Early_stopping_DL.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ANNAPAVAN/MachineLeaning-DeepLearning/blob/main/Early_stopping_DL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r6MqqScLzeNF"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "from mlxtend.plotting import plot_decision_regions\n",
        "from matplotlib.colors import ListedColormap\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import make_circles\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = make_circles(n_samples=100, noise=0.1, random_state=1)"
      ],
      "metadata": {
        "id": "i9RNS3GXzfkb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sns.scatterplot(X[:,0],X[:,1],hue=y)\n",
        "sns.scatterplot(x=X[:,0], y=X[:,1], hue=y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "HxPD3cyTzvcG",
        "outputId": "8d3f96e3-6c81-4b77-8aa5-2c68c34a73c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=2)"
      ],
      "metadata": {
        "id": "fNGCOgHFz396"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(256, input_dim=2, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "fN6s_E6-0S9c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "HJet6I080ZJc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3500, verbose=0)"
      ],
      "metadata": {
        "id": "vjZCAexQ0eVx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='train') #loss on training set\n",
        "plt.plot(history.history['val_loss'], label='test') #loss on test set\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "zTaTaYBa0kxh",
        "outputId": "ebf89319-2820-4f7a-caf3-ad8a4281dea8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(X_test, y_test.ravel(), clf = model, legend=2)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "jxrBHiX204K9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Early Stopping"
      ],
      "metadata": {
        "id": "n4fSIQyo3Ygb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(256, input_dim=2, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "lLmuwxI03kmC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "mN6J_WdZ4FBo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "callback = EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0.00001,\n",
        "    patience=20,\n",
        "    verbose=1,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=False\n",
        ")"
      ],
      "metadata": {
        "id": "G7mLk0WD4e5v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3500, callbacks=callback)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tubCoFf4-Xz",
        "outputId": "f705a201-ff7d-45fd-ae75-441f494499c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3500\n",
            "3/3 [==============================] - 0s 106ms/step - loss: 0.6774 - accuracy: 0.5625 - val_loss: 0.7244 - val_accuracy: 0.3500\n",
            "Epoch 2/3500\n",
            "3/3 [==============================] - 0s 65ms/step - loss: 0.6766 - accuracy: 0.5625 - val_loss: 0.7242 - val_accuracy: 0.3500\n",
            "Epoch 3/3500\n",
            "3/3 [==============================] - 0s 113ms/step - loss: 0.6763 - accuracy: 0.5625 - val_loss: 0.7245 - val_accuracy: 0.3500\n",
            "Epoch 4/3500\n",
            "3/3 [==============================] - 0s 134ms/step - loss: 0.6757 - accuracy: 0.5625 - val_loss: 0.7248 - val_accuracy: 0.3500\n",
            "Epoch 5/3500\n",
            "3/3 [==============================] - 0s 79ms/step - loss: 0.6747 - accuracy: 0.5625 - val_loss: 0.7253 - val_accuracy: 0.3500\n",
            "Epoch 6/3500\n",
            "3/3 [==============================] - 0s 105ms/step - loss: 0.6741 - accuracy: 0.5625 - val_loss: 0.7259 - val_accuracy: 0.3500\n",
            "Epoch 7/3500\n",
            "3/3 [==============================] - 0s 142ms/step - loss: 0.6737 - accuracy: 0.5625 - val_loss: 0.7265 - val_accuracy: 0.3500\n",
            "Epoch 8/3500\n",
            "3/3 [==============================] - 0s 61ms/step - loss: 0.6730 - accuracy: 0.5625 - val_loss: 0.7265 - val_accuracy: 0.3500\n",
            "Epoch 9/3500\n",
            "3/3 [==============================] - 0s 56ms/step - loss: 0.6725 - accuracy: 0.5625 - val_loss: 0.7258 - val_accuracy: 0.3500\n",
            "Epoch 10/3500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.6718 - accuracy: 0.5625 - val_loss: 0.7249 - val_accuracy: 0.3500\n",
            "Epoch 11/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6710 - accuracy: 0.5625 - val_loss: 0.7238 - val_accuracy: 0.3500\n",
            "Epoch 12/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6707 - accuracy: 0.5625 - val_loss: 0.7222 - val_accuracy: 0.3500\n",
            "Epoch 13/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.6700 - accuracy: 0.5875 - val_loss: 0.7206 - val_accuracy: 0.3500\n",
            "Epoch 14/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.6693 - accuracy: 0.5875 - val_loss: 0.7197 - val_accuracy: 0.3500\n",
            "Epoch 15/3500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.6688 - accuracy: 0.5750 - val_loss: 0.7190 - val_accuracy: 0.3500\n",
            "Epoch 16/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.6680 - accuracy: 0.5750 - val_loss: 0.7184 - val_accuracy: 0.3500\n",
            "Epoch 17/3500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.6674 - accuracy: 0.5625 - val_loss: 0.7176 - val_accuracy: 0.3500\n",
            "Epoch 18/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6668 - accuracy: 0.5625 - val_loss: 0.7172 - val_accuracy: 0.3500\n",
            "Epoch 19/3500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.6660 - accuracy: 0.5750 - val_loss: 0.7170 - val_accuracy: 0.4000\n",
            "Epoch 20/3500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.6653 - accuracy: 0.5750 - val_loss: 0.7169 - val_accuracy: 0.4000\n",
            "Epoch 21/3500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.6651 - accuracy: 0.5750 - val_loss: 0.7172 - val_accuracy: 0.4000\n",
            "Epoch 22/3500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.6642 - accuracy: 0.6000 - val_loss: 0.7153 - val_accuracy: 0.4000\n",
