dropout_classification_example.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "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": null,
      "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": 265
        },
        "id": "5DtHd0FlpjtK",
        "outputId": "cc8cece5-1f5b-4a27-a2a8-204fbd5d1be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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ipwMTMLl596vqNBEZC3yhqi/1dI5FAbXr0car0ptbNgS2Rcou65MYtynPfwJa7gS7BjzLI6UXIMEduz6W3Zzp7AGIQWoeGn4MKTmp2zaiLSChwmf1SADHDCJIr1J7E8FsljpsdvYwRNMVjEx0OC3L3LMXdxEB/0YFsku5/Yz7mPDAewhgeS0sy+L6N8ewxsb5Oewz7jieLfbZhDcfeh/bttn5iG3YZPcNB4Sm06KG9EfRTD6MGDFCv/hi0XgJUE2hNXtA6g9aX+stsKqQIW91fxMtT+yWB6DpP2RWWwZNtkdgmy6NpbGJ6PxT01IC7fBtgDV4fNfti7wJTVeZFaMEIHQ4UnpWwSpiAez556Qftm1z0kNQciZWybEFm8dx7sYrIfwUmYVpRlnUKj27V+c2D/tHofm29AO1GErOMBvWA8AhfvriZK494taFq/MFVAwr54nZ4/A4hGtceoaIfKmqjiFzt9K2EMQ/MTomtN1cso0EQfT1Xp1aNQXNd5Lp7AGiaNO/uz6gNQg0e5PMnOu6IJnGPoeGf6WFu1JmFRp+BG1yrM3rNlJ2RXpV2rYJ+B6miUkvI6XnQWCrzLkDOyAlZ/T63BoeD003maYrJM2/zTeb4wOA1+59O8vZA0TDMX6clL9ekEthcNUyC0HyF+dqRw2jiZ/p1QW+tjjovaRJzez6eN7VwTvcfCbapuiFuuU8tfl2HGWDw+PRkrO7lYbohFglSOVDaPJ3SM0G72qIZ1hBxu50bgkgFXehyVnme+5duSBx7wVoam767SUJge0zu1S1ODzsNQItd0Bx7/bfzYd41KkZvAkbJWI5FhYuvYa7wi8E3lWMfG57pKj3JYSlhJxPFM8KXR9OBKm4Ny0dEErHwENQeh7i37jr9uV86HjMfkOBEe+KSGCrnM5e1UbjX6KxD8x+RUHnHp6eu3DO3g6/iFbvhDbdiDb9G63ZG7v5jjYXzMtxY3W/aBy1Z8fDtyZY7LBBq7DW5vmlrLoUDneFXwj8W4K1VDqGv2BF4zHOMrh7r04tYqElp0PzLe1W+sG0omQ3xvQshVS9bCR67Xrwrp1T8bFTvGtDvBpo53xE0sqNfYcmfkbrj02rfQpoEi29CGsArISdULsOGi8hS7Su+R40sIPRzPcs7/xQ9QwfEDH8HQ/fmnce/4jvP/+ZaHMUr9+Lx2Nx/iNn4A903IO4fl4DH4z/lOb5LWy483qstelqA+IzLcq4m7YFQu35aOPVEH0Dk6WzHVI2pg+zdJ5sk6WzAlJ6PhLcodfn7tS2xHS09hAywzohKDkNq+TEvrNDU2j11g5vFUFk8GOIb90+syVfNPwM2nSlQ8jOgqJjscrOQ6Nvo/PPJvP7G4Tym7FCO/ehtbmxbZvJb3zN5Ne/onxIOTsftS1LrdhxBfQXb37D5fvfgNpKIp4kEPKz6R4bcdHjZ2JZbmCiIzratHUdvkuvo/Fv0KbrITkdrCooPgUJ7b9wtaaagNi7kPzVhMcCOxQ0gwfM5rHOP8XseWRgQWgUVvnVBZ2vEGj4aVO85ejwR2OVnW+ui71vNuhTM83DvuQsJLh93xtcIOKxBActdTwtDZmyzMHiAOc9eDpbj9qsnyxbNOjI4bshHZdex8gYP+54TlM1aN1BJnSkEbMfYQ2GwU8hVmXhjNDGHCfsLN171ThE30AT35kN0uBe3S44U7sRjbwEqT8Q33oQ3CV/pc3A9tA41uGEHwm1hgolsB0S2K5b9g00Is0R3nrkQ+xUdivGaEuMCQ++5zr8HuA6fJceocnfjG5MqgYJbJt2aB3HZjPub7wCUn+xMKVVWyAVQxuvQgZ1I60UUI1A5FU0Mc1k64T2Bv8I08ovixASbA19qF2H1h5oerxqGCUETf+GweMR78pdsyPxM1p3aHreCCpFJl9+8NOIVd7p/eKpQsvGQOOVGG0bG/BB0ZEDMgTVU568/nkeHfsMCMTCzo3oxXJj+D3Bdfgu3caOvAEN52GcdRKNvgnhB6HysbxWsaoKsXfIrF/AfB19q1s2aWouWjsK7GYgDITQ5luRwU9DyT/SNQtRQM3bhHc1CO7Ren/TvyH1PzL04DWKNlyEDH6ya7Y0nJcuYEuHTTUMqT/RptuQ8jF5jWEVHYT6N4fYBPPgCOyA+Nbokh2LAh8/P5HHrnyWWMTZ0YMJ6ex6zKIbqhoIuA7fpVuoxqDxQjI3C8OQ+BENP4MUH5br1nxn6N5djdeY1fnCGoKI6e/aOAar8iHUv4GReLYbkODuENo78+EUfYPsB5Aa9U07nHfdgNqNkPzJ4XMkIPYakJ/DB5PuibdrzUwWNcbf8CLRcHaBFoDHa+H1+9hyv03Zct9N+tiyxQvX4bt0j8RUnPVrohB9BfJw+CKCBrY3G7YZRV5eCO7UPbti77UbC4yI2kRUU4h/407qCTr4k+iSRo1F7odWppyA2vNNw/To22CVIUVHQXC3JSoFcf7cBsfj3oCXfU4byY6Hbc1qG3YtpOaSjevwXbqHhMjUTG97Lv/qWSm7DK2dZiQBtAUoAk8lUpr/CjhzQE8OP2uRU2CtLaF9IPwYmZo8HvBvhkj+fVTFKkF9G0LiSzIfQIF0dyqD2s1o7X6QqjZzpkAbv4fYu6hda94SvCsjJWci/sVXUXyDndblzQffI5XM/J0KFgU44boj8HhdzZ1C4Ca0LqaoxrGbbsOetyX23BHY8881JfqFwrsOyCCHEyGk6NC8hxHPUGTIm0jZ1UjJGcig65CqCYhncPfsCu4NtN809kJgp7xUJKXkTKMHv/BPQ8AqR8qv7bIpMuhGsIYaQTP85kHoWwcpOW3hNRp+ClK1ZDxgNALRl9IaTdXm7aTuWDT2YZdtWFQ4YswBFJUV4fW1OvZAkZ/Tbh3tOvsC4ubhL6bY9SdC7HNaY+wesCqMM7VKO71f1TarzOgrmDTAUUhg08xrEj+hdUdhKkHVtMcrOsxIM/dTOELtZrTuSNOfVVMgXrCGIYMfzyvNU+OT0bpjyVK+LLscq2hU1+3RBMQ+NHr9vrXBt1HG98auOxbiH+c3mGdFrCFvZs9h1xmxtMRU8K6BFB3aqY6QqvL5K1/y7hMf4/F62OXo7dhgh7/1axipZk4dT934Il+/+x3DVhjCQefuw7pbr9Vv9iyquIVXSxia+AmtPYAs0TIJQcnZWJ2IoKkqOv8fEP8o3clJgCAUHYlV9q921ybSK9H54N8Y8Sxb0M/SHVQVEpMh8RN4VwL/5nlrxNu1B0Hi6+wT1mBkyCc91prPmq/hYog8S87wWDtk2A8ZNmjyD/Oz1gjmIeUH8SGVjyM+Z2epqlx7xG189tLkjKYie5y0MyfflJ9A3tuPfcgDFz9B9exahi1fxXHXHs52B2+Z170uvYsrj7ykkfzBxLLboxFITOn8/vjnEP8w7ezBBMUjEH4ITf6RcamIzxT+hPYdEM4e0gJw/k2Q4iOQwJZdc9LJn52P240OVbo9x/SKzbMQS8qzPos2XZMuKlvwRhI37RUbL805zHcf/5Dh7MEUNb1815vM+tGpc1gmbz/6Af85aRzz/qhBbeWv36u56bj/8v74T/L7HC79huvwF0c8w3HeufSDZ5VOb9fY+zkklyX/8MOiirW083Hx90oHK/GtCeU3gpSnxw+AtQzQfoM4BMXHZQ8Q+xTHt4PEt6Zi2IGJr33lnAKpyhcTvunU5vsveiKrMCoWjnP/xU90eq9L/+I6/MUR39+NimL7JCzxIUUHd36/lGTfC4CV3oDse1QV1UivS/5K6ZlAe7lp42wL3poxjRXaFRn6GVI5HhnyBjLkPSg5Md1IPGTmLzoKKXYQm8uZOeQlVxJecVkIry/7nOWxCJV23LxBVameXet4bu7M6g7vdel/XIe/GCIiSOVDENiOhX/43jWRykcRT8cqhQAS2of2ueLmBBDoep/cnmKHn0Krt0DnbojO2wS75cFec/wS3BXKLgGpAHzploHHI8Wn9sp8C+cVL+JbA/Esi4hglZyODJ2IDH4FGTYRq+wc59BU0YGYnrpt8UNwj5yhrB0O2xrL43xuq/06LmwSEaqWc978Hrp8VYf3uvQ/rsMvAKqKxieh4SfR+FcDovGEWBVYFf9Fhk1Bhk7CqnoJ8a2T373e5aHsaiCYbrBi/pNBd3dbRKy72OEXoenq1upZbYCmW0wf117CKjrQrLiHfoYMnYxVckZe+wCqKdRuMG0nC4BIwDRV6SD/X0rOBP9mmJ9VMRAC39+Qstx1DMNWGMJ5D5xGoMhPUVmIorIQodIQVzx/HiWDOn+DG33VoQSKMh8ygSI/x17d0+pql97GzdLpIWo3pNMA/wC1TTWmZzWk8oE+d46FRu1ms4Er3nSmi0Pnol7Grt7BtCxsj1RgDZvY5/Y4oapoy73Qcjdo1IRiOmkkrslfIT4JrHLTtrALRV3O480wG86eFUxjlDwIN0X4+t3v8HgtNthxXfzBPDePgbce+YAHLnmSmtm1DBk+mGOvOYwdD9u6u+a7FBA3LbMXseefk9ZfaavE6E9rrF/RX2YtNth/rUPm93YBggybVnDd/O5gtzwITbeQ2Vs2BGWXYBUdmHGtqqKNl0PkOUDS2VQepPJBxPe3PrO5pzTPbyGVTFE2uHSJkoBYFHDTMnsJVXVw9gBxUynZJzbYaOxD7IZLsZtuNCu9xYlcfXmtpRydvab+wm6+E7vhYjTycs5MlYLSfBdZjcSJQNveswuIvQXRFzBplNF0E/pGtP4kU+w2wKmeXcu/dricA5c6nkOWO4mTNziXX775vb/NcsmTgjh8ERkpIj+KyAwRucDh/NkiMl1EporIOyLS9e7aAxIlW6hrwSmnVWmBZ1cbnX+aKZKKPAkt96M1+2OHn+71ubuC2s3YLQ9i15+C3XhdVi5/R0jZeWSnKAah9F9Z12p8Mlqzq3HAkafRhjFo7f5ZzcpVo9gt47HrT8VuGIsmcuTe54GqDVrvfNLOzlrR8HjnlFcNQ+LbbtvRF6RSKc7aegzffvQ9yXiSZDzJr1Nncva2l9JY29ThvbZtM++Paprnd62Woam+mXl/9Kwhu6oyb1ZNpzYuCfT4fVhMrtqdwM7AbGCyiLykqtPbXDYFGKGqYRE5BbgByCM/cGAjYqH+TSE+kcxcaAsC2/a+AbF3IPYZRvcdzMMnBY1j0eCuiFXW+zZ0gtp1aM2+phKXKOBDw09AxTgk0HnnIglsBxW3o003Q/J38CyLlJ6FBHfJnEdt09s1w5mGITkTbXkAKT0jbU+zaXCSmoNZlXvQyDNo+Y1YoV27/PlELNSznPM+g2fF7GM53ziETMG2gceXb06lsa4pqxtVMpHizYff54Cz9nK8b+JrX3HLiXfTXN+CbdtsuNN6nP/wGZRW5N7jaqxt4rqjbufrd75FPBZllSWcfe8pbLzr37tk8zfvT+PG0XdSP68BTdmss9WaXPTYmVQMc9KBWvwpxAp/E2CGqv6q5v35SWCftheo6nuqC8s2PweWK8C8AwIpGwtSRmvudsho1pRd0utza/Q1Wp19W6O8EP+s1+fPB226M51hs0DmId39qeH8vFdtEtgWq+olrKWmYg15PcvZA5D63VTDZhGD6Kut9oQfSTvnBQ+GlLGt8SIjE5GDebNquOWkcRy5ymmcudUlfPrS5NaTJefj9BYiZVkvu6b7Vlaefxrf+jnn7yqpVIrZP/+P+nnOssPdYe7v87CT2WGneCTOnBl/Od7z69SZXHnQzdTOqScWiZOIJfnyramM2fv6Due6eI9rmPLOVBLxJPFInJo/67hi1E3MnD4rb3v/99tcLtnzWubOrCYeiZOIJ/n2w+85d6exAyKTrj8ohMNfFmj7U5idPpaL44DXnU6IyIki8oWIfFFdvWgUcYh3BWTI21B6NoQOhNLzkKq3EE+Ois2CTh7EWfJXoB8yatqjmoToizhuutr1YM8p3GQSILdcc9sGJxPIFEZbgELye8fba/6s5eQNzmXCA+/x12/zmP7pj1x7+K088++XgXThVMVt4F3bpLB610Uq7kIC22QPFtoXfOsBC6p2fUAQKb8p/163nfDRcxM5aOkTOGXDczl8hVM4f5crCxLOWHXDlR03aEMlQdbabHXHe577zyskYpkNZZLxJDOm/JP34e0AACAASURBVMofPzjLOPz23R/89t0skvHMcGkiluD5217L296X/juBZCJz7lQyxdyZ1Xw/sfthvEWZPt20FZEjgBHAjU7nVfUeVR2hqiOGDBnSl6b1CLHKsIqPxiq/Gqv48D5Lx5TQAWSvLNP4t+gTGzpC55+ebvHnhJ2uIi0M4lnWCKVl/UqHINQmP1xy/Gw0mfPcE9e9QKQpQirZ6oCiLTEevHT8QokCCWyHVfUC1rCvsKqeRQLOQmIiPpORM+gWCB0OxacgQ15Hgjvk+1E7ZMaU37j+qNtorGki2hIjEUsw9YNpXLLXdT0ee81NVmWtzVfHH2p9MHn9XiqGlbPtgZs73vPnjL8cG5J7fV6qZ9U43jPvj5oMmeQF2CmbP9u9ScQiMX6dOpP6ufOzrp8z4y+Siew9NkuE6lnO1cKLO4Vw+H8Cw9t8vVz6WAYishNwMbC3qjr3MnPplLaZHOLfCEpOwCgkhkzhjRQjFeMKtlpsP3e+r8KamJ7WeXG63gL/hnnJFXcFGXQHWFWtBUgEILgD0iY1UoqPdHjQWOAZnrNJ+TfvfefoODxei1k5Vqkd2ikeJLg9VvllWKWnF1R07pl/v0w8mvlGlUyk+HXq7zlX1PkiIlz1yoUcesG+DBk+mIqlBrHniTtz+8Rrc+bwr7/93/AFspvax2MJVl5/Rcd7Vt1gpazPAOAP+lh/u9biwedue5UDhh3PWVuP4fAVT+XSfa8n0ty6h7P+dusQKMq2K5lIsvqIJbN7ViEc/mRgNRFZSYyXOQTIyEkUkQ2AcRhnP68Acy5x2JEJ2NU7oHPXxJ676UJ5AavkdGTI20jppUj5NcjQTztp4dd1NDUHu+54dO466Nx1sOvPQO26jm9KdCDCJaVI+c0FtRFMhbAMeR8ZdAtSdjEy+DmsQbdkauAEdkmv+APpCuJisJZGKu7KOe6Q4c6SAYl4koqlBtbm31+/V6N29kPW6/NS82fHPzNVpXl+C/FY7r0Mf8DHEWMO5PGZd/PUnP/jtNuOpawyd3+FfU8fSVFpCI+31dUEiwPsddLOVAwtd7xn8NIVjDxuh4xqXo/XQ1FZEXufYjbWP3v5C+6/6AmizVHCTRESsQRfTPia649qTYXddfT2lFaWtGuqEmCbAzdn6ZU67hewuNLjLB1VTYrI6cAEjADL/ao6TUTGAl+o6kuYEE4J8HQ6BviHqu7d07mXFDT2PjScy8KNT6038gKaREqORzxLQTeac+Q1t0ZMVotdy8IYeewdtPZHqHo9t6CYNSxHu0EfFJ2AeHonZCfiTWsI5TovSNn5aPHRRvfeGpxuSpJ77XPwefvw7UfTMxQifQEvf992HaqWKexbSk/ZYMe/8fOXv2StkBOxBKv+fcWc9331zrf856RxVM+qQSyL7Q7ZgjPuOJ5Qcc8qgAcNKefuKTfw0OVPMfn1rymtKGbUWXuy6+jtO7zvjNuPY9X1V+S5W1+leX6YzfbciCMuPYCywebh8uR1zxNrp/iZiCWZ9PoUGmubKBtcSnFZEf/94gYevfIZPn1hEqHSIPucNpI9T3bY9F9CcCttFwHsmn2cNxSlFBk6qddUHAE0/CzaeCVZ2UBSbFbSOZyragKt3h7sGjI2U6UIqXob8XRdaEs1CYlpJgvJu1bBm5F0xBsPvMvdZz+EnbJJJlJstPN6XPDoPyguK7xkckdoag7YDeBdxTFs11DTyAnrnk1jXTOpdBgqWBxg39N347hrD3cc87dvZ3LG5hdlPtCCPjbccV2uevnC3vkgPeTIlU/jr9+zgwXBkiD/nXwdw9cYGL0Z+oOOKm37vy7dpXNSOVLRNArabLTUewlTueuQ+qlxSP6aczUt4oPKx9H5Z0JyBiDgqULK/909Zx/7BJ1/FibjR0FKoeLuvAXhesrI0Tuw0xHb8OeMvyivKmXQkMJ+z1UjkPguneWzZlY2jKZq0PlnpK/xAhZaeilWUUYGNOVVZdw95UYeu+pZJr0+hdLKEg44a0+2P3SrnHM/ffPLWZk0iWiCKe98y9yZ1QxbYeAlUKy//TrMe7gma0PY47FYaqXOFWGXVFyHvyjgWQmSDlWYUmQcXxdRTZn+pyTBt36HG7ziWwOVojbdrxaeAK9zKt7CS7zLI1XPm+bpmjBFU93QXdHUXLT+VDLkCzSM1h0NQz9CCpjt0xFen5cV1ip8CYkdfgaarsRERG0TDqv4P6NamkbrTzSdzEjCgpyHxjGod0XEn5m/X7lUBWfccXze8//x/Z+OmTS+gI+/fp83IB3+EWMO4JPnJxFpji7MngoUBTjp5qPw+bM3iV0MrpbOIoCUno1jB6Q8ZXvbovGv0eqt0PpjjX7LvM3R2Ae5bwiOTBeWtQ0b+cCzbN6pn+IZhniX67bIlkZexFnCIgXRd7o1ZnfR1Fw08gIafYtCJJtpYio0jjUVwtpsHqypmWj96IUZUUYJcwaQbHd3DA0/0GMb1t5idcc0yHg0wQprD8wayaVWHMrdU25k5LE7sNzqS7PBjusy9oXz2O3Yvu/XsCjhrvAXASSwJVTcgTZeZypKrSFQcnqWEmNnqN2C1h9rHEvb4/VnwJA3zeZv+7klCIOfQRuvhti7ZiM2uBtSekHfxdDtGhxlBzRpCrj6CLv5Lmi+07zdACBQcR/i36DbY2rLY2R/Nttskie+Af/fIVVj5tRo+7sh9b9uz72AA87eizcffJ9UY2tHsUBRgF2O2a5goat4NM6nL06m7q/5rLv1Wqy2Yc/TIoetMIR/3u3QBcwlJ4uVw1e1If6J+UOxhkJw90Vek34BEtgGGeJQudkVYu/gnBdvo5GXkBLnPx7xDEUqbu3Z3D1AAlugkaeyw0oI+DftExs0/lVaFTOeoYej9SfA0E+7X/fQflN7IVbrw8y3dg4NngA4VfN2kaHDq7hj4rX83/mP8s370yipKGbUP/dgn9N36/HYYCpn/7X9ZSTiSZLxFJbHYsSu6zPmqbPxeHov4cAlm8XG4avG0LqjIPlj2jGEoOl6qHwU8a3V3+YNDOz5OVQ84326Uu4y/m3Auy4kp7YRRwtBcFfE1/E+QqHQyNM4SzLYpklMdx1vYHuIT6ZVa2jBhHGzusdUcmvJKdA8jtZ9DB9Yg5Ai58ybrrLc6stwxfPnFWSstqgql+9/I421mW+VX0z4htfvfZc9T9q54HO65GaxieFrywOQ+L7NKjAC2oTO/2e/2jWg8G+Go/aOFOWUAuhPVONo5EW04WzwrgZFx4FvQ/BtipRfjZT3XC4gb+wwzm9HOMsd54kUjQLPcmTu0YSg5HTEqlh4xCo5FRl0M/g2Bs8qUHwMUvUiYg2swq/2zP5pDrVzsgu+YuEYr937dj9YtGSz2KzwiTxP1ioJIDUHTc5GvANz86kvEd/qaGgviL7W5sEYAt9GA0J7py2qMbT2UEj+wgIZY/BB+XVYod373B4J7Y7GP8gOK2kC/M46MnmNKyGzRxIZb4TdpBwpPsrxASzBnZDgTt2eqz9IJe2cm/VttYlc+obFx+E7qkYuOOW2YFuAlF0NgW3R8NNAEgntA8G9+rSIKR80/FQ6M2XBQ3yB1v/FaHDHXumva5qPJ5z7ywZ2At8IiH+BqUuwAD+UXQxSikYnoOHHzJtAcHek+NC800XFKkKKR0Px6AJ+moHB8mstS3F5EdGWzHBYIORnpyOcw2C2bROPJgiE/G77xAKz+Dj80P7plnLtVvme5QoqTrWoIyIm9h3serOPPiX6Go5vbIipISigXpBqDG28Jt1nNoF6VkHKL8/QJBLxQMU9EPsAjU0AKUNCoxDfGtiNV0PkqdbQTvNPaPRFGPx0r4jYLUpYlsUl48/mwt2uxk7ZxCNxQiVBVvzb8uxz2siMa23b5pGxz/DcLa8QDceoWraSk/99DFvv3zcb80sCi420gmocrRttSu+JYcSxfEjlY72+saexz9Hm/5iUSe9qSMk/jZJlTlujaPO4tINRCO2JFJ+62GQUFQK77niIf5h9QorSP9PCVdja9adB7EMyN2VDSNWziHfVDu/V1J9o9UiyN3RDSPlY8wZVQGr+rOWZW15h2sc/sNway3DgOXuz8noDv2Po/OoG3nnsI6pn17H+tmuzye4bZGXo/N8Fj/LiHW9kaOQEivyMfeF8Ntxpvb42eZGlI2mFxcbhQ7qpeHyiEcXyDIPArojVu1onGn3PyAdkrEaDRqI4kB3bVVW07hBITKfVSfjBuxIy+HnHxtxLIub7+k8ym4MLWMsiQ94p2Ku+pv6HVu9Mdi68B0L7YpVf2+baP9HG6yH+kWk+EzoYPMubKll16NUa3AtrUOFUQef88henbXwB0XDUpDdagi/o44rnz2OjnQvXLas/iEfj7F81OkPPZwHrbLEG//n4qn6watGkI4c/sAK3PUREkMBmSMnJSGi/Xnf2ANp0NdmhhyjalCODJD7RpI5mrAjjRi8n9n6v2LhIEtgOio4A/EBxWsa4Cqm8B1BTRFaIxUpqVo7uYClo09xc7flozf4Qe9M4d7sWWu6HyBM5BvaaWpACct9FjxNuDC/sBGXbSiwc55aTxrVW5cY+wa7eHfuvNbDnbobdfF9Bvk+TXp/CsWudyS6egzho6eN54fbXCtomcH51I7n24do3PQFIxBO8fNcEztj8Is7c6mImPPgeqZS7CdwZ7nKyB6imIPWH88lkjhZqiW+di2g0jCamLnJZGL2FkTE+Fy0+0myUWoNQ36ZoyzgIP2icrpShJWdhFR/S/Yk8K7dq02TgTbciNGh4fDpG37ZIKgqJH4ymEe3TNr1I0UHmXo2aGgirqkdvcF+/+y22g9Z97Zx6IwlcNgOtP4VWGe06aL4N1SaktPvpyVPe/ZaxB9xELGJ+b+vnNnDvhY8TaYlx6AX7dXvctlQMK8fjcV5/rrze8hlf27bNRbtdw/cTf14Y/vn1m5lMem0KY546uyD2LK4sViv8vsfKrVRpDXY+7lk2x4oy5G4uOyCepZDQnmCVQ/U20HIbaCOQSvcFuBY7/EIPxq+C0N5k5sGbnsBSfGzrocQ3OG4iiw9KTgHP8DZdx0pNzrxnOHbj1ejcjdHqXdB5m2G3PNZtW0sGFTt/BjESyNp8q4ONEWh5oEe6Pw+OeXKhs19ALBzjyWufz+oZ2118fh+HXTKKYFHm30agyM/oqw7NOPbV29/yw+QZGbH+aEuMia99yU9f/lIQexZXXIffA0QEik/AtNNrSwiKT3G+KbhTusVe22+9mEbbwT16x9ACoqlaNP4lmip84zJVRRPT0cRUo32/cM4atPZIUKc+pBHzEOgBUnYllJxmNIoIgn9LpPKpzNoN72qY8FJ7Uoh/Y9O4vvIppOJBZOhnSHBntOlGCI/HhO+i5kHVfAMafaNbdu5/1p4ZXaDAKFpuPWozAqFAOo3V6QMCPfh5zfrRudl8MpEsSHP0BRz0r7057fZjWWqlofiDftbabDWumzCGNTdZLeO6b977jmhz9sM3lbSZ+sH0gtmzOOKGdHqIFB+PahjCD4CqERcrPgkpOtT5evFD5ZPo/LNbm5p4V0HKbx7QWTqqSbTxUoi8ZN5QNIYGd0HKrytI6qEmpptwhDZgPJQPBt2CBLZEI8+SrRTZhlR2jLcriHiQkpOg5KTc1xQdioYfbheO84Fn9daMId8aC8+oxiH8BNmSCRG0+Q4kmJmSmA97nbwLs3+cw6v3vI0/6CMRS7Detmu3Coh5V4V4dfaNCni6v58wfI1lmP7ZT1nHvT7vwg5UhUBEGDl6B0aO7rihe8VSg/AHfVldvXx+L4NytE10MSxWWTr9iWoc7DqwKvN2gGrXA1rwZt4551PbZJgkpoK1DARHIpZzmKA9dvOdaS2XzGwkig7FKutZVyTVKDpvq3Sopg0SQqreNCmvkWdzD+BZFWvIaz2yIR80MQ1tuCStS29BcBekbCxiZTs9TdWi1dviqPIp5VjDJnfbjvnVDcycPpuhy1dl9GbV+Fdo3TFk/oxCUDwaqwcx/K/f+45L9rw2I6wTKApw+CWjChbD7wr18xo4apXTsoq5isuLeGL2uB63ZVzUWWLSMl1yY3rTHgGpX9LyAEWmTmHwE53mmgPYczc1MfP2SBEydEqP0iQ18iraeIlDaqMfSs5ErEq0cSyZKZqt10jFnUhgW+exo++hLXcZGWHfBkjpmYh3lW7bCulNWDymq1fOa2x03ubO3zP/lliVPdexd5w39gnadI0J70gFlJyAFB3b4zTWSa9P4e5zHmL2T3MYNKScwy7en31OG9lvlbDffDCNqw6+hVgkBgolFcVc/ty5rL5Rz362iwOuw3fBbroNWv6PzHRQAe/qWFUvd37/X+tg2gu2R5Bh03vUV1dbHkGbbsBRjbLoGKT0bLRmj3Topo0NUoIMuhUJbO1sc3g8NF5D64PCAgkig5/tsdPPBzv8HDReTuuKW4AgMvgxxPe3Xp+/t1BVpn/2E1M/mE75kDK2PXAzisvze1MsJKlUil+/mYnH62GldZd3ZRjSuD1tu4FqBCKvockfEe+aENqtz1rp9QqRF8h2qArJ39DUPKSzGK9vfUg4PIC9a/a8ibp/E3KqePo3b23C0nyHERjDD0UHI8XH5lxlqyag6QYy3wps0CjadFuf6PtbRfujVgXacgek5oB3HaT0bMS3dof3/fTlL3z83EQ8Xg/bHbJlr7RV7C6pVIorRt3ElHe+JRFL4Av4uPucB7luwhjW3qxvpKoX4PF4CtJIZUnCXeE7oKn/obUHgN2Cya8uAqsEGfyMY1eoXrcn/gUaeRlQJLQX+EZ0eTVjz9sB7NkOZ/zIkPcQT8d9SzUxHa07LJ2znsKoV/qRygcQ/4ZdssXRvvnnpouaFjjoIPjWRSof7tYDRZOzzVuBUxjIGoo19OMe2dtbjDv3YV6+awLxaALLEjw+L6OvOpQDztqzv00D4I0H3uPOf9yXFT+vXHoQT8wah2W5iX/9zRJTaVsotPFyU0nJAincMNg1aOOVfW6L3XgdWn8cRJ6EyHi07ni0qRtl5qH9gPb5/2IyhDpx9gDiWxsZ/KIRqfOuDaG9jdZMAZw9AME9QQYDHlPIFDrYPEy6+/ZgDcK5Dy7gWbq7VvYqP3/1Ky/fNYFYOI7aSippxMYeuPhx5s2qKdg8Uz+czplbXcK+lUdzykbnMfHVL/O+d8L972Y5e4BIU5Rfvv69YDa69A4FcfgiMlJEfhSRGSJygcP5gIiMT5+fKCIrFmLe3kBV00Ja7dvO2X0ufaCJnyH8eHrVq+n/IhB+Gk1836WxpOQE8K2Vrgq1zL9Sjgz6d/5jeFfAKr8aq+oFrPLr89rszQc7MgHmn5F+A0mZTeXI+HQnqO4hVol5iGQ95EJIrhqJfubj5yZmpRoCIMLnL+fvlDtiyrvfctFuVzP90x9pmR9mxpTfuPLgf/P+U5/kdX9HEYHeiBZo4ifshvOxaw/EbrwG7WEK7pJOjx2+mCXYncBuwNrAoSLSPkh5HFCvqqsCtwDX93Te3iXXqrKPX4hi7+Ocf57o8sNHJIhUPokMuhMpORMpuxwZ+kGfbF52StN1dEmPKE+k/AoI7o4pmAqBlELp+Uiw4zxvJzT2IXbdUdjVu5nq2V4oPPP4PI4hERHB6ytM79d7zn3EoWo2zrh/PZKXw9519PYEi7MrxYPFQVbdYKWC2LgAjX2G1h4IkRdNpXP4MbRmDzT5e0HnWZIohAfbBJihqr+qahx4EmivCbsP8FD6/58BdpQBuqW+QC8e2m8G+iBYmKbO+RsTwPnh4zFqjV0dTiwksCVScgoS2ndAbEKrJsF2ruQk+WuPxhYJYA26Hhn6OVL1MjL0c6ziw7o8jt3yMFp/uuldm/ol7Xj2RlMORU49YNuDtsDj4NjtVApV5bojb+Pucx7ijx/+7PYcf3zvtI8DdXPqSMQ7l0nY5ejtWG/btY3TFyN9ECwJcunTZxc0fq+qaOMYzB7MgrftBGgL2nRTweZZ0ihEls6ywKw2X88G2ncsWHiNqiZFpAEYDGQEJkXkROBEgOWXX57+QsouQRPTwf7LtLATH1hLI2UX960hwZHQdKOThX3/8Ok1PCBl6Qrbdlid7y3kg1gl0M0qZtUINN9M5htI0vRLbrkX6WHRWVtWWGs5jr36UO6/6HEjHidCKpViyPAq7j7nYaItUTxeD6/c/SbnPnAa2x7U9baUg5ep4H+/Zr+dhMpC+PyduwOP18NVL1/I1A+nM/WD6QwaUsa2B29BWWXhKm4BU4SX+p/DCRvin+U9zB8//Mn/fvmLFf+2PMNWKMzv06LMgErLVNV7gHvAZOn0lx1iVUDVqxD/xPRU9a4K/i36vA2geIai5TdCw3lGsgFAk1B+bb9kC+VC1QaS3ZJYEBG0+GRovo3MjJqQ0bfpb5IzcH7LSkCs8Jk+o/65J1uP2oyJr3yJx+uhuaGFhy9/eqFQWCqZIpVMcfPxd7H53iPwB7v2PT/i0gO57dR72zUZCXDwefvmnfklIqy/7Tqsv23hmtBkTxIkZ9tSq6zT28NNES7d93p++PxnvH4PiViSLffblPMfOh2PtzDhsUWRQjj8P4Hhbb5eLn3M6ZrZYvRhywEnJawBg4gFga3Nf11AEz+ZqtDEl+kmGQchped0W2/GCo1EA1umN5IVAtsgefzC9wWmNeB1EHkGSKDe1ZCyK7qcuSPFx6LEoeWe9BtVEErOQEIHZM5nt0B8knn4+TcrjIaPxtJSyxXODs+qMjY50UsP3aHDq9jrFNOC8pztL8twzgsQS/h+4s9ddrq7HLUd4cYwD132FLFwHJ/fywH/2otDzt+3ILYXCpEAGtwNoq+TKU8RgqLOe//efvq9TP/0RxKxJLH0OuLTFybx5PUvcPjFo3rF5kWBQjj8ycBqIrISxrEfArQPlL4EHA18BhwAvKsDtQCgB2jqL9PNSpvTB1og/DiamolU3N3tccUqhdDAU9LU+WdntgZM/mjaTFY9j3jzL4gREaTkFLT4BPMqL+VZ6Zh25DVouDD9pqOAwKC7kED3+p2ah9UVRgwOjN5+6WVYoZ0zbfMsjfr+bh7gGRvoIaT4+G7N3RVCJc57NWprlpRwvux7+u7sdcquNNe3UDKoeMCueKXsCtSuSz/k/aYGJLQfUnREh/clE0k+GP9p1p5ELBLnpf9OWKIdfo9jFGp0bE8HJgDfA0+p6jQRGSsie6cvuw8YLCIzgLOBrNTNxQENP+LQTCMGsU/Q5Mx+sam30NQchz6wAHG05b5ujSniRazKLGevyVnQcAEQMQ9TbQFtRuefhNrN3bO/4XyIvIxZPcbBngcN56DxrzKus8PPp9tRLtg4TNcJlF4I/k3RxA9o4sdeSUkEo5DplBVTUlHMaht1v8rU4/FQXlU2YJ09gFhFWJX3IVWvIYP+iwz5AKv88k5Dq4l4Ettun1ZtiDQ56THlRyqVYsbXvzFz+qxOf96pVIoZU35j5veze+13ozsUJIavqq8Br7U7dmmb/48CBxZirgFNYhqOejPiMxkn3oHfbDpvkn+0rroySEEyW0q3J2jkJRyLqBSIvQ2hroUj1K6D6NtkK1lG0ea7020UQeOToPEyMjdsBbwbIb410eptWt/mpBwq7kB863bJls7YZPcN2fvUXXn+ttfx+jyIJfiDPq5+9aK8s2Js2+aT5yfxxgPvobbNLkdvxzYHbr7IVMWKdzh4h3d+YZpQcZDhay7L79/NyjgulnS7GfqUd7/lmsP+Y4riVKkYNogrXjiPlf6WnVzy5VvfcO3htxKPJdCUMniZCq548fwBIZHhSisUELvpBmh5iGynHzBpgd4V+8Gq3kFT89DqHch2mj4IHYJVPqZgc9mN10H4foczAaT0fKS441f89hiZiCNanXVbPCtjDTENSuy64yH+ocMIfhBvWnW0DVKCDPmwV/oazJtVw9QPplM2uJQNd1oXry//tdq1R97Gpy9MWlghGywOsMluG3LJ+LMWW8Gx6Z//xPk7jyURT5JKpPAFfASK/Nw56TqWWaVrey81c+o4ZvV/ZO2llFWV8sSscfgDrSnc82bVcOxa/8y4VgTKh5Tx+B934/PnVlgtFK60Qh8hRUeaVW8GAbPB2MvOXlVRu8no8vcB4hma7tDVLsYsfqTkWMd7uj1XYLt0lzAHAlt1fUDPCibbKfsE+P/e+mUqR30AAur0xmGnxd0Kz9DhVex0xDZsstsGXXL2P37xC588PylDDiHaEmPS6185NjXpb1SV5vktPW6duPZmq3PPNzez9ym7ssGO63LQv/bivmm3dNnZA7z18PvYqewQUSKWZOIrmRXQEx54FzuZ+buhCvFIgslvfN3luQvNgErLXNQRz9JQ+USbLJ0QhA5ASs/t1Xk19pnpRpX6E7DQ0F5I2aW9Xlgl5Vej3hUg/AjYzeDfCCm9uPC9ef2bgn8biH3EQn0jCUHokG49SMUqRouPh5b7aE0FFSOd3FZ2wb8pRH4nu9o5hbNOTwzswmneFIIp73xL0qGgKhaO89XbU1lnizUc7uo+NX/W8skLk1FVtth7BEOXzz/3/ePnJ3Lnmfczf14jHq/FHifsxAk3HNmlB1xbll55GKf+p/OMns6o/bOORCw7VJtKJqmfm1k/Ujun3rGALZWymT/Xodakj3EdfoER35rI4MdR1T55XdbEj2j9yWTksEdeQe06pGJcr84t4kVKToWSU3t5HoFBt0LsbaMaKj4kNAr8rYVHqgrxz9D4RMQaDKE9O+wkJiVnoJ7lTCqoXQv+ESZ9ts0+i5SchEZfTjdmWeDgQxDcC6Kv0Cqut+AGP/gd36T7jdKKYnx+L6l2q05f0EdpRWFDT6/d+zZ3/uN+E8NQ5f/Oe4Tjrz+C/c7YvdN7p344neuOvI1Y2LyhJuPw6j1vEw3HOWtc7taTfcEGO67HhIc+yOqjKwjrbrNW1rXvPv4xkXbXqirrbLVmr9vaGW4MfxHH+VFSrwAAIABJREFUnn8+RF8kW+wtgAyZgHiW6Q+zuoUmpqORF0ATSGj3vGWgVRNo/YmQmJKOqwdBBBl0T7fTNheOnfrT6PDHPgNPFVJ8IgR2RutHQ/wrWjd0QxDYFBk0LstmVWXapz/y/vhP8Hg97HDY1qwxIn8No4aaRj55YTLJeJLN9tywS6vmpvpmDlv+5CyFy2BRgId/vZOKAvWAnTerhtFr/CNL/M0f8nPPNzex7KpLo6p8+dZUPnlhEkWlIXY5ZruFG5nn7TyWKe98mzWuP+jjqf/9X8EarNi2zaTXpjDx1S8pqSxh5OjtWXbVjtVTU8kUZ20zhl+/mblQhyhYHGCLfTbhwkf/kXFtMpHkzC0vYea0WRnXbn3AZpz3wOkF+Qyd4Xa8Woyxa0ZBMvsPBSlFKu5G/BsvPKSagOgENDoBrDKk6JCCZ5V0F7t5HDTfidkEViBoJJjLxnbq9O2W8dDUtrNVGqlEhn7S8wYtDqgm0PDTrb12QwchRaMwdYWZ3Hnm/bx+37vEI3EQ48QO/Nc+HH35QZ3O8+Ezn3H90XdgWYJtK6hy1BUHc/C57eWqcvPN+9O4fNSN2EnbRK5EGPPU2Wy08/p5j9EZz9/2Gvde8GiWw/f6PBw99hAOOndvxh54M1+++Q3Rlhger4XH5+W0W0ez+/E7ccRKpzJ3ZrY2Uag0yJ2TrmP4Gj0PE6aSKS7e81qmffoj0WYjU+H1eTjnvlPZ/pAtO7w3Ho3z8t1v8s6jH+IL+NjzpF3Y8YitHTOdYpEYL9/1Ju889hH+kJ+9Tt6FHQ7bqs+yolyHvxhjN14J4SfIjjP7kaEfLgxrqCbQuqPTOeVhzH69H0rPxSo+sldtVLvFNDdJ1YB/I9Nbto0T19SfaPVIsnL6JYRUPIj4N+hwfLv2EEh8lX1CipHKhxBf91LxCsHPX/3KWduMWRiqWIA/6OOeqTd3uLpsrG3isOVPzlK3DIT83P75Nay0bv5pvslEkmmf/ggKa2+xesGzRZ695RXuu+jxrFi3x+vhqMsPYqV1l+eaw/6T9abhD/p48s97uOWkcXz83ETUzvRHweIAz8y7j0Coe0VmbXnnsY/4z8njHN92np53X7cL2QYabpbOYowUH5fOYGm7Cg5B6MDMGHb0jTbOHkwIKApNN6B2720maWI6Wr0N2nAF2nwLWj8arT/RqGQuIPZhO/sX3BxFo2/mMUuuFbx2cK5v+OylyY4a96ow8RWHh1Tbe1/+AvFk/4km4v/P3nmHR1V8f/iduz0FklBC70VQAemCFBFUBFGkKCKCoujPhmIBASkiImABxYZfQRSpgoAiWGhSFASk994JJT3bd35/bIhs9qbubhLgvs/DQ3L33pnZm91zZ86c8zkuVszKm46P3qD36t+0vTkkoYEtHmiC2kJMZ9DRsmtTVs/doFo4RW/Qs/WPnfQZ0QOTxTfCzRRm4pEhXYNi7AFWzl6nOgZFr7Bzbd7qS1yraAb/GkfoyiFKzAdTOxARoJSDyEGIYr5x8NK2DL9NRvAmhTk2hWRsUkqvrLBMTu/b5S3m4tiITJt31ZlG1IWycicDLcK6AyoRSSIC9HX8jxcgBrMRnYrRVnQCgzl7w+t2ebxPhkx4q2FlUc2rkChbLZa+bz+C0WJEp9eh0yuYLEYeGfwgletUwBRmVHfNpbu4qt5SiY/+HEPD9rcSVsxC+RplePGT/jw69KGgjdFkyeJ+S+8YbgS0KJ3rAKGvhoj+PPuTlGJ4jWpmAyJBBGdDzA/3kfRSkZmxgXU+XNGmN98FSaNVztN5a/jmhLkL2FaBfQ3epDcjCMVb7KWAFU4z07ZnC2aO+QGc/rHZdzyU/YZys04N+fQlf5kKo8VI6+63B3WcwaDna11o3rkRf/7wF9IjadWtOVVu9mbI3vvEnayctU5VCK5RB6/LrcZtVRn/2wi/14NFx6fa88/ybX6zfINJzy0tCz+CpiDQZvhBRkpZpLQzriAsj+Bf7g/vMWPTEPUqUV3nA1dHFQklCqI+whtdE55ehtEEkW/mSoRNCB0iajJEf+sNrSw+wpvxagzepmR+KVstlhen9MdoNmCJMGOJMGO0GBk844UcI2RKlI1mwMQ+GbNmoQhMYUY69m9HnWY1gzZGr7x1cKh0U3keG96dPiN6ZBh7gLq316bXkAcxmg2Yw01YIs1YIs28vXhwniWe80vju+vT5bl7MsYQFmkmvHgY7/z8ZpHWFAom2qZtkJCey8jEUV5tFyQY70AUH12kwiI9qdMg+UOvGwcAo7dQuCFzRcrgIKVEXmgLnsyFLMwQ+SpKeF/f8z3J3tKN0gmmNghdiRz7sKXZ+fK1b/ltxmqcdid1m9fipc+eplq9wHWLEi8m8e3o+axbuBGj2UDnZzrQ7ZXO2SYCSeduZPKH4NoNugqI8OcR5jtJvJjExqVbUXQKzTs3IiIq96uqk/tPs2rOepx2J3d0bUbtJsGpJSztq5FJY8F9HEQUhD+NCH8qpPkjF05dYstv27FEmGnaqSGW8LxXbguUc8fi+HfFTiKiwml6321B2yMoKmhROiFGSjfy4n3gPsl/0TIKKDGIUiuKRCnBK3jlZv/x+reNzfzCCKV0gfscKFFB0YSRju3I+H64nW42/m5k+4YoSlQoSYcBH1OibOmA2x9yzxh2rt3rszEaFmnhf7s/olSFnB8YWWFLs/PUza9w6cxlXOnuGJPFSKO76zP6xzdUr5HOnchLj+EbHmqGYqNRwrrmeyyhQNo3IuOfxlcYzgLhT6JEDiysYWkEAS1KJ9Q41nnldX1CIz3gSUOm/YR07kW6zxXW6HwQSgzCfI+3tm0mY+9J+xEZdzvyUidkXHM8Ca/hFToNoD9jfRwRv/FK1+aMf6k6i76O5rvxCv1qDQo4MuLEvtPsWrfPLwrGaXey+NPlAbW9YuafJF5MyjD24NVT3/Lbdo7uOqF6jUz+AL9cAGyQPD6obpNgIFMm4V843gpp0wpMj0mj4NEMfjBwHQHVL0kaJI9GXn4UeaEDnkuPeWfYRRBp3+CVApaJ3kgaHN4krcTASxcs+WwDR/ekYkv1Gj2HzYkt1c7YXh9lqVueG07tP6Na9NvpcHF429F8twuwc91e1RA+oQgObD6sfpFzl/pxmQIyIaDxBB13FvdHSiiin1GNwNEMfjDQ11RRybyCM12LxQ7Orcj4/8vivMJFpn6B/4zPDrY/kJ74gNpe8f1ab5ZpJlITrZzYm7kaZu6pVLeCzwz8CgaTIU/SBWpUqFUOg8k/VE8oIuti2FmWPNR5XWhFCV0W+wBCB9loEF0L2NLsbFr2L5t/245DRfTsRkYz+MHA2AJ05YCcYnld4Nwb9OpXl87G88fMP1m/aBMOWz6X4+4sDK8wZBFamXvUZuHgjQ7RZ/FabqhQsywN77oV41Xx1SI9rvv+5+7Nd7sAHfvf5Tc2nV4hpkw09dqob3KLiOdVZJwtENYrKPV3g4mIfAU/aWssED6gyI01L6xftIkesf0Z2+sjxvT8gJ5lnuLflSrSIzcomsEPAkIoiJhZYOmc/oU3gcii0LjQg8dfMyS/zBn/I32qPc/Hz33FhH5T6Fn2aW8KfV4xNEL94yBBl/tqQ2p0ero9pkxp60JAqQolKF8ze+GqnHhr3iC6PHcv4cXD0Bv0NGxfj8kbxlKibHRA7ZYoG83430ekz/T16I16bm1Vl/dXjcpSE0WYO0LEGyAi8RpTM4T1RES+luf+L5+L593ek7g/8jEeKP44k579ktTE1Hy9Fyklh/49ysZfthIf582qFsZG3jrL+psAHSilIfJ1RPiz+eqjKHDx9CXG9Z6MLdVOWpKVtCQrqYlpjHhgPCkJvvfO5XSxftEmFk5eyq71+4pkKHUo0KJ08on0pIBtGXjOg6EBGFv4JPl4kj9K11vPPOM2I0pvCEoEzJ6/9vNGh7f9dFoiosKZd+6rPKXQS9cJ5KUH09Um0/3qwgIRL6OEB6Yp7na7GdtrEpt+2Yr0SHQGHUaTkQ9Wj6Jy3cAeJgXB5XPxGEy5lxOW0uXVxVeiELnIFM6Mw+bgiZsGculMfEZGrd6op1Kd8ny+ZUKeRLgunY3nzXvf4eyR8+j0Ohx2J11f7MhT7z123VW7mjtxMTNGzMFp99WVMoebeH7yk9z7ZDvAG5b58h3DSUu24nK40ekVajWuzrhlw0KeExAfl8iauRtITUyjYYd63NS0RtD/DtlF6WiZtvlAOvell8hzAlZvopC+NsTMyPiCi/C+SOt88CSSUfJQWCD8+aCVwFs2bSUOq7+P0uPx8O+KXTTtmL3o2NUIfSUosRCZMtkbtqmUQkQ8izDfE/A4dTodI+a9yqFtR9m1bh8lykbTrHMjn9JwRY0/Zq5h+vA5XDx9mdgqpXhqXO9cZ7cKoc/Gn58zf/7wN8mXU3zkE1wOF2cPn2fbyl15qsv6do8POLH3lFemIZ0ln/1KzYbVaPtw9gqR1xqpianqxUdcblIT/5MVebf3ZOLPJXjVRwGnHfZtOsTcCYvpMyJ0pbf/+XUbo7tNREpw2Z3Mfu9HWnZtyuAZLxSYkqbm0skHMuFlkElkhODJNK9vPnV6xjlCiUGU+AnCHgNddTA0QRT/CCViQNDGYU2xqS5FpUQ1hT0nhL4KStRHKKXXoZT8MU/GXko30rYcT/xzeOIHIu1r/cZWo0FVHnyhI626Nc+zsZfSU2DL7l+/WcWkZ78i7sRFPG4PZw+fZ0K/KaxduLFA+j+07ZhfAQ3wGv3Mhbmz48KpSxzaesTH2IO3xOHCyUsDHmdRo+m9t6kqXiqKQqO7vVnXSZeTObjlSIaxv4LD6mD5tJUhG5vD7uSdhz/EnubAYXXg8UjsaXY2LPqHv5YUnCdDM/h5RLrPZLHBaQPrjz5HhK4ESrE3UUotQynxPcLcLqhjadOjBeZw/w+42+miQbtbgtpXdkgpkQkvekM47X+AfRky4QVk8rjA23buw3PpEeT5Osjz9fAkjkDKzLHuwWX68Nl+D0x7moOv3/w+pP1eoXKd8qp/V71JT/laud/zSElIzVIyIDk+f/sBRZmbW95Es06NfO6dOdzE3f3aZsg8uF2eLNU+1OrWBoudf+5RPW5LtfHrN6tC1m9mNJdOnik6fs8WDzTm1tZ12bl2L7YUG4pOwWDU8+yHfYNevi5bHH+BfT0+SUfSCmmzkWGP5ruAu3SfQ17ulR7WCmAH649I90lEzPRsr80vbrebS2fUw1DPHY0LSZ+ZaftwC6YNm5UxEwSvrnx0bBSN78m9PlClm8qjN/p/xQ0mPS0fbKJyhReHzcHSr/5g1ex1mCwmOj97N627Ny/yPn8hBENnDWT9on9YMfNPdHqFe55oR5N7/ytMH126OOVrluNYpuQ5g0lP2xyKoFzB4/Gweu4Glk79HZfDxV29W9PxqXbZ7plld+8K8r4GZPCFEDHAXKAKcAzoKaWMz3ROA+BzoBjewqBjpZRzA+m3MBG6skhdRXAfxld50gSWbgU6Fp1Oxzs/DWHj0q3esnHFLNz7RLug6MjkBWlfg3+GKYAAx3rIr8FPm5m+T3I1dnBsQboOIfTB0ZS5Gp1OR0zZaC6f9Tf6sVVyX1owECwRFj7+610mPTOVbat2IYSgeedGDPxiADpd7sNYdXodr0x9lvGPf4zT7sLj9mC0GIkqVYyer6lXzHI5XbzadiRHd57IKLyyb9NBtq/exUufPh2U9xdKFEWh1UPNaJWNEumbM19iUJsRuJwu7GkOLBFmSlUswWPDc/f9fb//Z6z94e+MxLzD24+xeu56Jq4cmeXf55ZWdRAqk0VzuIl7+t2Zq36DQUBROkKICcBlKeV7QoghQLSUcnCmc2oBUkp5UAhRDtgC1JEy+9TDohylI50HkJd7pxsjm1ezXV8HEfMNQlwfQkzSvhGZNsMbg29qhwjrneVmsyd5CqR+Tsbm9BVEmLdEoaWL1+2TNhfSvvHq45vuQES8jNBl7aLwXH7SK1uRGRGBKD4eYe6Q/zeYDcu+XsGnA6f7uHVMYUZen/Y8bXq2yObK4ON2uUGQJ0OfmSM7jvPjx79w/vgFGt9dn04D2mdZI3bN/L94v/9nfgW7jWYDX+38kHLV878ZXZRIjk9h5ay1nDl8nrrNa9HiwSa5imo7tvskLzQd4leFzBJhZuisl2neuVHGse2rd/PNyLmc2n+aynUr0qJLY6YNnwNIXA43eqOO1t1v5/Xpzwd1lh/KKJ0HgLbpP88AVgM+Bl9KeeCqn88IIeKAUkARyzXPPcJQC0qtAfuvXqExQwMwFv0lb27xpH4LV+vCOPd4I45KLFI1+sLyIDJ1Kn4GH8B0FwAy+R1I++G/Nq1LkLbVUPKXrFUxDfXSi7NkCm2VTgjB7P4KHfvfBQK+HTmPi2cuU7piSfqPe7TAjT0QFNneavUq8+r/cpfh/c/yf/2MPYCiU9jx595cG3ynw8mssQtZOvV37GkOGt1Tn2cmPp51lnIBExkdwQPPd8zzdTvW7EH61ZTwBlBs+WNHhsHf+MtWxvT4IOPBkBC3m32bDjLku5eIP5eQEZZZq1FgGeF5JVCDHyulvKJ9ew6Ize5kIURTvOWNVMVIhBADgAEAlSpVCnBooUUoYWApWgqIwUB6UiD5fXxlFuzgPo9Mm4VQiTIS+grI4uMhaQj/lRRUENGfI5RwpPsipM3F13C7QaYi075DRL6sOhYR1huZ9l36SurKl8wEptsR+qqBvtVs6fjkXXR88i6klEX+Qb5y1lqmDZtN3MmLlK5YkifG9uKuR1vlq62YstHoDTo/yQpFpxBVKotkQhXeefgjr7RBusFbv3Aj21fvZvq+yRSLiczTmC6djefTl6Z5Sz4KaPlgU57/+EmiSmVfTyA/OB1OpEdmGY9fvFQxdHo9mSc3BpOBmDL/jefzV77xWwXY0xx89/Z8vvz3/aCPO7fkGKUjhPhDCLFL5Z+PE1B6fUNZ+oeEEGWB74AnZBbSgVLKqVLKxlLKxqVKFY2ZwA2Hc/dVevlXYwf7iiwvUywdIWYhWB4AS3co+TPCmL4x6NoHqq4uR7blFYWuFKLEPDC2BAzeDNawxxBRU/L0lq7gdrm5ePpSnuQniryxn72ODwd8wfnjF5AeyfnjF/howBesmLU2X+11fLKdqhSG0WTI9Ybxyf2nfYw9gMcjsaXa+WXqH3kaj8Pu5KXbh7Jh8SZcDhdOu4u1CzcysOXwoJZ5TLiQyMiuE7g/sg/3R/ZhYMthnNjnH43XvHND9Hq1kpUKHfq0AbybuqcPZq4B4eX47tyH1YaCHA2+lLK9lPIWlX+LgfPphvyKQVcNYxBCFAOWAsOklH8H8w1oBBklCqR/8or3tZJZXuZJ/RouPQhpC8A6Fy50wGP9xfuirqzK5iuALscNXaGvjhIzDaXMbpTYLSjFBudL62XJZ8vpVvpJ+tV6iYdKPMEXg74Jel1Y6T6LdB0pUCnkaUNn+WVa29McTBs6K1/tla0Wy/A5g4iICiesmAVzuIkyVUszceWobAu/XM2RHSdUNZIcVgd7/jqgckXWrP9xU3oS2n/31O10E38+gY1Lsy8Cn1s8Hg+D2oxk4y9bcTvdeNwe9v59gIEth5Ecn+JzrsliYsKKkZSqWAJLhJmwSAuRMRGMWvg6Jct7XZOKohAZo77fFZVDlbNQE6hLZwnQF3gv/f/FmU8Q3m/nj8C3UsofAuxPI9Toa4G+IrgOcXUZQrAgwvupXiKdByF5MpC+yXllnZc4GGlqjtBXRxpuAed2fJfCBkRYYLINuWHNvA1MfWOmzybsz1N/R9HrGDChT8DtS/dpb7F21yG8hW8ioPhEhCn0Pv+4kxdVj184eSnf7qjmnRsx//z/OLj1KEazgWr1KuepnfI1yqjGtBuMeqrckjcpjRN7T6kmodnTHJzYe4oWD2QdXppbtq3azcVTl3Bf5caS0ltX4fdv1/DQwE4+59doUJXvj33O4e3HcDlc1GxYzW+vpefrDzBzzA+ZNv5N9HqzcN3AgSZevQd0EEIcBNqn/44QorEQ4n/p5/QEWgP9hBDb0v81UG9Oo7ARQiCivwJ9dcCSLutrhsjX/nPRZELafkJ1w1YoYPNmL4roL8DUBq+iqAmUMojoTxGG4NVmzYrMXzzwGowln/2K0xGYfK6UHuTlPuDai/eBZwXPBWT8/yFdpwJqOzeUrqi+6ipVsURA7ii9QU+dZjWpXr9KntupcVtVqt5SCUOmHAC9Sc/9/5c3qY5KdSpgifDXIzKFGalUp0Ke2sqKM4fOqdZlsKc5OL5H/W8ohKBGg6rc1LSm6sZ6z9e70G1QZ8zhJkxhJiwRZnq92ZUuAaq4BkpAM3wp5SXgLpXjm4Gn0n+eCcwMpB+NgkXoykKJn8F10Fu4Q38zQsmmBqt04bsauHIcrlQBE0oxRPRn3k1hmQJKbIH5xy+cVpd39rjdpCVZKV4yAE0fx0bwxOP//l1I61xE5Kv5bzsXPDG2Fx8N+MLHrWMKM/LE2F4h7Tcnxi0fxqRnp7Lux0143B6q1avMK1OfyXPZyZZdm/LV4O9w2BwZbh2dXkd06eI069QwKGOtVq+S6mfRHG7Kd10FRVF44u1H6D2sGwlxiUTHFs+TmGGo0DJtNVQRQoChVu7ONd+DTPse/+QrD5ja+p6rRAB5ywKWUrJy1jrmf7CEpEvJNL6nAY+P7JHhM82Jmg2rsW2lfzWq8OLhWfpac40nDvVYBSe4Qz/DvxKNM23oLC6cvESpiiV4Ymwv2vduHfK+syO8eDjDZr+Cy+nC5XSratzkBqPJwCd/j2PKi1/z90+bQYiMKJ1ghKwC1Glei+oNqnBwy5GMcpk6vY6IqHDufPSOgNo2mgxZrsIKA00eWSMoeJLGXhV6qQA6iHwNJbxvwG1/PfR7