Epoch 540/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 3.6015e-04 - accuracy: 1.0000 - val_loss: 0.0289 - val_accuracy: 0.9933\n",
            "Epoch 541/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 5.1509e-04 - accuracy: 1.0000 - val_loss: 0.0292 - val_accuracy: 0.9933\n",
            "Epoch 542/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 9.5420e-05 - accuracy: 1.0000 - val_loss: 0.0299 - val_accuracy: 0.9933\n",
            "Epoch 543/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 8.6101e-04 - accuracy: 1.0000 - val_loss: 0.0309 - val_accuracy: 0.9933\n",
            "Epoch 544/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 9.5648e-04 - accuracy: 1.0000 - val_loss: 0.0323 - val_accuracy: 0.9933\n",
            "Epoch 545/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 1.7233e-04 - accuracy: 1.0000 - val_loss: 0.0320 - val_accuracy: 0.9933\n",
            "Epoch 546/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0025 - accuracy: 0.9986 - val_loss: 0.0295 - val_accuracy: 0.9899\n",
            "Epoch 547/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0280 - val_accuracy: 0.9899\n",
            "Epoch 548/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0273 - val_accuracy: 0.9933\n",
            "Epoch 549/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0053 - accuracy: 0.9971 - val_loss: 0.0272 - val_accuracy: 0.9933\n",
            "Epoch 550/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0021 - accuracy: 0.9986 - val_loss: 0.0307 - val_accuracy: 0.9865\n",
            "Epoch 551/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0015 - accuracy: 0.9986 - val_loss: 0.0324 - val_accuracy: 0.9899\n",
            "Epoch 552/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0019 - accuracy: 0.9986 - val_loss: 0.0406 - val_accuracy: 0.9832\n",
            "Epoch 553/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 2.5505e-04 - accuracy: 1.0000 - val_loss: 0.0463 - val_accuracy: 0.9832\n",
            "Epoch 554/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0032 - accuracy: 0.9986 - val_loss: 0.0389 - val_accuracy: 0.9899\n",
            "Epoch 555/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0081 - accuracy: 0.9971 - val_loss: 0.0373 - val_accuracy: 0.9899\n",
            "Epoch 556/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0336 - val_accuracy: 0.9865\n",
            "Epoch 557/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 2.3894e-04 - accuracy: 1.0000 - val_loss: 0.0317 - val_accuracy: 0.9899\n",
            "Epoch 558/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 2.3180e-04 - accuracy: 1.0000 - val_loss: 0.0363 - val_accuracy: 0.9865\n",
            "Epoch 559/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0385 - val_accuracy: 0.9865\n",
            "Epoch 560/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 8.5443e-04 - accuracy: 1.0000 - val_loss: 0.0414 - val_accuracy: 0.9899\n",
            "Epoch 561/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0012 - accuracy: 0.9986 - val_loss: 0.0439 - val_accuracy: 0.9865\n",
            "Epoch 562/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 3.8406e-04 - accuracy: 1.0000 - val_loss: 0.0513 - val_accuracy: 0.9865\n",
            "Epoch 563/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 5.7050e-04 - accuracy: 1.0000 - val_loss: 0.0568 - val_accuracy: 0.9865\n",
            "Epoch 564/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 8.9931e-05 - accuracy: 1.0000 - val_loss: 0.0579 - val_accuracy: 0.9865\n",
            "Epoch 565/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0029 - accuracy: 0.9971 - val_loss: 0.0591 - val_accuracy: 0.9865\n",
            "Epoch 566/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0030 - accuracy: 0.9986 - val_loss: 0.0517 - val_accuracy: 0.9899\n",
            "Epoch 567/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0025 - accuracy: 0.9986 - val_loss: 0.0471 - val_accuracy: 0.9899\n",
            "Epoch 568/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 5.6707e-04 - accuracy: 1.0000 - val_loss: 0.0459 - val_accuracy: 0.9865\n",