            "Epoch 23/3500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.6636 - accuracy: 0.6000 - val_loss: 0.7138 - val_accuracy: 0.4000\n",
            "Epoch 24/3500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.6626 - accuracy: 0.6125 - val_loss: 0.7129 - val_accuracy: 0.4000\n",
            "Epoch 25/3500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.6621 - accuracy: 0.5875 - val_loss: 0.7126 - val_accuracy: 0.4000\n",
            "Epoch 26/3500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.6612 - accuracy: 0.5750 - val_loss: 0.7122 - val_accuracy: 0.4000\n",
            "Epoch 27/3500\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.6605 - accuracy: 0.5750 - val_loss: 0.7123 - val_accuracy: 0.4000\n",
            "Epoch 28/3500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.6596 - accuracy: 0.5750 - val_loss: 0.7127 - val_accuracy: 0.4000\n",
            "Epoch 29/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6590 - accuracy: 0.5875 - val_loss: 0.7139 - val_accuracy: 0.4500\n",
            "Epoch 30/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6585 - accuracy: 0.6125 - val_loss: 0.7153 - val_accuracy: 0.4000\n",
            "Epoch 31/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6576 - accuracy: 0.6125 - val_loss: 0.7151 - val_accuracy: 0.3500\n",
            "Epoch 32/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.6570 - accuracy: 0.6375 - val_loss: 0.7155 - val_accuracy: 0.5000\n",
            "Epoch 33/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6560 - accuracy: 0.6750 - val_loss: 0.7157 - val_accuracy: 0.4000\n",
            "Epoch 34/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.6555 - accuracy: 0.6625 - val_loss: 0.7153 - val_accuracy: 0.4500\n",
            "Epoch 35/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6543 - accuracy: 0.6500 - val_loss: 0.7152 - val_accuracy: 0.4500\n",
            "Epoch 36/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.6535 - accuracy: 0.6500 - val_loss: 0.7148 - val_accuracy: 0.4000\n",
            "Epoch 37/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6526 - accuracy: 0.6500 - val_loss: 0.7142 - val_accuracy: 0.4000\n",
            "Epoch 38/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6516 - accuracy: 0.6500 - val_loss: 0.7122 - val_accuracy: 0.4000\n",
            "Epoch 39/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.6504 - accuracy: 0.6500 - val_loss: 0.7108 - val_accuracy: 0.4000\n",
            "Epoch 40/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.6500 - accuracy: 0.6250 - val_loss: 0.7095 - val_accuracy: 0.5000\n",
            "Epoch 41/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6496 - accuracy: 0.6250 - val_loss: 0.7101 - val_accuracy: 0.5000\n",
            "Epoch 42/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6484 - accuracy: 0.6250 - val_loss: 0.7094 - val_accuracy: 0.5000\n",
            "Epoch 43/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.6475 - accuracy: 0.6250 - val_loss: 0.7090 - val_accuracy: 0.5000\n",
            "Epoch 44/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.6466 - accuracy: 0.6250 - val_loss: 0.7093 - val_accuracy: 0.5000\n",
            "Epoch 45/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.6458 - accuracy: 0.6250 - val_loss: 0.7101 - val_accuracy: 0.5500\n",
            "Epoch 46/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6449 - accuracy: 0.6250 - val_loss: 0.7104 - val_accuracy: 0.5000\n",
            "Epoch 47/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6436 - accuracy: 0.6375 - val_loss: 0.7093 - val_accuracy: 0.5000\n",
            "Epoch 48/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.6428 - accuracy: 0.6500 - val_loss: 0.7074 - val_accuracy: 0.5000\n",
            "Epoch 49/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.6426 - accuracy: 0.6375 - val_loss: 0.7050 - val_accuracy: 0.5500\n",
            "Epoch 50/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.6413 - accuracy: 0.6500 - val_loss: 0.7044 - val_accuracy: 0.6000\n",
            "Epoch 51/3500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.6403 - accuracy: 0.6500 - val_loss: 0.7031 - val_accuracy: 0.6000\n",
            "Epoch 52/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.6396 - accuracy: 0.6750 - val_loss: 0.7026 - val_accuracy: 0.6000\n",
            "Epoch 53/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6386 - accuracy: 0.6750 - val_loss: 0.7026 - val_accuracy: 0.6000\n",
            "Epoch 54/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.6373 - accuracy: 0.6750 - val_loss: 0.7035 - val_accuracy: 0.6000\n",
            "Epoch 55/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.6374 - accuracy: 0.6875 - val_loss: 0.7046 - val_accuracy: 0.6000\n",
            "Epoch 56/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6356 - accuracy: 0.6875 - val_loss: 0.7054 - val_accuracy: 0.6000\n",
            "Epoch 57/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.6345 - accuracy: 0.6875 - val_loss: 0.7061 - val_accuracy: 0.5500\n",
            "Epoch 58/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.6339 - accuracy: 0.6875 - val_loss: 0.7065 - val_accuracy: 0.5500\n",
            "Epoch 59/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.6326 - accuracy: 0.6750 - val_loss: 0.7067 - val_accuracy: 0.5500\n",
            "Epoch 60/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.6316 - accuracy: 0.7000 - val_loss: 0.7052 - val_accuracy: 0.5500\n",
            "Epoch 61/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.6306 - accuracy: 0.7250 - val_loss: 0.7031 - val_accuracy: 0.5500\n",
            "Epoch 62/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6300 - accuracy: 0.7125 - val_loss: 0.7018 - val_accuracy: 0.6000\n",
            "Epoch 63/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6286 - accuracy: 0.7375 - val_loss: 0.7013 - val_accuracy: 0.6000\n",
