dropout_classification_example.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fd0Ty4eIox-C"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array([[-1.58986e-01,  4.23977e-01],\n",
        "       [-3.47926e-01,  4.70760e-01],\n",
        "       [-5.04608e-01,  3.53801e-01],\n",
        "       [-5.96774e-01,  1.14035e-01],\n",
        "       [-5.18433e-01, -1.72515e-01],\n",
        "       [-2.92627e-01, -2.07602e-01],\n",
        "       [-1.58986e-01, -4.38596e-02],\n",
        "       [-5.76037e-02,  1.43275e-01],\n",
        "       [-7.14286e-02,  2.71930e-01],\n",
        "       [-2.97235e-01,  3.47953e-01],\n",
        "       [-4.17051e-01,  2.01754e-01],\n",
        "       [-4.40092e-01,  8.77193e-03],\n",
        "       [-3.24885e-01, -3.21637e-02],\n",
        "       [-2.46544e-01,  5.55556e-02],\n",
        "       [-2.18894e-01,  2.01754e-01],\n",
        "       [-3.43318e-01,  1.60819e-01],\n",
        "       [-5.09217e-01,  7.89474e-02],\n",
        "       [-3.84793e-01, -9.06433e-02],\n",
        "       [-1.49770e-01,  1.25731e-01],\n",
        "       [-1.95853e-01,  3.24561e-01],\n",
        "       [-3.91705e-02, -2.19298e-01],\n",
        "       [-1.08295e-01, -3.01170e-01],\n",
        "       [-1.86636e-01, -3.30409e-01],\n",
        "       [-2.18894e-01, -4.23977e-01],\n",
        "       [-8.06452e-02, -5.64327e-01],\n",
        "       [ 6.68203e-02, -5.17544e-01],\n",
        "       [ 9.44700e-02, -3.24561e-01],\n",
        "       [ 1.86636e-01, -1.66667e-01],\n",
        "       [ 6.22120e-02, -7.30994e-02],\n",
        "       [ 2.07373e-02, -1.95906e-01],\n",
        "       [ 2.99539e-02, -3.42105e-01],\n",
        "       [-9.90783e-02, -3.77193e-01],\n",
        "       [-6.91244e-03, -4.64912e-01],\n",
        "       [ 1.31336e-01, -4.29825e-01],\n",
        "       [ 2.32719e-01, -1.95906e-01],\n",
        "       [ 8.52535e-02, -8.47953e-02],\n",
        "       [-1.31336e-01, -2.36842e-01],\n",
        "       [ 2.30415e-03, -1.25731e-01],\n",
        "       [ 1.22120e-01, -2.92398e-03],\n",
        "       [-3.47926e-01, -3.12865e-01],\n",
        "       [-2.28111e-01, -1.25731e-01],\n",
        "       [-7.60369e-02,  1.46199e-02],\n",
        "       [ 4.37788e-02,  2.04678e-02],\n",
        "       [ 1.15207e-02,  1.54971e-01],\n",
        "       [-4.17051e-01, -1.60819e-01],\n",
        "       [-3.15668e-01, -3.18713e-01],\n",
        "       [ 1.26728e-01, -2.19298e-01],\n",
        "       [ 2.05069e-01, -3.12865e-01],\n",
        "       [ 2.18894e-01, -4.59064e-01],\n",
        "       [ 7.14286e-02, -6.46199e-01],\n",
        "       [-1.31336e-01, -6.05263e-01],\n",
        "       [-2.09677e-01, -5.81871e-01],\n",
        "       [-2.28111e-01, -4.29825e-01],\n",
        "       [-1.45161e-01, -4.12281e-01],\n",
        "       [-6.68203e-02, -4.82456e-01],\n",
        "       [ 1.35945e-01, -5.11696e-01],\n",
        "       [ 2.69585e-01, -4.06433e-01],\n",
        "       [ 2.97235e-01, -2.95322e-01],\n",
        "       [ 2.74194e-01, -1.72515e-01],\n",
        "       [ 2.55760e-01, -4.97076e-02],\n",
        "       [ 2.23502e-01, -4.97076e-02],\n",
        "       [ 1.82028e-01, -8.47953e-02],\n",
        "       [ 1.58986e-01, -1.54971e-01],\n",
        "       [ 7.14286e-02, -2.13450e-01],\n",
        "       [ 1.61290e-02, -2.66082e-01],\n",
        "       [-2.53456e-02, -3.83041e-01],\n",
        "       [-1.15207e-02, -4.82456e-01],\n",
        "       [-2.30415e-03, -5.05848e-01],\n",
        "       [ 2.53456e-02, -5.11696e-01],\n",
        "       [ 2.53456e-02, -5.58480e-01],\n",
        "       [ 1.15207e-02, -6.57895e-01],\n",
        "       [-4.83871e-02, -6.46199e-01],\n",
        "       [-8.52535e-02, -5.52632e-01],\n",
        "       [-9.90783e-02, -5.00000e-01],\n",
        "       [-1.61290e-02, -4.23977e-01],\n",
        "       [ 1.31336e-01, -3.59649e-01],\n",
        "       [ 2.23502e-01, -3.71345e-01],\n",
        "       [ 2.92627e-01, -3.01170e-01],\n",
        "       [ 2.60369e-01, -2.07602e-01],\n",
        "       [ 2.00461e-01, -2.25146e-01],\n",
        "       [ 1.72811e-01, -2.71930e-01],\n",
        "       [-1.31336e-01,  9.06433e-02],\n",
        "       [-1.49770e-01,  7.30994e-02],\n",
        "       [-2.41935e-01,  6.14035e-02],\n",
        "       [-3.01843e-01,  1.78363e-01],\n",
        "       [-2.97235e-01,  1.95906e-01],\n",
        "       [-2.74194e-01,  3.07018e-01],\n",
        "       [-3.24885e-01,  2.95322e-01],\n",
        "       [-3.98618e-01,  2.66082e-01],\n",
        "       [-4.35484e-01,  1.60819e-01],\n",
        "       [-4.72350e-01,  7.89474e-02],\n",
        "       [-3.38710e-01,  4.38596e-02],\n",
        "       [-2.69585e-01,  4.38596e-02],\n",
        "       [-2.55760e-01,  1.02339e-01],\n",
        "       [-1.68203e-01,  2.66082e-01],\n",
        "       [-1.12903e-01,  3.01170e-01],\n",
        "       [-3.91705e-02,  3.47953e-01],\n",
        "       [-1.26728e-01,  4.41520e-01],\n",
        "       [-2.32719e-01,  4.41520e-01],\n",
        "       [-3.38710e-01,  4.18129e-01],\n",
        "       [-4.12442e-01,  3.53801e-01],\n",
        "       [-5.09217e-01,  2.19298e-01],\n",
        "       [-5.41475e-01,  1.46199e-02],\n",
        "       [-5.04608e-01, -1.25731e-01],\n",
        "       [-4.90783e-01, -1.43275e-01],\n",
        "       [-3.61751e-01, -1.37427e-01],\n",
        "       [-2.69585e-01, -8.47953e-02],\n",
        "       [-2.23502e-01, -7.89474e-02],\n",
        "       [-1.86636e-01, -3.80117e-02],\n",
        "       [-1.54378e-01, -8.77193e-03],\n",
        "       [-1.12903e-01,  5.55556e-02],\n",
        "       [-8.52535e-02,  1.37427e-01],\n",
        "       [-8.52535e-02,  2.77778e-01],\n",
        "       [-1.68203e-01,  3.01170e-01],\n",
        "       [-1.91244e-01,  1.95906e-01],\n",
        "       [-1.40553e-01, -4.97076e-02],\n",
        "       [-2.99539e-02,  6.72515e-02],\n",
        "       [-2.00461e-01, -2.30994e-01],\n",
        "       [-1.08295e-01, -8.47953e-02],\n",
        "       [ 3.45622e-02,  6.72515e-02],\n",
        "       [ 8.06452e-02,  1.19883e-01],\n",
        "       [-3.85369e-01,  3.30409e-02],\n",
        "       [-3.81221e-01,  1.31287e-01],\n",
        "       [-3.52189e-01,  2.58187e-01],\n",
        "       [-3.54263e-01,  3.64620e-01],\n",
        "       [-4.14401e-01, -6.92982e-02],\n",
        "       [-4.99424e-01, -3.24561e-02],\n",
        "       [-2.98272e-01, -9.79532e-02],\n",
        "       [-3.16935e-01, -1.83918e-01],\n",
        "       [-3.68779e-01, -2.90351e-01],\n",
        "       [-3.56336e-01, -3.96784e-01],\n",
        "       [-2.71313e-01,  4.38596e-03],\n",
        "       [-1.77995e-01,  8.62573e-02],\n",
        "       [-2.46429e-01,  1.43567e-01],\n",
        "       [-2.50576e-01,  2.29532e-01],\n",
        "       [-2.21544e-01,  3.76901e-01],\n",
        "       [-2.15323e-01,  2.95029e-01],\n",
        "       [-1.30300e-01,  2.17251e-01],\n",
        "       [-2.07028e-01,  2.89474e-02],\n",
        "       [-9.71198e-02,  2.13158e-01],\n",
        "       [-3.90553e-02,  2.58187e-01],\n",
        "       [ 1.90092e-02,  4.01462e-01],\n",
        "       [-3.69816e-02,  4.21930e-01],\n",
        "       [-6.39401e-02,  3.31871e-01],\n",
        "       [-1.30300e-01,  3.76901e-01],\n",
        "       [-3.75000e-01, -4.37719e-01],\n",
        "       [-3.95737e-01, -3.51754e-01],\n",
        "       [-3.54263e-01, -2.08480e-01],\n",
        "       [-4.37212e-01, -3.76316e-01],\n",
        "       [-4.80760e-01, -5.03216e-01],\n",
        "       [-4.10253e-01, -4.66374e-01],\n",
        "       [-2.48502e-01, -2.57602e-01],\n",
        "       [-2.27765e-01, -3.14912e-01],\n",
        "       [-2.83756e-01, -3.84503e-01],\n",
        "       [-2.92051e-01, -4.54094e-01],\n",
        "       [-3.37673e-01, -5.19591e-01],\n",
        "       [-2.77535e-01, -5.48246e-01],\n",
        "       [-2.07028e-01, -5.35965e-01],\n",
        "       [-1.86290e-01, -4.78655e-01],\n",
        "       [-1.32373e-01, -5.07310e-01],\n",
        "       [-1.77995e-01, -2.98538e-01],\n",
        "       [-1.65553e-01, -1.75731e-01],\n",
        "       [-1.61406e-01, -1.26608e-01],\n",
        "       [ 3.45622e-04,  2.58187e-01],\n",
        "       [ 7.91475e-02,  3.56433e-01],\n",
        "       [-2.66129e-02,  1.80409e-01],\n",
        "       [-5.35714e-02,  7.80702e-02],\n",
        "       [-1.41705e-02, -5.29240e-02],\n",
        "       [-7.01613e-02, -1.63450e-01],\n",
        "       [-6.39401e-02, -2.94444e-01],\n",
        "       [-3.07604e-02, -4.66374e-01],\n",
        "       [-5.77189e-02, -5.27778e-01],\n",
        "       [-5.35714e-02, -3.96784e-01],\n",
        "       [ 5.21889e-02, -4.17251e-01],\n",
        "       [-1.62442e-02, -1.67544e-01],\n",
        "       [-6.39401e-02, -8.56725e-02],\n",
        "       [-6.18664e-02, -1.60819e-02],\n",
        "       [-3.80184e-03,  4.38596e-03],\n",
        "       [ 4.18203e-02,  2.04971e-01],\n",
        "       [ 7.91475e-02,  1.92690e-01],\n",
        "       [ 4.59677e-02,  2.54094e-01],\n",
        "       [ 1.18548e-01,  1.92690e-01],\n",
        "       [ 1.10253e-01,  8.62573e-02],\n",
        "       [ 1.08180e-01, -6.92982e-02],\n",
        "       [ 1.66244e-01, -2.42690e-02],\n",
        "       [ 1.41359e-01,  6.57895e-02],\n",
        "       [ 1.43433e-01,  1.68129e-01],\n",
        "       [ 1.70392e-01,  1.92690e-01],\n",
        "       [ 1.08180e-01,  2.99123e-01],\n",
        "       [ 1.18548e-01,  3.19591e-01],\n",
        "       [ 1.26843e-01,  3.93275e-01],\n",
        "       [-8.67512e-02,  4.21930e-01],\n",
        "       [-4.73502e-02,  5.07895e-01],\n",
        "       [ 2.52304e-02,  5.20175e-01],\n",
        "       [ 6.25576e-02,  5.52924e-01],\n",
        "       [-5.87558e-03,  4.42398e-01],\n",
        "       [-5.14977e-02,  5.73392e-01],\n",
        "       [-8.05300e-02,  5.07895e-01],\n",
        "       [-1.53111e-01,  5.52924e-01],\n",
        "       [-1.11636e-01,  5.48830e-01],\n",
        "       [-1.63479e-01,  4.91520e-01],\n",
        "       [-2.52650e-01, -1.88012e-01],\n",
        "       [-2.46429e-01, -3.65497e-02],\n",
        "       [-3.21083e-01, -4.33626e-01],\n",
        "       [-3.31452e-01, -6.05556e-01],\n",
        "       [-3.85369e-01, -5.15497e-01],\n",
        "       [-3.99885e-01, -6.21930e-01],\n",
        "       [-1.24078e-01, -1.26608e-01],\n",
        "       [-3.16935e-01, -2.28947e-01],\n",
        "       [-2.94124e-01, -1.34795e-01],\n",
        "       [-1.53111e-01,  1.84503e-01]])"
      ],
      "metadata": {
        "id": "givdvFnJpaB3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
        "       1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
        "       0., 0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
        "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
        "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.,\n",
        "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
        "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0.,\n",
        "       0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
        "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0.,\n",
        "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
        "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
        "       0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
        "       0., 1., 1., 1., 1., 0., 0.])"