Fn2yLCOz0ftlDOOrnR8SHRuV4/X7/znEq3eO8kukGvj5gIzIivwiXceRFzuTsX+RgQVR7C1EWPeA2s/TWIIQQmpNtbFuwUYunblMnea1qNembpGJVLpiq0IxHluanW9GzOH3GWtwOVzc3qUxT0/oE3BdhcIgu8QrzeBrBA3p3IG0/QYYEZZOCH3gSSVJl5N5pPwzOO2Z4571dHvlfvq/+2iu2jmw5TDThs3m8L9HKV25FI+P7EGzTo1yvjAXeBKHgHUZ/61wjKArjyi5OF96+IXF0V0neLXtSJwOFw6rA6PFSK1G1Ri3fHiRlrLOCbfLzcalW9m5bi+lKpagfe/WFCuRt1yAawlND1+jQBCGeghD7rXac8PRnScwmg1+Bt9pd7FtVRZFw1Wo1ag67y0fHtSxXUEUexdpaARpM0HawHwvIvypa8rYA7zz8IckX/4vDNGWYmP/pkMs+vgXer6urr1T1LGm2ni17UhO7j+DLcWGyWJkxltzmfDHCGo3CV22dlFFk0fWKNKULB+jWtRCKIKy1bJN7C4whFBQwnqglFyMUupXlMhXEMq1NYM8f/wC5475l960Wx0sn15w8r3BZsGHP3F898kMuQi71UFaspWxvSbdMGUNr0Yz+BpI9zk8yVPwJA5HWn9BqiZJFQ7la5SlduPqfjK/RrOB7oM6F9Korj+klFkLf1/DhnHF9+syNmKv5vK5+AKTuy5KaAb/BkfaNyAv3AOpX4B1HjJxKPJSj5AXGckLby8eTKMO9TCY9JjCTESVLs6Q714KqvCUlJIjO46zbdUuUpPScr6ggLCm2vjitRl0K/0kXWP6MfGJTzMKkQeT2MqlKKkSMmm0GLm7b9ug91dQ6FRkEMD7DMvqtcLC7XLz0xe/8VyTwTzb8HUWTPoZhz24ky9t0/YGRko38kJL8FzO9IoZIl4IajnGYJB0OZnUhDRKVy4ZkFxwZuJOXmTYfe9y7lgcOr0Op8PFk2N70e3l4KwgrKk25k5YzIqZf6LoFO7p15bur3bJcSNUSsnLrYZzcMvRjD0MnV5HifLRTNszCZMluFLch7Yd5bU7R+F2ubGl2rFEmKl6ayUm/DEi6H0VFAs++onpb83xyVMQQlD55gp8tePDQhyZL9vX7Obt7h+QdCk545jJYqRmo2p8sHp0nmrealE6Gqp4i7E/4q3Jmxn9TSgllxT8oAoYKSUD6r/Kib2nfeQATGEmxiwZzG3tbg2ofbfbzQvN3uTEnlMZrgWjxchNTWrw/qpR2YYY7lq/jzc7jvWTK7ZEmHnhk/4hmXmnJqWxZt5fXDp9mZua16RRh3oFVmA7FLicLkY8MIGda/fgdrnRG/WYzEY+/PNtKtYuX9jDA2Dj0i2M7v6BX2ACeP/Wb81/lSb35F6cQIvS0VBHmLL2zwoTUkqWTv2d78cuIP58IpXrVOCZD/rS8K6sjaCUEpfTVaBZhSf3n+bnL38n7sRFGnWoT/s+rXOd6HN8zynOHYnz036xp9n5cfIvARv8jUu3cvrAWR8/ssPq4MDWI+xcu5d6retmee3hbcdUNWmsKTb2bz4UEoMfXiyM+57yS56/ZtEb9Ixd+ib7Nh1i718HKFE+htu7NM5ydbVz7V4+H/QNR3ccp1jJYjwy+AEefPG+kOYifDpwuqqxB+/fesefe/Jk8LNDM/g3MroqoCsD7mP4ZotaEGG9mPf+EmaOno8tPWHpyI7jjOjyHuOWD+fWVnV8mvJ4PMwau4AfPvyZtCQrsVVK8X8f9aNFl8DFrbJj49ItjOn5IS6nG7fLzeZft7Hgo5+Ysuk9wouF5Xh98uUUlCx8uQkXAveV7914UFX8y2l3sn/ToWwNfrnqsap+ZlOYiUo3FY3ZaaCkJnrrFWdVgSsYCCGo06wmdZplr9u0/59DvNnxnQz3z+Wz8UwbOpvES8n0G/1ISMbmdDiz3Tw2WYxBTf66dtdqGgHjFUr7HJQSIMIBC2AG8924dZ2Z9c6CDGN/BbvVwfS35vi1NX3YbOaMX0xqYhpSSs4djePdRyflKVY+r7jdbib0+xS71ZEhdWykYWUVAAAgAElEQVRLtXP+xEUWTlqaqzbKVC3tV+wDvFFAwXhYxVYqpbraMJoNlKqYvTREww71iI6N8pEQEEJgMOm5q5ClEwLlzOFzDLxjGN1L96d76f4MvGMYpw+pa8gXFN+OmucnNW1Ls/PDBz9jt2bOpA4OeoMeS2TW+RqKTqFdr8DkHXzaC1pLGtckQl8NUWoNovgHiGJDESUXokRNJPFSKq4s9OIzF3Fw2Bz8+MkyVUXKGaPmhWzsJ/acUl0KO21O1szfkO211hQro7u/T9+aL+Jx+75Po8VIiXIx3P9c3pQd1bjzkRZ+BUWEEBjNxhylfXU6HR/9+TaN76mPzqBDp1eo07wmk9ePJSIqdDPiUGO32hnYcjh7/z6Iy+nG5XSz9++DvHzH8JAZ1txwZOcJ1eOKIrh4OnNgQ3AQQtDtlc6YwvxrPIRHhfHer8ODmhWsuXQ0EMIA5nY+x4qViEDRqc8HKtTyLTyekE2Y4OkDZwIfYBaYI8y4VXzcAGGRlmyvHdf7Yzb/tt3ngaEogkp1K9L+sVZ0fvbuXLmEciK8eDgfrB7Nu49O5uzR8yAllepUYPicV7Iso3c1MWWieeenN3HYnXjcnnyLkAWC2+Vm78aDeNwe6jSvGfD+zNoFG7Gn2ZGe/9yI0iOxpzlYt3ATd/XOX3nGQKl0UzkunvJXVvW4PcSEUFOn9/BuWFNs/PTZrwhFID2Se55sx/OTnwj6hrlm8DVUMRgN9Hj1fuZNXOIrOmYx0vdtX39mdJkoFEV9U6vqraGrTVy2aiwVa5fj6I7jeK4yHuZwEw++kHWB6vjzCX7GHrxl+EqUi+bhNx4M6jir16/C17s/4uLpSwhFyZdPtrC0bHZv2M/IB8fjtLtAeGV/h85+OaBNxPPHLmQI4V2NLdVeqMlQfUb2ZPeG/Zmkpk3c/393YwkPnUyGTqfjmYmP03f0w8SfS6BEuehcTQbyg+bSuY6RUiJtf+C53B/PpUfxpM5CytzXc+0zogd9RnTPkBAuVz2WobNe9ovSMRgN9Br6kN/s0xRmpN+Y0Oqyj/7xDUpXLoUl0ltuzmAycE+/O7kzG7/n5XMJGEzqc5244/7yAsGiZPkS15T6YmpSGm92fIfEi8mkJVtJS7KSkpDK6G4TuXQ2Pt/t1ritCuZw/5WKOdxEjduqBDDiwLil5U2MXPA6FWqXA7wulUcGP8DT4x8rkP7NYSbKVosNmbEHbYZ/XSOTx6VLFqdnzTp3IW2LIOZ7rxsnB4QQPPzGgzz8xoO43e5sk50eGfwgkdHhzHp3IQlxSVS9tSLPvN83x8iIQImtXIoZBz9h17p9xJ9LoE7zmpSuVCrba8rXLOtTI/UKOr2Oem1uDtVQrznW/7jJx+1yBemRrJq9ju6D7s9Xu43vbUDZ6rGc3HcmY5VlMBkoWz2WxvdmvXLweDxs/WMnR3ccp3zNsjTr1DBomvhXaHJPA6bvnZzj5/1aRTP41ynSdQrSZuOr024D1wGw/Q6W+/LUXk4ffiEEnZ+5m87P3J33wQaIoijZhjdmxhxmou/onswYOS/DXaXoFMwRJh4dWrg1R4sSyZdTVCOYHDYniVdlhOYV72b0GL57ez4rvl8LQLtH7+DxkT2z/JylJqYyqO1Izh4+j9PuxGA2UCwmkskbxoZk1XQ9GnvQDP71i/MfEDr/YkwyDelYg8ijwb/e6PFqF8pWi2Xu+EVcPpdA/bY38/ionjmuDm4kGrS7BZ1ewZXJC2gON9GofWAy2GGRFp6Z+DjPTHw8V+d//eYsTuw9jStdOdXldOOwOvhowBe889ObAY0lN0gp2fHnHtYu+BuDyUD7x1pTvX6VkPcbbDSDfw0gpQOZNhesCwABlu6IsJ7Zu2VElPdcP/SgFJ2Sa4XJHV2bcUfXZgXe7+4N+1n+9QqsqXba9GxBiwcaF8kZZfX6VWjd43bW/vB3xiarOdxE/TtvoX7bgnV9rZqzPsPYX8Ht8rD51+24nC70htCZMiklHz79BavnrseeZkcIwU+f/crjox+m52tdQtZvKNAMfhFHSomMfxoc/wLpGZvJh5H2lRD9v6xTvk0tAROQmukFPcLSI3QDziWpiamsXbCR5Msp1L/z5qAqXxZlZr27gFnv/ojD6kBKycalW6jf9mbeXjw4qCF4O9fuZfGny0iIS6Llg024t/9d+Yo0eX3a8zTv1Ijl01bicrm5+/G23NmrZYGXPfR41MNvpZQh17XfvX4fq+euz3joSSmxWx3MGDGHOx9pmefC7IWJZvCLOo6/wbmdDGMP3p+dW8C5GYzqyTtCGCFmBjJ+AMhEvAFZEoq9h9BXCf24s2HPX/sZcu87SA+4HE50Bj2339+IN78feE0LdeXExTOXmTlmgU84qC3VzvY1e/hn2b9BK7m46JNf+N+bs3BY7UgJ+zYdZOnUP/hk47g8G30hBK27307r7rcHZWz55Y6uzVgxay3uq/YUFEVQr3XdkOs2rV240S8DF7xFeDb9spVOAzqEtP9gcv1+u64XnFtATZte2sCxJdtLhaE2otRqRMxMRPSXiNIbUSw5Z49K6UTafkWmfIq0Ls1TKGdOuN1uRnadiDXZhi3Vhsvpxp5m5++ft7B6bvbZsdc6//6xE73B33VjS7Gx7seNuWrD7XazcekWZo75gd+/W+OXmZqalMZXQ773JjalT3ztaQ7OHYtj+bSVAb+HwmLAxD6UrlACS4T3gWUON1GsZCSD/vdsyPs2WYwoOv8VjaIoIQ2hDAUBzfCFEDHAXKAKcAzoKaVUDdAVQhQD9gCLpJQvBNLvDYVSEoRZxeibQMl5g1EIAYZbct2d9FxGXuoJnkte2WRhgeQJUGIeQhd4ScEDm4+ops/bUu0s/3pFUHVDihrmCLOqK0TRKYTnQiohLdnKK63f4uzh81hTbVjCzUx97VsmrX+H8jW82c/7Nh7EYNTjsPo+pO1pDtb/uImuL16bm/XFSxbj672TWLdwE4e3HaVC7fK06Xl7SBOirnBX71YsmLQUd6bda49HcnsXVRXiIkugM/whwAopZU1gRfrvWTEG+DPA/m48zPeh+mcSOjAHrvWSGZk0DtxnQKYC0mv0PXHIpJHBad/jydL/61GJ+S7KZOVXzoqmHRsgVDKS9UY99/S7M8frvx09j5P7znjVN6VXOjfxUjLjH5+ScU5EdISqpLIQEFWqWJ7GW9QwGA3c+UhLnnrvMe594s4CMfYAletW5Onxj2E0GzCHm7BEmDGFGRk+55Vcaxrt+HMPLzQbQqewR3ms2nMsm7aiUGrqBurDfwBom/7zDGA1MDjzSUKIRkAssBy4th6JhYxQikH0N8iEl9J98RJENCJ6CkKJCH6Htl+BzEXD3WBfg5QehAhsjlC7SQ3VZBlzuClXRq+wkVKyeMoyvh+7kIS4RMpWi2XAxD65ivYxWUy88/ObDL9/XEZCk8vh5rlJ/ah6S84SFCtnrfOTg5AeycEth0lNTCW8eDi1GlUjukwUtiPnfZKmjBYTD76YtdxEIKQmpXHqwFlKVYghpsy1k0mcFx58oSOtuzfnn+XbMBj1NOvUMNeSznv+2s/QjmOxp6+6zh+7wGcDp5MSn0qPVws2yiegildCiAQpZVT6zwKIv/L7VecowErgMaA90Dg3Lh2t4pUvUkpwHfJO1XTVQxYl4TlXD98N4ivoELG7Azb4AP+u3MmIB8bjcXtw2JzeUL+2NzP6xzeCkjmZlmwl+XIKJSvEBD3ccd77S9JldH31hUb88BpNO96WqzYcdifbVu7CbnXQ4M6biYzO3YP74XJPc/lcgt9xvUHHD3FfZxigM4fP8WbHscSfS0BRFJxOF31H96RNjxaULB8TtOxUKSXfjJzLD+//hN7oLQ3ZrGNDBn/3YqGIvBVVXrtrFNtX7fY7HlbMwoIL04IeUhpQiUMhxB9AGZWXhgEzrjbwQoh4KaXPI14I8QIQJqWcIIToRzYGXwgxABgAUKlSpUbHjx/PdmwawceT8DrYluI7y9eBqS1K9OdB6yc+LpHVc9aTeDGJ29rdSr02dQN6iMWduEB8XCILPlrKuoUbUXQKJouB//uoH+0faxOUMbvdbrqVfJLURP+SkDVuq8rnWyYEpZ+s+PL1b1k8ZZlXyCwdoXiLe0xeP9bnXCklB7YcIeF8AqvmrOfPBX+jUxT0Rj0DJvahY//Aq1r9On0VU1762kcIzWg20ObhFrwxPTTbdKlJaZw7GkepiiUoFhM82eBQ0j22P4kXkvyOmyxGvjnwMSXLBzesM2Q1bYUQ+4G2UsqzQoiywGopZe1M53wPtAI8QARgBD6TUmbn79dm+IWEd9P2YfBc9G4UCwuIYohsNm2lJwVpXQKu3aCvhbB09bqiCoCLpy8xqtv7HN1xHLfL7aeRYwoz8vbiIdmWZcwtyfEp9Cz7tF8CEHhna4sTvg24j+xIS7YyqM0Izhw6hy3NjjnMhCnMyOT1YylXXW1OBhP6TWHN/A04rP+5gkxhJkbMfzXXK5KseLLuQE7u85e/NpgM/Hh5elALn0sp+d+b37Po41/QG/U47S7aPXoHL38xIKRJV8HgpRbD2Pv3Ab/j5nATCy5OD7oSaihr2i4B+gLvpf+/OPMJUsreVw2kH94ZfrbGXiP0SOlEpn7p1duRVjDegYh8A6GvACWXgX0VuA6CvhqY2nnj+tXacZ9FXuoGnlS8Im0WZMqnUGI+Ql85xO9BMvjuMZw6cFZ1oxK80Smzxy0MisEPK2bBHGYiRcXgV6hVLuD2c+w/0sJnm8ez+dftHNx6hDJVSnPHQ02zNKypSWmsnrvBz+9vT7Pz/dgfAjb4iRey1tNJS7IG1eAvmrKMxVOW47A5M+oDr56znsjoCJ55P3fyDIVF39E9Gdl1gp/scteX7itw2etAHbLvAR2EEAfx+uffAxBCNBZC/C/QwWmEDpnwKqRMBc8FkClg/w156SGk5zJC6BHmDoiI5xDme7M09gAyaSx4LpOhyIkVZBIyaUTI38OBzYeJO3kpS2N/hWBJHut0OvqM6uEvA20x8uTY0MpAX0FRFJp2vI3ew7pxV+9W2RrVhLhE1Zq4AHEn/At95JV6rdXdcMVLRhJVunjA7V/N/Ex1GcBbbvPnL3/Lc7RUQdOoQ30Gf/sSpSuXRFEEYcUsPDL4AfqNCU2d3OwIaIYvpbwE+DkDpZSbgadUjn8DfBNInxqBI10nvDN4HyVND0grMm02IuL53DdmX+O91gcPODYipRshfDcIj+89xbmjcVSrVznglPRLZ+OzLLxyBUWncMsddbI9Jy90ffE+TBYj349ZwOVz8ZSvVY4BE/rQqEP9oPURLEpXKqlatUwogptb1Fa5Im88+W4vtq7YgT3NjtvlQQgwmo28OOWpoAcVJF1WX004bE6cdmdQVxOhoNVDzbija1Ov0qfJUODSFFco2s4vjdDg2gfCADJzApQdHNvy1pbQq7QDoOPqBWRqUhpv3f8eBzYfRm/U47A5ubNXSwZ99Wy+I2lualrDZwMzM4oiMIeb6D28W77aV0MIQaenO9Dp6aKfTm8wGuj/7qNMfWNmxuxYKAJzmInHR/UMuP2Ktcvzxb8TmfPeIvZs2E+5GmV4ZEjXkNRAqN2kBjvW7PE7XrZabMiNvTXFyp8//E3ciYvc1LQGje6uny8JkCu1jAsTzeBfR0jpBsdmr4vG2BihZLGs1lUCqVag3ACGPH5ZzV3SVTyvzkI0gPken1nMR898yb5NB3HaXRnxyGvm/UWVmyvmOxY5pkw0XZ6/h6Vf/p4RKaLT6xCKIDI6gnpt6tJvzCNZbmhej0gpObj1CHEnLlKzYTW6PHcvJcuX4PuxC7h4+jI3Na1O9fpVWfLZcqrXr0LbR1oGlMBUtmosr3z5TBDfgTrPftCXQW1G4LA68HikdzVhMfLCJ/1D2u/xPSd5pfUInA4XtvTs5kp1yvP+qtHXZOhpQFE6oUSL0skb0rkPGf9kugSDAOmEyNdQwvuqnu+59Ag4d+FjqEUYouRShK587vv1pCAv9wP3IZAeEAroKnr1e9IfOHarnQej+6lGt5SuXIrvj36Wh3eaqX8pWTNvAz9+/AspCanc0bUZ3V+9P9ex7dcTCRcSGXL3O5w+dBZFp2REsgz66lkUReHcsThebD4UW6oNW6odc7iJsGJhTNk47ppQfDy+9xSzxi7gwObDVKpTgUeHPkTtJjVC2ueA+q9ybNcJrjaTRrOBnm88QN9RD4e07/wSsrDMUKIZ/NwjpRt5oZU3lNIHMyLmW4TRv2yc9CR75RJsvwIe0NdAFHsHYcy7L1pKCc6t/0X1GJr4zO6TLifzcLkBqgY/IjqcHy99k+c+g4GUkn2bDnH2yHlq3FaVSjfl/kFXFBlyzxi2r97tU6XKFGbi6QmP8cBz9zL0vnfZ8ts2HwkLRafQoksTRi54rTCGXKS5fC6ex6o+p+o2jK1SiplH8j9RCSWhDMvUKAo4NqsramJHWmerGnyhRCKiPvQqYUoXQgnLd/dCCDA28v5TITI6gjJVSnHqwFmf44oiaHx34Wx2Jl1K5o0Ob3P64FmEouBxuWnYoR4j5r9aZOK6pZT8Of8vfv7ydxw2B+0evYOOT7VXDeVLjk9hx5o9fiUJ7Wl2Fk9Zxr1PtuOfX//1q4DmcXvY+Ev2qqsa/hTWpmugaPLI1wMyBfXqVhI8idleKoQxIGOfG4QQDPrq/zCFmTLCBA0mA+FR4fQf1zuHq0PDB099zvHdJ7