            "Epoch 64/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.6281 - accuracy: 0.7250 - val_loss: 0.7006 - val_accuracy: 0.7000\n",
            "Epoch 65/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.6263 - accuracy: 0.7375 - val_loss: 0.7016 - val_accuracy: 0.7000\n",
            "Epoch 66/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.6255 - accuracy: 0.7125 - val_loss: 0.7025 - val_accuracy: 0.6500\n",
            "Epoch 67/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6243 - accuracy: 0.7250 - val_loss: 0.7034 - val_accuracy: 0.6500\n",
            "Epoch 68/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6238 - accuracy: 0.7250 - val_loss: 0.7041 - val_accuracy: 0.7000\n",
            "Epoch 69/3500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.6227 - accuracy: 0.7250 - val_loss: 0.7039 - val_accuracy: 0.7000\n",
            "Epoch 70/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6218 - accuracy: 0.7250 - val_loss: 0.7043 - val_accuracy: 0.7000\n",
            "Epoch 71/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6205 - accuracy: 0.7250 - val_loss: 0.7046 - val_accuracy: 0.7000\n",
            "Epoch 72/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6195 - accuracy: 0.7125 - val_loss: 0.7039 - val_accuracy: 0.6500\n",
            "Epoch 73/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.6187 - accuracy: 0.7375 - val_loss: 0.7016 - val_accuracy: 0.6500\n",
            "Epoch 74/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6175 - accuracy: 0.7375 - val_loss: 0.6994 - val_accuracy: 0.6500\n",
            "Epoch 75/3500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.6163 - accuracy: 0.7375 - val_loss: 0.6974 - val_accuracy: 0.6500\n",
            "Epoch 76/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.6153 - accuracy: 0.7125 - val_loss: 0.6966 - val_accuracy: 0.6500\n",
            "Epoch 77/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6148 - accuracy: 0.7125 - val_loss: 0.6939 - val_accuracy: 0.6500\n",
            "Epoch 78/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6130 - accuracy: 0.7125 - val_loss: 0.6931 - val_accuracy: 0.6500\n",
            "Epoch 79/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6122 - accuracy: 0.7125 - val_loss: 0.6921 - val_accuracy: 0.6000\n",
            "Epoch 80/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.6112 - accuracy: 0.7125 - val_loss: 0.6913 - val_accuracy: 0.6000\n",
            "Epoch 81/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.6099 - accuracy: 0.7250 - val_loss: 0.6905 - val_accuracy: 0.6000\n",
            "Epoch 82/3500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.6085 - accuracy: 0.7375 - val_loss: 0.6890 - val_accuracy: 0.6000\n",
            "Epoch 83/3500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.6075 - accuracy: 0.7625 - val_loss: 0.6871 - val_accuracy: 0.6000\n",
            "Epoch 84/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.6071 - accuracy: 0.7750 - val_loss: 0.6855 - val_accuracy: 0.6500\n",
            "Epoch 85/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.6055 - accuracy: 0.7875 - val_loss: 0.6850 - val_accuracy: 0.6500\n",
            "Epoch 86/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6045 - accuracy: 0.7875 - val_loss: 0.6851 - val_accuracy: 0.6500\n",
            "Epoch 87/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.6035 - accuracy: 0.7875 - val_loss: 0.6854 - val_accuracy: 0.6500\n",
            "Epoch 88/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.6029 - accuracy: 0.8000 - val_loss: 0.6856 - val_accuracy: 0.6500\n",
            "Epoch 89/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.6011 - accuracy: 0.8000 - val_loss: 0.6859 - val_accuracy: 0.6500\n",
            "Epoch 90/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5995 - accuracy: 0.8000 - val_loss: 0.6869 - val_accuracy: 0.6500\n",
            "Epoch 91/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.5990 - accuracy: 0.8000 - val_loss: 0.6874 - val_accuracy: 0.6500\n",
            "Epoch 92/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5981 - accuracy: 0.8000 - val_loss: 0.6875 - val_accuracy: 0.6500\n",
            "Epoch 93/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5961 - accuracy: 0.7750 - val_loss: 0.6870 - val_accuracy: 0.7000\n",
            "Epoch 94/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5947 - accuracy: 0.7875 - val_loss: 0.6876 - val_accuracy: 0.7000\n",
            "Epoch 95/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5942 - accuracy: 0.7750 - val_loss: 0.6877 - val_accuracy: 0.7000\n",
            "Epoch 96/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5925 - accuracy: 0.7750 - val_loss: 0.6877 - val_accuracy: 0.7000\n",
            "Epoch 97/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5915 - accuracy: 0.7875 - val_loss: 0.6880 - val_accuracy: 0.6500\n",
            "Epoch 98/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5913 - accuracy: 0.7875 - val_loss: 0.6898 - val_accuracy: 0.6500\n",
            "Epoch 99/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5893 - accuracy: 0.7875 - val_loss: 0.6886 - val_accuracy: 0.6500\n",
            "Epoch 100/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.5881 - accuracy: 0.7875 - val_loss: 0.6879 - val_accuracy: 0.6000\n",
            "Epoch 101/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.5869 - accuracy: 0.8000 - val_loss: 0.6861 - val_accuracy: 0.6500\n",
            "Epoch 102/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5857 - accuracy: 0.8000 - val_loss: 0.6849 - val_accuracy: 0.6500\n",
            "Epoch 103/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5845 - accuracy: 0.8000 - val_loss: 0.6829 - val_accuracy: 0.7000\n",
            "Epoch 104/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.5837 - accuracy: 0.8000 - val_loss: 0.6801 - val_accuracy: 0.7000\n",
            "Epoch 105/3500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.5820 - accuracy: 0.7875 - val_loss: 0.6781 - val_accuracy: 0.6500\n",