      ],
      "metadata": {
        "id": "6cHwXzDBviun"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X[:,0], X[:,1], c=y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "5DtHd0FlpjtK",
        "outputId": "de3943e5-cfdc-49ae-ab4b-9a5deac894a0"
      },
      "execution_count": 4,
      "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": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "metadata": {
        "id": "384kyBc5qgUK"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(128, input_dim=2, activation=\"relu\"))\n",
        "model.add(Dense(128, activation=\"relu\"))\n",
        "model.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "adam = Adam(learning_rate=0.01)\n",
        "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X, y, epochs=500, validation_split = 0.2,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWWWfVWIws0r",
        "outputId": "dd6f93a6-b061-4a49-dd37-4d49d63f991b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "6/6 [==============================] - 1s 50ms/step - loss: 0.5531 - accuracy: 0.7857 - val_loss: 0.6904 - val_accuracy: 0.5814\n",
            "Epoch 2/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2919 - accuracy: 0.8690 - val_loss: 0.7067 - val_accuracy: 0.6977\n",
            "Epoch 3/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2367 - accuracy: 0.9048 - val_loss: 0.2905 - val_accuracy: 0.8837\n",
            "Epoch 4/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2398 - accuracy: 0.9226 - val_loss: 0.3414 - val_accuracy: 0.9302\n",
            "Epoch 5/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2845 - accuracy: 0.8690 - val_loss: 0.4219 - val_accuracy: 0.8605\n",
            "Epoch 6/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2127 - accuracy: 0.9167 - val_loss: 0.3760 - val_accuracy: 0.8372\n",
            "Epoch 7/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2156 - accuracy: 0.9167 - val_loss: 0.3405 - val_accuracy: 0.8605\n",
            "Epoch 8/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2096 - accuracy: 0.9286 - val_loss: 0.3643 - val_accuracy: 0.8605\n",
            "Epoch 9/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2084 - accuracy: 0.9167 - val_loss: 0.4047 - val_accuracy: 0.8372\n",
            "Epoch 10/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.2063 - accuracy: 0.9048 - val_loss: 0.3837 - val_accuracy: 0.8605\n",
            "Epoch 11/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2059 - accuracy: 0.9226 - val_loss: 0.3578 - val_accuracy: 0.8605\n",
            "Epoch 12/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.2018 - accuracy: 0.9345 - val_loss: 0.3553 - val_accuracy: 0.8605\n",
            "Epoch 13/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2133 - accuracy: 0.9286 - val_loss: 0.3769 - val_accuracy: 0.8605\n",
            "Epoch 14/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.2610 - accuracy: 0.8690 - val_loss: 0.3485 - val_accuracy: 0.8605\n",
            "Epoch 15/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2064 - accuracy: 0.9286 - val_loss: 0.3953 - val_accuracy: 0.8605\n",
            "Epoch 16/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2161 - accuracy: 0.9226 - val_loss: 0.3553 - val_accuracy: 0.8605\n",
            "Epoch 17/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.2211 - accuracy: 0.9167 - val_loss: 0.3990 - val_accuracy: 0.8372\n",
            "Epoch 18/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1997 - accuracy: 0.9286 - val_loss: 0.3836 - val_accuracy: 0.8605\n",
            "Epoch 19/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2147 - accuracy: 0.9167 - val_loss: 0.3833 - val_accuracy: 0.8605\n",
            "Epoch 20/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.2034 - accuracy: 0.9345 - val_loss: 0.3978 - val_accuracy: 0.8605\n",
            "Epoch 21/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1945 - accuracy: 0.9226 - val_loss: 0.3967 - val_accuracy: 0.8605\n",
            "Epoch 22/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1998 - accuracy: 0.9226 - val_loss: 0.3706 - val_accuracy: 0.8605\n",
            "Epoch 23/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2022 - accuracy: 0.9167 - val_loss: 0.3769 - val_accuracy: 0.8605\n",
            "Epoch 24/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1956 - accuracy: 0.9345 - val_loss: 0.3863 - val_accuracy: 0.8605\n",
            "Epoch 25/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1970 - accuracy: 0.9167 - val_loss: 0.3719 - val_accuracy: 0.8605\n",
            "Epoch 26/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1912 - accuracy: 0.9405 - val_loss: 0.3910 - val_accuracy: 0.8605\n",
            "Epoch 27/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1896 - accuracy: 0.9405 - val_loss: 0.4248 - val_accuracy: 0.8605\n",
            "Epoch 28/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1842 - accuracy: 0.9226 - val_loss: 0.4067 - val_accuracy: 0.8605\n",
            "Epoch 29/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.1833 - accuracy: 0.9226 - val_loss: 0.3594 - val_accuracy: 0.8605\n",
            "Epoch 30/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1931 - accuracy: 0.9464 - val_loss: 0.3078 - val_accuracy: 0.8605\n",
            "Epoch 31/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1898 - accuracy: 0.9405 - val_loss: 0.3662 - val_accuracy: 0.8605\n",
            "Epoch 32/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1897 - accuracy: 0.9226 - val_loss: 0.4447 - val_accuracy: 0.8605\n",
            "Epoch 33/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2009 - accuracy: 0.9107 - val_loss: 0.3775 - val_accuracy: 0.8605\n",
            "Epoch 34/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1802 - accuracy: 0.9345 - val_loss: 0.3716 - val_accuracy: 0.8372\n",
            "Epoch 35/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1816 - accuracy: 0.9405 - val_loss: 0.3836 - val_accuracy: 0.8605\n",
            "Epoch 36/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1751 - accuracy: 0.9464 - val_loss: 0.4063 - val_accuracy: 0.8605\n",
            "Epoch 37/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1727 - accuracy: 0.9464 - val_loss: 0.3923 - val_accuracy: 0.8605\n",
            "Epoch 38/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1757 - accuracy: 0.9345 - val_loss: 0.4078 - val_accuracy: 0.8605\n",
            "Epoch 39/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1818 - accuracy: 0.9345 - val_loss: 0.3882 - val_accuracy: 0.8605\n",
            "Epoch 40/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.2069 - accuracy: 0.9167 - val_loss: 0.3420 - val_accuracy: 0.8837\n",
            "Epoch 41/500\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.1657 - accuracy: 0.9524 - val_loss: 0.4729 - val_accuracy: 0.8372\n",
            "Epoch 42/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1848 - accuracy: 0.9345 - val_loss: 0.4165 - val_accuracy: 0.8605\n",
            "Epoch 43/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1655 - accuracy: 0.9345 - val_loss: 0.4786 - val_accuracy: 0.8372\n",
            "Epoch 44/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1874 - accuracy: 0.9226 - val_loss: 0.3539 - val_accuracy: 0.8837\n",
            "Epoch 45/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1801 - accuracy: 0.9226 - val_loss: 0.3982 - val_accuracy: 0.8605\n",
            "Epoch 46/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1599 - accuracy: 0.9405 - val_loss: 0.3607 - val_accuracy: 0.8605\n",
            "Epoch 47/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1681 - accuracy: 0.9405 - val_loss: 0.3775 - val_accuracy: 0.8605\n",
            "Epoch 48/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1600 - accuracy: 0.9464 - val_loss: 0.4370 - val_accuracy: 0.8605\n",
            "Epoch 49/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1665 - accuracy: 0.9286 - val_loss: 0.4335 - val_accuracy: 0.8605\n",
            "Epoch 50/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1546 - accuracy: 0.9524 - val_loss: 0.4151 - val_accuracy: 0.8605\n",
            "Epoch 51/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1547 - accuracy: 0.9524 - val_loss: 0.4124 - val_accuracy: 0.8605\n",
            "Epoch 52/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1532 - accuracy: 0.9345 - val_loss: 0.4204 - val_accuracy: 0.8372\n",
            "Epoch 53/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1579 - accuracy: 0.9345 - val_loss: 0.4189 - val_accuracy: 0.8605\n",
            "Epoch 54/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1632 - accuracy: 0.9405 - val_loss: 0.3967 - val_accuracy: 0.8837\n",
            "Epoch 55/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1636 - accuracy: 0.9345 - val_loss: 0.3892 - val_accuracy: 0.8605\n",
            "Epoch 56/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1442 - accuracy: 0.9524 - val_loss: 0.4110 - val_accuracy: 0.8837\n",
            "Epoch 57/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1688 - accuracy: 0.9286 - val_loss: 0.4899 - val_accuracy: 0.8372\n",
            "Epoch 58/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1592 - accuracy: 0.9286 - val_loss: 0.4149 - val_accuracy: 0.8837\n",
            "Epoch 59/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1508 - accuracy: 0.9345 - val_loss: 0.4225 - val_accuracy: 0.8372\n",
            "Epoch 60/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1524 - accuracy: 0.9405 - val_loss: 0.4809 - val_accuracy: 0.8372\n",
            "Epoch 61/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1451 - accuracy: 0.9405 - val_loss: 0.4946 - val_accuracy: 0.8372\n",
            "Epoch 62/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1407 - accuracy: 0.9464 - val_loss: 0.3936 - val_accuracy: 0.8605\n",
            "Epoch 63/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1460 - accuracy: 0.9464 - val_loss: 0.4431 - val_accuracy: 0.8372\n",
            "Epoch 64/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1354 - accuracy: 0.9524 - val_loss: 0.4801 - val_accuracy: 0.8372\n",
            "Epoch 65/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1302 - accuracy: 0.9583 - val_loss: 0.4713 - val_accuracy: 0.8372\n",
            "Epoch 66/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1283 - accuracy: 0.9583 - val_loss: 0.4711 - val_accuracy: 0.8372\n",
            "Epoch 67/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1291 - accuracy: 0.9524 - val_loss: 0.5068 - val_accuracy: 0.8372\n",
            "Epoch 68/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1375 - accuracy: 0.9464 - val_loss: 0.5158 - val_accuracy: 0.8372\n",
            "Epoch 69/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1361 - accuracy: 0.9524 - val_loss: 0.4279 - val_accuracy: 0.8837\n",
            "Epoch 70/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1351 - accuracy: 0.9524 - val_loss: 0.5951 - val_accuracy: 0.7907\n",
            "Epoch 71/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1516 - accuracy: 0.9167 - val_loss: 0.4702 - val_accuracy: 0.8605\n",
            "Epoch 72/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1386 - accuracy: 0.9345 - val_loss: 0.4649 - val_accuracy: 0.8605\n",
            "Epoch 73/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1521 - accuracy: 0.9286 - val_loss: 0.5099 - val_accuracy: 0.8140\n",
            "Epoch 74/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1387 - accuracy: 0.9464 - val_loss: 0.4539 - val_accuracy: 0.8605\n",
            "Epoch 75/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1217 - accuracy: 0.9524 - val_loss: 0.5354 - val_accuracy: 0.8372\n",
            "Epoch 76/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1159 - accuracy: 0.9524 - val_loss: 0.5160 - val_accuracy: 0.8372\n",
            "Epoch 77/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1175 - accuracy: 0.9464 - val_loss: 0.4815 - val_accuracy: 0.8605\n",
            "Epoch 78/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1192 - accuracy: 0.9524 - val_loss: 0.6204 - val_accuracy: 0.8140\n",
            "Epoch 79/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1277 - accuracy: 0.9583 - val_loss: 0.5049 - val_accuracy: 0.8605\n",
            "Epoch 80/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1299 - accuracy: 0.9345 - val_loss: 0.5139 - val_accuracy: 0.8372\n",
            "Epoch 81/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2887 - accuracy: 0.8869 - val_loss: 0.5629 - val_accuracy: 0.8140\n",
            "Epoch 82/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1550 - accuracy: 0.9345 - val_loss: 0.7265 - val_accuracy: 0.7209\n",
            "Epoch 83/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2015 - accuracy: 0.8929 - val_loss: 0.6893 - val_accuracy: 0.8140\n",
            "Epoch 84/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1400 - accuracy: 0.9464 - val_loss: 0.4454 - val_accuracy: 0.8372\n",
            "Epoch 85/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1399 - accuracy: 0.9464 - val_loss: 0.4514 - val_accuracy: 0.8837\n",
            "Epoch 86/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1345 - accuracy: 0.9524 - val_loss: 0.5930 - val_accuracy: 0.8372\n",
            "Epoch 87/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1226 - accuracy: 0.9643 - val_loss: 0.5214 - val_accuracy: 0.8605\n",
            "Epoch 88/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1731 - accuracy: 0.9345 - val_loss: 0.4788 - val_accuracy: 0.8605\n",
            "Epoch 89/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1988 - accuracy: 0.9286 - val_loss: 0.6554 - val_accuracy: 0.8372\n",
            "Epoch 90/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1702 - accuracy: 0.9167 - val_loss: 0.5436 - val_accuracy: 0.8372\n",
            "Epoch 91/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1608 - accuracy: 0.9345 - val_loss: 0.3551 - val_accuracy: 0.9302\n",
            "Epoch 92/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1402 - accuracy: 0.9583 - val_loss: 0.5846 - val_accuracy: 0.8372\n",
            "Epoch 93/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1301 - accuracy: 0.9583 - val_loss: 0.5207 - val_accuracy: 0.8605\n",
            "Epoch 94/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1263 - accuracy: 0.9405 - val_loss: 0.4813 - val_accuracy: 0.8837\n",
            "Epoch 95/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1211 - accuracy: 0.9583 - val_loss: 0.5012 - val_accuracy: 0.8605\n",
            "Epoch 96/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1248 - accuracy: 0.9524 - val_loss: 0.6324 - val_accuracy: 0.8140\n",
            "Epoch 97/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1138 - accuracy: 0.9524 - val_loss: 0.6147 - val_accuracy: 0.8372\n",
            "Epoch 98/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1191 - accuracy: 0.9405 - val_loss: 0.5520 - val_accuracy: 0.8605\n",
            "Epoch 99/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1094 - accuracy: 0.9524 - val_loss: 0.5694 - val_accuracy: 0.8372\n",
            "Epoch 100/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1225 - accuracy: 0.9464 - val_loss: 0.6163 - val_accuracy: 0.8140\n",
            "Epoch 101/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1159 - accuracy: 0.9583 - val_loss: 0.4814 - val_accuracy: 0.8837\n",
            "Epoch 102/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1061 - accuracy: 0.9583 - val_loss: 0.5014 - val_accuracy: 0.8605\n",
            "Epoch 103/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1045 - accuracy: 0.9524 - val_loss: 0.5419 - val_accuracy: 0.8605\n",
            "Epoch 104/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0977 - accuracy: 0.9524 - val_loss: 0.6659 - val_accuracy: 0.8372\n",
            "Epoch 105/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1026 - accuracy: 0.9524 - val_loss: 0.6746 - val_accuracy: 0.8372\n",
            "Epoch 106/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0997 - accuracy: 0.9643 - val_loss: 0.6589 - val_accuracy: 0.8372\n",
            "Epoch 107/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1102 - accuracy: 0.9524 - val_loss: 0.5337 - val_accuracy: 0.8605\n",
            "Epoch 108/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1073 - accuracy: 0.9524 - val_loss: 0.6165 - val_accuracy: 0.8140\n",
            "Epoch 109/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0984 - accuracy: 0.9702 - val_loss: 0.7080 - val_accuracy: 0.8140\n",
            "Epoch 110/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0982 - accuracy: 0.9643 - val_loss: 0.6087 - val_accuracy: 0.8372\n",
            "Epoch 111/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1072 - accuracy: 0.9583 - val_loss: 0.5489 - val_accuracy: 0.8372\n",
            "Epoch 112/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0960 - accuracy: 0.9583 - val_loss: 0.6426 - val_accuracy: 0.8372\n",
            "Epoch 113/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0928 - accuracy: 0.9583 - val_loss: 0.6425 - val_accuracy: 0.8605\n",
            "Epoch 114/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0935 - accuracy: 0.9643 - val_loss: 0.6894 - val_accuracy: 0.8372\n",
            "Epoch 115/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0931 - accuracy: 0.9643 - val_loss: 0.6590 - val_accuracy: 0.8605\n",
            "Epoch 116/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0922 - accuracy: 0.9583 - val_loss: 0.6697 - val_accuracy: 0.8372\n",
            "Epoch 117/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0891 - accuracy: 0.9702 - val_loss: 0.6227 - val_accuracy: 0.8605\n",
            "Epoch 118/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0882 - accuracy: 0.9643 - val_loss: 0.5879 - val_accuracy: 0.8372\n",
            "Epoch 119/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0925 - accuracy: 0.9524 - val_loss: 0.6289 - val_accuracy: 0.8372\n",
            "Epoch 120/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0908 - accuracy: 0.9583 - val_loss: 0.7077 - val_accuracy: 0.8140\n",
            "Epoch 121/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0953 - accuracy: 0.9583 - val_loss: 0.7044 - val_accuracy: 0.8372\n",
            "Epoch 122/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0888 - accuracy: 0.9524 - val_loss: 0.7471 - val_accuracy: 0.8140\n",
            "Epoch 123/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0953 - accuracy: 0.9583 - val_loss: 0.6756 - val_accuracy: 0.8605\n",
            "Epoch 124/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1177 - accuracy: 0.9583 - val_loss: 0.6196 - val_accuracy: 0.8605\n",
            "Epoch 125/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1274 - accuracy: 0.9345 - val_loss: 0.8305 - val_accuracy: 0.7907\n",
            "Epoch 126/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1105 - accuracy: 0.9405 - val_loss: 0.8726 - val_accuracy: 0.8605\n",
            "Epoch 127/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1052 - accuracy: 0.9583 - val_loss: 1.0634 - val_accuracy: 0.8140\n",
            "Epoch 128/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1238 - accuracy: 0.9226 - val_loss: 0.6531 - val_accuracy: 0.8605\n",
            "Epoch 129/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0931 - accuracy: 0.9524 - val_loss: 0.5882 - val_accuracy: 0.8837\n",
            "Epoch 130/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0975 - accuracy: 0.9643 - val_loss: 0.6350 - val_accuracy: 0.8372\n",
            "Epoch 131/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0988 - accuracy: 0.9583 - val_loss: 0.7328 - val_accuracy: 0.7907\n",
            "Epoch 132/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0898 - accuracy: 0.9583 - val_loss: 0.6827 - val_accuracy: 0.8605\n",
            "Epoch 133/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0959 - accuracy: 0.9583 - val_loss: 0.6745 - val_accuracy: 0.8605\n",
            "Epoch 134/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0889 - accuracy: 0.9583 - val_loss: 0.7543 - val_accuracy: 0.8605\n",
            "Epoch 135/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0879 - accuracy: 0.9643 - val_loss: 0.6491 - val_accuracy: 0.8837\n",
            "Epoch 136/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0872 - accuracy: 0.9643 - val_loss: 0.6951 - val_accuracy: 0.8605\n",
            "Epoch 137/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0815 - accuracy: 0.9643 - val_loss: 0.7397 - val_accuracy: 0.8605\n",
            "Epoch 138/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0795 - accuracy: 0.9702 - val_loss: 0.7960 - val_accuracy: 0.8605\n",
            "Epoch 139/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0807 - accuracy: 0.9643 - val_loss: 0.6803 - val_accuracy: 0.8605\n",
            "Epoch 140/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0986 - accuracy: 0.9583 - val_loss: 0.7364 - val_accuracy: 0.7907\n",
            "Epoch 141/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1063 - accuracy: 0.9583 - val_loss: 0.7534 - val_accuracy: 0.8372\n",