            "Epoch 569/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 2.2834e-04 - accuracy: 1.0000 - val_loss: 0.0456 - val_accuracy: 0.9865\n",
            "Epoch 570/2500\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 1.4505e-04 - accuracy: 1.0000 - val_loss: 0.0466 - val_accuracy: 0.9865\n",
            "Epoch 571/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0065 - accuracy: 0.9986 - val_loss: 0.0413 - val_accuracy: 0.9865\n",
            "Epoch 572/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.0450 - val_accuracy: 0.9832\n",
            "Epoch 573/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 5.5690e-04 - accuracy: 1.0000 - val_loss: 0.0608 - val_accuracy: 0.9832\n",
            "Epoch 574/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0057 - accuracy: 0.9986 - val_loss: 0.0381 - val_accuracy: 0.9865\n",
            "Epoch 575/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0025 - accuracy: 0.9986 - val_loss: 0.0390 - val_accuracy: 0.9865\n",
            "Epoch 576/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0445 - val_accuracy: 0.9899\n",
            "Epoch 577/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 1.5852e-04 - accuracy: 1.0000 - val_loss: 0.0484 - val_accuracy: 0.9899\n",
            "Epoch 578/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0096 - accuracy: 0.9986 - val_loss: 0.0468 - val_accuracy: 0.9865\n",
            "Epoch 579/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 5.7992e-04 - accuracy: 1.0000 - val_loss: 0.0424 - val_accuracy: 0.9865\n",
            "Epoch 580/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 8.8106e-05 - accuracy: 1.0000 - val_loss: 0.0427 - val_accuracy: 0.9865\n",
            "Epoch 581/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 9.7554e-04 - accuracy: 1.0000 - val_loss: 0.0444 - val_accuracy: 0.9899\n",
            "Epoch 582/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 4.7911e-04 - accuracy: 1.0000 - val_loss: 0.0461 - val_accuracy: 0.9899\n",
            "Epoch 583/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 2.1856e-05 - accuracy: 1.0000 - val_loss: 0.0477 - val_accuracy: 0.9899\n",
            "Epoch 584/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 1.6127e-04 - accuracy: 1.0000 - val_loss: 0.0493 - val_accuracy: 0.9899\n",
            "Epoch 585/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 3.7006e-05 - accuracy: 1.0000 - val_loss: 0.0508 - val_accuracy: 0.9899\n",
            "Epoch 586/2500\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 2.5308e-04 - accuracy: 1.0000 - val_loss: 0.0517 - val_accuracy: 0.9865\n",
            "Epoch 587/2500\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 7.3929e-05 - accuracy: 1.0000 - val_loss: 0.0524 - val_accuracy: 0.9865\n",
            "Epoch 588/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 2.3446e-04 - accuracy: 1.0000 - val_loss: 0.0559 - val_accuracy: 0.9832\n",
            "Epoch 589/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 3.8530e-04 - accuracy: 1.0000 - val_loss: 0.0571 - val_accuracy: 0.9865\n",
            "Epoch 590/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 2.0364e-05 - accuracy: 1.0000 - val_loss: 0.0570 - val_accuracy: 0.9865\n",
            "Epoch 591/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 2.9345e-05 - accuracy: 1.0000 - val_loss: 0.0579 - val_accuracy: 0.9865\n",
            "Epoch 592/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 3.0954e-04 - accuracy: 1.0000 - val_loss: 0.0583 - val_accuracy: 0.9865\n",
            "Epoch 593/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 1.8827e-04 - accuracy: 1.0000 - val_loss: 0.0582 - val_accuracy: 0.9865\n",
            "Epoch 594/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 1.8185e-05 - accuracy: 1.0000 - val_loss: 0.0580 - val_accuracy: 0.9865\n",
            "Epoch 595/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 3.6869e-05 - accuracy: 1.0000 - val_loss: 0.0579 - val_accuracy: 0.9865\n",
            "Epoch 596/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 6.7524e-04 - accuracy: 1.0000 - val_loss: 0.0578 - val_accuracy: 0.9865\n",