            "Epoch 106/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5814 - accuracy: 0.8250 - val_loss: 0.6754 - val_accuracy: 0.7000\n",
            "Epoch 107/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.5798 - accuracy: 0.8125 - val_loss: 0.6756 - val_accuracy: 0.6500\n",
            "Epoch 108/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5785 - accuracy: 0.8125 - val_loss: 0.6764 - val_accuracy: 0.6500\n",
            "Epoch 109/3500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5771 - accuracy: 0.8000 - val_loss: 0.6791 - val_accuracy: 0.6500\n",
            "Epoch 110/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5765 - accuracy: 0.7875 - val_loss: 0.6820 - val_accuracy: 0.6500\n",
            "Epoch 111/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5752 - accuracy: 0.7875 - val_loss: 0.6856 - val_accuracy: 0.6500\n",
            "Epoch 112/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5746 - accuracy: 0.7750 - val_loss: 0.6868 - val_accuracy: 0.6000\n",
            "Epoch 113/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5730 - accuracy: 0.7875 - val_loss: 0.6827 - val_accuracy: 0.6000\n",
            "Epoch 114/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5714 - accuracy: 0.7875 - val_loss: 0.6792 - val_accuracy: 0.6500\n",
            "Epoch 115/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5707 - accuracy: 0.8000 - val_loss: 0.6775 - val_accuracy: 0.6000\n",
            "Epoch 116/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5696 - accuracy: 0.8125 - val_loss: 0.6741 - val_accuracy: 0.6500\n",
            "Epoch 117/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5683 - accuracy: 0.8250 - val_loss: 0.6727 - val_accuracy: 0.6500\n",
            "Epoch 118/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5677 - accuracy: 0.8250 - val_loss: 0.6699 - val_accuracy: 0.6500\n",
            "Epoch 119/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5661 - accuracy: 0.8250 - val_loss: 0.6701 - val_accuracy: 0.6500\n",
            "Epoch 120/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5642 - accuracy: 0.8125 - val_loss: 0.6678 - val_accuracy: 0.6500\n",
            "Epoch 121/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.5634 - accuracy: 0.8250 - val_loss: 0.6673 - val_accuracy: 0.6500\n",
            "Epoch 122/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5630 - accuracy: 0.8125 - val_loss: 0.6673 - val_accuracy: 0.6000\n",
            "Epoch 123/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5602 - accuracy: 0.8250 - val_loss: 0.6662 - val_accuracy: 0.6500\n",
            "Epoch 124/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5602 - accuracy: 0.8250 - val_loss: 0.6648 - val_accuracy: 0.6500\n",
            "Epoch 125/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5580 - accuracy: 0.8250 - val_loss: 0.6657 - val_accuracy: 0.6500\n",
            "Epoch 126/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5573 - accuracy: 0.8250 - val_loss: 0.6670 - val_accuracy: 0.6500\n",
            "Epoch 127/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5561 - accuracy: 0.8250 - val_loss: 0.6673 - val_accuracy: 0.6500\n",
            "Epoch 128/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5548 - accuracy: 0.8250 - val_loss: 0.6661 - val_accuracy: 0.6500\n",
            "Epoch 129/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5536 - accuracy: 0.8375 - val_loss: 0.6642 - val_accuracy: 0.6500\n",
            "Epoch 130/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5521 - accuracy: 0.8375 - val_loss: 0.6614 - val_accuracy: 0.6500\n",
            "Epoch 131/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5510 - accuracy: 0.8125 - val_loss: 0.6588 - val_accuracy: 0.6500\n",
            "Epoch 132/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5497 - accuracy: 0.8125 - val_loss: 0.6562 - val_accuracy: 0.6500\n",
            "Epoch 133/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5488 - accuracy: 0.8250 - val_loss: 0.6526 - val_accuracy: 0.6500\n",
            "Epoch 134/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.5476 - accuracy: 0.8250 - val_loss: 0.6496 - val_accuracy: 0.6500\n",
            "Epoch 135/3500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.5460 - accuracy: 0.8250 - val_loss: 0.6491 - val_accuracy: 0.6500\n",
            "Epoch 136/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5451 - accuracy: 0.8250 - val_loss: 0.6487 - val_accuracy: 0.6500\n",
            "Epoch 137/3500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.5442 - accuracy: 0.8250 - val_loss: 0.6492 - val_accuracy: 0.6500\n",
            "Epoch 138/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5426 - accuracy: 0.8250 - val_loss: 0.6515 - val_accuracy: 0.6500\n",
            "Epoch 139/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5412 - accuracy: 0.8375 - val_loss: 0.6526 - val_accuracy: 0.6500\n",
            "Epoch 140/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.5400 - accuracy: 0.8375 - val_loss: 0.6531 - val_accuracy: 0.6500\n",
            "Epoch 141/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5386 - accuracy: 0.8375 - val_loss: 0.6543 - val_accuracy: 0.6500\n",
            "Epoch 142/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5377 - accuracy: 0.8375 - val_loss: 0.6556 - val_accuracy: 0.6500\n",
            "Epoch 143/3500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.5365 - accuracy: 0.8250 - val_loss: 0.6557 - val_accuracy: 0.6000\n",
            "Epoch 144/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5354 - accuracy: 0.8250 - val_loss: 0.6537 - val_accuracy: 0.6500\n",
            "Epoch 145/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5342 - accuracy: 0.8375 - val_loss: 0.6518 - val_accuracy: 0.6500\n",
            "Epoch 146/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5328 - accuracy: 0.8375 - val_loss: 0.6505 - val_accuracy: 0.6500\n",
            "Epoch 147/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5321 - accuracy: 0.8375 - val_loss: 0.6486 - val_accuracy: 0.6500\n",