            "Epoch 142/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0883 - accuracy: 0.9524 - val_loss: 0.5978 - val_accuracy: 0.9070\n",
            "Epoch 143/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1250 - accuracy: 0.9464 - val_loss: 0.7304 - val_accuracy: 0.8605\n",
            "Epoch 144/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0828 - accuracy: 0.9643 - val_loss: 0.7592 - val_accuracy: 0.8140\n",
            "Epoch 145/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0922 - accuracy: 0.9702 - val_loss: 0.6381 - val_accuracy: 0.8837\n",
            "Epoch 146/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0968 - accuracy: 0.9583 - val_loss: 0.8928 - val_accuracy: 0.8140\n",
            "Epoch 147/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0851 - accuracy: 0.9762 - val_loss: 1.0844 - val_accuracy: 0.8140\n",
            "Epoch 148/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0883 - accuracy: 0.9643 - val_loss: 0.9151 - val_accuracy: 0.8372\n",
            "Epoch 149/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0788 - accuracy: 0.9643 - val_loss: 0.7740 - val_accuracy: 0.8372\n",
            "Epoch 150/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0763 - accuracy: 0.9702 - val_loss: 0.8983 - val_accuracy: 0.8140\n",
            "Epoch 151/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0764 - accuracy: 0.9643 - val_loss: 0.9669 - val_accuracy: 0.8140\n",
            "Epoch 152/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0722 - accuracy: 0.9702 - val_loss: 0.9817 - val_accuracy: 0.8372\n",
            "Epoch 153/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0728 - accuracy: 0.9643 - val_loss: 0.9412 - val_accuracy: 0.8372\n",
            "Epoch 154/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0750 - accuracy: 0.9643 - val_loss: 0.9732 - val_accuracy: 0.7907\n",
            "Epoch 155/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0661 - accuracy: 0.9821 - val_loss: 0.8468 - val_accuracy: 0.8372\n",
            "Epoch 156/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0691 - accuracy: 0.9762 - val_loss: 0.9071 - val_accuracy: 0.8372\n",
            "Epoch 157/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0727 - accuracy: 0.9762 - val_loss: 0.8204 - val_accuracy: 0.8372\n",
            "Epoch 158/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0718 - accuracy: 0.9702 - val_loss: 0.8454 - val_accuracy: 0.8372\n",
            "Epoch 159/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0697 - accuracy: 0.9702 - val_loss: 0.8651 - val_accuracy: 0.8372\n",
            "Epoch 160/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0662 - accuracy: 0.9762 - val_loss: 0.9240 - val_accuracy: 0.8140\n",
            "Epoch 161/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0752 - accuracy: 0.9762 - val_loss: 0.9640 - val_accuracy: 0.7907\n",
            "Epoch 162/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0863 - accuracy: 0.9583 - val_loss: 0.9271 - val_accuracy: 0.8140\n",
            "Epoch 163/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1468 - accuracy: 0.9464 - val_loss: 1.1808 - val_accuracy: 0.7907\n",
            "Epoch 164/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1253 - accuracy: 0.9464 - val_loss: 0.6375 - val_accuracy: 0.8837\n",
            "Epoch 165/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0977 - accuracy: 0.9583 - val_loss: 0.8337 - val_accuracy: 0.8140\n",
            "Epoch 166/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1081 - accuracy: 0.9464 - val_loss: 1.0032 - val_accuracy: 0.7442\n",
            "Epoch 167/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0844 - accuracy: 0.9702 - val_loss: 0.9829 - val_accuracy: 0.7674\n",
            "Epoch 168/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1188 - accuracy: 0.9464 - val_loss: 1.0462 - val_accuracy: 0.8605\n",
            "Epoch 169/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1133 - accuracy: 0.9524 - val_loss: 0.9678 - val_accuracy: 0.7907\n",
            "Epoch 170/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0865 - accuracy: 0.9702 - val_loss: 0.6925 - val_accuracy: 0.8605\n",
            "Epoch 171/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1097 - accuracy: 0.9643 - val_loss: 0.9341 - val_accuracy: 0.8372\n",
            "Epoch 172/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0832 - accuracy: 0.9583 - val_loss: 1.2231 - val_accuracy: 0.8140\n",
            "Epoch 173/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0948 - accuracy: 0.9524 - val_loss: 0.7614 - val_accuracy: 0.8372\n",
            "Epoch 174/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0962 - accuracy: 0.9583 - val_loss: 0.7246 - val_accuracy: 0.8605\n",
            "Epoch 175/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0705 - accuracy: 0.9762 - val_loss: 1.0369 - val_accuracy: 0.8140\n",
            "Epoch 176/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0840 - accuracy: 0.9583 - val_loss: 0.9999 - val_accuracy: 0.7907\n",
            "Epoch 177/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0747 - accuracy: 0.9762 - val_loss: 0.9856 - val_accuracy: 0.8140\n",
            "Epoch 178/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0746 - accuracy: 0.9762 - val_loss: 0.9945 - val_accuracy: 0.8140\n",
            "Epoch 179/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0730 - accuracy: 0.9702 - val_loss: 0.9049 - val_accuracy: 0.8140\n",
            "Epoch 180/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0708 - accuracy: 0.9762 - val_loss: 0.8109 - val_accuracy: 0.8140\n",
            "Epoch 181/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0918 - accuracy: 0.9583 - val_loss: 0.8268 - val_accuracy: 0.8372\n",
            "Epoch 182/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0788 - accuracy: 0.9643 - val_loss: 0.8738 - val_accuracy: 0.8372\n",
            "Epoch 183/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0680 - accuracy: 0.9702 - val_loss: 0.8925 - val_accuracy: 0.8605\n",
            "Epoch 184/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0658 - accuracy: 0.9702 - val_loss: 0.8678 - val_accuracy: 0.8372\n",
            "Epoch 185/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0601 - accuracy: 0.9762 - val_loss: 0.8251 - val_accuracy: 0.8372\n",
            "Epoch 186/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0631 - accuracy: 0.9643 - val_loss: 0.8914 - val_accuracy: 0.8372\n",
            "Epoch 187/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0582 - accuracy: 0.9762 - val_loss: 0.9784 - val_accuracy: 0.8372\n",
            "Epoch 188/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0601 - accuracy: 0.9821 - val_loss: 1.1663 - val_accuracy: 0.7907\n",
            "Epoch 189/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0674 - accuracy: 0.9702 - val_loss: 0.9647 - val_accuracy: 0.8372\n",
            "Epoch 190/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0607 - accuracy: 0.9762 - val_loss: 0.8998 - val_accuracy: 0.8372\n",
            "Epoch 191/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0557 - accuracy: 0.9821 - val_loss: 0.9566 - val_accuracy: 0.8605\n",
            "Epoch 192/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0596 - accuracy: 0.9762 - val_loss: 0.9534 - val_accuracy: 0.8140\n",
            "Epoch 193/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0558 - accuracy: 0.9762 - val_loss: 0.7739 - val_accuracy: 0.8140\n",
            "Epoch 194/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0641 - accuracy: 0.9762 - val_loss: 0.8359 - val_accuracy: 0.8372\n",
            "Epoch 195/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0548 - accuracy: 0.9881 - val_loss: 1.0045 - val_accuracy: 0.8372\n",
            "Epoch 196/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0558 - accuracy: 0.9762 - val_loss: 0.9653 - val_accuracy: 0.8372\n",
            "Epoch 197/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0513 - accuracy: 0.9821 - val_loss: 1.0221 - val_accuracy: 0.8372\n",
            "Epoch 198/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0527 - accuracy: 0.9821 - val_loss: 1.0193 - val_accuracy: 0.8372\n",
            "Epoch 199/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0534 - accuracy: 0.9821 - val_loss: 1.0785 - val_accuracy: 0.8140\n",
            "Epoch 200/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0542 - accuracy: 0.9821 - val_loss: 1.1946 - val_accuracy: 0.7907\n",
            "Epoch 201/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0504 - accuracy: 0.9762 - val_loss: 1.2167 - val_accuracy: 0.8140\n",
            "Epoch 202/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0587 - accuracy: 0.9762 - val_loss: 1.3867 - val_accuracy: 0.7907\n",
            "Epoch 203/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0510 - accuracy: 0.9881 - val_loss: 0.9091 - val_accuracy: 0.8372\n",
            "Epoch 204/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0504 - accuracy: 0.9762 - val_loss: 0.8591 - val_accuracy: 0.8372\n",
            "Epoch 205/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0475 - accuracy: 0.9821 - val_loss: 1.0072 - val_accuracy: 0.8140\n",
            "Epoch 206/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0472 - accuracy: 0.9821 - val_loss: 1.1123 - val_accuracy: 0.8140\n",
            "Epoch 207/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0526 - accuracy: 0.9821 - val_loss: 1.1820 - val_accuracy: 0.7907\n",
            "Epoch 208/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0445 - accuracy: 0.9821 - val_loss: 0.9589 - val_accuracy: 0.8140\n",
            "Epoch 209/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0562 - accuracy: 0.9821 - val_loss: 0.9941 - val_accuracy: 0.8372\n",
            "Epoch 210/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0476 - accuracy: 0.9762 - val_loss: 1.1028 - val_accuracy: 0.8372\n",
            "Epoch 211/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0485 - accuracy: 0.9821 - val_loss: 1.3893 - val_accuracy: 0.7209\n",
            "Epoch 212/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0612 - accuracy: 0.9762 - val_loss: 1.2112 - val_accuracy: 0.8140\n",
            "Epoch 213/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0503 - accuracy: 0.9821 - val_loss: 0.9200 - val_accuracy: 0.8605\n",
            "Epoch 214/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0560 - accuracy: 0.9821 - val_loss: 1.1336 - val_accuracy: 0.8140\n",
            "Epoch 215/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0463 - accuracy: 0.9821 - val_loss: 1.1986 - val_accuracy: 0.8140\n",
            "Epoch 216/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0439 - accuracy: 0.9881 - val_loss: 1.1386 - val_accuracy: 0.8372\n",
            "Epoch 217/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0450 - accuracy: 0.9762 - val_loss: 1.1608 - val_accuracy: 0.8372\n",
            "Epoch 218/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0426 - accuracy: 0.9881 - val_loss: 1.0125 - val_accuracy: 0.8140\n",
            "Epoch 219/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0478 - accuracy: 0.9821 - val_loss: 1.1234 - val_accuracy: 0.8372\n",
            "Epoch 220/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0447 - accuracy: 0.9821 - val_loss: 1.2315 - val_accuracy: 0.8372\n",
            "Epoch 221/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0395 - accuracy: 0.9762 - val_loss: 1.1075 - val_accuracy: 0.8140\n",
            "Epoch 222/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0409 - accuracy: 0.9881 - val_loss: 1.2464 - val_accuracy: 0.8140\n",
            "Epoch 223/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0403 - accuracy: 0.9821 - val_loss: 1.2836 - val_accuracy: 0.8140\n",
            "Epoch 224/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0452 - accuracy: 0.9702 - val_loss: 1.1761 - val_accuracy: 0.8140\n",
            "Epoch 225/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0373 - accuracy: 0.9940 - val_loss: 1.3354 - val_accuracy: 0.7907\n",
            "Epoch 226/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0399 - accuracy: 0.9881 - val_loss: 1.2409 - val_accuracy: 0.8140\n",
            "Epoch 227/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0536 - accuracy: 0.9762 - val_loss: 1.1642 - val_accuracy: 0.8140\n",
            "Epoch 228/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0400 - accuracy: 0.9881 - val_loss: 1.2932 - val_accuracy: 0.7907\n",
            "Epoch 229/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0405 - accuracy: 0.9821 - val_loss: 1.4205 - val_accuracy: 0.7907\n",
            "Epoch 230/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0492 - accuracy: 0.9821 - val_loss: 1.4931 - val_accuracy: 0.8140\n",
            "Epoch 231/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0407 - accuracy: 0.9821 - val_loss: 1.1921 - val_accuracy: 0.8140\n",
            "Epoch 232/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0405 - accuracy: 0.9881 - val_loss: 1.2146 - val_accuracy: 0.8140\n",
            "Epoch 233/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0350 - accuracy: 0.9940 - val_loss: 1.4538 - val_accuracy: 0.8140\n",
            "Epoch 234/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0409 - accuracy: 0.9821 - val_loss: 1.4407 - val_accuracy: 0.8140\n",
            "Epoch 235/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0372 - accuracy: 0.9881 - val_loss: 1.2117 - val_accuracy: 0.8140\n",
            "Epoch 236/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9762 - val_loss: 1.0884 - val_accuracy: 0.8140\n",
            "Epoch 237/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0436 - accuracy: 0.9821 - val_loss: 1.2618 - val_accuracy: 0.8372\n",
            "Epoch 238/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0442 - accuracy: 0.9821 - val_loss: 1.3732 - val_accuracy: 0.8372\n",
            "Epoch 239/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0395 - accuracy: 0.9821 - val_loss: 1.3066 - val_accuracy: 0.8140\n",
            "Epoch 240/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0360 - accuracy: 0.9881 - val_loss: 1.4593 - val_accuracy: 0.7674\n",
            "Epoch 241/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0683 - accuracy: 0.9821 - val_loss: 1.5890 - val_accuracy: 0.8140\n",
            "Epoch 242/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0364 - accuracy: 0.9881 - val_loss: 1.3318 - val_accuracy: 0.8140\n",
            "Epoch 243/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0450 - accuracy: 0.9762 - val_loss: 1.3274 - val_accuracy: 0.8140\n",
            "Epoch 244/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0454 - accuracy: 0.9881 - val_loss: 1.4614 - val_accuracy: 0.7907\n",
            "Epoch 245/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0451 - accuracy: 0.9821 - val_loss: 1.4793 - val_accuracy: 0.8140\n",
            "Epoch 246/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0404 - accuracy: 0.9821 - val_loss: 1.5236 - val_accuracy: 0.8140\n",
            "Epoch 247/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0336 - accuracy: 0.9881 - val_loss: 1.4277 - val_accuracy: 0.8140\n",
            "Epoch 248/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0330 - accuracy: 0.9881 - val_loss: 1.4830 - val_accuracy: 0.7907\n",
            "Epoch 249/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0355 - accuracy: 0.9821 - val_loss: 1.4315 - val_accuracy: 0.8372\n",
            "Epoch 250/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0324 - accuracy: 0.9881 - val_loss: 1.4535 - val_accuracy: 0.7907\n",
            "Epoch 251/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0370 - accuracy: 0.9881 - val_loss: 1.3610 - val_accuracy: 0.8140\n",
            "Epoch 252/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0318 - accuracy: 0.9881 - val_loss: 1.3679 - val_accuracy: 0.8140\n",
            "Epoch 253/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0303 - accuracy: 0.9940 - val_loss: 1.4635 - val_accuracy: 0.8140\n",
            "Epoch 254/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0350 - accuracy: 0.9881 - val_loss: 1.4603 - val_accuracy: 0.8140\n",
            "Epoch 255/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0330 - accuracy: 0.9881 - val_loss: 1.4552 - val_accuracy: 0.8140\n",
            "Epoch 256/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0289 - accuracy: 0.9881 - val_loss: 1.4321 - val_accuracy: 0.8140\n",
            "Epoch 257/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0279 - accuracy: 0.9881 - val_loss: 1.4241 - val_accuracy: 0.8140\n",
            "Epoch 258/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0319 - accuracy: 0.9821 - val_loss: 1.6831 - val_accuracy: 0.8140\n",
            "Epoch 259/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0380 - accuracy: 0.9881 - val_loss: 1.4534 - val_accuracy: 0.8140\n",
            "Epoch 260/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0322 - accuracy: 0.9881 - val_loss: 1.3813 - val_accuracy: 0.8140\n",
            "Epoch 261/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0488 - accuracy: 0.9762 - val_loss: 1.3602 - val_accuracy: 0.7907\n",
            "Epoch 262/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0526 - accuracy: 0.9762 - val_loss: 1.6355 - val_accuracy: 0.8140\n",
            "Epoch 263/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0469 - accuracy: 0.9821 - val_loss: 1.4899 - val_accuracy: 0.8372\n",
            "Epoch 264/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0381 - accuracy: 0.9881 - val_loss: 1.2644 - val_accuracy: 0.8140\n",
            "Epoch 265/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0363 - accuracy: 0.9940 - val_loss: 1.3382 - val_accuracy: 0.8140\n",
            "Epoch 266/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0311 - accuracy: 0.9881 - val_loss: 1.5457 - val_accuracy: 0.7907\n",
            "Epoch 267/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0289 - accuracy: 0.9821 - val_loss: 1.5847 - val_accuracy: 0.8140\n",
            "Epoch 268/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0275 - accuracy: 0.9940 - val_loss: 1.5522 - val_accuracy: 0.8140\n",
            "Epoch 269/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0269 - accuracy: 0.9940 - val_loss: 1.5780 - val_accuracy: 0.7907\n",
            "Epoch 270/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0282 - accuracy: 0.9881 - val_loss: 1.6029 - val_accuracy: 0.7907\n",
            "Epoch 271/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0284 - accuracy: 0.9940 - val_loss: 1.5019 - val_accuracy: 0.8372\n",
            "Epoch 272/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0317 - accuracy: 0.9881 - val_loss: 1.4608 - val_accuracy: 0.7907\n",
            "Epoch 273/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0397 - accuracy: 0.9762 - val_loss: 1.5323 - val_accuracy: 0.7907\n",
            "Epoch 274/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0371 - accuracy: 0.9881 - val_loss: 1.4520 - val_accuracy: 0.8140\n",
            "Epoch 275/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0379 - accuracy: 0.9821 - val_loss: 1.7669 - val_accuracy: 0.8140\n",
            "Epoch 276/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0311 - accuracy: 0.9940 - val_loss: 1.3118 - val_accuracy: 0.8140\n",
            "Epoch 277/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0280 - accuracy: 0.9940 - val_loss: 1.4134 - val_accuracy: 0.8140\n",
            "Epoch 278/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0370 - accuracy: 0.9821 - val_loss: 1.5864 - val_accuracy: 0.8140\n",
            "Epoch 279/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0244 - accuracy: 0.9940 - val_loss: 1.5949 - val_accuracy: 0.8140\n",
            "Epoch 280/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0395 - accuracy: 0.9881 - val_loss: 1.7191 - val_accuracy: 0.7907\n",
            "Epoch 281/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0950 - accuracy: 0.9702 - val_loss: 1.8971 - val_accuracy: 0.7907\n",
            "Epoch 282/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1802 - accuracy: 0.9345 - val_loss: 1.4965 - val_accuracy: 0.8372\n",
            "Epoch 283/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2961 - accuracy: 0.9107 - val_loss: 1.3750 - val_accuracy: 0.7674\n",
            "Epoch 284/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2755 - accuracy: 0.8929 - val_loss: 1.1108 - val_accuracy: 0.7674\n",
            "Epoch 285/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1094 - accuracy: 0.9464 - val_loss: 1.1747 - val_accuracy: 0.8605\n",
            "Epoch 286/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1445 - accuracy: 0.9286 - val_loss: 1.0637 - val_accuracy: 0.8372\n",
            "Epoch 287/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0856 - accuracy: 0.9643 - val_loss: 0.7684 - val_accuracy: 0.9302\n",
            "Epoch 288/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0845 - accuracy: 0.9762 - val_loss: 0.8271 - val_accuracy: 0.9070\n",