Gl2rEmW7FbHWz9fQdz3vuxUMajxqT/m8r7/T9j26pd7PnrAF8Nnsnr7Ubhdvnvv9hS7QiViByA1EQrb7R/27/cZTrBLgR+vRBTJpqKtcuT2bYbzQY6PB6c7O2CRjP41wPGRl6fvR8WhPneAh+OGre2qsMX/06k04AONGh3Cz1f78LXeyZRpkrpAh+L3Wpn07J//WfDVgc/f/lHgY9HjVMHzvDHd3/6yBbY0xwc2XmCDUv8XZ0ly8eoqmHqDDqq3lqJIzvUZUr0Bh1tH24RvIFfZwyd/TKRMRFYIswIRWCJMFO9QRUefuOBwh5avigaa9cbBOlJ88a/y1QwtvBmtQYBoUQhI1+D5A/wxtZLwAKGm9LllYsGFWqW5cUpfukZBY7L4fJRkrwaNa3+YOKwOfh29Hx+nbYSh91F8/sb8fT4xyhZLsbnvB1/7lV1G9hSbGz5bRutHvJV5xRC8Nq053iry3hcThdupxuTxUh4VDgxscWxpagJ4kFUbHGeeV99Y18DKtepwPfHv2DtD38Td9IblnnbXbdes5XZNINfQEjHZm9tWvBGs+BBhj+BEjkoKO0r4X2RhvpI62zwJHpn9ub7ss2SvVEJLx5OpTrlObrzhM9xRafkq8xgcnwKiz5Zxl9LNhMVW4yHBnbOcm9iWOdx7NmwP0MeYM3c9WxbuYvp+yYTFmnJOK94yUh0Ki4avVFPdGyU33GA29rdypfbJrLk0185ffAs9drezH1P3cXCyUsxGPU4VTbNL59LYOITn/LCJ/1VI3U8Hg92qwNzmKlI+a1dThcet6dA4trNYaZr1oWTGS1KpwCQ0oGMawEys2KexVt60NS8gMYhwbkNnDtBVwZMbW/YB8L+zYd5vd0oXE4XTrsLU5gxQ6smL+qFKQmpPHvb68SfT8gw4qb0xKaer/nmFxzceoRXWo/wkwgwhZkYMKEPXZ77r6CNw+6kV8VnSLqYnOlcI1/vnkRs5ZyrnV3h/PEL9L/5ZdW6rOB90EWVLs6Mg59kxJZLKVk4eSnfv7OAtKQ0ImMieXJsr1yrbLpdbuLjEikWExFUo5wcn8KkZ6eyYdEmPB5JzUbVePWrZ6l6a2h1m64lsovSuTbXJdcajk34yw8A2JDWHwpkCFI6kPFPIC/3QyZPQCYORl640yuzkEusKVZmvbuAAQ1eZWDLYfwx889CqdoTDGo3rs60vZPo+foDtOrWnH5vP8K0vZPzLFW75PNffYw9eCNjZoyYS2piqs+5h7cf99sAvHL+vk0HfY4ZTQbeXzGS0pVLYY4wE1bMQkRUOG/NHZQnYw8QW7kUb817lYjocExh/sbX4/aQlpzmU1N40SfL+Gb4HJIvp+B2eUiIS+TTgdNZ8f3aHPtb8tlyupV+kr41X+ShEk/w5evf4narJfrlDSklr981mg2L/8HldONxe9i/6RCvtB5BfFz2wQkaXjSXTkGQZRFw6S1GXhBDSJ0Ojq1kFDeRDq92TsIgRMmcHzoOm4OXWgzjzKFzGcbtyI7j7Fq/l5c/D32mZSgoWb4E/d4OTMBq49KtPsb+CgaTnoNbj9Lgzv/qCZerHqvqFjFajFSq47+fU/XWysw88imHtx/DYXNSrnosGxZvZv4HP9GoQz2q1cv9rLbZfQ2Zf+5/TH1jJj99ttxvw9qWYufojmOA17DOfOcHbJnFytLszBg5h7t6t8qynzXzNvhIOQD89PlvKIrg6fF9cj1eNfb+fYDTB8/55XM47U6Wfb2CR998KKD2bwS0GX5BYGwKUkXzRYQhLJ0LZgzW+fhXsvKAax/SnbNy4uq5Gzh3NM7HuNlS7fw+Yw1nj5wP7liDTGpiKqcOnsXpUItkCoyS5aJVZ+0up9tPMfLWVnWIrVIKvcE3DNJg1HPvk+rlHYUQ1GhQFYfVQZ9qz/P5K9OZNux7XmoxlA+f/iJPKyy9QU+TextgMPvH8ZvDTRluEbfLTfIldbGyC6cuZ9vHd2/P91e1TLOz+NNfcTmz1j3KDacPnlO91w6bk2M7c79SvZHRDH4BIJQIKDYGMJOxqBJhYGwGpgIS4VJ74HgHgn8NW3+2/L7dJ0TwCopex671+wIbW4hw2ByMf/wTepR5mv9r9AbdS/dnyWfLg9pH14GdMFp83SSKTqF8zTJUubmiz3EhBO+vHEWzTo3QG3To9Aq1m9Tgoz/fJqpU1nLCToeTUQ9NxJZqx5Zqx+VwY09zsGrOOv7+OW9JU4061KNUhRI+Dx1FEYRFWmj7SEvA+2AoUT5G9foKNctm2/7F0+oPBLfLTVqSWnJg7qlar5KqFLIpzESd5sEXbLse0Qx+AaGEPYAouRjCnwLLo4ioTxBRn/vJB4cMy/14i41lQlchy0pWV1OqYgm/mSl4S+zGlFGPGsktiReTOLrrRNBDIic9O5U/F/yN0+7ElmIjLcnK1DdmsmHJP0Hr45aWN/HcpCfSdWksmMKMVK9fmbFLh6qeX7xkMUYtfJ3FSd+xKOFbpmwcl+OG4861+1Rn8rZUO79+sypP41UUhY/WjqF1j9sxmPTo9ApN72vIJxvH+YiBPfXeY37+fpPFyNMTsnfLVG9QRfV4RFQ4EdG5K/qdFTUaVOXmFrUxXrVCUXQKlggzd/dtG1DbNwpalM4Nglfk7BFwnwKZBphB6L0iZ4a6OV5/5vA5BtR/zWe5LoSgRPloZh79LF8Sx7Y0OxP6TeHvn7agN+qQEvqOfpjurwTu5kpLttK9dH+/ak/glVX+5O9xAfdxNXarncPbj1MsJiJf1a8ObTvK1t93EF48jFbdm/vUa93y+3be7vGB6gy5+f2NGLM4/wXkpJRZhluu+3Ej04fP5vyxC1SoVY7+4x6lyb3ZV8nat+kgr7Ublam6k5GBnw+gQ5/AQxsdNgffjprH8mkrcdicNL2vIQMm9qF0xZIBt329oImnaQDesobYVyAdW70ze0sXhJL72fmmZf8y/vFPcDqceNweylSN5e1Fb+Rbfnjso5PYsGiTz76AOdzE4G9f5I6uzbK5MmfOHYvj6VsHqbqhSpSLYc6pLwNqP1hIKfngqc9ZPXc9LqcbvUGPEDB60eCMsowOm4Pusf2xJvvuwZjDTbw+/Xlad789JGOzptpYM3cDJ/adolq9KrTu3jxXIZb7Nx9m+vDZHPr3KLGVS/H4yB75ym/QyB+awdcIGm63m2O7TmIKM+Xoz82O1MRUepR5SlWJsHaTGkzZGNgM3O1y0710f1ISfEMjFUXQqvvtDJ/zSkDtB4sNS/5hXO/Jfg8mo8XInFNfZsg8b1y6hTE9P0RKicPuxBxmolmnhgyd9XJIsj4zSylbIsxExkTwyd/vElMmOuj9aQQPLQ5fI2jodDqq168SkLEHSLqckqVo16Uz2UeC5AadXseAiX0wXeWXVhSBKdxE39GBV3sKFr99s1p1FeKwOniy7sskXvQm6zXr1IhvD0/hybGP0ntYN8YtH86w2a+ELMV/0jNfknQxKWNs1hQbl87E88WrM0LSn0bBoMXhaxQKpSuWRG/UQyZjp+gU6rW9OSh9dOx/FyXKxTD73YXEnbzIzS1q8/ionvnysYeK7FbYSReT+XbUvAz9oZgy0XQLwv5GTrjdbv5ductHNx+8q6a/Fmur7msZbYavUSjo9Dqe/bCvTySIolMwh5t4fGSPoPRx6N+jLJ+2kqTLydzaui6PjehRpIw9QPs+bXxWIVfjcXtYt3BjAY/Iuxmf1UauWlF0jWsH7a+nUWjc0/dO3l48hNvuupVyNcrQ4fE2fLF1IuVrBOYuAti6YicvtxrOuoUbObH3NKtmr+P5JkM4uPVIEEYePFo+2ISmHf0L1FzBqJIkFWoURaH5/Y38XG4Go542mpTyNY22aatxXfJEnYGc2n/G73i9NnX5YNXoQhhR1kgpeb7pEA5tPerj4jFZjPQe3o1ehSAZEH8+gZdbvUX8+QScdhcGo56y1WL5cM1owosHFk+vEVpCVuJQCBEDzAWqAMeAnlLKeJXzKgH/AyriFWu/T0p5LJC+NTSywmF3cubgWdXX9m08FLJ+PR4P0iPzXEFKCMGYJUMY1GYE8ecTkB6JlJLb2t1Kj0yKmwVFdGwU0/ZOYvPybZw6cJYqt1S8pnXgNbwEumk7BFghpXxPCDEk/ffBKud9C4yVUv4uhIhAXTpS4xohPi6Rz1+ezvpFmwBo2bUpz016Ilt5gIJEb9BhMBv9NF0AImMigu/izGMAAAnnSURBVN5famIqn7w4jTXzNuB2ubn59tq8/OUAKtetmPPF6ZQoG830fZPZvno3549doFbj6nkSRwsFOp2OZp0a0axToQ5DI4gE5NIRQuwH2kopzwohygKrpZS1M51TF5gqpbwjL21rLp2iicvp4sk6LxN34mJGbVWdXkfpSiWZtndSkSkA/sWr3/DzF79jt16d8WniiTEP0+2V+4PWj5SSF5sP5cj2YxkFRoSAsGJhfHPg4yLzENS4cQhlHH6slPLK2vkcoCbKUgtIEEIsFEL8K4SYKApMQObGRrpPI61LkY5NSBmcRdVfSzaTcCHRp5C22+Um4UJinoW8QslT7z3m1YsxGwgrZsFoNtB5QHu6DgzudHX/P4c4vuekTzUpKdMle/+3Iqh9aWgESo7TMSHEH4Ba7vywq3+RUkohhNpyQQ+0Am4DTuD1+fcDvlbpawAwAKBSpUo5DU0jC6SUyKRRYF0AGLyCmEo0xHyH0JUPqO1ju09iVamPaku1c3z3qYAlEYKF3qDnjW9e4Jn3H+f88QuUq16GiKjgbzae3H9GNYTRYXNmWThcQ6OwyNHgSynbZ/WaEOK8EKLsVS6dOJXTTgHbpJRH0q9ZBDRHxeBLKacCU8Hr0sndW9Dww7YYrIsAh/efBNxWZPzziJKLAmq60k3lsUSYVXVdKtUJ7GESCoqXLEbxksVC1n7VWyv5JSiBVzCsdpMaIetXQyM/BOrSWQJcKXnfF1iscs4/QJQQ4kpdtnbAngD71cgGmTYTyKys6AHXYaTrVEBtt3iwCZHREej0/310dHodkTER3N5F1W14XVOjQVVualbDp6iIoghMYSbueUK9qImGRmERqMF/D+gghDgItE//HSFEYyHE/wCklG7gNWCFEGInXgfDVwH2q5EdnjT140KXLo2cfwxGAx//9S7N72+MTq9Dp9dx+/2N+eSvd4vMhm1BM/bnN7n/2buJiArHaDHSrHMjPt30XobwWShw2J2smf8X8z/4iZ1r9xbJ2sLJ8Sks+3oFCyct5fjewCYaGsFBS7y6DvEkfwypXwGZwhJFDKL0+qAVXbny2ckqDV8jNJw+dJZXWr2FLc2B0+5Ab9BTs1F13ls+LFfyxQXBlt+3M7LrRITwbuoLRaHzgPY8+2E/7fMSYjS1zBsMEf4E6MoBlvQjesCMiBof1Apb2WmuaISOd3tNIuFCEtZkKy6HG1uqnf2bDjJvoppHNXe43W7WL9rE5Oe+4tvR8zh3TG07LnfYrXZGd/8Ae5q3JKPT7sJhdfDL/1bw74qd+W5XI3BuzDX4dY5QIqHkImTaEnCsA105RFgvhL5KYQ9NI0Di4xI5uvMEMtNGscPm5NdvVvPYW3kXnnM6nLzR/m0ObTuGLcWG3qhn3oTFvDVvUL4Kl2xbuUu12Lgt1c5vM1bTsH29PLepERw0g3+dIoQFEf4whD9c2EPRCCLS4/Hugqngcecv12L5tFUc3Ho0IzPZ5XDhAsb1+Zgfzn+d570ZdzbjcF2Vv6FR8GguHQ2Na4iYMtGqJSUNJgPtHs1TMnsGK79fqypDIT2S/f8cznN7t7W7BbfL3+ibw0207906X2PUCA6awdfQuMYYOutlIqLCMafr6FsizFSsXS7fqppGi/pGr/RIDKa8OwEsERbe+OZ5jBajt8iN8Br7Fg80oVmnhvkao0Zw0Fw6GhrXGNXqVWbm0U9ZOXs954/FUad5LZp39tevzy2dBrRnz1/7/UotRkSFU+O2qvlqs3X327mpWU1WzV5HSkIazTs3ou7ttbRN/kJGC8vU0LjBkVIy+bmp/D5jDUJR0OkUdAYdE/4YQY0G+TP4GoVHdmGZmsHX0NAA4NSBM+xYs4fIEpE069QQo6ngq21pBE7ICqBoaGhcP1SoVa7I1fzVCC7apq2GhobGDYJm8DU0NDRuEDSDr6GhoXGDoBl8DQ0NjRsEzeBraGho3CAU2bBMIcQFIL814koCF4M4nGsd7X74ot0PX7T74cu1fj8qSylLqb1QZA1+IAghNmcVh3ojot0PX7T74Yt2P3y5nu+H5tLR0NDQuEHQDL6GhobGDcL1avCnFvYAihja/fBFux++aPfDl+v2flyXPnwNDQ0NDX+u1xm+hoaGhkYmNIOvoaGhcYNwXRh8IUSMEOJ3IcTB9P+jszivkhDiNyHEXiHEHiFElYIdacGQ2/uRfm4xIcQpIcSUghxjQZKb+yGEaCCE+EsIsVsIsUMIcd0VAxZC3CuE2C+EOCSEGKLyukkIMTf99Y3X6/fjCrm4H4PS7cQOIcQKIUTlwhhnMLkuDD4wBFghpawJrEj/XY1vgYlSyjpAUyCugMZX0OT2fgCMAf4skFEVHrm5H2nA41LKm4F7gUlCiKgCHGNIEULogE+BjkBdoJcQom6m0/oD8VLKGsBHwPiCHWXBkcv78S/QWEpZD/gBmFCwoww+14vBfwCYkf7zDODBzCek/zH1UsrfAaSUKVLKtIIbYoGS4/0AEEI0AmKB3wpoXIVFjvdDSnlASnkw/eczeCcDqtmK1yhNgUNSyiNSSgcwB+99uZqr79MPwF3i+q1JmOP9kFKuuspG/A1UKOAxBp3rxeDHSinPpv98Dq8Ry0wtIEEIsVAI8a8QYmL6U/56JMf7IYRQgA+A1wpyYIVEbj4fGQghmgJG4HCoB1aAlAdOXvX7qfRjqudIKV1AIlCiQEZX8OTmflxNf2BZSEdUAFwzFa+EEH8AZVReGnb1L1JKKYRQizXVA62A24ATwFygH/B1cEdaMAThfjwH/CKlPHU9TOKCcD+utFMW+A7oK6X0BHeUGtciQojHgMZAm8IeS6BcMwZfStk+q9eEEOeFEGWllGfTv7BqvvlTwDYp5ZH0axYBzblGDX4Q7sftQCshxHNABGAUQqRIKbPz9xdZgnA/EEIUA5YCw6SUf4doqP/fzh2rNBAEYRz/D0iwU6xVVLBXsBdEGwsrCyFCfA87n8FGsBBbBTF19AFMIwoWahkLCx9ALMZiT9gicAF1l9x+P1hIli3mPsJwzCXJ5Q2Yi97PVnvDzgzMbAKYAj7SlJfcKHlgZpuEm4Z1d/9MVNu/acpIpwt0qtcd4HrImT4wbWY/c9kN4ClBbTnU5uHubXefd/cFwljnfFyb/Qhq8zCzFnBFyOEyYW2p9IFlM1usrnWPkEsszmkXuPXm/jKzNg8zWwVOgB13b8YXPNx97BdhzngDvAA9YKbaXwNOo3NbwAPwCJwBrdy158wjOn8AHOeuO2cewD7wBdxHayV37X+cwzbwTHg2cVjtHREaGsAkcAG8AnfAUu6aM+fRA96jz0M3d82/XfprBRGRQjRlpCMiIjXU8EVECqGGLyJSCDV8EZFCqOGLiBRCDV9EpBBq+CIihfgGrGys/FBN0ioAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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": null,
      "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": "b894645e-6ce9-4c2e-9d60-cdcd854e4495"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "6/6 [==============================] - 1s 36ms/step - loss: 0.5667 - accuracy: 0.8393 - val_loss: 0.6492 - val_accuracy: 0.6279\n",
            "Epoch 2/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.3443 - accuracy: 0.8393 - val_loss: 0.5098 - val_accuracy: 0.6977\n",
            "Epoch 3/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2406 - accuracy: 0.8929 - val_loss: 0.3613 - val_accuracy: 0.8605\n",
            "Epoch 4/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2168 - accuracy: 0.9167 - val_loss: 0.3645 - val_accuracy: 0.8605\n",
            "Epoch 5/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2301 - accuracy: 0.8988 - val_loss: 0.4342 - val_accuracy: 0.8605\n",
            "Epoch 6/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2191 - accuracy: 0.9167 - val_loss: 0.4084 - val_accuracy: 0.8605\n",