            "Epoch 148/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5309 - accuracy: 0.8375 - val_loss: 0.6485 - val_accuracy: 0.6500\n",
            "Epoch 149/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5297 - accuracy: 0.8375 - val_loss: 0.6509 - val_accuracy: 0.6500\n",
            "Epoch 150/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5290 - accuracy: 0.8375 - val_loss: 0.6507 - val_accuracy: 0.6500\n",
            "Epoch 151/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.5271 - accuracy: 0.8375 - val_loss: 0.6495 - val_accuracy: 0.6500\n",
            "Epoch 152/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5260 - accuracy: 0.8375 - val_loss: 0.6490 - val_accuracy: 0.6500\n",
            "Epoch 153/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5245 - accuracy: 0.8375 - val_loss: 0.6487 - val_accuracy: 0.6500\n",
            "Epoch 154/3500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.5232 - accuracy: 0.8250 - val_loss: 0.6476 - val_accuracy: 0.6500\n",
            "Epoch 155/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5217 - accuracy: 0.8250 - val_loss: 0.6443 - val_accuracy: 0.6500\n",
            "Epoch 156/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.5209 - accuracy: 0.8250 - val_loss: 0.6421 - val_accuracy: 0.6500\n",
            "Epoch 157/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5202 - accuracy: 0.8125 - val_loss: 0.6396 - val_accuracy: 0.6500\n",
            "Epoch 158/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.5187 - accuracy: 0.8250 - val_loss: 0.6384 - val_accuracy: 0.6500\n",
            "Epoch 159/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5181 - accuracy: 0.8250 - val_loss: 0.6382 - val_accuracy: 0.6500\n",
            "Epoch 160/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5164 - accuracy: 0.8375 - val_loss: 0.6360 - val_accuracy: 0.7000\n",
            "Epoch 161/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.5150 - accuracy: 0.8375 - val_loss: 0.6341 - val_accuracy: 0.7000\n",
            "Epoch 162/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5135 - accuracy: 0.8375 - val_loss: 0.6338 - val_accuracy: 0.7000\n",
            "Epoch 163/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5129 - accuracy: 0.8375 - val_loss: 0.6343 - val_accuracy: 0.6500\n",
            "Epoch 164/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.5114 - accuracy: 0.8250 - val_loss: 0.6333 - val_accuracy: 0.6000\n",
            "Epoch 165/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.5100 - accuracy: 0.8375 - val_loss: 0.6335 - val_accuracy: 0.6000\n",
            "Epoch 166/3500\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.5088 - accuracy: 0.8500 - val_loss: 0.6360 - val_accuracy: 0.6500\n",
            "Epoch 167/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.5082 - accuracy: 0.8375 - val_loss: 0.6384 - val_accuracy: 0.6000\n",
            "Epoch 168/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5067 - accuracy: 0.8500 - val_loss: 0.6396 - val_accuracy: 0.6500\n",
            "Epoch 169/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.5058 - accuracy: 0.8500 - val_loss: 0.6383 - val_accuracy: 0.6000\n",
            "Epoch 170/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.5040 - accuracy: 0.8500 - val_loss: 0.6355 - val_accuracy: 0.6000\n",
            "Epoch 171/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.5028 - accuracy: 0.8250 - val_loss: 0.6322 - val_accuracy: 0.6000\n",
            "Epoch 172/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.5013 - accuracy: 0.8250 - val_loss: 0.6315 - val_accuracy: 0.6000\n",
            "Epoch 173/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.5006 - accuracy: 0.8375 - val_loss: 0.6322 - val_accuracy: 0.6000\n",
            "Epoch 174/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4994 - accuracy: 0.8375 - val_loss: 0.6349 - val_accuracy: 0.6000\n",
            "Epoch 175/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.4980 - accuracy: 0.8375 - val_loss: 0.6352 - val_accuracy: 0.6000\n",
            "Epoch 176/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4968 - accuracy: 0.8500 - val_loss: 0.6344 - val_accuracy: 0.6000\n",
            "Epoch 177/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4958 - accuracy: 0.8375 - val_loss: 0.6334 - val_accuracy: 0.6000\n",
            "Epoch 178/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4944 - accuracy: 0.8500 - val_loss: 0.6323 - val_accuracy: 0.6000\n",
            "Epoch 179/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4937 - accuracy: 0.8500 - val_loss: 0.6288 - val_accuracy: 0.6500\n",
            "Epoch 180/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4931 - accuracy: 0.8625 - val_loss: 0.6270 - val_accuracy: 0.6500\n",
            "Epoch 181/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4914 - accuracy: 0.8625 - val_loss: 0.6270 - val_accuracy: 0.6500\n",
            "Epoch 182/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4907 - accuracy: 0.8625 - val_loss: 0.6266 - val_accuracy: 0.6500\n",
            "Epoch 183/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4886 - accuracy: 0.8625 - val_loss: 0.6266 - val_accuracy: 0.7000\n",
            "Epoch 184/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4877 - accuracy: 0.8625 - val_loss: 0.6270 - val_accuracy: 0.7000\n",
            "Epoch 185/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4868 - accuracy: 0.8500 - val_loss: 0.6266 - val_accuracy: 0.7000\n",
            "Epoch 186/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.4853 - accuracy: 0.8500 - val_loss: 0.6250 - val_accuracy: 0.7000\n",
            "Epoch 187/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4845 - accuracy: 0.8625 - val_loss: 0.6225 - val_accuracy: 0.7000\n",
            "Epoch 188/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4830 - accuracy: 0.8625 - val_loss: 0.6246 - val_accuracy: 0.7000\n",