            "Epoch 289/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0760 - accuracy: 0.9821 - val_loss: 1.0029 - val_accuracy: 0.8140\n",
            "Epoch 290/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0702 - accuracy: 0.9762 - val_loss: 1.0185 - val_accuracy: 0.8140\n",
            "Epoch 291/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0639 - accuracy: 0.9702 - val_loss: 1.0086 - val_accuracy: 0.8140\n",
            "Epoch 292/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0694 - accuracy: 0.9762 - val_loss: 1.0036 - val_accuracy: 0.7674\n",
            "Epoch 293/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0919 - accuracy: 0.9702 - val_loss: 1.0396 - val_accuracy: 0.8605\n",
            "Epoch 294/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0754 - accuracy: 0.9762 - val_loss: 1.1540 - val_accuracy: 0.7907\n",
            "Epoch 295/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0617 - accuracy: 0.9762 - val_loss: 1.0721 - val_accuracy: 0.8140\n",
            "Epoch 296/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0560 - accuracy: 0.9702 - val_loss: 1.0864 - val_accuracy: 0.8140\n",
            "Epoch 297/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0534 - accuracy: 0.9762 - val_loss: 1.0412 - val_accuracy: 0.8372\n",
            "Epoch 298/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0551 - accuracy: 0.9821 - val_loss: 1.2195 - val_accuracy: 0.8140\n",
            "Epoch 299/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0488 - accuracy: 0.9821 - val_loss: 1.3026 - val_accuracy: 0.7674\n",
            "Epoch 300/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0483 - accuracy: 0.9881 - val_loss: 1.3668 - val_accuracy: 0.7442\n",
            "Epoch 301/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0464 - accuracy: 0.9821 - val_loss: 1.3497 - val_accuracy: 0.7442\n",
            "Epoch 302/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0470 - accuracy: 0.9821 - val_loss: 1.2976 - val_accuracy: 0.7907\n",
            "Epoch 303/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0398 - accuracy: 0.9881 - val_loss: 1.1964 - val_accuracy: 0.7907\n",
            "Epoch 304/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0557 - accuracy: 0.9762 - val_loss: 1.2336 - val_accuracy: 0.7907\n",
            "Epoch 305/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0656 - accuracy: 0.9702 - val_loss: 1.3533 - val_accuracy: 0.7442\n",
            "Epoch 306/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0623 - accuracy: 0.9702 - val_loss: 1.2734 - val_accuracy: 0.7907\n",
            "Epoch 307/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0511 - accuracy: 0.9821 - val_loss: 1.2955 - val_accuracy: 0.7907\n",
            "Epoch 308/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0500 - accuracy: 0.9762 - val_loss: 1.3826 - val_accuracy: 0.7907\n",
            "Epoch 309/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0506 - accuracy: 0.9762 - val_loss: 1.3579 - val_accuracy: 0.8372\n",
            "Epoch 310/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9762 - val_loss: 1.4164 - val_accuracy: 0.7907\n",
            "Epoch 311/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0808 - accuracy: 0.9524 - val_loss: 1.4144 - val_accuracy: 0.7674\n",
            "Epoch 312/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0528 - accuracy: 0.9881 - val_loss: 1.2999 - val_accuracy: 0.8372\n",
            "Epoch 313/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0883 - accuracy: 0.9762 - val_loss: 1.2706 - val_accuracy: 0.8140\n",
            "Epoch 314/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0501 - accuracy: 0.9762 - val_loss: 1.3037 - val_accuracy: 0.7674\n",
            "Epoch 315/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0486 - accuracy: 0.9821 - val_loss: 1.2875 - val_accuracy: 0.7674\n",
            "Epoch 316/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0417 - accuracy: 0.9762 - val_loss: 1.3321 - val_accuracy: 0.7674\n",
            "Epoch 317/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0455 - accuracy: 0.9762 - val_loss: 1.3738 - val_accuracy: 0.7442\n",
            "Epoch 318/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0384 - accuracy: 0.9881 - val_loss: 1.3164 - val_accuracy: 0.7907\n",
            "Epoch 319/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0394 - accuracy: 0.9821 - val_loss: 1.3890 - val_accuracy: 0.7674\n",
            "Epoch 320/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0359 - accuracy: 0.9821 - val_loss: 1.3914 - val_accuracy: 0.7907\n",
            "Epoch 321/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0363 - accuracy: 0.9881 - val_loss: 1.4475 - val_accuracy: 0.7907\n",
            "Epoch 322/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0308 - accuracy: 0.9881 - val_loss: 1.4199 - val_accuracy: 0.7907\n",
            "Epoch 323/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0388 - accuracy: 0.9821 - val_loss: 1.4516 - val_accuracy: 0.7674\n",
            "Epoch 324/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0506 - accuracy: 0.9702 - val_loss: 1.8213 - val_accuracy: 0.7209\n",
            "Epoch 325/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0531 - accuracy: 0.9881 - val_loss: 1.4970 - val_accuracy: 0.7674\n",
            "Epoch 326/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0530 - accuracy: 0.9762 - val_loss: 1.2514 - val_accuracy: 0.8372\n",
            "Epoch 327/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0443 - accuracy: 0.9881 - val_loss: 1.2373 - val_accuracy: 0.8140\n",
            "Epoch 328/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0399 - accuracy: 0.9881 - val_loss: 1.3489 - val_accuracy: 0.7907\n",
            "Epoch 329/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0411 - accuracy: 0.9881 - val_loss: 1.4327 - val_accuracy: 0.7907\n",
            "Epoch 330/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0339 - accuracy: 0.9940 - val_loss: 1.5394 - val_accuracy: 0.7674\n",
            "Epoch 331/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0429 - accuracy: 0.9821 - val_loss: 1.5307 - val_accuracy: 0.7674\n",
            "Epoch 332/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0307 - accuracy: 0.9881 - val_loss: 1.4393 - val_accuracy: 0.7907\n",
            "Epoch 333/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0328 - accuracy: 0.9881 - val_loss: 1.4703 - val_accuracy: 0.8140\n",
            "Epoch 334/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0321 - accuracy: 0.9881 - val_loss: 1.5498 - val_accuracy: 0.7674\n",
            "Epoch 335/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0321 - accuracy: 0.9821 - val_loss: 1.4244 - val_accuracy: 0.7907\n",
            "Epoch 336/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0303 - accuracy: 0.9881 - val_loss: 1.5064 - val_accuracy: 0.7907\n",
            "Epoch 337/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0469 - accuracy: 0.9821 - val_loss: 1.6910 - val_accuracy: 0.7442\n",
            "Epoch 338/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0652 - accuracy: 0.9821 - val_loss: 1.4738 - val_accuracy: 0.8140\n",
            "Epoch 339/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9821 - val_loss: 1.4935 - val_accuracy: 0.7674\n",
            "Epoch 340/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0569 - accuracy: 0.9643 - val_loss: 1.6935 - val_accuracy: 0.7442\n",
            "Epoch 341/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0425 - accuracy: 0.9821 - val_loss: 1.4285 - val_accuracy: 0.8605\n",
            "Epoch 342/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0472 - accuracy: 0.9821 - val_loss: 1.3858 - val_accuracy: 0.8372\n",
            "Epoch 343/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0474 - accuracy: 0.9821 - val_loss: 1.3454 - val_accuracy: 0.8140\n",
            "Epoch 344/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0447 - accuracy: 0.9762 - val_loss: 1.4371 - val_accuracy: 0.8140\n",
            "Epoch 345/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0343 - accuracy: 0.9821 - val_loss: 1.4487 - val_accuracy: 0.7674\n",
            "Epoch 346/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0310 - accuracy: 0.9881 - val_loss: 1.5482 - val_accuracy: 0.7907\n",
            "Epoch 347/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0393 - accuracy: 0.9821 - val_loss: 1.6849 - val_accuracy: 0.7674\n",
            "Epoch 348/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0380 - accuracy: 0.9881 - val_loss: 1.6226 - val_accuracy: 0.7674\n",
            "Epoch 349/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0347 - accuracy: 0.9821 - val_loss: 1.5035 - val_accuracy: 0.7674\n",
            "Epoch 350/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0308 - accuracy: 0.9940 - val_loss: 1.5396 - val_accuracy: 0.7907\n",
            "Epoch 351/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0300 - accuracy: 0.9881 - val_loss: 1.6389 - val_accuracy: 0.7907\n",
            "Epoch 352/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0291 - accuracy: 0.9940 - val_loss: 1.6085 - val_accuracy: 0.7907\n",
            "Epoch 353/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0237 - accuracy: 1.0000 - val_loss: 1.5755 - val_accuracy: 0.7907\n",
            "Epoch 354/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0367 - accuracy: 0.9762 - val_loss: 1.6712 - val_accuracy: 0.7907\n",
            "Epoch 355/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0236 - accuracy: 0.9940 - val_loss: 1.8389 - val_accuracy: 0.7674\n",
            "Epoch 356/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0357 - accuracy: 0.9881 - val_loss: 1.6809 - val_accuracy: 0.7907\n",
            "Epoch 357/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0310 - accuracy: 0.9821 - val_loss: 1.5486 - val_accuracy: 0.7907\n",
            "Epoch 358/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0286 - accuracy: 0.9940 - val_loss: 1.5300 - val_accuracy: 0.7907\n",
            "Epoch 359/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0264 - accuracy: 0.9940 - val_loss: 1.6082 - val_accuracy: 0.7907\n",
            "Epoch 360/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0304 - accuracy: 0.9881 - val_loss: 1.6366 - val_accuracy: 0.7907\n",
            "Epoch 361/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0467 - accuracy: 0.9821 - val_loss: 1.7270 - val_accuracy: 0.8140\n",
            "Epoch 362/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0335 - accuracy: 0.9762 - val_loss: 1.6632 - val_accuracy: 0.7674\n",
            "Epoch 363/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0258 - accuracy: 0.9940 - val_loss: 1.6538 - val_accuracy: 0.7907\n",
            "Epoch 364/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0230 - accuracy: 0.9940 - val_loss: 1.6718 - val_accuracy: 0.7907\n",
            "Epoch 365/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0269 - accuracy: 0.9940 - val_loss: 1.6078 - val_accuracy: 0.8140\n",
            "Epoch 366/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0200 - accuracy: 0.9940 - val_loss: 1.5855 - val_accuracy: 0.8140\n",
            "Epoch 367/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0230 - accuracy: 0.9940 - val_loss: 1.6008 - val_accuracy: 0.8140\n",
            "Epoch 368/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0252 - accuracy: 0.9881 - val_loss: 1.6141 - val_accuracy: 0.8140\n",
            "Epoch 369/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0346 - accuracy: 0.9762 - val_loss: 1.5830 - val_accuracy: 0.8372\n",
            "Epoch 370/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 1.6686 - val_accuracy: 0.7907\n",
            "Epoch 371/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0297 - accuracy: 0.9940 - val_loss: 1.6880 - val_accuracy: 0.8140\n",
            "Epoch 372/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0515 - accuracy: 0.9821 - val_loss: 1.7094 - val_accuracy: 0.8140\n",
            "Epoch 373/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0334 - accuracy: 0.9881 - val_loss: 1.8497 - val_accuracy: 0.7907\n",
            "Epoch 374/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0330 - accuracy: 0.9881 - val_loss: 1.7684 - val_accuracy: 0.7907\n",
            "Epoch 375/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0364 - accuracy: 0.9881 - val_loss: 1.5890 - val_accuracy: 0.8140\n",
            "Epoch 376/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0327 - accuracy: 0.9881 - val_loss: 1.6959 - val_accuracy: 0.7907\n",
            "Epoch 377/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0372 - accuracy: 0.9881 - val_loss: 1.6997 - val_accuracy: 0.7674\n",
            "Epoch 378/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0456 - accuracy: 0.9762 - val_loss: 1.7157 - val_accuracy: 0.7907\n",
            "Epoch 379/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0284 - accuracy: 0.9940 - val_loss: 1.8158 - val_accuracy: 0.7674\n",
            "Epoch 380/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0240 - accuracy: 0.9940 - val_loss: 1.9221 - val_accuracy: 0.7907\n",
            "Epoch 381/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0386 - accuracy: 0.9881 - val_loss: 1.7829 - val_accuracy: 0.7907\n",
            "Epoch 382/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0367 - accuracy: 0.9821 - val_loss: 1.6777 - val_accuracy: 0.8140\n",
            "Epoch 383/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0240 - accuracy: 0.9940 - val_loss: 1.6507 - val_accuracy: 0.8140\n",
            "Epoch 384/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0281 - accuracy: 0.9940 - val_loss: 1.7238 - val_accuracy: 0.7907\n",
            "Epoch 385/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0288 - accuracy: 0.9940 - val_loss: 1.8027 - val_accuracy: 0.7674\n",
            "Epoch 386/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0208 - accuracy: 0.9940 - val_loss: 1.6970 - val_accuracy: 0.7907\n",
            "Epoch 387/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0225 - accuracy: 0.9940 - val_loss: 1.6837 - val_accuracy: 0.7907\n",
            "Epoch 388/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0198 - accuracy: 1.0000 - val_loss: 1.7711 - val_accuracy: 0.7907\n",
            "Epoch 389/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0211 - accuracy: 0.9940 - val_loss: 1.8516 - val_accuracy: 0.7674\n",
            "Epoch 390/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0241 - accuracy: 0.9940 - val_loss: 1.7771 - val_accuracy: 0.7907\n",
            "Epoch 391/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0258 - accuracy: 0.9940 - val_loss: 1.7011 - val_accuracy: 0.7907\n",
            "Epoch 392/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 1.7696 - val_accuracy: 0.7674\n",
            "Epoch 393/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0199 - accuracy: 0.9940 - val_loss: 1.7714 - val_accuracy: 0.7907\n",
            "Epoch 394/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0232 - accuracy: 0.9881 - val_loss: 1.6814 - val_accuracy: 0.8140\n",
            "Epoch 395/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0208 - accuracy: 1.0000 - val_loss: 1.7453 - val_accuracy: 0.8140\n",
            "Epoch 396/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0166 - accuracy: 1.0000 - val_loss: 1.8280 - val_accuracy: 0.7674\n",
            "Epoch 397/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0204 - accuracy: 0.9940 - val_loss: 1.8476 - val_accuracy: 0.7442\n",
            "Epoch 398/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0218 - accuracy: 0.9940 - val_loss: 1.7664 - val_accuracy: 0.7907\n",
            "Epoch 399/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0173 - accuracy: 0.9940 - val_loss: 1.7446 - val_accuracy: 0.7907\n",
            "Epoch 400/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0205 - accuracy: 0.9881 - val_loss: 1.7680 - val_accuracy: 0.7907\n",
            "Epoch 401/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0180 - accuracy: 1.0000 - val_loss: 1.8424 - val_accuracy: 0.7907\n",
            "Epoch 402/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0512 - accuracy: 0.9940 - val_loss: 2.0764 - val_accuracy: 0.7674\n",
            "Epoch 403/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0449 - accuracy: 0.9821 - val_loss: 1.9917 - val_accuracy: 0.7442\n",
            "Epoch 404/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0386 - accuracy: 0.9881 - val_loss: 1.7871 - val_accuracy: 0.7907\n",
            "Epoch 405/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0301 - accuracy: 0.9762 - val_loss: 1.7110 - val_accuracy: 0.7907\n",
            "Epoch 406/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0350 - accuracy: 0.9881 - val_loss: 1.8679 - val_accuracy: 0.7907\n",
            "Epoch 407/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0244 - accuracy: 0.9881 - val_loss: 1.8539 - val_accuracy: 0.8140\n",
            "Epoch 408/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0228 - accuracy: 0.9940 - val_loss: 1.7780 - val_accuracy: 0.8372\n",
            "Epoch 409/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0180 - accuracy: 0.9940 - val_loss: 1.9499 - val_accuracy: 0.7674\n",
            "Epoch 410/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 1.8830 - val_accuracy: 0.7907\n",
            "Epoch 411/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0222 - accuracy: 0.9881 - val_loss: 1.8362 - val_accuracy: 0.7907\n",
            "Epoch 412/500\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.0219 - accuracy: 0.9940 - val_loss: 1.8460 - val_accuracy: 0.7907\n",
            "Epoch 413/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0432 - accuracy: 0.9821 - val_loss: 1.6895 - val_accuracy: 0.8372\n",
            "Epoch 414/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0906 - accuracy: 0.9702 - val_loss: 1.7161 - val_accuracy: 0.8372\n",
            "Epoch 415/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0521 - accuracy: 0.9881 - val_loss: 1.7980 - val_accuracy: 0.7907\n",
            "Epoch 416/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0299 - accuracy: 0.9881 - val_loss: 1.8992 - val_accuracy: 0.7674\n",
            "Epoch 417/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0351 - accuracy: 0.9881 - val_loss: 1.7391 - val_accuracy: 0.8372\n",
            "Epoch 418/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0270 - accuracy: 0.9881 - val_loss: 1.8922 - val_accuracy: 0.7907\n",
            "Epoch 419/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0380 - accuracy: 0.9881 - val_loss: 1.9100 - val_accuracy: 0.7907\n",
            "Epoch 420/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0326 - accuracy: 0.9881 - val_loss: 1.6944 - val_accuracy: 0.8372\n",
            "Epoch 421/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0277 - accuracy: 0.9821 - val_loss: 1.6517 - val_accuracy: 0.8372\n",
            "Epoch 422/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0218 - accuracy: 0.9881 - val_loss: 1.7384 - val_accuracy: 0.7907\n",
            "Epoch 423/500\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 1.8595 - val_accuracy: 0.7907\n",
            "Epoch 424/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 1.8407 - val_accuracy: 0.7907\n",
            "Epoch 425/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0174 - accuracy: 1.0000 - val_loss: 1.8377 - val_accuracy: 0.7907\n",
            "Epoch 426/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0402 - accuracy: 0.9821 - val_loss: 1.9385 - val_accuracy: 0.7674\n",
            "Epoch 427/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0358 - accuracy: 0.9821 - val_loss: 1.8760 - val_accuracy: 0.7907\n",
            "Epoch 428/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0254 - accuracy: 0.9940 - val_loss: 1.7575 - val_accuracy: 0.7907\n",
            "Epoch 429/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0310 - accuracy: 0.9881 - val_loss: 1.7588 - val_accuracy: 0.7907\n",
            "Epoch 430/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0146 - accuracy: 1.0000 - val_loss: 1.7585 - val_accuracy: 0.8372\n",
            "Epoch 431/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0243 - accuracy: 0.9881 - val_loss: 1.8145 - val_accuracy: 0.8140\n",
            "Epoch 432/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0183 - accuracy: 0.9940 - val_loss: 1.8786 - val_accuracy: 0.7907\n",
            "Epoch 433/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0173 - accuracy: 1.0000 - val_loss: 1.8613 - val_accuracy: 0.7907\n",
            "Epoch 434/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0167 - accuracy: 1.0000 - val_loss: 1.8707 - val_accuracy: 0.8140\n",
            "Epoch 435/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 1.8024 - val_accuracy: 0.8140\n",
            "Epoch 436/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0156 - accuracy: 1.0000 - val_loss: 1.7473 - val_accuracy: 0.8372\n",
            "Epoch 437/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0193 - accuracy: 0.9940 - val_loss: 1.8356 - val_accuracy: 0.8140\n",