            "Epoch 7/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2567 - accuracy: 0.8869 - val_loss: 0.3422 - val_accuracy: 0.8605\n",
            "Epoch 8/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2513 - accuracy: 0.8988 - val_loss: 0.4096 - val_accuracy: 0.8372\n",
            "Epoch 9/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2139 - accuracy: 0.9107 - val_loss: 0.4799 - val_accuracy: 0.7209\n",
            "Epoch 10/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2231 - accuracy: 0.9107 - val_loss: 0.3972 - val_accuracy: 0.8605\n",
            "Epoch 11/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2033 - accuracy: 0.9226 - val_loss: 0.3831 - val_accuracy: 0.8605\n",
            "Epoch 12/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1916 - accuracy: 0.9405 - val_loss: 0.3639 - val_accuracy: 0.8605\n",
            "Epoch 13/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2005 - accuracy: 0.9405 - val_loss: 0.3492 - val_accuracy: 0.8605\n",
            "Epoch 14/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1945 - accuracy: 0.9345 - val_loss: 0.4050 - val_accuracy: 0.8605\n",
            "Epoch 15/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1898 - accuracy: 0.9286 - val_loss: 0.4337 - val_accuracy: 0.8605\n",
            "Epoch 16/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1908 - accuracy: 0.9226 - val_loss: 0.4427 - val_accuracy: 0.8605\n",
            "Epoch 17/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1897 - accuracy: 0.9286 - val_loss: 0.3872 - val_accuracy: 0.8605\n",
            "Epoch 18/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1871 - accuracy: 0.9524 - val_loss: 0.3717 - val_accuracy: 0.8605\n",
            "Epoch 19/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1961 - accuracy: 0.9345 - val_loss: 0.4220 - val_accuracy: 0.8605\n",
            "Epoch 20/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1949 - accuracy: 0.9286 - val_loss: 0.3772 - val_accuracy: 0.8605\n",
            "Epoch 21/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1796 - accuracy: 0.9524 - val_loss: 0.4178 - val_accuracy: 0.8605\n",
            "Epoch 22/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1835 - accuracy: 0.9345 - val_loss: 0.4119 - val_accuracy: 0.8605\n",
            "Epoch 23/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1781 - accuracy: 0.9405 - val_loss: 0.4477 - val_accuracy: 0.8605\n",
            "Epoch 24/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.1796 - accuracy: 0.9345 - val_loss: 0.4339 - val_accuracy: 0.8605\n",
            "Epoch 25/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1763 - accuracy: 0.9464 - val_loss: 0.3802 - val_accuracy: 0.8605\n",
            "Epoch 26/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1839 - accuracy: 0.9345 - val_loss: 0.4218 - val_accuracy: 0.8605\n",
            "Epoch 27/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1782 - accuracy: 0.9286 - val_loss: 0.4185 - val_accuracy: 0.8605\n",
            "Epoch 28/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1849 - accuracy: 0.9226 - val_loss: 0.3679 - val_accuracy: 0.8605\n",
            "Epoch 29/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1710 - accuracy: 0.9524 - val_loss: 0.4132 - val_accuracy: 0.8605\n",
            "Epoch 30/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1711 - accuracy: 0.9345 - val_loss: 0.4188 - val_accuracy: 0.8605\n",
            "Epoch 31/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1711 - accuracy: 0.9345 - val_loss: 0.4058 - val_accuracy: 0.8605\n",
            "Epoch 32/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1657 - accuracy: 0.9405 - val_loss: 0.3362 - val_accuracy: 0.8837\n",
            "Epoch 33/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1695 - accuracy: 0.9524 - val_loss: 0.4052 - val_accuracy: 0.8605\n",
            "Epoch 34/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1619 - accuracy: 0.9405 - val_loss: 0.4034 - val_accuracy: 0.8605\n",
            "Epoch 35/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1764 - accuracy: 0.9345 - val_loss: 0.4267 - val_accuracy: 0.8605\n",
            "Epoch 36/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1630 - accuracy: 0.9286 - val_loss: 0.4148 - val_accuracy: 0.8605\n",
            "Epoch 37/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1663 - accuracy: 0.9524 - val_loss: 0.3838 - val_accuracy: 0.8837\n",
            "Epoch 38/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1573 - accuracy: 0.9464 - val_loss: 0.5332 - val_accuracy: 0.8372\n",
            "Epoch 39/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1657 - accuracy: 0.9345 - val_loss: 0.4669 - val_accuracy: 0.8605\n",
            "Epoch 40/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1660 - accuracy: 0.9345 - val_loss: 0.4267 - val_accuracy: 0.8605\n",
            "Epoch 41/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1611 - accuracy: 0.9345 - val_loss: 0.4860 - val_accuracy: 0.8140\n",
            "Epoch 42/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.2036 - accuracy: 0.9167 - val_loss: 0.5028 - val_accuracy: 0.7907\n",
            "Epoch 43/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1688 - accuracy: 0.9464 - val_loss: 0.5672 - val_accuracy: 0.8605\n",
            "Epoch 44/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1705 - accuracy: 0.9107 - val_loss: 0.5138 - val_accuracy: 0.8605\n",
            "Epoch 45/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1503 - accuracy: 0.9464 - val_loss: 0.4203 - val_accuracy: 0.8837\n",
            "Epoch 46/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1554 - accuracy: 0.9524 - val_loss: 0.4916 - val_accuracy: 0.8372\n",
            "Epoch 47/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1503 - accuracy: 0.9345 - val_loss: 0.4633 - val_accuracy: 0.8605\n",
            "Epoch 48/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1429 - accuracy: 0.9524 - val_loss: 0.4613 - val_accuracy: 0.8372\n",
            "Epoch 49/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1428 - accuracy: 0.9583 - val_loss: 0.4645 - val_accuracy: 0.8372\n",
            "Epoch 50/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1406 - accuracy: 0.9583 - val_loss: 0.4272 - val_accuracy: 0.8837\n",
            "Epoch 51/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1443 - accuracy: 0.9524 - val_loss: 0.4582 - val_accuracy: 0.8605\n",
            "Epoch 52/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1695 - accuracy: 0.9226 - val_loss: 0.5647 - val_accuracy: 0.8372\n",
            "Epoch 53/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1586 - accuracy: 0.9345 - val_loss: 0.4300 - val_accuracy: 0.8837\n",
            "Epoch 54/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1540 - accuracy: 0.9405 - val_loss: 0.4684 - val_accuracy: 0.8605\n",
            "Epoch 55/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1809 - accuracy: 0.9286 - val_loss: 0.5720 - val_accuracy: 0.8372\n",
            "Epoch 56/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1511 - accuracy: 0.9464 - val_loss: 0.4173 - val_accuracy: 0.8605\n",
            "Epoch 57/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1375 - accuracy: 0.9524 - val_loss: 0.4836 - val_accuracy: 0.8605\n",
            "Epoch 58/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1400 - accuracy: 0.9524 - val_loss: 0.5516 - val_accuracy: 0.8372\n",
            "Epoch 59/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1445 - accuracy: 0.9286 - val_loss: 0.4529 - val_accuracy: 0.8605\n",
            "Epoch 60/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1619 - accuracy: 0.9405 - val_loss: 0.4599 - val_accuracy: 0.8837\n",
            "Epoch 61/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1621 - accuracy: 0.9286 - val_loss: 0.6293 - val_accuracy: 0.8372\n",
            "Epoch 62/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1394 - accuracy: 0.9583 - val_loss: 0.4824 - val_accuracy: 0.9302\n",
            "Epoch 63/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1482 - accuracy: 0.9524 - val_loss: 0.4631 - val_accuracy: 0.8605\n",
            "Epoch 64/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1489 - accuracy: 0.9405 - val_loss: 0.4692 - val_accuracy: 0.8837\n",
            "Epoch 65/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1302 - accuracy: 0.9524 - val_loss: 0.7568 - val_accuracy: 0.8372\n",
            "Epoch 66/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1395 - accuracy: 0.9405 - val_loss: 0.5548 - val_accuracy: 0.8605\n",
            "Epoch 67/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1277 - accuracy: 0.9524 - val_loss: 0.5705 - val_accuracy: 0.8372\n",