            "Epoch 189/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4817 - accuracy: 0.8500 - val_loss: 0.6235 - val_accuracy: 0.7000\n",
            "Epoch 190/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.4806 - accuracy: 0.8500 - val_loss: 0.6221 - val_accuracy: 0.6500\n",
            "Epoch 191/3500\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 0.4800 - accuracy: 0.8500 - val_loss: 0.6217 - val_accuracy: 0.6500\n",
            "Epoch 192/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4787 - accuracy: 0.8500 - val_loss: 0.6215 - val_accuracy: 0.6500\n",
            "Epoch 193/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4772 - accuracy: 0.8500 - val_loss: 0.6225 - val_accuracy: 0.6500\n",
            "Epoch 194/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4759 - accuracy: 0.8500 - val_loss: 0.6214 - val_accuracy: 0.6500\n",
            "Epoch 195/3500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.4745 - accuracy: 0.8500 - val_loss: 0.6197 - val_accuracy: 0.6500\n",
            "Epoch 196/3500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.4737 - accuracy: 0.8500 - val_loss: 0.6178 - val_accuracy: 0.7000\n",
            "Epoch 197/3500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.4726 - accuracy: 0.8500 - val_loss: 0.6172 - val_accuracy: 0.7000\n",
            "Epoch 198/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.4714 - accuracy: 0.8500 - val_loss: 0.6184 - val_accuracy: 0.7000\n",
            "Epoch 199/3500\n",
            "3/3 [==============================] - 0s 42ms/step - loss: 0.4706 - accuracy: 0.8375 - val_loss: 0.6214 - val_accuracy: 0.7000\n",
            "Epoch 200/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.4696 - accuracy: 0.8375 - val_loss: 0.6223 - val_accuracy: 0.6500\n",
            "Epoch 201/3500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.4680 - accuracy: 0.8375 - val_loss: 0.6217 - val_accuracy: 0.6500\n",
            "Epoch 202/3500\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 0.4673 - accuracy: 0.8375 - val_loss: 0.6210 - val_accuracy: 0.6500\n",
            "Epoch 203/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4665 - accuracy: 0.8375 - val_loss: 0.6196 - val_accuracy: 0.6000\n",
            "Epoch 204/3500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.4659 - accuracy: 0.8625 - val_loss: 0.6187 - val_accuracy: 0.6000\n",
            "Epoch 205/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4641 - accuracy: 0.8625 - val_loss: 0.6186 - val_accuracy: 0.6000\n",
            "Epoch 206/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4634 - accuracy: 0.8625 - val_loss: 0.6187 - val_accuracy: 0.6000\n",
            "Epoch 207/3500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.4624 - accuracy: 0.8500 - val_loss: 0.6180 - val_accuracy: 0.6500\n",
            "Epoch 208/3500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.4615 - accuracy: 0.8500 - val_loss: 0.6156 - val_accuracy: 0.6000\n",
            "Epoch 209/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4601 - accuracy: 0.8500 - val_loss: 0.6159 - val_accuracy: 0.6000\n",
            "Epoch 210/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.4586 - accuracy: 0.8500 - val_loss: 0.6155 - val_accuracy: 0.6500\n",
            "Epoch 211/3500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.4575 - accuracy: 0.8500 - val_loss: 0.6152 - val_accuracy: 0.7000\n",
            "Epoch 212/3500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.4569 - accuracy: 0.8500 - val_loss: 0.6115 - val_accuracy: 0.7000\n",
            "Epoch 213/3500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.4566 - accuracy: 0.8625 - val_loss: 0.6103 - val_accuracy: 0.6500\n",
            "Epoch 214/3500\n",
            "3/3 [==============================] - 0s 47ms/step - loss: 0.4548 - accuracy: 0.8750 - val_loss: 0.6085 - val_accuracy: 0.6500\n",
            "Epoch 215/3500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.4535 - accuracy: 0.8625 - val_loss: 0.6084 - val_accuracy: 0.6500\n",
            "Epoch 216/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.4520 - accuracy: 0.8750 - val_loss: 0.6077 - val_accuracy: 0.7000\n",
            "Epoch 217/3500\n",
            "3/3 [==============================] - 0s 40ms/step - loss: 0.4518 - accuracy: 0.8750 - val_loss: 0.6097 - val_accuracy: 0.7000\n",
            "Epoch 218/3500\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.4501 - accuracy: 0.8750 - val_loss: 0.6096 - val_accuracy: 0.7000\n",
            "Epoch 219/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.4490 - accuracy: 0.8750 - val_loss: 0.6115 - val_accuracy: 0.6000\n",
            "Epoch 220/3500\n",
            "3/3 [==============================] - 0s 45ms/step - loss: 0.4481 - accuracy: 0.8750 - val_loss: 0.6144 - val_accuracy: 0.6500\n",
            "Epoch 221/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4468 - accuracy: 0.8500 - val_loss: 0.6153 - val_accuracy: 0.7000\n",
            "Epoch 222/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4465 - accuracy: 0.8625 - val_loss: 0.6174 - val_accuracy: 0.6500\n",
            "Epoch 223/3500\n",
            "3/3 [==============================] - 0s 39ms/step - loss: 0.4464 - accuracy: 0.8375 - val_loss: 0.6158 - val_accuracy: 0.6500\n",
            "Epoch 224/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.4440 - accuracy: 0.8375 - val_loss: 0.6142 - val_accuracy: 0.6500\n",
            "Epoch 225/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.4426 - accuracy: 0.8500 - val_loss: 0.6130 - val_accuracy: 0.6500\n",
            "Epoch 226/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4425 - accuracy: 0.8625 - val_loss: 0.6098 - val_accuracy: 0.7000\n",
            "Epoch 227/3500\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 0.4408 - accuracy: 0.8500 - val_loss: 0.6093 - val_accuracy: 0.6500\n",
            "Epoch 228/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4396 - accuracy: 0.8625 - val_loss: 0.6085 - val_accuracy: 0.6500\n",