            "Epoch 597/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 1.8266e-04 - accuracy: 1.0000 - val_loss: 0.0580 - val_accuracy: 0.9865\n",
            "Epoch 598/2500\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 4.6197e-05 - accuracy: 1.0000 - val_loss: 0.0584 - val_accuracy: 0.9865\n",
            "Epoch 599/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0025 - accuracy: 0.9986 - val_loss: 0.0595 - val_accuracy: 0.9865\n",
            "Epoch 600/2500\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 1.9733e-05 - accuracy: 1.0000 - val_loss: 0.0608 - val_accuracy: 0.9865\n",
            "Epoch 601/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0013 - accuracy: 0.9986 - val_loss: 0.0701 - val_accuracy: 0.9798\n",
            "Epoch 602/2500\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 1.7159e-04 - accuracy: 1.0000 - val_loss: 0.0800 - val_accuracy: 0.9798\n",
            "Epoch 603/2500\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 1.3910e-04 - accuracy: 1.0000 - val_loss: 0.0875 - val_accuracy: 0.9798\n",
            "CPU times: user 2min 1s, sys: 4.99 s, total: 2min 6s\n",
            "Wall time: 1min 32s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('val_acc: ',max(history.history['val_accuracy']))\n",
        "print('val_loss: ',min(history.history['val_loss']))\n",
        "print('train_acc: ',max(history.history['accuracy']))\n",
        "print('train_loss: ',min(history.history['loss']))\n",
        "\n",
        "print()\n",
        "print(\"train/val loss ratio: \", min(history.history['loss'])/min(history.history['val_loss']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5W51q1Yhd66I",
        "outputId": "03c7e8a6-0c4d-4c8a-8ae6-786ce70e3a0e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "val_acc:  1.0\n",
            "val_loss:  0.004055082332342863\n",
            "train_acc:  1.0\n",
            "train_loss:  1.8184629880124703e-05\n",
            "\n",
            "train/val loss ratio:  0.004484404603843976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Kayıp için geçmişi özetle\n",
        "## Kaybı yineleme sayısı ile çizme\n",
        "plt.semilogy(history.history['loss'])\n",
        "plt.semilogy(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "plt.savefig('model_loss.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "81iW4xTvd8Ei",
        "outputId": "a5ad5970-0317-4a9f-84b6-1689aec6d6fb"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Hatayı yineleme sayısı ile çizme\n",
        "## Her yinelemede hata sorunsuz şekilde çalışır\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "plt.savefig('model_accuracy.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "AT-CftG5eA4d",
        "outputId": "850449c4-8ac8-4155-9a40-fb422d3e5bd9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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yoJx+3YtpfOQqDl/+W7YW9uGOw37H6FGjwYKaGMDarQ2MLNoCtxwP21prw25xzFMALD7iOrb3Ppi1y9/ihMPHYKV9WV5XQHn/UTzyxOMcWb6RkR85h3nLN5OIGYWbljKs6W0aRp9C//efDMqhwjI49BOQaDspnbvz1pptNCRTjOlXnrWmlCsz+6e7T866rQMDwc3AU83zw5rZm8DU3TUN7dNAsOk9qNuIE7TFbKWEegroG17Be0EJ1msYSSsgEQMagisCisrbhPKzzz6bWbNmcdCIIRQUJCguKqRXj+68sXQZby34B584/xJWhLe3Xnrppcy88Auw9nWGHXUK859/lm1NcPLJJ3Pcccfx3HPPMah/X/76l7vo1nPHf9i9zms+vfsMbAwLn17DYcRH9/w9ko2weBZUTYameogXQMVIWL8UcKgc/cHT+cz18MS18JHLYN2bsPx5+OIT0Cuc8rhhKyz+G6QaYc3rMPdmOPoSmP4/e3ae354WvNeXnt5x28Z34N2/w8BJQWAF+L9RULuudZ9uvaG+JqipNvv3d6Gkd/B6+YtQ3h82vQv3XwxbVrbud/bt0H0AvHATvNoyaRoNfcZTtO5VALb0m0KitDdvHfsjFqxOMm1sP95/8ibGrHuMuSsbqTjrp0waPx5euRu2b4CjvxzUnBfPCs557NfZtL2JXqWF8Kez4a1HWs9fdSRUz4MJ58Ihp8Od52T/jEaeAMdcElyIPfhNOPg0asedS1H/sdjzP2PsPz7MF+KPcHnBnW2P+8xfYPSJzFu2kZgZvef8O8OXBflsOOF7FH34q8F+6XTwP5rtkvutx2Der6DPQbDwjuDvv3ZxkPaPfBtevBmOvhjefCi40NmwFLaugWnXBt9Ld3j2x1Czm4ndRn88+N9I1gXv3Wt48J1+6n+D7T2HQM3y1v27D4KeQ6GobMf3GjQZpl6+6/PtxoEaCE4FvkIwpd9RwA3uPqX9fu3tLhBc+7fXeH3VbtqR00lIBlcUaYw6LyRmUBRG26Dt12muzo4d2J2rTz90p2+3bNkyTjvtNBYtmM9TD9/Hqed8kUWvvMzw4cMglmDjxo307t2buro6jjzySJ5++mkqevdi2PARzJ8/n23btjFq1Cjmz5/PxIkTOeuss5gxYwbnnXfeDuc6IANBZ+EeXH1169Vaq4nv4pmM7Rtb990T6TTgQXNjLhbdC3+7FBrC7+3nH4HnbggKIoAx0+Hcu7If21QHvzy2NRB/bSH0Hh6k4e7PwhsPBOvHfQoW3dP22E/dBuM+GQTe/x0U/F8ADPswnHAV3DodPAVl/WDbmtbjBh4On38YnrgOXvgFFPeEsWdA7XrYugr6Hgqn/yT4bN9+Ev5wZnBc1RSYchGM/USwzSz4jH+YcTv0iKnwzlM8O+57bFn2Eqdsu5c2Tv4/OGombK4Gi8FvTsK3rmqpRXLhbJh/a1DDbP7cVr4U/C36HgpNtfDj8ZBugqbtwTEX/C0ISLO+uuPn2388xIvguG/AIae1rk82BJ/Xw5fDgnDK54rRsG0tFJYGP0VlUFIRfIc2LIU1rwUXGb1HwuApsO4NGH1SUMtddA88+h+tn2+m7RuDgPHJX8HgI6HXsB3TmYNdBYK89RGY2R3AVKDSzKqBqyFoXHf3m4CHCILAUmA78Pl8pWUH6SSEzUDp8MapeCzWrvNvL9qiC4qh+wCmTJnC8JGjWlbfcMMN3HfffQCsWLGCJUuWUHH00W0OHT58OBMnTgTgiCOOYNmyZXt+ftk1s9arath1EIC2++6J2B7ejDfuk8HPurdgwxIYegwkCoNAMPhomPGznR9b0A0mngtP/heceE0QBJrT8K+/hf+qDJZHT2sNBAedCm8+GFzVFpaF/WPJ1vdc9gz8Zhr0GAyllbBqAUw4J7iC3rwyuJq++wJY8mhw1fuJm6C0Inv6Rn4MvrkYVswNmj7aK+kd1GLeew5euBHeeQqA49Lz8LH9YW67/de8GhSMP269MLOP/3eQh0euhHsvCgr1nkNhyWx452n4/Yxgx4nnwepXoGEznPKj4LPr1huGfyQIfvEi2LwiDEZzgiv48Z/Knq9EEVAEZ/wcDj4tOG7yhbv+2y9/Ed5fCJPOD86d6ZhLgnTUbQp+Z9r4LvzsCLj3i3Dq9XDkhTs/x17K511DO6kTtmx3YMdG2w9oV1fuLZojM7DSK9jg3anqVULv0p13Fu2J0tLSltdPPfUUjz/+OM8//zwlJSVMnTo167MARUWtbYPxeJy6urp9khbpRPqMCX4ABh0B314SXFHurmZx3Ddg0gVQ1m6E4cxAd9jZ0KMq6DOa8TN4ugpe+j08fg0MOy7Y5xuvBc0iz90QLB9xAXzoa1CzAirDCxv3oIloyaNBc8Wn/7T7gNp9YPYg0OyQ04KfLdVBwNj6Prz+V2zMycH2gZPgX2+Dh6+ABbcH6c409Jjg83r/5aAmEC+Ek/4H7vpM0HTVbOEfg9+n/GjHQtsMJmTsW5X1wjm7g3Z1l3yGIUcFPzvTf3z29b2Hw9cWBM2F3atyT9ce6BJPFu8R95YgAFDvQeFftItb63anvLycrVuzz/i3efNmevXqRUlJCW+88QYvvPDCXp9HIqZs1526LWLxHYNAs7P+AD0GBQXdsOPgi2HH/ik/DI558nuw9rVgXY+qoN262ZEXBVe+la21W8yCK9Y3H4F/+fXug8CeOCss4BfcDn+9GN56ODjXBX8L1p9xIzx7PTz/87bH9R0b/J5wThAIpswMairHfQPqamDI0dDvULjpODj0k0HzVGfTaygwNG9vH71AkApuAaWonHRDLfUUMaBHN0oK9743vqKigmOPPZZx48bRrVs3+vXr17Jt+vTp3HTTTRxyyCEcdNBBHN2uSUgkr8bO2Pm24R8Fvhe8nnBu8HvUiRArgAsfg247uU3x5B/Cx76z123Vu3XYWUGt4O//B0NabyultAJO+u+gGaasH8y9JVjf3Mwy4qMw82nof1hwtX/iNW3f90vPtAYNaaPTzVn8ge8aaqwN7gToPYIlm2PEY8aIPll66Q9Q6iyWfSadhuuC2yS5ZvOu9z0QvfLnoIays3Z8aaNDOosPWGGNoDZp1DWl6Ne9uIMTJNJBYjG4ZF7udzcdaA77145OQZcRvUCQbg4EMQynT1nRbg4Q6cKaO6cl0qI3MU2yAYhRnzIKEkYsl8E+RES6sGgGgkQhDck0RYlOWiUWEdmHIhkIPFFEYyoYBVBEJOqiVxKmm/BYIam073JYXhGRqIheSehOKrxj9oM8RJappqaGX/ziF3t17E9+8hO2b9++T9IhIrI3ohUI3AEnGQ7quK9qBAoEItKZRez20aAq0JQOZpfaV4Hgiiuu4O2332bixIlMmzaNvn37cvfdd9PQ0MCZZ57JtddeS21tLWeddRbV1dWkUim++93vsmbNGlatWsXxxx9PZWUlc+bM2SfpERHZE10vEDx8Bax+dScbHRq3UUwBo6yAWK6TPPQfDyd/f6ebv//977No0SIWLlzIY489xj333MPcuXNxd2bMmMHf//531q1bx8CBA3nwwQeBYAyiHj16cP311zNnzhwqKyv3MKMiIvtGtJqGwhqBs+dDzOfqscce47HHHuPwww9n0qRJvPHGGyxZsoTx48cze/ZsLr/8cp555hl69OiRnwSIiOyhrlcj2MWVezDz1Gus80oSZX3o32PfDy/h7lx55ZV86Utf2mHbSy+9xEMPPcR3vvMdTjjhBK666qp9fn4RkT0VrRpBOMCeu+3x3CG7kjkM9UknncStt97Ktm3BtJYrV65k7dq1rFq1ipKSEs477zwuu+wyXnrppR2OFRHpCF2vRrArzYEAI7Y3M5DtROYw1CeffDLnnnsuxxwTDJ9bVlbGH//4R5YuXcpll11GLBajoKCAX/7ylwDMnDmT6dOnM3DgQHUWi0iHiNYw1E3bYd2bvJfuS1mvSipKO9+AcxqGWkT2xq6GoY5m0xDWbn5iEZHoimQgSGNo0FERkUCXCQS5NXG11gisE9YIOlsznoh0Dl0iEBQXF7Nhw4bdF5SduGnI3dmwYQPFxZpRTUT2rS5x11BVVRXV1dWsW7du1zs21UHtOtZ6E7Wb1ne60UeLi4upqqrq6GSISBfTJQJBQUEBw4cP3/2Oi+6FRz/P1xp+yM8vPZeD+pfnP3EiIge4znVJ/EGlGgFoJEFxQbSyLiKyM9EqDZsDgRfQLdcB50REurhoBYJkAwBNJChSIBARAaIWCFqahuL7bHYyEZHOLlqlYToJBDUCTVwvIhKIVmkYBoJYPEFMjxaLiACRCwQpABKJgg5OiIjIgSNigSCoEcTjXeLxCRGRfSJigSBFijiFCd0xJCLSLK+BwMymm9mbZrbUzK7Isn2Imc0xswVm9oqZnZLP9JBOkrYYRXqYTESkRd5KRDOLAzcCJwNjgXPMbGy73b4D3O3uhwOfBn6Rr/QAkE4GNQLdMSQi0iKfJeIUYKm7v+PujcCdwBnt9nGge/i6B7Aqj+mBdIo0sU432JyISD7ls0QcBKzIWK4O12W6BjjPzKqBh4CvZnsjM5tpZvPNbP5uRxjdFU+RMj1MJiKSqaNLxHOA37p7FXAK8Acz2yFN7n6Lu09298l9+vTZ+7M1Nw0pEIiItMhnibgSGJyxXBWuy3QhcDeAuz8PFAOVeUtRSyDQXUMiIs3yGQjmAaPNbLiZFRJ0Bs9qt89y4AQAMzuEIBB8gLaf3UinSHpMTUMiIhnyViK6exL4CvAosJjg7qDXzOw6M5sR7vYt4CIzexm4A/ic53Ni3nSSlDqLRUTayOsjtu7+EEEncOa6qzJevw4cm880tJFOkSJGkW4fFRFpEa0SMZ2kyeN6oExEJEO0SsR0kiQxPVAmIpIhWiViOkXKYyQUCEREWkSrRAxrBIm45iIQEWkWrUDgKZIeJ6FJaUREWkQqEHg6SRMx4rFIZVtEZJeiVSKmkqRUIxARaSNSgcDTSVIYcQUCEZEWEQwEcQUCEZEMkQoEpFMkUdOQiEimiAWCYKwh1QhERFpFKhB4SjUCEZH2IhUISCdJ6/ZREZE2olUieip4slg1AhGRFtEKBLprSERkB5ELBEmPa6whEZEMEQsEwcQ0MVMgEBFpFrFAENw+qj4CEZFW0QoEHtw+qj4CEZFWkQoEFjYNqY9ARKRVpAIBntJzBCIi7USqRDRPk1YfgYhIG5EKBLiT1jDUIiJtRCwQpEljqhGIiGSIVCAwgkAQUyAQEWkRrUDgaVx9BCIibUQnELgDkHb1EYiIZIpQIEgDhH0E0cm2iMjuRKdEbAkEmqFMRCRT5AKB664hEZE2IhkIVCMQEWmVUyAws3vN7FQz67yBI7OPQGMNiYi0yLVg/wVwLrDEzL5vZgflcpCZTTezN81sqZldsZN9zjKz183sNTP7U47p2XPNdw1hxDUfgYhIi0QuO7n748DjZtYDOCd8vQL4FfBHd29qf4yZxYEbgWlANTDPzGa5++sZ+4wGrgSOdfdNZtb3A+dop5lorRGoaUhEpFXOTT1mVgF8DvgisAD4KTAJmL2TQ6YAS939HXdvBO4Ezmi3z0XAje6+CcDd1+5R6vdESx9BTLePiohkyLWP4D7gGaAEON3dZ7j7Xe7+VaBsJ4cNAlZkLFeH6zKNAcaY2T/M7AUzm76T8880s/lmNn/dunW5JHlHGTUCxQERkVY5NQ0BN7j7nGwb3H3yBzz/aGAqUAX83czGu3tNu3PcAtwCMHnyZN+rM2X2EahpSESkRa7XxmPNrGfzgpn1MrOLd3PMSmBwxnJVuC5TNTDL3Zvc/V3gLYLAsO9lPFCmyetFRFrlGgguyrxKD9v0L9rNMfOA0WY23MwKgU8Ds9rtcz9BbQAzqyRoKnonxzTtmYznCBQIRERa5RoI4matpWd4R1Dhrg5w9yTwFeBRYDFwt7u/ZmbXmdmMcLdHgQ1m9jowB7jM3TfsaSZyktlHoDggItIi1z6CR4C7zOzmcPlL4bpdcveHgIfarbsq47UD3wx/8ktNQyIiWeUaCC4nKPy/HC7PBn6dlxTlS0aNQHFARKRVrg+UpYFfhj+dUxgIwDBFAhGRFjkFgvAJ4P8FxgLFzevdfUSe0rXvNXcWd+LhkiNsbiAAAA4hSURBVERE8iHXUvE2gtpAEjge+D3wx3wlKi9aagQKBCIimXItFbu5+xOAuft77n4NcGr+kpUH4QNl6iAQEWkr187ihnAI6iVm9hWCB8N2NrTEgam5RqCmIRGRNnItFS8lGGfoa8ARwHnABflKVH401wgUCEREMu22RhA+PHa2u38b2AZ8Pu+pyoeWzmI1DYmIZNrt5bG7p4Dj9kNa8itjGGoREWmVax/BAjObBfwZqG1e6e735iVV+RAGAtMY1CIibeQaCIqBDcDHMtY50OkCAahpSEQkU65PFnfOfoFMeqBMRCSrXJ8svo2W225aufsX9nmK8kW3j4qIZJVr09ADGa+LgTOBVfs+OXnU/ECZOotFRNrItWnoL5nLZnYH8GxeUpQvahoSEclqb0vF0UDffZmQvGtuGtKsNCIibeTaR7CVtn0EqwnmKOg8mm8fVY1ARKSNXJuGyvOdkLzT6KMiIlnlVCqa2Zlm1iNjuaeZfSJ/ycoD9RGIiGSVa6l4tbtvbl5w9xrg6vwkKU9amobURyAikinXQJBtv1xvPT0waIgJEZGsci0V55vZ9WY2Mvy5HvhnPhO2z4XPEbjFOzghIiIHllwDwVeBRuAu4E6gHrgkX4nKC81QJiKSVa53DdUCV+Q5Lfmlu4ZERLLK9a6h2WbWM2O5l5k9mr9k5YH6CEREssq1VKwM7xQCwN030VmfLNbtoyIibeRaKqbNbEjzgpkNI8topAc0BQIRkaxyvQX0P4FnzexpgpldPgzMzFuq8kGBQEQkq1w7ix8xs8kEhf8C4H6gLp8J2+c01pCISFa5Djr3ReBSoApYCBwNPE/bqSsPbKoRiIhklWupeClwJPCeux8PHA7U7PqQA4yGoRYRySrXQFDv7vUAZlbk7m8AB+UvWXkQPlBmerJYRKSNXDuLq8PnCO4HZpvZJuC9/CUrD9Q0JCKSVU6loruf6e417n4N8F3gN8Buh6E2s+lm9qaZLTWznT6ZbGb/YmYedkjnhzqLRUSy2uMRRN396Vz2s6AN5kZgGlANzDOzWe7+erv9ygn6IF7c07TsEQ1DLSKSVT4vj6cAS939HXdvJBis7ows+/0X8AOCgezyp6WzWH0EIiKZ8hkIBgErMparw3UtzGwSMNjdH9zVG5nZTDObb2bz161bt5fJCTuLddeQiEgbHdZgbkFj/fXAt3a3r7vf4u6T3X1ynz599u6E6iwWEckqn6XiSmBwxnJVuK5ZOTAOeMrMlhE8pDYrbx3GLX0EahoSEcmUz0AwDxhtZsPNrBD4NDCreaO7b3b3Sncf5u7DgBeAGe4+Py+p0TDUIiJZ5a1UdPck8BXgUWAxcLe7v2Zm15nZjHyddxcJCn6raUhEpI28TkDv7g8BD7Vbd9VO9p2az7ToOQIRkeyiUyq23D4anSyLiOQiOqViGAhiqhGIiLQRnVKx5fZR3TUkIpIpcoHA4nqgTEQkU147iw8oo6ZxzRNrSMcKOzolIiIHlOjUCPqNZZYdj8UKOjolIiIHlOgEAiDtrgnKRETaiVYgSLuGoRYRaSdagcAhpkAgItJGxAKBE49UjkVEdi9SxWLQR6AagYhIpmgFgrSmqhQRaS9agUBNQyIiO4hUsaimIRGRHUUsEKhpSESkvcgEgnQ6mJhGD5SJiLQVnUAQzlAWV41ARKSNCAWC4HdMVQIRkTYiFAiCSKAKgYhIW5ELBLprSESkrQgFguC3+ghERNqKUCBQ05CISDaRCQTNUxaraUhEpK3IBIKU6zkCEZFsIhMIWp4jUCQQEWkjcoFAQ0yIiLQVmUDgzQ+UKRCIiLQRmUCQ0lhDIiJZRSYQtDxQpkggItJGZAKBmoZERLKLTCBQ05CISHaRCQS6fVREJLu8BgIzm25mb5rZUjO7Isv2b5rZ62b2ipk9YWZD85WW5rGGdPuoiEhbeQsEZhYHbgROBsYC55jZ2Ha7LQAmu/thwD3AD/OVHteTxSIiWeWzRjAFWOru77h7I3AncEbmDu4+x923h4svAFX5SkxKw1CLiGSVz0AwCFiRsVwdrtuZC4GH85WYtAadExHJKtHRCQAws/OAycBHd7J9JjATYMiQIXt1jrSahkREsspnjWAlMDhjuSpc14aZnQj8JzDD3RuyvZG73+Luk919cp8+ffYqMXqOQEQku3wGgnnAaDMbbmaFwKeBWZk7mNnhwM0EQWBtHtPS2kcQmRtmRURyk7di0d2TwFeAR4HFwN3u/pqZXWdmM8Ld/g8oA/5sZgvNbNZO3u4D05zFIiLZ5bWPwN0fAh5qt+6qjNcn5vP87c4LKBCIiLQXmYaStPoIRESyikwg0FhDIiLZRSYQaBhqEZHsIhMIdPuoiEh2kQkEeqBMRCS7yASC5j4CjT4qItJWZAJBc9OQ5iMQEWkrMoFATUMiItlFJhC03j6qSCAikikygUAPlImIZBeZQOAadE5EJKvIFIuqEYiIZBeZQJBSZ7GISFaRCQQafVREJLvIBALNRyAikl10AoEmrxcRySoygaC5j0BxQESkrcgEguY+Ag0xISLSVmQCgW4fFRHJLkKBQLePiohkE51AoGGoRUSyik4g0DDUIiJZRSgQqGlIRCSbCAWC4LeahkRE2opOIEirRiAikk10AoGeIxARySoygWB4ZSmnjh+gQCAi0k6ioxOwv3z80P58/ND+HZ0MEZEDTmRqBCIikp0CgYhIxCkQiIhEnAKBiEjEKRCIiEScAoGISMQpEIiIRJwCgYhIxFnzFI6dhZmtA97by8MrgfX7MDkdqavkpavkA5SXA1FXyQd88LwMdfc+2TZ0ukDwQZjZfHef3NHp2Be6Sl66Sj5AeTkQdZV8QH7zoqYhEZGIUyAQEYm4qAWCWzo6AftQV8lLV8kHKC8Hoq6SD8hjXiLVRyAiIjuKWo1ARETaUSAQEYm4yAQCM5tuZm+a2VIzu6Kj07M7Znarma01s0UZ63qb2WwzWxL+7hWuNzO7IczbK2Y2qeNS3paZDTazOWb2upm9ZmaXhus7VV7MrNjM5prZy2E+rg3XDzezF8P03mVmheH6onB5abh9WEemPxszi5vZAjN7IFzudHkxs2Vm9qqZLTSz+eG6TvXdamZmPc3sHjN7w8wWm9kx+ysvkQgEZhYHbgROBsYC55jZ2I5N1W79Fpjebt0VwBPuPhp4IlyGIF+jw5+ZwC/3UxpzkQS+5e5jgaOBS8LPvrPlpQH4mLtPACYC083saOAHwI/dfRSwCbgw3P9CYFO4/sfhfgeaS4HFGcudNS/Hu/vEjHvsO9t3q9lPgUfc/WBgAsHfZv/kxd27/A9wDPBoxvKVwJUdna4c0j0MWJSx/CYwIHw9AHgzfH0zcE62/Q60H+CvwLTOnBegBHgJOIrgSc9E++8Z8ChwTPg6Ee5nHZ32jDxUhQXLx4AHAOuMeQGWAZXt1nW67xbQA3i3/ee6v/ISiRoBMAhYkbFcHa7rbPq5+/vh69VAv/B1p8hf2KRwOPAinTAvYVPKQmAtMBt4G6hx92S4S2ZaW/IRbt8MVOzfFO/ST4B/B9LhcgWdMy8OPGZm/zSzmeG6TvfdAoYD64Dbwua6X5tZKfspL1EJBF2OB5cBnebeXzMrA/4CfN3dt2Ru6yx5cfeUu08kuJqeAhzcwUnaK2Z2GrDW3f/Z0WnZB45z90kETSWXmNlHMjd2lu8WQU1rEvBLdz8cqKW1GQjIb16iEghWAoMzlqvCdZ3NGjMbABD+XhuuP6DzZ2YFBEHgdne/N1zdKfMC4O41wByC5pOeZpYIN2WmtSUf4fYewIb9nNSdORaYYWbLgDsJmod+SifMi7uvDH+vBe4jCNCd8btVDVS7+4vh8j0EgWG/5CUqgWAeMDq8K6IQ+DQwq4PTtDdmAReEry8gaG9vXn9+eCfB0cDmjOpkhzIzA34DLHb36zM2daq8mFkfM+sZvu5G0M+xmCAgfCrcrX0+mvP3KeDJ8Iquw7n7le5e5e7DCP4XnnT3z9DJ8mJmpWZW3vwa+DiwiE723QJw99XACjM7KFx1AvA6+ysvHd1Jsh87Y04B3iJo1/3Pjk5PDum9A3gfaCK4WriQoF32CWAJ8DjQO9zXCO6Keht4FZjc0enPyMdxBNXZV4CF4c8pnS0vwGHAgjAfi4CrwvUjgLnAUuDPQFG4vjhcXhpuH9HRedhJvqYCD3TGvITpfTn8ea35/7qzfbcy8jMRmB9+x+4Heu2vvGiICRGRiItK05CIiOyEAoGISMQpEIiIRJwCgYhIxCkQiIhEnAKByH5kZlObR/sUOVAoEIiIRJwCgUgWZnZeOP/AQjO7ORxwbpuZ/diC+QieMLM+4b4TzeyFcFz4+zLGjB9lZo9bMIfBS2Y2Mnz7soxx528Pn74W6TAKBCLtmNkhwNnAsR4MMpcCPgOUAvPd/VDgaeDq8JDfA5e7+2EET3k2r78duNGDOQw+RPCkOAQjsH6dYG6MEQRj/4h0mMTudxGJnBOAI4B54cV6N4LBvtLAXeE+fwTuNbMeQE93fzpc/zvgz+EYOIPc/T4Ad68HCN9vrrtXh8sLCeadeDb/2RLJToFAZEcG/M7dr2yz0uy77fbb2/FZGjJep9D/oXQwNQ2J7OgJ4FNm1hda5sAdSvD/0jw657nAs+6+GdhkZh8O138WeNrdtwLVZvaJ8D2KzKxkv+ZCJEe6EhFpx91fN7PvEMx8FSMYAfYSgslCpoTb1hL0I0AwPPBNYUH/DvD5cP1ngZvN7LrwPf51P2ZDJGcafVQkR2a2zd3LOjodIvuamoZERCJONQIRkYhTjUBEJOIUCEREIk6BQEQk4hQIREQiToFARCTi/j/mRSY5EuVWOgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yPred = model.predict_on_batch(test)"
      ],
      "metadata": {
        "id": "01HS1mbGeBpM"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Converting the test predictions in a dataframe as depicted by sample submission\n",
        "submission = pd.DataFrame(yPred,index=testId,columns=sort(parent_data.species.unique()))"
      ],
      "metadata": {
        "id": "_z19Hm4jeEO9"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission.to_csv('leafClassificationSubmission.csv')\n",
        "\n",
        "end = time.time()\n",
        "print(round((end-start),2), \"seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXCUQRRkeGcW",
        "outputId": "8358a80e-3d4c-4da6-ecf9-83584d1d3e9d"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "181.61 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "Dl2qBWMDeIgG",
        "outputId": "41a48f4e-add2-4097-ea1f-669fd170db7e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Acer_Capillipes  Acer_Circinatum     Acer_Mono   Acer_Opalus  \\\n",
              "id                                                                 \n",
              "4      2.917364e-19     1.601428e-19  9.898620e-25  1.681752e-16   \n",
              "7      5.922716e-19     2.996160e-16  5.454980e-12  7.333194e-14   \n",
              "9      2.312113e-14     9.999999e-01  9.936026e-19  1.004096e-18   \n",
              "12     1.730318e-15     1.054542e-07  8.941590e-12  9.048728e-17   \n",
              "13     8.850307e-19     1.153815e-15  3.920244e-22  1.127199e-23   \n",
              "\n",
              "    Acer_Palmatum   Acer_Pictum  Acer_Platanoids   Acer_Rubrum  \\\n",
              "id                                                               \n",
              "4    9.797785e-18  5.234284e-18     2.807636e-19  1.232271e-22   \n",
              "7    1.952818e-15  7.970609e-13     1.757541e-10  6.610248e-17   \n",
              "9    1.535450e-07  3.124955e-14     1.274681e-20  6.840905e-15   \n",
              "12   1.311519e-16  2.609470e-17     8.500660e-16  1.422018e-16   \n",
              "13   4.098076e-17  4.687162e-21     7.332263e-20  3.638508e-20   \n",
              "\n",
              "    Acer_Rufinerve  Acer_Saccharinum  ...  Salix_Fragilis  Salix_Intergra  \\\n",
              "id                                    ...                                   \n",
              "4     5.558559e-24      2.884295e-22  ...    7.336963e-20    1.922539e-18   \n",
              "7     3.814030e-18      3.196192e-16  ...    2.973455e-17    6.175424e-19   \n",
              "9     7.692347e-15      3.650696e-15  ...    3.240483e-18    1.823617e-23   \n",