            "Epoch 68/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1207 - accuracy: 0.9583 - val_loss: 0.5437 - val_accuracy: 0.8605\n",
            "Epoch 69/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1340 - accuracy: 0.9464 - val_loss: 0.5551 - val_accuracy: 0.8372\n",
            "Epoch 70/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1260 - accuracy: 0.9524 - val_loss: 0.5215 - val_accuracy: 0.8605\n",
            "Epoch 71/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1212 - accuracy: 0.9524 - val_loss: 0.5889 - val_accuracy: 0.8605\n",
            "Epoch 72/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1232 - accuracy: 0.9524 - val_loss: 0.6109 - val_accuracy: 0.8372\n",
            "Epoch 73/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1147 - accuracy: 0.9524 - val_loss: 0.6046 - val_accuracy: 0.8372\n",
            "Epoch 74/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1166 - accuracy: 0.9583 - val_loss: 0.5631 - val_accuracy: 0.8837\n",
            "Epoch 75/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.1162 - accuracy: 0.9583 - val_loss: 0.6869 - val_accuracy: 0.8372\n",
            "Epoch 76/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1123 - accuracy: 0.9643 - val_loss: 0.6311 - val_accuracy: 0.8605\n",
            "Epoch 77/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1167 - accuracy: 0.9524 - val_loss: 0.6711 - val_accuracy: 0.8372\n",
            "Epoch 78/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1222 - accuracy: 0.9464 - val_loss: 0.6401 - val_accuracy: 0.8605\n",
            "Epoch 79/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1167 - accuracy: 0.9524 - val_loss: 0.6523 - val_accuracy: 0.8605\n",
            "Epoch 80/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1113 - accuracy: 0.9524 - val_loss: 0.6155 - val_accuracy: 0.8605\n",
            "Epoch 81/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1172 - accuracy: 0.9464 - val_loss: 0.7933 - val_accuracy: 0.8372\n",
            "Epoch 82/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1378 - accuracy: 0.9464 - val_loss: 0.6854 - val_accuracy: 0.8372\n",
            "Epoch 83/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1437 - accuracy: 0.9345 - val_loss: 0.7063 - val_accuracy: 0.8605\n",
            "Epoch 84/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1199 - accuracy: 0.9464 - val_loss: 0.6761 - val_accuracy: 0.8605\n",
            "Epoch 85/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1148 - accuracy: 0.9583 - val_loss: 0.6433 - val_accuracy: 0.8605\n",
            "Epoch 86/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1131 - accuracy: 0.9583 - val_loss: 0.7015 - val_accuracy: 0.8605\n",
            "Epoch 87/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1117 - accuracy: 0.9583 - val_loss: 0.7292 - val_accuracy: 0.8605\n",
            "Epoch 88/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1041 - accuracy: 0.9583 - val_loss: 0.7536 - val_accuracy: 0.8605\n",
            "Epoch 89/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1035 - accuracy: 0.9583 - val_loss: 0.7690 - val_accuracy: 0.8372\n",
            "Epoch 90/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1076 - accuracy: 0.9524 - val_loss: 0.7636 - val_accuracy: 0.8605\n",
            "Epoch 91/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0982 - accuracy: 0.9643 - val_loss: 0.8119 - val_accuracy: 0.8372\n",
            "Epoch 92/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0974 - accuracy: 0.9643 - val_loss: 0.8322 - val_accuracy: 0.8372\n",
            "Epoch 93/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1061 - accuracy: 0.9702 - val_loss: 0.8067 - val_accuracy: 0.8372\n",
            "Epoch 94/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0994 - accuracy: 0.9643 - val_loss: 0.8015 - val_accuracy: 0.8372\n",
            "Epoch 95/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1019 - accuracy: 0.9643 - val_loss: 0.7555 - val_accuracy: 0.8605\n",
            "Epoch 96/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1102 - accuracy: 0.9583 - val_loss: 0.7257 - val_accuracy: 0.8605\n",
            "Epoch 97/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1041 - accuracy: 0.9524 - val_loss: 0.8780 - val_accuracy: 0.8372\n",
            "Epoch 98/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1104 - accuracy: 0.9405 - val_loss: 0.9965 - val_accuracy: 0.8140\n",
            "Epoch 99/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1131 - accuracy: 0.9524 - val_loss: 0.8121 - val_accuracy: 0.8605\n",
            "Epoch 100/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.1295 - accuracy: 0.9524 - val_loss: 0.8393 - val_accuracy: 0.8605\n",
            "Epoch 101/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1107 - accuracy: 0.9524 - val_loss: 0.8952 - val_accuracy: 0.8140\n",
            "Epoch 102/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1102 - accuracy: 0.9583 - val_loss: 0.9054 - val_accuracy: 0.8372\n",
            "Epoch 103/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1102 - accuracy: 0.9583 - val_loss: 0.7808 - val_accuracy: 0.8605\n",
            "Epoch 104/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1088 - accuracy: 0.9643 - val_loss: 0.9629 - val_accuracy: 0.8140\n",
            "Epoch 105/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1050 - accuracy: 0.9524 - val_loss: 0.9799 - val_accuracy: 0.8372\n",
            "Epoch 106/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1023 - accuracy: 0.9583 - val_loss: 0.8535 - val_accuracy: 0.8605\n",
            "Epoch 107/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1671 - accuracy: 0.9405 - val_loss: 1.1075 - val_accuracy: 0.8372\n",
            "Epoch 108/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1238 - accuracy: 0.9286 - val_loss: 0.7978 - val_accuracy: 0.8605\n",
            "Epoch 109/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1082 - accuracy: 0.9524 - val_loss: 0.7965 - val_accuracy: 0.9070\n",
            "Epoch 110/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1442 - accuracy: 0.9464 - val_loss: 1.0871 - val_accuracy: 0.8140\n",
            "Epoch 111/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2296 - accuracy: 0.8988 - val_loss: 0.6856 - val_accuracy: 0.9070\n",
            "Epoch 112/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1612 - accuracy: 0.9345 - val_loss: 0.8658 - val_accuracy: 0.8605\n",
            "Epoch 113/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1513 - accuracy: 0.9464 - val_loss: 0.7534 - val_accuracy: 0.8140\n",
            "Epoch 114/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1588 - accuracy: 0.9226 - val_loss: 0.8493 - val_accuracy: 0.8140\n",
            "Epoch 115/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1226 - accuracy: 0.9405 - val_loss: 0.7881 - val_accuracy: 0.8605\n",
            "Epoch 116/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1066 - accuracy: 0.9643 - val_loss: 0.8020 - val_accuracy: 0.8605\n",
            "Epoch 117/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1046 - accuracy: 0.9524 - val_loss: 0.7440 - val_accuracy: 0.8605\n",
            "Epoch 118/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0993 - accuracy: 0.9583 - val_loss: 0.7795 - val_accuracy: 0.8605\n",
            "Epoch 119/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0998 - accuracy: 0.9583 - val_loss: 0.7807 - val_accuracy: 0.8605\n",
            "Epoch 120/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0970 - accuracy: 0.9643 - val_loss: 0.9497 - val_accuracy: 0.8140\n",
            "Epoch 121/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0951 - accuracy: 0.9583 - val_loss: 0.9382 - val_accuracy: 0.8372\n",
            "Epoch 122/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0956 - accuracy: 0.9702 - val_loss: 0.8711 - val_accuracy: 0.8605\n",
            "Epoch 123/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0913 - accuracy: 0.9643 - val_loss: 0.9336 - val_accuracy: 0.8372\n",
            "Epoch 124/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1127 - accuracy: 0.9464 - val_loss: 1.0427 - val_accuracy: 0.8372\n",
            "Epoch 125/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0959 - accuracy: 0.9583 - val_loss: 0.8052 - val_accuracy: 0.8837\n",
            "Epoch 126/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1045 - accuracy: 0.9524 - val_loss: 0.7925 - val_accuracy: 0.8605\n",
            "Epoch 127/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0950 - accuracy: 0.9583 - val_loss: 1.0376 - val_accuracy: 0.8140\n",