            "Epoch 438/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0270 - accuracy: 0.9940 - val_loss: 1.9895 - val_accuracy: 0.7907\n",
            "Epoch 439/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0215 - accuracy: 0.9940 - val_loss: 1.7847 - val_accuracy: 0.8140\n",
            "Epoch 440/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 1.7567 - val_accuracy: 0.8372\n",
            "Epoch 441/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0423 - accuracy: 0.9881 - val_loss: 1.7200 - val_accuracy: 0.8140\n",
            "Epoch 442/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0232 - accuracy: 0.9881 - val_loss: 1.9028 - val_accuracy: 0.7907\n",
            "Epoch 443/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0231 - accuracy: 0.9821 - val_loss: 2.0512 - val_accuracy: 0.7907\n",
            "Epoch 444/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0407 - accuracy: 0.9762 - val_loss: 1.9270 - val_accuracy: 0.7907\n",
            "Epoch 445/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0232 - accuracy: 1.0000 - val_loss: 1.9628 - val_accuracy: 0.7907\n",
            "Epoch 446/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0248 - accuracy: 0.9940 - val_loss: 1.8430 - val_accuracy: 0.8140\n",
            "Epoch 447/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0275 - accuracy: 0.9881 - val_loss: 1.8360 - val_accuracy: 0.8140\n",
            "Epoch 448/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0195 - accuracy: 1.0000 - val_loss: 1.8792 - val_accuracy: 0.8140\n",
            "Epoch 449/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0199 - accuracy: 0.9940 - val_loss: 1.9210 - val_accuracy: 0.7907\n",
            "Epoch 450/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 1.8560 - val_accuracy: 0.7907\n",
            "Epoch 451/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0242 - accuracy: 0.9881 - val_loss: 1.8456 - val_accuracy: 0.7907\n",
            "Epoch 452/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 1.9927 - val_accuracy: 0.7907\n",
            "Epoch 453/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0223 - accuracy: 0.9940 - val_loss: 1.9073 - val_accuracy: 0.7907\n",
            "Epoch 454/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0468 - accuracy: 0.9881 - val_loss: 1.7687 - val_accuracy: 0.7907\n",
            "Epoch 455/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0869 - accuracy: 0.9821 - val_loss: 2.1266 - val_accuracy: 0.8140\n",
            "Epoch 456/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1474 - accuracy: 0.9583 - val_loss: 2.3678 - val_accuracy: 0.7442\n",
            "Epoch 457/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1080 - accuracy: 0.9643 - val_loss: 2.4016 - val_accuracy: 0.7442\n",
            "Epoch 458/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1178 - accuracy: 0.9762 - val_loss: 2.8918 - val_accuracy: 0.7442\n",
            "Epoch 459/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1526 - accuracy: 0.9643 - val_loss: 1.8614 - val_accuracy: 0.7907\n",
            "Epoch 460/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1163 - accuracy: 0.9643 - val_loss: 1.9839 - val_accuracy: 0.7907\n",
            "Epoch 461/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0766 - accuracy: 0.9821 - val_loss: 2.2622 - val_accuracy: 0.7442\n",
            "Epoch 462/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0556 - accuracy: 0.9821 - val_loss: 2.2727 - val_accuracy: 0.7674\n",
            "Epoch 463/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0509 - accuracy: 0.9821 - val_loss: 1.9842 - val_accuracy: 0.7674\n",
            "Epoch 464/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0729 - accuracy: 0.9643 - val_loss: 1.9340 - val_accuracy: 0.7907\n",
            "Epoch 465/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1105 - accuracy: 0.9702 - val_loss: 2.2596 - val_accuracy: 0.7442\n",
            "Epoch 466/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1502 - accuracy: 0.9702 - val_loss: 2.3196 - val_accuracy: 0.7442\n",
            "Epoch 467/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0821 - accuracy: 0.9702 - val_loss: 1.8384 - val_accuracy: 0.7442\n",
            "Epoch 468/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0681 - accuracy: 0.9762 - val_loss: 1.9876 - val_accuracy: 0.7442\n",
            "Epoch 469/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0591 - accuracy: 0.9643 - val_loss: 2.1412 - val_accuracy: 0.7442\n",
            "Epoch 470/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0476 - accuracy: 0.9821 - val_loss: 2.0664 - val_accuracy: 0.7442\n",
            "Epoch 471/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0495 - accuracy: 0.9821 - val_loss: 2.1203 - val_accuracy: 0.7442\n",
            "Epoch 472/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0527 - accuracy: 0.9821 - val_loss: 2.2696 - val_accuracy: 0.7674\n",
            "Epoch 473/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0469 - accuracy: 0.9821 - val_loss: 2.2050 - val_accuracy: 0.7674\n",
            "Epoch 474/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0374 - accuracy: 0.9881 - val_loss: 2.0567 - val_accuracy: 0.7674\n",
            "Epoch 475/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0381 - accuracy: 0.9881 - val_loss: 2.0334 - val_accuracy: 0.7442\n",
            "Epoch 476/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0410 - accuracy: 0.9821 - val_loss: 2.0762 - val_accuracy: 0.7442\n",
            "Epoch 477/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0392 - accuracy: 0.9881 - val_loss: 2.0704 - val_accuracy: 0.7674\n",
            "Epoch 478/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0515 - accuracy: 0.9762 - val_loss: 2.0405 - val_accuracy: 0.7442\n",
            "Epoch 479/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0343 - accuracy: 0.9881 - val_loss: 2.1594 - val_accuracy: 0.7442\n",
            "Epoch 480/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0441 - accuracy: 0.9821 - val_loss: 2.0970 - val_accuracy: 0.7674\n",
            "Epoch 481/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0472 - accuracy: 0.9821 - val_loss: 2.1213 - val_accuracy: 0.7674\n",
            "Epoch 482/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0361 - accuracy: 0.9821 - val_loss: 2.3115 - val_accuracy: 0.7442\n",
            "Epoch 483/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0378 - accuracy: 0.9881 - val_loss: 2.1627 - val_accuracy: 0.7442\n",
            "Epoch 484/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0327 - accuracy: 0.9881 - val_loss: 2.0742 - val_accuracy: 0.7907\n",
            "Epoch 485/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0321 - accuracy: 0.9881 - val_loss: 2.1360 - val_accuracy: 0.7907\n",
            "Epoch 486/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0322 - accuracy: 0.9881 - val_loss: 2.1122 - val_accuracy: 0.7674\n",
            "Epoch 487/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0308 - accuracy: 0.9881 - val_loss: 2.1132 - val_accuracy: 0.7442\n",
            "Epoch 488/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0300 - accuracy: 0.9940 - val_loss: 2.1587 - val_accuracy: 0.7674\n",
            "Epoch 489/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0311 - accuracy: 0.9940 - val_loss: 2.1702 - val_accuracy: 0.7442\n",
            "Epoch 490/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0294 - accuracy: 0.9881 - val_loss: 2.2198 - val_accuracy: 0.7442\n",
            "Epoch 491/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0301 - accuracy: 0.9881 - val_loss: 2.1517 - val_accuracy: 0.7442\n",
            "Epoch 492/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0276 - accuracy: 0.9940 - val_loss: 2.1402 - val_accuracy: 0.7442\n",
            "Epoch 493/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0287 - accuracy: 0.9881 - val_loss: 2.3151 - val_accuracy: 0.7442\n",
            "Epoch 494/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0283 - accuracy: 0.9940 - val_loss: 2.3669 - val_accuracy: 0.7442\n",
            "Epoch 495/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0281 - accuracy: 0.9940 - val_loss: 2.3401 - val_accuracy: 0.7442\n",
            "Epoch 496/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0272 - accuracy: 0.9881 - val_loss: 2.2557 - val_accuracy: 0.7442\n",
            "Epoch 497/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0263 - accuracy: 0.9940 - val_loss: 2.2489 - val_accuracy: 0.7442\n",
            "Epoch 498/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0336 - accuracy: 0.9881 - val_loss: 2.3441 - val_accuracy: 0.7209\n",
            "Epoch 499/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0330 - accuracy: 0.9821 - val_loss: 1.9989 - val_accuracy: 0.7442\n",
            "Epoch 500/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0399 - accuracy: 0.9881 - val_loss: 2.0575 - val_accuracy: 0.7442\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_decision_regions"
      ],
      "metadata": {
        "id": "onv45VRexAz2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(X, y.astype('int'), clf=model, legend=2)\n",
        "plt.xlim(-0.7,0.5)\n",
        "plt.ylim(-0.8,0.8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "EAb_i8vPxd9W",
        "outputId": "6b27e1f0-86d6-4851-a9e1-084e62f2c8c8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9600/9600 [==============================] - 14s 1ms/step\n"
          ]
        },
        {
          "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": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "acsg1tSSx0zS",
        "outputId": "07c18196-1f30-4737-abbf-cb65acf4b694"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ab4fa7a9060>]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "--RJquSDzhS_",
        "outputId": "0198bc96-3977-498d-e5c6-996e1b36b6c9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ab4fa81cee0>]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "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": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(128, input_dim=2, activation=\"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(128, activation=\"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "adam = Adam(learning_rate=0.01)\n",
        "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X, y, epochs=500, validation_split = 0.2,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCkRC1hvzmsK",
        "outputId": "87b8e915-639e-468f-9ea0-f0ce0c667182"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "6/6 [==============================] - 2s 113ms/step - loss: 0.6279 - accuracy: 0.6250 - val_loss: 0.6213 - val_accuracy: 0.5814\n",
            "Epoch 2/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.3580 - accuracy: 0.8690 - val_loss: 0.6779 - val_accuracy: 0.6512\n",
            "Epoch 3/500\n",
            "6/6 [==============================] - 0s 24ms/step - loss: 0.2762 - accuracy: 0.8750 - val_loss: 0.6132 - val_accuracy: 0.7209\n",
            "Epoch 4/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.2403 - accuracy: 0.8988 - val_loss: 0.4353 - val_accuracy: 0.8140\n",
            "Epoch 5/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.2469 - accuracy: 0.8988 - val_loss: 0.3177 - val_accuracy: 0.8605\n",
            "Epoch 6/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.2639 - accuracy: 0.9107 - val_loss: 0.3417 - val_accuracy: 0.8605\n",
            "Epoch 7/500\n",
            "6/6 [==============================] - 0s 32ms/step - loss: 0.2330 - accuracy: 0.9048 - val_loss: 0.3731 - val_accuracy: 0.8605\n",
            "Epoch 8/500\n",
            "6/6 [==============================] - 0s 22ms/step - loss: 0.2027 - accuracy: 0.9226 - val_loss: 0.3943 - val_accuracy: 0.8372\n",
            "Epoch 9/500\n",
            "6/6 [==============================] - 0s 21ms/step - loss: 0.2390 - accuracy: 0.9048 - val_loss: 0.4234 - val_accuracy: 0.8605\n",
            "Epoch 10/500\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.2115 - accuracy: 0.9167 - val_loss: 0.4384 - val_accuracy: 0.8140\n",
            "Epoch 11/500\n",
            "6/6 [==============================] - 0s 38ms/step - loss: 0.2338 - accuracy: 0.9107 - val_loss: 0.4102 - val_accuracy: 0.8605\n",
            "Epoch 12/500\n",
            "6/6 [==============================] - 0s 42ms/step - loss: 0.2455 - accuracy: 0.9048 - val_loss: 0.3765 - val_accuracy: 0.8605\n",
            "Epoch 13/500\n",
            "6/6 [==============================] - 0s 37ms/step - loss: 0.2328 - accuracy: 0.9167 - val_loss: 0.4028 - val_accuracy: 0.8605\n",
            "Epoch 14/500\n",
            "6/6 [==============================] - 0s 53ms/step - loss: 0.2007 - accuracy: 0.9286 - val_loss: 0.3945 - val_accuracy: 0.8605\n",
            "Epoch 15/500\n",
            "6/6 [==============================] - 0s 49ms/step - loss: 0.2217 - accuracy: 0.9167 - val_loss: 0.4237 - val_accuracy: 0.8605\n",
            "Epoch 16/500\n",
            "6/6 [==============================] - 0s 53ms/step - loss: 0.2544 - accuracy: 0.8869 - val_loss: 0.4271 - val_accuracy: 0.8605\n",
            "Epoch 17/500\n",
            "6/6 [==============================] - 0s 53ms/step - loss: 0.2237 - accuracy: 0.9048 - val_loss: 0.3726 - val_accuracy: 0.8837\n",
            "Epoch 18/500\n",
            "6/6 [==============================] - 0s 38ms/step - loss: 0.2044 - accuracy: 0.9286 - val_loss: 0.3557 - val_accuracy: 0.8605\n",
            "Epoch 19/500\n",
            "6/6 [==============================] - 0s 27ms/step - loss: 0.2516 - accuracy: 0.8988 - val_loss: 0.3937 - val_accuracy: 0.8372\n",
            "Epoch 20/500\n",
            "6/6 [==============================] - 0s 41ms/step - loss: 0.2151 - accuracy: 0.9107 - val_loss: 0.4359 - val_accuracy: 0.8140\n",
            "Epoch 21/500\n",
            "6/6 [==============================] - 0s 22ms/step - loss: 0.2146 - accuracy: 0.9226 - val_loss: 0.4102 - val_accuracy: 0.8605\n",
            "Epoch 22/500\n",
            "6/6 [==============================] - 0s 28ms/step - loss: 0.2187 - accuracy: 0.8988 - val_loss: 0.3968 - val_accuracy: 0.8372\n",
            "Epoch 23/500\n",
            "6/6 [==============================] - 0s 35ms/step - loss: 0.1915 - accuracy: 0.9048 - val_loss: 0.3761 - val_accuracy: 0.8605\n",
            "Epoch 24/500\n",
            "6/6 [==============================] - 0s 21ms/step - loss: 0.1925 - accuracy: 0.9286 - val_loss: 0.3705 - val_accuracy: 0.8605\n",
            "Epoch 25/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1992 - accuracy: 0.9286 - val_loss: 0.3695 - val_accuracy: 0.8605\n",
            "Epoch 26/500\n",
            "6/6 [==============================] - 0s 25ms/step - loss: 0.1993 - accuracy: 0.9345 - val_loss: 0.3837 - val_accuracy: 0.8605\n",
            "Epoch 27/500\n",
            "6/6 [==============================] - 0s 25ms/step - loss: 0.2252 - accuracy: 0.8988 - val_loss: 0.4172 - val_accuracy: 0.8605\n",
            "Epoch 28/500\n",
            "6/6 [==============================] - 0s 44ms/step - loss: 0.2655 - accuracy: 0.8869 - val_loss: 0.4022 - val_accuracy: 0.8372\n",
            "Epoch 29/500\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.1967 - accuracy: 0.9107 - val_loss: 0.3559 - val_accuracy: 0.8837\n",
            "Epoch 30/500\n",
            "6/6 [==============================] - 0s 26ms/step - loss: 0.2381 - accuracy: 0.9107 - val_loss: 0.3527 - val_accuracy: 0.8837\n",
            "Epoch 31/500\n",
            "6/6 [==============================] - 0s 44ms/step - loss: 0.2293 - accuracy: 0.8988 - val_loss: 0.3972 - val_accuracy: 0.8605\n",
            "Epoch 32/500\n",
            "6/6 [==============================] - 0s 30ms/step - loss: 0.2120 - accuracy: 0.9226 - val_loss: 0.4024 - val_accuracy: 0.8605\n",
            "Epoch 33/500\n",
            "6/6 [==============================] - 0s 21ms/step - loss: 0.2043 - accuracy: 0.9167 - val_loss: 0.3719 - val_accuracy: 0.8605\n",
            "Epoch 34/500\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.2295 - accuracy: 0.9167 - val_loss: 0.3602 - val_accuracy: 0.8605\n",
            "Epoch 35/500\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.1767 - accuracy: 0.9405 - val_loss: 0.3898 - val_accuracy: 0.8605\n",
            "Epoch 36/500\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.2206 - accuracy: 0.9167 - val_loss: 0.3913 - val_accuracy: 0.8605\n",
            "Epoch 37/500\n",
            "6/6 [==============================] - 0s 39ms/step - loss: 0.2055 - accuracy: 0.9226 - val_loss: 0.4139 - val_accuracy: 0.8605\n",
            "Epoch 38/500\n",
            "6/6 [==============================] - 0s 45ms/step - loss: 0.2059 - accuracy: 0.9286 - val_loss: 0.4252 - val_accuracy: 0.8605\n",
            "Epoch 39/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1918 - accuracy: 0.9405 - val_loss: 0.3709 - val_accuracy: 0.8605\n",
            "Epoch 40/500\n",
            "6/6 [==============================] - 0s 44ms/step - loss: 0.2287 - accuracy: 0.9107 - val_loss: 0.3355 - val_accuracy: 0.8837\n",
            "Epoch 41/500\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.2268 - accuracy: 0.9226 - val_loss: 0.3554 - val_accuracy: 0.8605\n",
            "Epoch 42/500\n",
            "6/6 [==============================] - 0s 34ms/step - loss: 0.1981 - accuracy: 0.9286 - val_loss: 0.4029 - val_accuracy: 0.8605\n",
            "Epoch 43/500\n",
            "6/6 [==============================] - 0s 24ms/step - loss: 0.1671 - accuracy: 0.9345 - val_loss: 0.4382 - val_accuracy: 0.8605\n",
            "Epoch 44/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2010 - accuracy: 0.9226 - val_loss: 0.4400 - val_accuracy: 0.8605\n",
            "Epoch 45/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2267 - accuracy: 0.9167 - val_loss: 0.4357 - val_accuracy: 0.8605\n",
            "Epoch 46/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2096 - accuracy: 0.9226 - val_loss: 0.4352 - val_accuracy: 0.8605\n",
            "Epoch 47/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1997 - accuracy: 0.9167 - val_loss: 0.3675 - val_accuracy: 0.8605\n",
            "Epoch 48/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2440 - accuracy: 0.9167 - val_loss: 0.3622 - val_accuracy: 0.8605\n",
            "Epoch 49/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2016 - accuracy: 0.9226 - val_loss: 0.3881 - val_accuracy: 0.8605\n",
            "Epoch 50/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1936 - accuracy: 0.9286 - val_loss: 0.3759 - val_accuracy: 0.8605\n",
            "Epoch 51/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2115 - accuracy: 0.9107 - val_loss: 0.3581 - val_accuracy: 0.8837\n",
            "Epoch 52/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2171 - accuracy: 0.9107 - val_loss: 0.3834 - val_accuracy: 0.8605\n",
            "Epoch 53/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2115 - accuracy: 0.9107 - val_loss: 0.3807 - val_accuracy: 0.8605\n",
            "Epoch 54/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2062 - accuracy: 0.9286 - val_loss: 0.3691 - val_accuracy: 0.8605\n",
            "Epoch 55/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1915 - accuracy: 0.9107 - val_loss: 0.3613 - val_accuracy: 0.8605\n",
            "Epoch 56/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1980 - accuracy: 0.9167 - val_loss: 0.3809 - val_accuracy: 0.8605\n",
            "Epoch 57/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1930 - accuracy: 0.9226 - val_loss: 0.4292 - val_accuracy: 0.8605\n",
            "Epoch 58/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2233 - accuracy: 0.9167 - val_loss: 0.4102 - val_accuracy: 0.8372\n",
            "Epoch 59/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2194 - accuracy: 0.9286 - val_loss: 0.3733 - val_accuracy: 0.8605\n",
            "Epoch 60/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2509 - accuracy: 0.8929 - val_loss: 0.3667 - val_accuracy: 0.8605\n",
            "Epoch 61/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2156 - accuracy: 0.9167 - val_loss: 0.3682 - val_accuracy: 0.8605\n",
            "Epoch 62/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1793 - accuracy: 0.9405 - val_loss: 0.4095 - val_accuracy: 0.8605\n",
            "Epoch 63/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1764 - accuracy: 0.9107 - val_loss: 0.4567 - val_accuracy: 0.8605\n",
            "Epoch 64/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1804 - accuracy: 0.9345 - val_loss: 0.4836 - val_accuracy: 0.8605\n",
            "Epoch 65/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2071 - accuracy: 0.9167 - val_loss: 0.4682 - val_accuracy: 0.8605\n",
            "Epoch 66/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2434 - accuracy: 0.9107 - val_loss: 0.4439 - val_accuracy: 0.8605\n",
            "Epoch 67/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1886 - accuracy: 0.9286 - val_loss: 0.4558 - val_accuracy: 0.8605\n",
            "Epoch 68/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1973 - accuracy: 0.9167 - val_loss: 0.4446 - val_accuracy: 0.8605\n",
            "Epoch 69/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1869 - accuracy: 0.9405 - val_loss: 0.3861 - val_accuracy: 0.8837\n",
            "Epoch 70/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.2252 - accuracy: 0.9167 - val_loss: 0.3846 - val_accuracy: 0.9070\n",
            "Epoch 71/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2035 - accuracy: 0.9286 - val_loss: 0.3969 - val_accuracy: 0.8605\n",
            "Epoch 72/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2246 - accuracy: 0.9345 - val_loss: 0.4193 - val_accuracy: 0.8605\n",
            "Epoch 73/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1684 - accuracy: 0.9405 - val_loss: 0.4225 - val_accuracy: 0.8605\n",
            "Epoch 74/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1813 - accuracy: 0.9226 - val_loss: 0.4741 - val_accuracy: 0.8605\n",
            "Epoch 75/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1982 - accuracy: 0.9226 - val_loss: 0.4502 - val_accuracy: 0.8372\n",
            "Epoch 76/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1980 - accuracy: 0.9167 - val_loss: 0.4035 - val_accuracy: 0.8605\n",
            "Epoch 77/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2050 - accuracy: 0.9345 - val_loss: 0.4050 - val_accuracy: 0.8605\n",
            "Epoch 78/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1945 - accuracy: 0.9405 - val_loss: 0.4384 - val_accuracy: 0.8605\n",
            "Epoch 79/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1670 - accuracy: 0.9345 - val_loss: 0.4370 - val_accuracy: 0.8605\n",
            "Epoch 80/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1818 - accuracy: 0.9167 - val_loss: 0.4651 - val_accuracy: 0.8605\n",
            "Epoch 81/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1812 - accuracy: 0.9405 - val_loss: 0.4807 - val_accuracy: 0.8605\n",
            "Epoch 82/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1867 - accuracy: 0.9286 - val_loss: 0.4768 - val_accuracy: 0.8605\n",
            "Epoch 83/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2023 - accuracy: 0.9226 - val_loss: 0.4636 - val_accuracy: 0.8605\n",
            "Epoch 84/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1677 - accuracy: 0.9345 - val_loss: 0.5004 - val_accuracy: 0.8605\n",
            "Epoch 85/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2010 - accuracy: 0.9286 - val_loss: 0.4770 - val_accuracy: 0.8605\n",
            "Epoch 86/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1907 - accuracy: 0.9286 - val_loss: 0.4347 - val_accuracy: 0.8605\n",
            "Epoch 87/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1709 - accuracy: 0.9405 - val_loss: 0.4165 - val_accuracy: 0.8605\n",
            "Epoch 88/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1550 - accuracy: 0.9405 - val_loss: 0.4123 - val_accuracy: 0.8605\n",
            "Epoch 89/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1816 - accuracy: 0.9167 - val_loss: 0.4841 - val_accuracy: 0.8605\n",
            "Epoch 90/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2059 - accuracy: 0.9345 - val_loss: 0.5204 - val_accuracy: 0.8605\n",
            "Epoch 91/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1986 - accuracy: 0.9107 - val_loss: 0.5433 - val_accuracy: 0.8372\n",
            "Epoch 92/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2010 - accuracy: 0.9048 - val_loss: 0.4715 - val_accuracy: 0.8605\n",
            "Epoch 93/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1854 - accuracy: 0.9226 - val_loss: 0.3956 - val_accuracy: 0.8837\n",
            "Epoch 94/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1710 - accuracy: 0.9345 - val_loss: 0.4544 - val_accuracy: 0.8605\n",
            "Epoch 95/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2152 - accuracy: 0.9226 - val_loss: 0.4383 - val_accuracy: 0.8605\n",
            "Epoch 96/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2097 - accuracy: 0.9286 - val_loss: 0.3611 - val_accuracy: 0.8605\n",
            "Epoch 97/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2131 - accuracy: 0.9345 - val_loss: 0.3733 - val_accuracy: 0.8605\n",
            "Epoch 98/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1711 - accuracy: 0.9345 - val_loss: 0.4197 - val_accuracy: 0.8605\n",
            "Epoch 99/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2088 - accuracy: 0.9107 - val_loss: 0.4653 - val_accuracy: 0.8605\n",
            "Epoch 100/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1963 - accuracy: 0.9345 - val_loss: 0.4249 - val_accuracy: 0.8605\n",
            "Epoch 101/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2022 - accuracy: 0.9345 - val_loss: 0.4339 - val_accuracy: 0.8605\n",
            "Epoch 102/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1872 - accuracy: 0.9167 - val_loss: 0.4495 - val_accuracy: 0.8605\n",
            "Epoch 103/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1900 - accuracy: 0.9167 - val_loss: 0.4768 - val_accuracy: 0.8605\n",
            "Epoch 104/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1782 - accuracy: 0.9226 - val_loss: 0.4721 - val_accuracy: 0.8605\n",
            "Epoch 105/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1759 - accuracy: 0.9286 - val_loss: 0.4734 - val_accuracy: 0.8605\n",
            "Epoch 106/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1773 - accuracy: 0.9405 - val_loss: 0.5236 - val_accuracy: 0.8605\n",
            "Epoch 107/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1707 - accuracy: 0.9405 - val_loss: 0.5520 - val_accuracy: 0.8372\n",
            "Epoch 108/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2071 - accuracy: 0.9107 - val_loss: 0.4927 - val_accuracy: 0.8605\n",
            "Epoch 109/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1891 - accuracy: 0.9286 - val_loss: 0.4766 - val_accuracy: 0.8605\n",
            "Epoch 110/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1918 - accuracy: 0.9226 - val_loss: 0.6091 - val_accuracy: 0.8605\n",
            "Epoch 111/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1981 - accuracy: 0.9167 - val_loss: 0.5653 - val_accuracy: 0.8605\n",
            "Epoch 112/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1592 - accuracy: 0.9464 - val_loss: 0.5369 - val_accuracy: 0.8605\n",
            "Epoch 113/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1741 - accuracy: 0.9405 - val_loss: 0.4767 - val_accuracy: 0.8605\n",
            "Epoch 114/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1820 - accuracy: 0.9226 - val_loss: 0.4761 - val_accuracy: 0.8605\n",
            "Epoch 115/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1923 - accuracy: 0.9286 - val_loss: 0.5221 - val_accuracy: 0.8605\n",
            "Epoch 116/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1851 - accuracy: 0.9345 - val_loss: 0.5784 - val_accuracy: 0.8605\n",
            "Epoch 117/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1728 - accuracy: 0.9167 - val_loss: 0.5018 - val_accuracy: 0.8605\n",
            "Epoch 118/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1975 - accuracy: 0.8988 - val_loss: 0.4380 - val_accuracy: 0.8605\n",
            "Epoch 119/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1891 - accuracy: 0.9464 - val_loss: 0.4522 - val_accuracy: 0.8605\n",
            "Epoch 120/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1831 - accuracy: 0.9345 - val_loss: 0.4832 - val_accuracy: 0.8605\n",
            "Epoch 121/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1844 - accuracy: 0.9345 - val_loss: 0.5213 - val_accuracy: 0.8605\n",
            "Epoch 122/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1561 - accuracy: 0.9345 - val_loss: 0.5947 - val_accuracy: 0.8605\n",
            "Epoch 123/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1774 - accuracy: 0.9345 - val_loss: 0.5280 - val_accuracy: 0.8605\n",
            "Epoch 124/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1700 - accuracy: 0.9405 - val_loss: 0.4485 - val_accuracy: 0.8605\n",
            "Epoch 125/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1948 - accuracy: 0.9286 - val_loss: 0.5258 - val_accuracy: 0.8605\n",
            "Epoch 126/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1717 - accuracy: 0.9345 - val_loss: 0.5840 - val_accuracy: 0.8605\n",
            "Epoch 127/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1676 - accuracy: 0.9345 - val_loss: 0.5543 - val_accuracy: 0.8605\n",
            "Epoch 128/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1745 - accuracy: 0.9226 - val_loss: 0.5122 - val_accuracy: 0.8372\n",
            "Epoch 129/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1695 - accuracy: 0.9405 - val_loss: 0.5671 - val_accuracy: 0.8605\n",
            "Epoch 130/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1551 - accuracy: 0.9405 - val_loss: 0.5510 - val_accuracy: 0.8605\n",
            "Epoch 131/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1547 - accuracy: 0.9464 - val_loss: 0.5761 - val_accuracy: 0.8605\n",
            "Epoch 132/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1602 - accuracy: 0.9464 - val_loss: 0.6050 - val_accuracy: 0.8605\n",
            "Epoch 133/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1930 - accuracy: 0.9286 - val_loss: 0.6038 - val_accuracy: 0.8605\n",
            "Epoch 134/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1847 - accuracy: 0.9286 - val_loss: 0.5967 - val_accuracy: 0.8605\n",
            "Epoch 135/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1869 - accuracy: 0.9345 - val_loss: 0.6674 - val_accuracy: 0.8372\n",
            "Epoch 136/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1775 - accuracy: 0.9286 - val_loss: 0.6528 - val_accuracy: 0.8605\n",
            "Epoch 137/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1904 - accuracy: 0.9226 - val_loss: 0.5697 - val_accuracy: 0.8605\n",
            "Epoch 138/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1964 - accuracy: 0.9107 - val_loss: 0.3893 - val_accuracy: 0.8837\n",
            "Epoch 139/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1981 - accuracy: 0.9286 - val_loss: 0.3746 - val_accuracy: 0.8372\n",
            "Epoch 140/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2186 - accuracy: 0.9286 - val_loss: 0.3636 - val_accuracy: 0.8837\n",
            "Epoch 141/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1877 - accuracy: 0.9167 - val_loss: 0.3964 - val_accuracy: 0.8605\n",
            "Epoch 142/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1947 - accuracy: 0.9107 - val_loss: 0.4791 - val_accuracy: 0.8605\n",
            "Epoch 143/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1849 - accuracy: 0.9286 - val_loss: 0.4884 - val_accuracy: 0.8605\n",
            "Epoch 144/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2139 - accuracy: 0.9107 - val_loss: 0.4959 - val_accuracy: 0.8605\n",
            "Epoch 145/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1682 - accuracy: 0.9405 - val_loss: 0.4619 - val_accuracy: 0.8605\n",
            "Epoch 146/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1755 - accuracy: 0.9345 - val_loss: 0.4703 - val_accuracy: 0.8605\n",
            "Epoch 147/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1697 - accuracy: 0.9464 - val_loss: 0.5333 - val_accuracy: 0.8605\n",
            "Epoch 148/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1618 - accuracy: 0.9405 - val_loss: 0.5804 - val_accuracy: 0.8605\n",
            "Epoch 149/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2054 - accuracy: 0.9286 - val_loss: 0.5243 - val_accuracy: 0.8605\n",
            "Epoch 150/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1987 - accuracy: 0.9226 - val_loss: 0.4495 - val_accuracy: 0.8605\n",
            "Epoch 151/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1865 - accuracy: 0.9345 - val_loss: 0.4655 - val_accuracy: 0.8605\n",
            "Epoch 152/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1582 - accuracy: 0.9464 - val_loss: 0.4796 - val_accuracy: 0.8605\n",
            "Epoch 153/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1461 - accuracy: 0.9464 - val_loss: 0.4963 - val_accuracy: 0.8605\n",
            "Epoch 154/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1749 - accuracy: 0.9405 - val_loss: 0.5309 - val_accuracy: 0.8605\n",
            "Epoch 155/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1829 - accuracy: 0.9167 - val_loss: 0.5265 - val_accuracy: 0.8605\n",
            "Epoch 156/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1790 - accuracy: 0.9345 - val_loss: 0.5004 - val_accuracy: 0.8605\n",
            "Epoch 157/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1750 - accuracy: 0.9167 - val_loss: 0.5420 - val_accuracy: 0.8605\n",
            "Epoch 158/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1879 - accuracy: 0.9226 - val_loss: 0.5660 - val_accuracy: 0.8605\n",
            "Epoch 159/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1611 - accuracy: 0.9345 - val_loss: 0.3926 - val_accuracy: 0.8837\n",
            "Epoch 160/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2097 - accuracy: 0.9345 - val_loss: 0.3779 - val_accuracy: 0.8837\n",
            "Epoch 161/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1705 - accuracy: 0.9226 - val_loss: 0.3741 - val_accuracy: 0.8837\n",
            "Epoch 162/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1793 - accuracy: 0.9167 - val_loss: 0.4216 - val_accuracy: 0.8605\n",
            "Epoch 163/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1925 - accuracy: 0.9226 - val_loss: 0.4495 - val_accuracy: 0.8605\n",
            "Epoch 164/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1689 - accuracy: 0.9464 - val_loss: 0.4898 - val_accuracy: 0.8605\n",
            "Epoch 165/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1919 - accuracy: 0.9167 - val_loss: 0.4858 - val_accuracy: 0.8605\n",
            "Epoch 166/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1688 - accuracy: 0.9226 - val_loss: 0.5370 - val_accuracy: 0.8605\n",
            "Epoch 167/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2004 - accuracy: 0.9107 - val_loss: 0.5467 - val_accuracy: 0.8372\n",
            "Epoch 168/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1501 - accuracy: 0.9286 - val_loss: 0.4302 - val_accuracy: 0.8837\n",
            "Epoch 169/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1865 - accuracy: 0.9167 - val_loss: 0.4330 - val_accuracy: 0.8837\n",
            "Epoch 170/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1836 - accuracy: 0.9167 - val_loss: 0.5520 - val_accuracy: 0.8605\n",
            "Epoch 171/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1853 - accuracy: 0.9107 - val_loss: 0.4444 - val_accuracy: 0.8837\n",
            "Epoch 172/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1807 - accuracy: 0.9167 - val_loss: 0.4064 - val_accuracy: 0.8837\n",
            "Epoch 173/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1939 - accuracy: 0.9167 - val_loss: 0.4310 - val_accuracy: 0.8605\n",
            "Epoch 174/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.2003 - accuracy: 0.9167 - val_loss: 0.4710 - val_accuracy: 0.8605\n",
            "Epoch 175/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.1726 - accuracy: 0.9226 - val_loss: 0.4558 - val_accuracy: 0.8605\n",
            "Epoch 176/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1529 - accuracy: 0.9583 - val_loss: 0.4404 - val_accuracy: 0.8372\n",
            "Epoch 177/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1678 - accuracy: 0.9345 - val_loss: 0.4035 - val_accuracy: 0.8837\n",
            "Epoch 178/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1753 - accuracy: 0.9345 - val_loss: 0.4280 - val_accuracy: 0.8837\n",
            "Epoch 179/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1557 - accuracy: 0.9405 - val_loss: 0.4241 - val_accuracy: 0.8837\n",
            "Epoch 180/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1678 - accuracy: 0.9405 - val_loss: 0.4496 - val_accuracy: 0.8605\n",
            "Epoch 181/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1648 - accuracy: 0.9405 - val_loss: 0.4981 - val_accuracy: 0.8605\n",
            "Epoch 182/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1701 - accuracy: 0.9405 - val_loss: 0.5307 - val_accuracy: 0.8605\n",
            "Epoch 183/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1455 - accuracy: 0.9464 - val_loss: 0.5315 - val_accuracy: 0.8605\n",
            "Epoch 184/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1731 - accuracy: 0.9286 - val_loss: 0.5113 - val_accuracy: 0.8605\n",
            "Epoch 185/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1788 - accuracy: 0.9286 - val_loss: 0.4791 - val_accuracy: 0.8605\n",
            "Epoch 186/500\n",
            "6/6 [==============================] - 0s 22ms/step - loss: 0.1343 - accuracy: 0.9524 - val_loss: 0.4712 - val_accuracy: 0.8837\n",
            "Epoch 187/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1848 - accuracy: 0.9405 - val_loss: 0.4808 - val_accuracy: 0.8605\n",
            "Epoch 188/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1703 - accuracy: 0.9464 - val_loss: 0.5315 - val_accuracy: 0.8605\n",
            "Epoch 189/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1709 - accuracy: 0.9286 - val_loss: 0.5605 - val_accuracy: 0.8372\n",
            "Epoch 190/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2008 - accuracy: 0.9107 - val_loss: 0.5279 - val_accuracy: 0.8605\n",
            "Epoch 191/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1595 - accuracy: 0.9286 - val_loss: 0.4696 - val_accuracy: 0.8837\n",
            "Epoch 192/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1657 - accuracy: 0.9286 - val_loss: 0.4557 - val_accuracy: 0.8837\n",
            "Epoch 193/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1686 - accuracy: 0.9226 - val_loss: 0.6842 - val_accuracy: 0.8372\n",
            "Epoch 194/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1578 - accuracy: 0.9464 - val_loss: 0.6509 - val_accuracy: 0.8605\n",
            "Epoch 195/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1787 - accuracy: 0.9107 - val_loss: 0.5271 - val_accuracy: 0.8837\n",
            "Epoch 196/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1641 - accuracy: 0.9107 - val_loss: 0.5019 - val_accuracy: 0.8837\n",
            "Epoch 197/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1917 - accuracy: 0.9345 - val_loss: 0.5296 - val_accuracy: 0.8605\n",
            "Epoch 198/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1781 - accuracy: 0.9345 - val_loss: 0.5762 - val_accuracy: 0.8605\n",
            "Epoch 199/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1647 - accuracy: 0.9345 - val_loss: 0.5303 - val_accuracy: 0.8372\n",
            "Epoch 200/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.2016 - accuracy: 0.9107 - val_loss: 0.4507 - val_accuracy: 0.9070\n",
            "Epoch 201/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1932 - accuracy: 0.9286 - val_loss: 0.4796 - val_accuracy: 0.8837\n",
            "Epoch 202/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1574 - accuracy: 0.9345 - val_loss: 0.5843 - val_accuracy: 0.8605\n",
            "Epoch 203/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1550 - accuracy: 0.9286 - val_loss: 0.7149 - val_accuracy: 0.8605\n",
            "Epoch 204/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1514 - accuracy: 0.9286 - val_loss: 0.7480 - val_accuracy: 0.8605\n",
            "Epoch 205/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1648 - accuracy: 0.9167 - val_loss: 0.5516 - val_accuracy: 0.8605\n",
            "Epoch 206/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1851 - accuracy: 0.9167 - val_loss: 0.4837 - val_accuracy: 0.8837\n",
            "Epoch 207/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1768 - accuracy: 0.9226 - val_loss: 0.4680 - val_accuracy: 0.8837\n",
            "Epoch 208/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1868 - accuracy: 0.9345 - val_loss: 0.4292 - val_accuracy: 0.8837\n",
            "Epoch 209/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.1743 - accuracy: 0.9286 - val_loss: 0.3889 - val_accuracy: 0.8837\n",
            "Epoch 210/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1627 - accuracy: 0.9524 - val_loss: 0.4451 - val_accuracy: 0.8837\n",
            "Epoch 211/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1597 - accuracy: 0.9345 - val_loss: 0.5211 - val_accuracy: 0.8605\n",
            "Epoch 212/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1692 - accuracy: 0.9107 - val_loss: 0.5659 - val_accuracy: 0.8372\n",
            "Epoch 213/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1766 - accuracy: 0.9167 - val_loss: 0.5160 - val_accuracy: 0.8605\n",
            "Epoch 214/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1903 - accuracy: 0.9286 - val_loss: 0.4280 - val_accuracy: 0.8837\n",