            "Epoch 229/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4391 - accuracy: 0.8625 - val_loss: 0.6065 - val_accuracy: 0.6500\n",
            "Epoch 230/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4380 - accuracy: 0.8625 - val_loss: 0.6043 - val_accuracy: 0.6500\n",
            "Epoch 231/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4368 - accuracy: 0.8375 - val_loss: 0.6023 - val_accuracy: 0.6500\n",
            "Epoch 232/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4361 - accuracy: 0.8375 - val_loss: 0.6012 - val_accuracy: 0.6500\n",
            "Epoch 233/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4351 - accuracy: 0.8500 - val_loss: 0.6021 - val_accuracy: 0.6500\n",
            "Epoch 234/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4338 - accuracy: 0.8625 - val_loss: 0.6039 - val_accuracy: 0.6500\n",
            "Epoch 235/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4325 - accuracy: 0.8750 - val_loss: 0.6054 - val_accuracy: 0.6500\n",
            "Epoch 236/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4320 - accuracy: 0.8750 - val_loss: 0.6058 - val_accuracy: 0.6500\n",
            "Epoch 237/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4309 - accuracy: 0.8750 - val_loss: 0.6046 - val_accuracy: 0.6500\n",
            "Epoch 238/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4301 - accuracy: 0.8750 - val_loss: 0.6037 - val_accuracy: 0.6500\n",
            "Epoch 239/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4289 - accuracy: 0.8750 - val_loss: 0.6024 - val_accuracy: 0.6500\n",
            "Epoch 240/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4288 - accuracy: 0.8750 - val_loss: 0.6016 - val_accuracy: 0.6500\n",
            "Epoch 241/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4274 - accuracy: 0.8750 - val_loss: 0.5994 - val_accuracy: 0.6500\n",
            "Epoch 242/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4263 - accuracy: 0.8750 - val_loss: 0.5987 - val_accuracy: 0.6500\n",
            "Epoch 243/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4251 - accuracy: 0.8750 - val_loss: 0.5963 - val_accuracy: 0.6500\n",
            "Epoch 244/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4243 - accuracy: 0.8750 - val_loss: 0.5972 - val_accuracy: 0.6500\n",
            "Epoch 245/3500\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 0.4233 - accuracy: 0.8750 - val_loss: 0.5999 - val_accuracy: 0.6500\n",
            "Epoch 246/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.4224 - accuracy: 0.8750 - val_loss: 0.6000 - val_accuracy: 0.6500\n",
            "Epoch 247/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4214 - accuracy: 0.8750 - val_loss: 0.6015 - val_accuracy: 0.6500\n",
            "Epoch 248/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4204 - accuracy: 0.8750 - val_loss: 0.5999 - val_accuracy: 0.7000\n",
            "Epoch 249/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.4197 - accuracy: 0.8750 - val_loss: 0.5976 - val_accuracy: 0.7000\n",
            "Epoch 250/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.4191 - accuracy: 0.8750 - val_loss: 0.5952 - val_accuracy: 0.6500\n",
            "Epoch 251/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4177 - accuracy: 0.8750 - val_loss: 0.5944 - val_accuracy: 0.6500\n",
            "Epoch 252/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4167 - accuracy: 0.8750 - val_loss: 0.5957 - val_accuracy: 0.6500\n",
            "Epoch 253/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4158 - accuracy: 0.8750 - val_loss: 0.5944 - val_accuracy: 0.6500\n",
            "Epoch 254/3500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.4154 - accuracy: 0.8750 - val_loss: 0.5948 - val_accuracy: 0.6500\n",
            "Epoch 255/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4141 - accuracy: 0.8750 - val_loss: 0.5936 - val_accuracy: 0.6500\n",
            "Epoch 256/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.4130 - accuracy: 0.8750 - val_loss: 0.5930 - val_accuracy: 0.6500\n",
            "Epoch 257/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4134 - accuracy: 0.8750 - val_loss: 0.5925 - val_accuracy: 0.6500\n",
            "Epoch 258/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4121 - accuracy: 0.8750 - val_loss: 0.5949 - val_accuracy: 0.6500\n",
            "Epoch 259/3500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.4103 - accuracy: 0.8750 - val_loss: 0.5946 - val_accuracy: 0.6500\n",
            "Epoch 260/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4094 - accuracy: 0.8750 - val_loss: 0.5944 - val_accuracy: 0.6500\n",
            "Epoch 261/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4095 - accuracy: 0.8750 - val_loss: 0.5930 - val_accuracy: 0.6500\n",
            "Epoch 262/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.4075 - accuracy: 0.8750 - val_loss: 0.5932 - val_accuracy: 0.6500\n",
            "Epoch 263/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.4073 - accuracy: 0.8750 - val_loss: 0.5940 - val_accuracy: 0.6500\n",
            "Epoch 264/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4061 - accuracy: 0.8625 - val_loss: 0.5929 - val_accuracy: 0.6500\n",
            "Epoch 265/3500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.4053 - accuracy: 0.8625 - val_loss: 0.5922 - val_accuracy: 0.6500\n",
            "Epoch 266/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.4047 - accuracy: 0.8750 - val_loss: 0.5920 - val_accuracy: 0.6500\n",
            "Epoch 267/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.4037 - accuracy: 0.8750 - val_loss: 0.5905 - val_accuracy: 0.6500\n",
            "Epoch 268/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4037 - accuracy: 0.8750 - val_loss: 0.5886 - val_accuracy: 0.6500\n",
            "Epoch 269/3500\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.4016 - accuracy: 0.8875 - val_loss: 0.5882 - val_accuracy: 0.7000\n",
            "Epoch 270/3500\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.4009 - accuracy: 0.8750 - val_loss: 0.5896 - val_accuracy: 0.7000\n",