              "12    7.086167e-16      3.608291e-14  ...    1.431750e-17    2.184886e-21   \n",
              "13    1.316987e-11      4.890106e-18  ...    4.151936e-18    3.834314e-22   \n",
              "\n",
              "     Sorbus_Aria  Tilia_Oliveri  Tilia_Platyphyllos  Tilia_Tomentosa  \\\n",
              "id                                                                     \n",
              "4   2.852463e-24   2.308004e-24        7.125684e-25     2.478850e-23   \n",
              "7   1.225312e-19   2.030325e-20        3.937733e-23     1.249676e-13   \n",
              "9   1.539725e-20   7.683660e-24        6.580053e-26     5.059081e-23   \n",
              "12  1.049988e-14   3.216959e-18        3.551410e-24     3.706192e-12   \n",
              "13  1.366071e-16   7.127660e-20        2.115034e-19     1.471872e-16   \n",
              "\n",
              "    Ulmus_Bergmanniana  Viburnum_Tinus  Viburnum_x_Rhytidophylloides  \\\n",
              "id                                                                     \n",
              "4         2.377948e-22    2.533677e-24                  5.237684e-16   \n",
              "7         9.839365e-17    2.313780e-11                  4.359894e-20   \n",
              "9         1.134738e-19    3.742711e-20                  2.348274e-20   \n",
              "12        2.949346e-07    8.105726e-12                  6.810432e-18   \n",
              "13        1.604714e-09    1.347876e-16                  9.270423e-16   \n",
              "\n",
              "    Zelkova_Serrata  \n",
              "id                   \n",
              "4      1.136788e-25  \n",
              "7      1.817777e-15  \n",
              "9      1.947404e-09  \n",
              "12     5.678588e-10  \n",
              "13     1.175521e-17  \n",
              "\n",
              "[5 rows x 99 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e89e9e6f-e857-451f-a6e6-844fba02f77f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Acer_Capillipes</th>\n",
              "      <th>Acer_Circinatum</th>\n",
              "      <th>Acer_Mono</th>\n",
              "      <th>Acer_Opalus</th>\n",
              "      <th>Acer_Palmatum</th>\n",
              "      <th>Acer_Pictum</th>\n",
              "      <th>Acer_Platanoids</th>\n",
              "      <th>Acer_Rubrum</th>\n",
              "      <th>Acer_Rufinerve</th>\n",
              "      <th>Acer_Saccharinum</th>\n",
              "      <th>...</th>\n",
              "      <th>Salix_Fragilis</th>\n",
              "      <th>Salix_Intergra</th>\n",
              "      <th>Sorbus_Aria</th>\n",
              "      <th>Tilia_Oliveri</th>\n",
              "      <th>Tilia_Platyphyllos</th>\n",
              "      <th>Tilia_Tomentosa</th>\n",
              "      <th>Ulmus_Bergmanniana</th>\n",
              "      <th>Viburnum_Tinus</th>\n",
              "      <th>Viburnum_x_Rhytidophylloides</th>\n",
              "      <th>Zelkova_Serrata</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.917364e-19</td>\n",
              "      <td>1.601428e-19</td>\n",
              "      <td>9.898620e-25</td>\n",
              "      <td>1.681752e-16</td>\n",
              "      <td>9.797785e-18</td>\n",
              "      <td>5.234284e-18</td>\n",
              "      <td>2.807636e-19</td>\n",
              "      <td>1.232271e-22</td>\n",
              "      <td>5.558559e-24</td>\n",
              "      <td>2.884295e-22</td>\n",
              "      <td>...</td>\n",
              "      <td>7.336963e-20</td>\n",
              "      <td>1.922539e-18</td>\n",
              "      <td>2.852463e-24</td>\n",
              "      <td>2.308004e-24</td>\n",
              "      <td>7.125684e-25</td>\n",
              "      <td>2.478850e-23</td>\n",
              "      <td>2.377948e-22</td>\n",
              "      <td>2.533677e-24</td>\n",
              "      <td>5.237684e-16</td>\n",
              "      <td>1.136788e-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.922716e-19</td>\n",
              "      <td>2.996160e-16</td>\n",
              "      <td>5.454980e-12</td>\n",