            "Epoch 215/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2126 - accuracy: 0.9226 - val_loss: 0.4290 - val_accuracy: 0.8605\n",
            "Epoch 216/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1674 - accuracy: 0.9405 - val_loss: 0.4614 - val_accuracy: 0.8837\n",
            "Epoch 217/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1566 - accuracy: 0.9464 - val_loss: 0.5312 - val_accuracy: 0.8605\n",
            "Epoch 218/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1536 - accuracy: 0.9405 - val_loss: 0.6005 - val_accuracy: 0.8372\n",
            "Epoch 219/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1805 - accuracy: 0.9167 - val_loss: 0.5632 - val_accuracy: 0.8372\n",
            "Epoch 220/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1789 - accuracy: 0.9226 - val_loss: 0.4892 - val_accuracy: 0.8837\n",
            "Epoch 221/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1800 - accuracy: 0.9167 - val_loss: 0.4454 - val_accuracy: 0.8837\n",
            "Epoch 222/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1723 - accuracy: 0.9286 - val_loss: 0.4790 - val_accuracy: 0.9070\n",
            "Epoch 223/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1645 - accuracy: 0.9464 - val_loss: 0.6006 - val_accuracy: 0.8605\n",
            "Epoch 224/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1800 - accuracy: 0.9226 - val_loss: 0.6322 - val_accuracy: 0.8372\n",
            "Epoch 225/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1852 - accuracy: 0.9226 - val_loss: 0.6180 - val_accuracy: 0.8605\n",
            "Epoch 226/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1742 - accuracy: 0.9226 - val_loss: 0.5145 - val_accuracy: 0.8605\n",
            "Epoch 227/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1718 - accuracy: 0.9167 - val_loss: 0.4316 - val_accuracy: 0.9070\n",
            "Epoch 228/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1461 - accuracy: 0.9405 - val_loss: 0.4568 - val_accuracy: 0.8837\n",
            "Epoch 229/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1509 - accuracy: 0.9464 - val_loss: 0.4877 - val_accuracy: 0.8605\n",
            "Epoch 230/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1774 - accuracy: 0.9405 - val_loss: 0.5254 - val_accuracy: 0.8837\n",
            "Epoch 231/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1746 - accuracy: 0.9464 - val_loss: 0.5747 - val_accuracy: 0.8605\n",
            "Epoch 232/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1772 - accuracy: 0.9405 - val_loss: 0.5903 - val_accuracy: 0.8372\n",
            "Epoch 233/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1625 - accuracy: 0.9524 - val_loss: 0.5650 - val_accuracy: 0.8605\n",
            "Epoch 234/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1940 - accuracy: 0.9226 - val_loss: 0.5451 - val_accuracy: 0.8605\n",
            "Epoch 235/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1478 - accuracy: 0.9286 - val_loss: 0.5559 - val_accuracy: 0.8837\n",
            "Epoch 236/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1728 - accuracy: 0.9167 - val_loss: 0.6300 - val_accuracy: 0.8605\n",
            "Epoch 237/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1680 - accuracy: 0.9405 - val_loss: 0.6703 - val_accuracy: 0.8605\n",
            "Epoch 238/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1550 - accuracy: 0.9464 - val_loss: 0.5550 - val_accuracy: 0.8837\n",
            "Epoch 239/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1490 - accuracy: 0.9405 - val_loss: 0.4938 - val_accuracy: 0.8837\n",
            "Epoch 240/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1521 - accuracy: 0.9464 - val_loss: 0.5856 - val_accuracy: 0.8837\n",
            "Epoch 241/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1469 - accuracy: 0.9345 - val_loss: 0.6200 - val_accuracy: 0.8605\n",
            "Epoch 242/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1491 - accuracy: 0.9405 - val_loss: 0.6188 - val_accuracy: 0.8372\n",
            "Epoch 243/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1298 - accuracy: 0.9405 - val_loss: 0.6039 - val_accuracy: 0.8605\n",
            "Epoch 244/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1622 - accuracy: 0.9345 - val_loss: 0.6102 - val_accuracy: 0.8605\n",
            "Epoch 245/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1661 - accuracy: 0.9345 - val_loss: 0.6382 - val_accuracy: 0.8605\n",
            "Epoch 246/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1482 - accuracy: 0.9464 - val_loss: 0.6665 - val_accuracy: 0.8605\n",
            "Epoch 247/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1793 - accuracy: 0.9345 - val_loss: 0.6623 - val_accuracy: 0.8605\n",
            "Epoch 248/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2250 - accuracy: 0.9226 - val_loss: 0.6432 - val_accuracy: 0.8605\n",
            "Epoch 249/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2189 - accuracy: 0.9107 - val_loss: 0.5937 - val_accuracy: 0.8605\n",
            "Epoch 250/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1480 - accuracy: 0.9405 - val_loss: 0.6656 - val_accuracy: 0.8605\n",
            "Epoch 251/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1683 - accuracy: 0.9226 - val_loss: 0.6143 - val_accuracy: 0.8605\n",
            "Epoch 252/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1598 - accuracy: 0.9286 - val_loss: 0.5737 - val_accuracy: 0.8837\n",
            "Epoch 253/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1407 - accuracy: 0.9405 - val_loss: 0.6489 - val_accuracy: 0.8605\n",
            "Epoch 254/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1774 - accuracy: 0.9226 - val_loss: 0.6437 - val_accuracy: 0.8605\n",
            "Epoch 255/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1614 - accuracy: 0.9286 - val_loss: 0.6109 - val_accuracy: 0.8837\n",
            "Epoch 256/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1386 - accuracy: 0.9464 - val_loss: 0.6427 - val_accuracy: 0.8605\n",
            "Epoch 257/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1423 - accuracy: 0.9405 - val_loss: 0.6435 - val_accuracy: 0.8605\n",
            "Epoch 258/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1725 - accuracy: 0.9345 - val_loss: 0.6030 - val_accuracy: 0.8605\n",
            "Epoch 259/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1442 - accuracy: 0.9464 - val_loss: 0.6512 - val_accuracy: 0.8605\n",
            "Epoch 260/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1594 - accuracy: 0.9286 - val_loss: 0.6716 - val_accuracy: 0.8605\n",
            "Epoch 261/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1437 - accuracy: 0.9345 - val_loss: 0.7176 - val_accuracy: 0.8605\n",
            "Epoch 262/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1350 - accuracy: 0.9405 - val_loss: 0.7218 - val_accuracy: 0.8605\n",
            "Epoch 263/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1651 - accuracy: 0.9286 - val_loss: 0.7309 - val_accuracy: 0.8605\n",
            "Epoch 264/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1412 - accuracy: 0.9345 - val_loss: 0.7877 - val_accuracy: 0.8605\n",
            "Epoch 265/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1633 - accuracy: 0.9405 - val_loss: 0.8111 - val_accuracy: 0.8605\n",
            "Epoch 266/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1465 - accuracy: 0.9345 - val_loss: 0.6331 - val_accuracy: 0.8605\n",
            "Epoch 267/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1366 - accuracy: 0.9524 - val_loss: 0.5501 - val_accuracy: 0.8837\n",
            "Epoch 268/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1881 - accuracy: 0.9464 - val_loss: 0.5984 - val_accuracy: 0.8837\n",
            "Epoch 269/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1710 - accuracy: 0.9286 - val_loss: 0.7322 - val_accuracy: 0.8372\n",
            "Epoch 270/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1433 - accuracy: 0.9405 - val_loss: 0.7780 - val_accuracy: 0.8372\n",
            "Epoch 271/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1782 - accuracy: 0.9345 - val_loss: 0.6617 - val_accuracy: 0.8372\n",
            "Epoch 272/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1752 - accuracy: 0.9167 - val_loss: 0.6380 - val_accuracy: 0.8140\n",
            "Epoch 273/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1962 - accuracy: 0.9107 - val_loss: 0.5602 - val_accuracy: 0.8372\n",
            "Epoch 274/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1837 - accuracy: 0.9226 - val_loss: 0.5154 - val_accuracy: 0.8837\n",
            "Epoch 275/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1689 - accuracy: 0.9286 - val_loss: 0.5594 - val_accuracy: 0.8372\n",
            "Epoch 276/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1469 - accuracy: 0.9464 - val_loss: 0.5952 - val_accuracy: 0.8605\n",
            "Epoch 277/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1561 - accuracy: 0.9345 - val_loss: 0.6684 - val_accuracy: 0.8605\n",
            "Epoch 278/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1309 - accuracy: 0.9524 - val_loss: 0.8075 - val_accuracy: 0.8605\n",
            "Epoch 279/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1431 - accuracy: 0.9405 - val_loss: 0.8054 - val_accuracy: 0.8372\n",
            "Epoch 280/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1679 - accuracy: 0.9405 - val_loss: 0.7206 - val_accuracy: 0.8372\n",
            "Epoch 281/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1430 - accuracy: 0.9405 - val_loss: 0.6013 - val_accuracy: 0.8605\n",
            "Epoch 282/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1983 - accuracy: 0.9286 - val_loss: 0.5925 - val_accuracy: 0.8837\n",
            "Epoch 283/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1807 - accuracy: 0.9226 - val_loss: 0.6317 - val_accuracy: 0.8837\n",
            "Epoch 284/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1506 - accuracy: 0.9345 - val_loss: 0.7901 - val_accuracy: 0.8372\n",
            "Epoch 285/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1660 - accuracy: 0.9226 - val_loss: 0.7518 - val_accuracy: 0.8372\n",
            "Epoch 286/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1529 - accuracy: 0.9464 - val_loss: 0.6676 - val_accuracy: 0.8372\n",
            "Epoch 287/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1757 - accuracy: 0.9345 - val_loss: 0.6460 - val_accuracy: 0.8605\n",
            "Epoch 288/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1425 - accuracy: 0.9405 - val_loss: 0.6516 - val_accuracy: 0.8605\n",
            "Epoch 289/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1631 - accuracy: 0.9167 - val_loss: 0.6688 - val_accuracy: 0.8605\n",
            "Epoch 290/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1443 - accuracy: 0.9405 - val_loss: 0.8180 - val_accuracy: 0.8605\n",
            "Epoch 291/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1872 - accuracy: 0.9345 - val_loss: 1.1005 - val_accuracy: 0.8605\n",
            "Epoch 292/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1916 - accuracy: 0.9226 - val_loss: 0.9415 - val_accuracy: 0.8372\n",
            "Epoch 293/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1538 - accuracy: 0.9286 - val_loss: 0.7461 - val_accuracy: 0.8372\n",
            "Epoch 294/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1535 - accuracy: 0.9405 - val_loss: 0.6271 - val_accuracy: 0.8605\n",
            "Epoch 295/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1429 - accuracy: 0.9464 - val_loss: 0.5691 - val_accuracy: 0.8372\n",
            "Epoch 296/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1885 - accuracy: 0.9286 - val_loss: 0.5734 - val_accuracy: 0.8372\n",
            "Epoch 297/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1709 - accuracy: 0.9167 - val_loss: 0.6736 - val_accuracy: 0.8605\n",
            "Epoch 298/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1418 - accuracy: 0.9524 - val_loss: 0.6834 - val_accuracy: 0.8605\n",
            "Epoch 299/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1950 - accuracy: 0.9048 - val_loss: 0.6403 - val_accuracy: 0.8605\n",
            "Epoch 300/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1420 - accuracy: 0.9464 - val_loss: 0.7806 - val_accuracy: 0.8605\n",
            "Epoch 301/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2036 - accuracy: 0.9226 - val_loss: 0.6454 - val_accuracy: 0.8605\n",
            "Epoch 302/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1620 - accuracy: 0.9405 - val_loss: 0.5529 - val_accuracy: 0.8605\n",
            "Epoch 303/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1881 - accuracy: 0.9226 - val_loss: 0.6171 - val_accuracy: 0.8372\n",
            "Epoch 304/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1736 - accuracy: 0.9286 - val_loss: 0.7170 - val_accuracy: 0.8372\n",
            "Epoch 305/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1678 - accuracy: 0.9405 - val_loss: 0.6764 - val_accuracy: 0.8605\n",
            "Epoch 306/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1701 - accuracy: 0.9345 - val_loss: 0.6399 - val_accuracy: 0.8605\n",
            "Epoch 307/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1840 - accuracy: 0.9107 - val_loss: 0.6860 - val_accuracy: 0.8605\n",
            "Epoch 308/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2134 - accuracy: 0.9286 - val_loss: 0.6748 - val_accuracy: 0.8372\n",
            "Epoch 309/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1712 - accuracy: 0.9345 - val_loss: 0.7322 - val_accuracy: 0.8372\n",
            "Epoch 310/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1853 - accuracy: 0.8929 - val_loss: 0.7331 - val_accuracy: 0.8372\n",
            "Epoch 311/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1995 - accuracy: 0.9048 - val_loss: 0.6365 - val_accuracy: 0.8605\n",
            "Epoch 312/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1607 - accuracy: 0.9345 - val_loss: 0.5947 - val_accuracy: 0.8605\n",
            "Epoch 313/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1619 - accuracy: 0.9286 - val_loss: 0.6824 - val_accuracy: 0.8605\n",
            "Epoch 314/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1861 - accuracy: 0.9286 - val_loss: 0.6869 - val_accuracy: 0.8605\n",
            "Epoch 315/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1673 - accuracy: 0.9167 - val_loss: 0.5724 - val_accuracy: 0.8605\n",
            "Epoch 316/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1743 - accuracy: 0.9167 - val_loss: 0.5437 - val_accuracy: 0.8837\n",
            "Epoch 317/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1834 - accuracy: 0.9107 - val_loss: 0.5705 - val_accuracy: 0.8837\n",
            "Epoch 318/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1449 - accuracy: 0.9464 - val_loss: 0.5986 - val_accuracy: 0.8605\n",
            "Epoch 319/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1728 - accuracy: 0.9405 - val_loss: 0.5984 - val_accuracy: 0.8605\n",
            "Epoch 320/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1516 - accuracy: 0.9405 - val_loss: 0.6723 - val_accuracy: 0.8605\n",
            "Epoch 321/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1431 - accuracy: 0.9524 - val_loss: 0.6629 - val_accuracy: 0.8605\n",
            "Epoch 322/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1417 - accuracy: 0.9524 - val_loss: 0.6366 - val_accuracy: 0.8605\n",
            "Epoch 323/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1336 - accuracy: 0.9405 - val_loss: 0.6465 - val_accuracy: 0.8605\n",
            "Epoch 324/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1909 - accuracy: 0.9345 - val_loss: 0.6238 - val_accuracy: 0.8605\n",
            "Epoch 325/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1706 - accuracy: 0.9345 - val_loss: 0.5582 - val_accuracy: 0.8837\n",
            "Epoch 326/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1540 - accuracy: 0.9464 - val_loss: 0.4919 - val_accuracy: 0.8837\n",
            "Epoch 327/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1536 - accuracy: 0.9405 - val_loss: 0.4955 - val_accuracy: 0.8837\n",
            "Epoch 328/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1473 - accuracy: 0.9405 - val_loss: 0.5423 - val_accuracy: 0.8837\n",
            "Epoch 329/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1767 - accuracy: 0.9345 - val_loss: 0.6305 - val_accuracy: 0.8605\n",
            "Epoch 330/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1455 - accuracy: 0.9405 - val_loss: 0.7266 - val_accuracy: 0.8605\n",
            "Epoch 331/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1526 - accuracy: 0.9405 - val_loss: 0.7116 - val_accuracy: 0.8605\n",
            "Epoch 332/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1626 - accuracy: 0.9345 - val_loss: 0.7216 - val_accuracy: 0.8605\n",
            "Epoch 333/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1633 - accuracy: 0.9345 - val_loss: 0.6740 - val_accuracy: 0.8605\n",
            "Epoch 334/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1560 - accuracy: 0.9345 - val_loss: 0.6760 - val_accuracy: 0.8605\n",
            "Epoch 335/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1585 - accuracy: 0.9405 - val_loss: 0.7276 - val_accuracy: 0.8605\n",
            "Epoch 336/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1473 - accuracy: 0.9464 - val_loss: 0.7703 - val_accuracy: 0.8605\n",
            "Epoch 337/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1409 - accuracy: 0.9524 - val_loss: 0.8850 - val_accuracy: 0.8605\n",
            "Epoch 338/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1490 - accuracy: 0.9345 - val_loss: 0.6487 - val_accuracy: 0.8605\n",
            "Epoch 339/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1677 - accuracy: 0.9286 - val_loss: 0.6609 - val_accuracy: 0.8605\n",
            "Epoch 340/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1643 - accuracy: 0.9286 - val_loss: 0.7637 - val_accuracy: 0.8372\n",
            "Epoch 341/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1766 - accuracy: 0.9167 - val_loss: 0.7326 - val_accuracy: 0.8372\n",
            "Epoch 342/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1560 - accuracy: 0.9524 - val_loss: 0.6705 - val_accuracy: 0.8605\n",
            "Epoch 343/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1689 - accuracy: 0.9226 - val_loss: 0.6137 - val_accuracy: 0.8837\n",
            "Epoch 344/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1258 - accuracy: 0.9464 - val_loss: 0.6512 - val_accuracy: 0.8605\n",
            "Epoch 345/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1444 - accuracy: 0.9226 - val_loss: 0.7775 - val_accuracy: 0.8605\n",
            "Epoch 346/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1734 - accuracy: 0.9226 - val_loss: 0.7991 - val_accuracy: 0.8605\n",
            "Epoch 347/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1488 - accuracy: 0.9286 - val_loss: 0.8410 - val_accuracy: 0.8372\n",
            "Epoch 348/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1537 - accuracy: 0.9286 - val_loss: 0.7849 - val_accuracy: 0.8605\n",
            "Epoch 349/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1509 - accuracy: 0.9464 - val_loss: 0.7008 - val_accuracy: 0.8605\n",
            "Epoch 350/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1664 - accuracy: 0.9345 - val_loss: 0.6764 - val_accuracy: 0.8605\n",
            "Epoch 351/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1412 - accuracy: 0.9345 - val_loss: 0.5973 - val_accuracy: 0.8837\n",
            "Epoch 352/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1513 - accuracy: 0.9345 - val_loss: 0.6064 - val_accuracy: 0.8605\n",
            "Epoch 353/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1454 - accuracy: 0.9464 - val_loss: 0.5913 - val_accuracy: 0.8837\n",
            "Epoch 354/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1444 - accuracy: 0.9405 - val_loss: 0.6292 - val_accuracy: 0.8837\n",
            "Epoch 355/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1329 - accuracy: 0.9583 - val_loss: 0.6671 - val_accuracy: 0.8837\n",
            "Epoch 356/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1537 - accuracy: 0.9345 - val_loss: 0.7243 - val_accuracy: 0.9070\n",
            "Epoch 357/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.2287 - accuracy: 0.9286 - val_loss: 0.8068 - val_accuracy: 0.8605\n",
            "Epoch 358/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1713 - accuracy: 0.9226 - val_loss: 0.7859 - val_accuracy: 0.8372\n",
            "Epoch 359/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.2428 - accuracy: 0.8810 - val_loss: 0.7740 - val_accuracy: 0.8372\n",
            "Epoch 360/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1889 - accuracy: 0.9107 - val_loss: 0.7626 - val_accuracy: 0.8605\n",
            "Epoch 361/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1694 - accuracy: 0.9286 - val_loss: 0.6817 - val_accuracy: 0.8605\n",