            "Epoch 271/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.4004 - accuracy: 0.8750 - val_loss: 0.5946 - val_accuracy: 0.7000\n",
            "Epoch 272/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.3998 - accuracy: 0.8750 - val_loss: 0.5976 - val_accuracy: 0.7000\n",
            "Epoch 273/3500\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 0.3988 - accuracy: 0.8625 - val_loss: 0.5969 - val_accuracy: 0.6500\n",
            "Epoch 274/3500\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 0.3983 - accuracy: 0.8750 - val_loss: 0.5952 - val_accuracy: 0.6000\n",
            "Epoch 275/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3996 - accuracy: 0.8750 - val_loss: 0.5931 - val_accuracy: 0.6500\n",
            "Epoch 276/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3973 - accuracy: 0.8750 - val_loss: 0.5956 - val_accuracy: 0.6500\n",
            "Epoch 277/3500\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.3949 - accuracy: 0.8750 - val_loss: 0.5980 - val_accuracy: 0.6500\n",
            "Epoch 278/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.3958 - accuracy: 0.8750 - val_loss: 0.5989 - val_accuracy: 0.6500\n",
            "Epoch 279/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3956 - accuracy: 0.8625 - val_loss: 0.5995 - val_accuracy: 0.6500\n",
            "Epoch 280/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3944 - accuracy: 0.8625 - val_loss: 0.5959 - val_accuracy: 0.6500\n",
            "Epoch 281/3500\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 0.3922 - accuracy: 0.8750 - val_loss: 0.5913 - val_accuracy: 0.6500\n",
            "Epoch 282/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3916 - accuracy: 0.8750 - val_loss: 0.5885 - val_accuracy: 0.6500\n",
            "Epoch 283/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3914 - accuracy: 0.8750 - val_loss: 0.5860 - val_accuracy: 0.6000\n",
            "Epoch 284/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3915 - accuracy: 0.8750 - val_loss: 0.5875 - val_accuracy: 0.6000\n",
            "Epoch 285/3500\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.3898 - accuracy: 0.8750 - val_loss: 0.5849 - val_accuracy: 0.6000\n",
            "Epoch 286/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3887 - accuracy: 0.8750 - val_loss: 0.5813 - val_accuracy: 0.6000\n",
            "Epoch 287/3500\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.3876 - accuracy: 0.8875 - val_loss: 0.5795 - val_accuracy: 0.7500\n",
            "Epoch 288/3500\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.3870 - accuracy: 0.8875 - val_loss: 0.5797 - val_accuracy: 0.7000\n",
            "Epoch 289/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3868 - accuracy: 0.8875 - val_loss: 0.5789 - val_accuracy: 0.7000\n",
            "Epoch 290/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3857 - accuracy: 0.8750 - val_loss: 0.5798 - val_accuracy: 0.7000\n",
            "Epoch 291/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.3856 - accuracy: 0.9000 - val_loss: 0.5808 - val_accuracy: 0.7000\n",
            "Epoch 292/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3850 - accuracy: 0.8875 - val_loss: 0.5825 - val_accuracy: 0.7000\n",
            "Epoch 293/3500\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.3838 - accuracy: 0.8875 - val_loss: 0.5840 - val_accuracy: 0.7000\n",
            "Epoch 294/3500\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 0.3829 - accuracy: 0.8875 - val_loss: 0.5854 - val_accuracy: 0.7000\n",
            "Epoch 295/3500\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 0.3827 - accuracy: 0.8750 - val_loss: 0.5879 - val_accuracy: 0.7000\n",
            "Epoch 296/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3819 - accuracy: 0.8875 - val_loss: 0.5867 - val_accuracy: 0.7000\n",
            "Epoch 297/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3800 - accuracy: 0.8875 - val_loss: 0.5880 - val_accuracy: 0.7000\n",
            "Epoch 298/3500\n",
            "3/3 [==============================] - 0s 33ms/step - loss: 0.3798 - accuracy: 0.8750 - val_loss: 0.5891 - val_accuracy: 0.7000\n",
            "Epoch 299/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3795 - accuracy: 0.8875 - val_loss: 0.5879 - val_accuracy: 0.7000\n",
            "Epoch 300/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.3789 - accuracy: 0.8875 - val_loss: 0.5885 - val_accuracy: 0.7000\n",
            "Epoch 301/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3793 - accuracy: 0.8875 - val_loss: 0.5891 - val_accuracy: 0.6500\n",
            "Epoch 302/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3778 - accuracy: 0.8875 - val_loss: 0.5918 - val_accuracy: 0.7000\n",
            "Epoch 303/3500\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.3772 - accuracy: 0.8875 - val_loss: 0.5891 - val_accuracy: 0.7000\n",
            "Epoch 304/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3754 - accuracy: 0.8875 - val_loss: 0.5892 - val_accuracy: 0.7000\n",
            "Epoch 305/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3746 - accuracy: 0.8750 - val_loss: 0.5893 - val_accuracy: 0.7000\n",
            "Epoch 306/3500\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 0.3743 - accuracy: 0.8750 - val_loss: 0.5881 - val_accuracy: 0.7000\n",
            "Epoch 307/3500\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.3740 - accuracy: 0.8750 - val_loss: 0.5861 - val_accuracy: 0.7000\n",
            "Epoch 308/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3731 - accuracy: 0.9000 - val_loss: 0.5817 - val_accuracy: 0.7000\n",
            "Epoch 309/3500\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.3721 - accuracy: 0.9000 - val_loss: 0.5796 - val_accuracy: 0.7000\n",
            "Epoch 309: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ShQgHQnq5KqV",
        "outputId": "54636a3d-0061-471e-b21c-18588c6d26ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mAzmNtAN5Y43"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}