              "      <td>7.333194e-14</td>\n",
              "      <td>1.952818e-15</td>\n",
              "      <td>7.970609e-13</td>\n",
              "      <td>1.757541e-10</td>\n",
              "      <td>6.610248e-17</td>\n",
              "      <td>3.814030e-18</td>\n",
              "      <td>3.196192e-16</td>\n",
              "      <td>...</td>\n",
              "      <td>2.973455e-17</td>\n",
              "      <td>6.175424e-19</td>\n",
              "      <td>1.225312e-19</td>\n",
              "      <td>2.030325e-20</td>\n",
              "      <td>3.937733e-23</td>\n",
              "      <td>1.249676e-13</td>\n",
              "      <td>9.839365e-17</td>\n",
              "      <td>2.313780e-11</td>\n",
              "      <td>4.359894e-20</td>\n",
              "      <td>1.817777e-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.312113e-14</td>\n",
              "      <td>9.999999e-01</td>\n",
              "      <td>9.936026e-19</td>\n",
              "      <td>1.004096e-18</td>\n",
              "      <td>1.535450e-07</td>\n",
              "      <td>3.124955e-14</td>\n",
              "      <td>1.274681e-20</td>\n",
              "      <td>6.840905e-15</td>\n",
              "      <td>7.692347e-15</td>\n",
              "      <td>3.650696e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>3.240483e-18</td>\n",
              "      <td>1.823617e-23</td>\n",
              "      <td>1.539725e-20</td>\n",
              "      <td>7.683660e-24</td>\n",
              "      <td>6.580053e-26</td>\n",
              "      <td>5.059081e-23</td>\n",
              "      <td>1.134738e-19</td>\n",
              "      <td>3.742711e-20</td>\n",
              "      <td>2.348274e-20</td>\n",
              "      <td>1.947404e-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.730318e-15</td>\n",
              "      <td>1.054542e-07</td>\n",
              "      <td>8.941590e-12</td>\n",
              "      <td>9.048728e-17</td>\n",
              "      <td>1.311519e-16</td>\n",
              "      <td>2.609470e-17</td>\n",
              "      <td>8.500660e-16</td>\n",
              "      <td>1.422018e-16</td>\n",
              "      <td>7.086167e-16</td>\n",
              "      <td>3.608291e-14</td>\n",
              "      <td>...</td>\n",
              "      <td>1.431750e-17</td>\n",
              "      <td>2.184886e-21</td>\n",
              "      <td>1.049988e-14</td>\n",
              "      <td>3.216959e-18</td>\n",
              "      <td>3.551410e-24</td>\n",
              "      <td>3.706192e-12</td>\n",
              "      <td>2.949346e-07</td>\n",
              "      <td>8.105726e-12</td>\n",
              "      <td>6.810432e-18</td>\n",
              "      <td>5.678588e-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>8.850307e-19</td>\n",
              "      <td>1.153815e-15</td>\n",
              "      <td>3.920244e-22</td>\n",
              "      <td>1.127199e-23</td>\n",
              "      <td>4.098076e-17</td>\n",
              "      <td>4.687162e-21</td>\n",
              "      <td>7.332263e-20</td>\n",
              "      <td>3.638508e-20</td>\n",
              "      <td>1.316987e-11</td>\n",
              "      <td>4.890106e-18</td>\n",
              "      <td>...</td>\n",
              "      <td>4.151936e-18</td>\n",
              "      <td>3.834314e-22</td>\n",
              "      <td>1.366071e-16</td>\n",
              "      <td>7.127660e-20</td>\n",
              "      <td>2.115034e-19</td>\n",
              "      <td>1.471872e-16</td>\n",
              "      <td>1.604714e-09</td>\n",
              "      <td>1.347876e-16</td>\n",
              "      <td>9.270423e-16</td>\n",
              "      <td>1.175521e-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 99 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e89e9e6f-e857-451f-a6e6-844fba02f77f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e89e9e6f-e857-451f-a6e6-844fba02f77f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e89e9e6f-e857-451f-a6e6-844fba02f77f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    }
  ]
}