            "Epoch 362/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1795 - accuracy: 0.9345 - val_loss: 0.7675 - val_accuracy: 0.8605\n",
            "Epoch 363/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1720 - accuracy: 0.9286 - val_loss: 0.8509 - val_accuracy: 0.8605\n",
            "Epoch 364/500\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.1708 - accuracy: 0.9226 - val_loss: 0.8574 - val_accuracy: 0.8605\n",
            "Epoch 365/500\n",
            "6/6 [==============================] - 0s 29ms/step - loss: 0.1475 - accuracy: 0.9405 - val_loss: 0.9409 - val_accuracy: 0.8605\n",
            "Epoch 366/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1684 - accuracy: 0.9167 - val_loss: 0.8870 - val_accuracy: 0.8605\n",
            "Epoch 367/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1556 - accuracy: 0.9226 - val_loss: 0.7847 - val_accuracy: 0.8372\n",
            "Epoch 368/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1820 - accuracy: 0.9167 - val_loss: 0.6746 - val_accuracy: 0.8605\n",
            "Epoch 369/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1727 - accuracy: 0.9226 - val_loss: 0.6489 - val_accuracy: 0.8605\n",
            "Epoch 370/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1999 - accuracy: 0.9226 - val_loss: 0.6499 - val_accuracy: 0.8605\n",
            "Epoch 371/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1887 - accuracy: 0.9226 - val_loss: 0.7341 - val_accuracy: 0.8605\n",
            "Epoch 372/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1437 - accuracy: 0.9464 - val_loss: 0.8374 - val_accuracy: 0.8605\n",
            "Epoch 373/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1944 - accuracy: 0.9167 - val_loss: 0.6065 - val_accuracy: 0.8605\n",
            "Epoch 374/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1635 - accuracy: 0.9226 - val_loss: 0.6034 - val_accuracy: 0.8605\n",
            "Epoch 375/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.1607 - accuracy: 0.9286 - val_loss: 0.6313 - val_accuracy: 0.8605\n",
            "Epoch 376/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.1692 - accuracy: 0.9167 - val_loss: 0.8015 - val_accuracy: 0.8605\n",
            "Epoch 377/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1794 - accuracy: 0.9345 - val_loss: 0.6205 - val_accuracy: 0.8605\n",
            "Epoch 378/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.1648 - accuracy: 0.9345 - val_loss: 0.6076 - val_accuracy: 0.8605\n",
            "Epoch 379/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1925 - accuracy: 0.9167 - val_loss: 0.6076 - val_accuracy: 0.8605\n",
            "Epoch 380/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1697 - accuracy: 0.9345 - val_loss: 0.6713 - val_accuracy: 0.8605\n",
            "Epoch 381/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1802 - accuracy: 0.9286 - val_loss: 0.6880 - val_accuracy: 0.8605\n",
            "Epoch 382/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1987 - accuracy: 0.9286 - val_loss: 0.7269 - val_accuracy: 0.8605\n",
            "Epoch 383/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1793 - accuracy: 0.9167 - val_loss: 0.7754 - val_accuracy: 0.8605\n",
            "Epoch 384/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1737 - accuracy: 0.9286 - val_loss: 0.6497 - val_accuracy: 0.8605\n",
            "Epoch 385/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1642 - accuracy: 0.9167 - val_loss: 0.5841 - val_accuracy: 0.8605\n",
            "Epoch 386/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.1585 - accuracy: 0.9286 - val_loss: 0.6289 - val_accuracy: 0.8605\n",
            "Epoch 387/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.1406 - accuracy: 0.9524 - val_loss: 0.7817 - val_accuracy: 0.8605\n",
            "Epoch 388/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1760 - accuracy: 0.9107 - val_loss: 0.8866 - val_accuracy: 0.8605\n",
            "Epoch 389/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1985 - accuracy: 0.9107 - val_loss: 0.7578 - val_accuracy: 0.8605\n",
            "Epoch 390/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1389 - accuracy: 0.9405 - val_loss: 0.7538 - val_accuracy: 0.8605\n",
            "Epoch 391/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1610 - accuracy: 0.9226 - val_loss: 0.8504 - val_accuracy: 0.8605\n",
            "Epoch 392/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.1907 - accuracy: 0.9286 - val_loss: 0.8906 - val_accuracy: 0.8605\n",
            "Epoch 393/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1804 - accuracy: 0.9345 - val_loss: 0.7226 - val_accuracy: 0.8605\n",
            "Epoch 394/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1586 - accuracy: 0.9286 - val_loss: 0.6528 - val_accuracy: 0.8837\n",
            "Epoch 395/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.1654 - accuracy: 0.9167 - val_loss: 0.6335 - val_accuracy: 0.8837\n",
            "Epoch 396/500\n",
            "6/6 [==============================] - 0s 21ms/step - loss: 0.1389 - accuracy: 0.9405 - val_loss: 0.6648 - val_accuracy: 0.8372\n",
            "Epoch 397/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.1851 - accuracy: 0.9167 - val_loss: 0.7739 - val_accuracy: 0.8605\n",
            "Epoch 398/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2008 - accuracy: 0.9226 - val_loss: 0.7521 - val_accuracy: 0.8372\n",
            "Epoch 399/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.2016 - accuracy: 0.9048 - val_loss: 0.6995 - val_accuracy: 0.8605\n",
            "Epoch 400/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1322 - accuracy: 0.9524 - val_loss: 0.7033 - val_accuracy: 0.8837\n",
            "Epoch 401/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1375 - accuracy: 0.9286 - val_loss: 0.7147 - val_accuracy: 0.8837\n",
            "Epoch 402/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1748 - accuracy: 0.9167 - val_loss: 0.7726 - val_accuracy: 0.8605\n",
            "Epoch 403/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1403 - accuracy: 0.9226 - val_loss: 0.7480 - val_accuracy: 0.8837\n",
            "Epoch 404/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1569 - accuracy: 0.9405 - val_loss: 0.7336 - val_accuracy: 0.8605\n",
            "Epoch 405/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1474 - accuracy: 0.9464 - val_loss: 0.7719 - val_accuracy: 0.8605\n",
            "Epoch 406/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1600 - accuracy: 0.9405 - val_loss: 0.9324 - val_accuracy: 0.8372\n",
            "Epoch 407/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1686 - accuracy: 0.9345 - val_loss: 1.0547 - val_accuracy: 0.8372\n",
            "Epoch 408/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1428 - accuracy: 0.9464 - val_loss: 1.0729 - val_accuracy: 0.8372\n",
            "Epoch 409/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1347 - accuracy: 0.9524 - val_loss: 1.1461 - val_accuracy: 0.8605\n",
            "Epoch 410/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1842 - accuracy: 0.9167 - val_loss: 0.9906 - val_accuracy: 0.8605\n",
            "Epoch 411/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1689 - accuracy: 0.9107 - val_loss: 0.8355 - val_accuracy: 0.8605\n",
            "Epoch 412/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1714 - accuracy: 0.9286 - val_loss: 0.8124 - val_accuracy: 0.8605\n",
            "Epoch 413/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1390 - accuracy: 0.9345 - val_loss: 1.0215 - val_accuracy: 0.8605\n",
            "Epoch 414/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1719 - accuracy: 0.9167 - val_loss: 1.0664 - val_accuracy: 0.8605\n",
            "Epoch 415/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1592 - accuracy: 0.9226 - val_loss: 1.0654 - val_accuracy: 0.8605\n",
            "Epoch 416/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1656 - accuracy: 0.9286 - val_loss: 0.9200 - val_accuracy: 0.8605\n",
            "Epoch 417/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1374 - accuracy: 0.9464 - val_loss: 0.9823 - val_accuracy: 0.8140\n",
            "Epoch 418/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1670 - accuracy: 0.9048 - val_loss: 0.9303 - val_accuracy: 0.8140\n",
            "Epoch 419/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1384 - accuracy: 0.9405 - val_loss: 0.9518 - val_accuracy: 0.8140\n",
            "Epoch 420/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1905 - accuracy: 0.9286 - val_loss: 0.9377 - val_accuracy: 0.8140\n",
            "Epoch 421/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1751 - accuracy: 0.9405 - val_loss: 0.8895 - val_accuracy: 0.8372\n",
            "Epoch 422/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1520 - accuracy: 0.9524 - val_loss: 0.8487 - val_accuracy: 0.8372\n",
            "Epoch 423/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1620 - accuracy: 0.9286 - val_loss: 0.8287 - val_accuracy: 0.8372\n",
            "Epoch 424/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1495 - accuracy: 0.9286 - val_loss: 0.8322 - val_accuracy: 0.8372\n",
            "Epoch 425/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1925 - accuracy: 0.9286 - val_loss: 0.8524 - val_accuracy: 0.8372\n",
            "Epoch 426/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2050 - accuracy: 0.9226 - val_loss: 0.9108 - val_accuracy: 0.8605\n",
            "Epoch 427/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1738 - accuracy: 0.9405 - val_loss: 0.9641 - val_accuracy: 0.8605\n",
            "Epoch 428/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1602 - accuracy: 0.9286 - val_loss: 0.7765 - val_accuracy: 0.8605\n",
            "Epoch 429/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1433 - accuracy: 0.9345 - val_loss: 0.7159 - val_accuracy: 0.8837\n",
            "Epoch 430/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1657 - accuracy: 0.9405 - val_loss: 0.7561 - val_accuracy: 0.8605\n",
            "Epoch 431/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1611 - accuracy: 0.9345 - val_loss: 0.7268 - val_accuracy: 0.8605\n",
            "Epoch 432/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1719 - accuracy: 0.9107 - val_loss: 0.8202 - val_accuracy: 0.8605\n",
            "Epoch 433/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1426 - accuracy: 0.9345 - val_loss: 0.7494 - val_accuracy: 0.8605\n",
            "Epoch 434/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1549 - accuracy: 0.9405 - val_loss: 0.7284 - val_accuracy: 0.8605\n",
            "Epoch 435/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1399 - accuracy: 0.9464 - val_loss: 0.7311 - val_accuracy: 0.8605\n",
            "Epoch 436/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1483 - accuracy: 0.9405 - val_loss: 0.8035 - val_accuracy: 0.7674\n",
            "Epoch 437/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1501 - accuracy: 0.9643 - val_loss: 0.9204 - val_accuracy: 0.7442\n",
            "Epoch 438/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1935 - accuracy: 0.9286 - val_loss: 0.8921 - val_accuracy: 0.7674\n",
            "Epoch 439/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1450 - accuracy: 0.9405 - val_loss: 0.8512 - val_accuracy: 0.8372\n",
            "Epoch 440/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1423 - accuracy: 0.9345 - val_loss: 0.9049 - val_accuracy: 0.8605\n",
            "Epoch 441/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1379 - accuracy: 0.9524 - val_loss: 0.8020 - val_accuracy: 0.8605\n",
            "Epoch 442/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1461 - accuracy: 0.9345 - val_loss: 0.8126 - val_accuracy: 0.8605\n",
            "Epoch 443/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1425 - accuracy: 0.9345 - val_loss: 0.9478 - val_accuracy: 0.8372\n",
            "Epoch 444/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1821 - accuracy: 0.9107 - val_loss: 1.0754 - val_accuracy: 0.8605\n",
            "Epoch 445/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2103 - accuracy: 0.9226 - val_loss: 1.0424 - val_accuracy: 0.8605\n",
            "Epoch 446/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1696 - accuracy: 0.9167 - val_loss: 0.8916 - val_accuracy: 0.8605\n",
            "Epoch 447/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1574 - accuracy: 0.9226 - val_loss: 0.8149 - val_accuracy: 0.8605\n",
            "Epoch 448/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1374 - accuracy: 0.9464 - val_loss: 0.7792 - val_accuracy: 0.8605\n",
            "Epoch 449/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1639 - accuracy: 0.9286 - val_loss: 0.7542 - val_accuracy: 0.8605\n",
            "Epoch 450/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2225 - accuracy: 0.8929 - val_loss: 0.7524 - val_accuracy: 0.8605\n",
            "Epoch 451/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1556 - accuracy: 0.9464 - val_loss: 0.7610 - val_accuracy: 0.8837\n",
            "Epoch 452/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1337 - accuracy: 0.9524 - val_loss: 0.7825 - val_accuracy: 0.8837\n",
            "Epoch 453/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1508 - accuracy: 0.9464 - val_loss: 0.7581 - val_accuracy: 0.8605\n",
            "Epoch 454/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1589 - accuracy: 0.9345 - val_loss: 0.7880 - val_accuracy: 0.8605\n",
            "Epoch 455/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1484 - accuracy: 0.9464 - val_loss: 0.8630 - val_accuracy: 0.8605\n",
            "Epoch 456/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1484 - accuracy: 0.9405 - val_loss: 0.9291 - val_accuracy: 0.8605\n",
            "Epoch 457/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1379 - accuracy: 0.9345 - val_loss: 1.0457 - val_accuracy: 0.8605\n",
            "Epoch 458/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1469 - accuracy: 0.9345 - val_loss: 1.0875 - val_accuracy: 0.8605\n",
            "Epoch 459/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2179 - accuracy: 0.9226 - val_loss: 1.0060 - val_accuracy: 0.8605\n",
            "Epoch 460/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1624 - accuracy: 0.9286 - val_loss: 1.0429 - val_accuracy: 0.8605\n",
            "Epoch 461/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1686 - accuracy: 0.9286 - val_loss: 0.9964 - val_accuracy: 0.8605\n",
            "Epoch 462/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1704 - accuracy: 0.9345 - val_loss: 0.8067 - val_accuracy: 0.8837\n",
            "Epoch 463/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1443 - accuracy: 0.9464 - val_loss: 0.7081 - val_accuracy: 0.8837\n",
            "Epoch 464/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1724 - accuracy: 0.9226 - val_loss: 0.7296 - val_accuracy: 0.8837\n",
            "Epoch 465/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1715 - accuracy: 0.9286 - val_loss: 0.7609 - val_accuracy: 0.8837\n",
            "Epoch 466/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1410 - accuracy: 0.9464 - val_loss: 0.7914 - val_accuracy: 0.8837\n",
            "Epoch 467/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1437 - accuracy: 0.9464 - val_loss: 0.7593 - val_accuracy: 0.8837\n",
            "Epoch 468/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1444 - accuracy: 0.9405 - val_loss: 0.7840 - val_accuracy: 0.8837\n",
            "Epoch 469/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1478 - accuracy: 0.9405 - val_loss: 0.8463 - val_accuracy: 0.8605\n",
            "Epoch 470/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1355 - accuracy: 0.9464 - val_loss: 0.9256 - val_accuracy: 0.8605\n",
            "Epoch 471/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1660 - accuracy: 0.9345 - val_loss: 0.9761 - val_accuracy: 0.8605\n",
            "Epoch 472/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1475 - accuracy: 0.9464 - val_loss: 0.9366 - val_accuracy: 0.8372\n",
            "Epoch 473/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1434 - accuracy: 0.9286 - val_loss: 0.8611 - val_accuracy: 0.8605\n",
            "Epoch 474/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1682 - accuracy: 0.9226 - val_loss: 0.8344 - val_accuracy: 0.8605\n",
            "Epoch 475/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1422 - accuracy: 0.9524 - val_loss: 0.8664 - val_accuracy: 0.8605\n",
            "Epoch 476/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1622 - accuracy: 0.9464 - val_loss: 0.9954 - val_accuracy: 0.8605\n",
            "Epoch 477/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1312 - accuracy: 0.9345 - val_loss: 1.0014 - val_accuracy: 0.8605\n",
            "Epoch 478/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1523 - accuracy: 0.9345 - val_loss: 1.0038 - val_accuracy: 0.8372\n",
            "Epoch 479/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1237 - accuracy: 0.9524 - val_loss: 1.0180 - val_accuracy: 0.8372\n",
            "Epoch 480/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1704 - accuracy: 0.9286 - val_loss: 1.0456 - val_accuracy: 0.8605\n",
            "Epoch 481/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1797 - accuracy: 0.9107 - val_loss: 0.9100 - val_accuracy: 0.8372\n",
            "Epoch 482/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1499 - accuracy: 0.9345 - val_loss: 0.9132 - val_accuracy: 0.8372\n",
            "Epoch 483/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1429 - accuracy: 0.9464 - val_loss: 0.9689 - val_accuracy: 0.8140\n",
            "Epoch 484/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1303 - accuracy: 0.9345 - val_loss: 1.0399 - val_accuracy: 0.7907\n",
            "Epoch 485/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1584 - accuracy: 0.9167 - val_loss: 1.1016 - val_accuracy: 0.8140\n",
            "Epoch 486/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1208 - accuracy: 0.9464 - val_loss: 1.2191 - val_accuracy: 0.8140\n",
            "Epoch 487/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1538 - accuracy: 0.9226 - val_loss: 1.2760 - val_accuracy: 0.8605\n",
            "Epoch 488/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1426 - accuracy: 0.9524 - val_loss: 1.0220 - val_accuracy: 0.8372\n",
            "Epoch 489/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1604 - accuracy: 0.9286 - val_loss: 0.9910 - val_accuracy: 0.8372\n",
            "Epoch 490/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1447 - accuracy: 0.9345 - val_loss: 1.1170 - val_accuracy: 0.8140\n",
            "Epoch 491/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1465 - accuracy: 0.9464 - val_loss: 1.2281 - val_accuracy: 0.8140\n",
            "Epoch 492/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1558 - accuracy: 0.9345 - val_loss: 1.1401 - val_accuracy: 0.8605\n",
            "Epoch 493/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1406 - accuracy: 0.9464 - val_loss: 0.8660 - val_accuracy: 0.8140\n",
            "Epoch 494/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1652 - accuracy: 0.9345 - val_loss: 0.8334 - val_accuracy: 0.8372\n",
            "Epoch 495/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1885 - accuracy: 0.9226 - val_loss: 0.8453 - val_accuracy: 0.8372\n",
            "Epoch 496/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1843 - accuracy: 0.9107 - val_loss: 0.8248 - val_accuracy: 0.8605\n",
            "Epoch 497/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1519 - accuracy: 0.9226 - val_loss: 0.7789 - val_accuracy: 0.8605\n",
            "Epoch 498/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1953 - accuracy: 0.9107 - val_loss: 0.7174 - val_accuracy: 0.8837\n",
            "Epoch 499/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2119 - accuracy: 0.9167 - val_loss: 0.6933 - val_accuracy: 0.8837\n",
            "Epoch 500/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1723 - accuracy: 0.9405 - val_loss: 0.7408 - val_accuracy: 0.8837\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(X, y.astype('int'), clf=model, legend=2)\n",
        "plt.xlim(-0.7,0.5)\n",
        "plt.ylim(-0.8,0.8)\n",
        "plt.title('p = 0.5')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "IfZwa5IVz1xS",
        "outputId": "995ebf2e-a890-4a61-c442-337d0064a72e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9600/9600 [==============================] - 14s 1ms/step\n"
          ]
        },
        {
          "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": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "k61Sm-2W0DQi",
        "outputId": "5d1cf703-1184-449c-b4c5-da1281ec142f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ab4fe588880>]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "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": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "di2HOJBZ0Ib-",
        "outputId": "d09c7a5d-a00d-4239-bb22-cd2e0fe9a645"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ab4fe634220>]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": "poeo3a1J0LiH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}