            "Epoch 128/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0918 - accuracy: 0.9524 - val_loss: 1.0371 - val_accuracy: 0.8140\n",
            "Epoch 129/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0893 - accuracy: 0.9702 - val_loss: 0.9447 - val_accuracy: 0.8605\n",
            "Epoch 130/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0893 - accuracy: 0.9643 - val_loss: 0.9959 - val_accuracy: 0.8372\n",
            "Epoch 131/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0942 - accuracy: 0.9583 - val_loss: 1.0524 - val_accuracy: 0.8140\n",
            "Epoch 132/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1090 - accuracy: 0.9524 - val_loss: 0.9151 - val_accuracy: 0.8605\n",
            "Epoch 133/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0975 - accuracy: 0.9583 - val_loss: 1.1732 - val_accuracy: 0.8140\n",
            "Epoch 134/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1042 - accuracy: 0.9583 - val_loss: 1.0522 - val_accuracy: 0.8605\n",
            "Epoch 135/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1059 - accuracy: 0.9583 - val_loss: 0.9635 - val_accuracy: 0.8605\n",
            "Epoch 136/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0936 - accuracy: 0.9643 - val_loss: 0.9396 - val_accuracy: 0.8372\n",
            "Epoch 137/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0957 - accuracy: 0.9583 - val_loss: 1.0305 - val_accuracy: 0.8605\n",
            "Epoch 138/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1163 - accuracy: 0.9464 - val_loss: 1.2026 - val_accuracy: 0.8372\n",
            "Epoch 139/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1097 - accuracy: 0.9524 - val_loss: 1.1095 - val_accuracy: 0.8372\n",
            "Epoch 140/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0989 - accuracy: 0.9583 - val_loss: 0.9653 - val_accuracy: 0.8140\n",
            "Epoch 141/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0928 - accuracy: 0.9524 - val_loss: 1.0130 - val_accuracy: 0.8140\n",
            "Epoch 142/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0822 - accuracy: 0.9702 - val_loss: 1.0850 - val_accuracy: 0.8372\n",
            "Epoch 143/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0894 - accuracy: 0.9583 - val_loss: 1.1122 - val_accuracy: 0.8372\n",
            "Epoch 144/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0901 - accuracy: 0.9583 - val_loss: 1.1109 - val_accuracy: 0.8372\n",
            "Epoch 145/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1099 - accuracy: 0.9464 - val_loss: 1.2288 - val_accuracy: 0.7442\n",
            "Epoch 146/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1036 - accuracy: 0.9464 - val_loss: 0.9512 - val_accuracy: 0.8605\n",
            "Epoch 147/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1120 - accuracy: 0.9524 - val_loss: 1.0620 - val_accuracy: 0.8372\n",
            "Epoch 148/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1277 - accuracy: 0.9524 - val_loss: 1.1727 - val_accuracy: 0.8372\n",
            "Epoch 149/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1030 - accuracy: 0.9524 - val_loss: 0.9696 - val_accuracy: 0.8605\n",
            "Epoch 150/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0857 - accuracy: 0.9762 - val_loss: 0.9313 - val_accuracy: 0.9070\n",
            "Epoch 151/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0885 - accuracy: 0.9702 - val_loss: 1.0874 - val_accuracy: 0.8605\n",
            "Epoch 152/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0868 - accuracy: 0.9583 - val_loss: 1.1865 - val_accuracy: 0.8372\n",
            "Epoch 153/500\n",
            "6/6 [==============================] - 0s 28ms/step - loss: 0.1008 - accuracy: 0.9524 - val_loss: 0.9681 - val_accuracy: 0.8140\n",
            "Epoch 154/500\n",
            "6/6 [==============================] - 0s 51ms/step - loss: 0.0985 - accuracy: 0.9464 - val_loss: 1.0075 - val_accuracy: 0.8140\n",
            "Epoch 155/500\n",
            "6/6 [==============================] - 0s 23ms/step - loss: 0.1052 - accuracy: 0.9464 - val_loss: 1.0851 - val_accuracy: 0.8605\n",
            "Epoch 156/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.1021 - accuracy: 0.9524 - val_loss: 1.1219 - val_accuracy: 0.8605\n",
            "Epoch 157/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0975 - accuracy: 0.9524 - val_loss: 1.2945 - val_accuracy: 0.8372\n",
            "Epoch 158/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0918 - accuracy: 0.9643 - val_loss: 1.0375 - val_accuracy: 0.8605\n",
            "Epoch 159/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1061 - accuracy: 0.9583 - val_loss: 0.9615 - val_accuracy: 0.8605\n",
            "Epoch 160/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0898 - accuracy: 0.9643 - val_loss: 1.1138 - val_accuracy: 0.8372\n",
            "Epoch 161/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0851 - accuracy: 0.9524 - val_loss: 1.2938 - val_accuracy: 0.8140\n",
            "Epoch 162/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0991 - accuracy: 0.9405 - val_loss: 1.0395 - val_accuracy: 0.8605\n",
            "Epoch 163/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0882 - accuracy: 0.9524 - val_loss: 0.9613 - val_accuracy: 0.8837\n",
            "Epoch 164/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0780 - accuracy: 0.9762 - val_loss: 1.1226 - val_accuracy: 0.8372\n",
            "Epoch 165/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0766 - accuracy: 0.9702 - val_loss: 1.1960 - val_accuracy: 0.8140\n",
            "Epoch 166/500\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.0863 - accuracy: 0.9583 - val_loss: 1.1182 - val_accuracy: 0.7907\n",
            "Epoch 167/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0957 - accuracy: 0.9702 - val_loss: 1.0102 - val_accuracy: 0.9070\n",
            "Epoch 168/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0893 - accuracy: 0.9643 - val_loss: 1.2507 - val_accuracy: 0.8372\n",
            "Epoch 169/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0844 - accuracy: 0.9643 - val_loss: 1.2544 - val_accuracy: 0.8140\n",
            "Epoch 170/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0768 - accuracy: 0.9702 - val_loss: 1.0938 - val_accuracy: 0.8140\n",
            "Epoch 171/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0911 - accuracy: 0.9643 - val_loss: 1.1011 - val_accuracy: 0.8372\n",
            "Epoch 172/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0831 - accuracy: 0.9702 - val_loss: 1.1187 - val_accuracy: 0.8605\n",
            "Epoch 173/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0811 - accuracy: 0.9643 - val_loss: 1.1384 - val_accuracy: 0.8605\n",
            "Epoch 174/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0724 - accuracy: 0.9702 - val_loss: 1.2505 - val_accuracy: 0.7907\n",
            "Epoch 175/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0788 - accuracy: 0.9702 - val_loss: 1.1740 - val_accuracy: 0.8372\n",
            "Epoch 176/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0804 - accuracy: 0.9702 - val_loss: 1.2096 - val_accuracy: 0.8140\n",
            "Epoch 177/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0754 - accuracy: 0.9583 - val_loss: 1.2827 - val_accuracy: 0.8140\n",
            "Epoch 178/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0689 - accuracy: 0.9762 - val_loss: 1.1371 - val_accuracy: 0.8605\n",
            "Epoch 179/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0712 - accuracy: 0.9762 - val_loss: 1.0799 - val_accuracy: 0.8605\n",
            "Epoch 180/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0722 - accuracy: 0.9702 - val_loss: 1.2038 - val_accuracy: 0.8372\n",
            "Epoch 181/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0750 - accuracy: 0.9643 - val_loss: 1.3934 - val_accuracy: 0.8140\n",
            "Epoch 182/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0868 - accuracy: 0.9643 - val_loss: 1.0517 - val_accuracy: 0.8837\n",
            "Epoch 183/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0961 - accuracy: 0.9583 - val_loss: 1.0518 - val_accuracy: 0.8372\n",
            "Epoch 184/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1069 - accuracy: 0.9524 - val_loss: 1.1463 - val_accuracy: 0.8372\n",
            "Epoch 185/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0906 - accuracy: 0.9643 - val_loss: 1.2720 - val_accuracy: 0.8372\n",
            "Epoch 186/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0696 - accuracy: 0.9762 - val_loss: 1.6484 - val_accuracy: 0.8140\n",
            "Epoch 187/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0816 - accuracy: 0.9524 - val_loss: 1.3411 - val_accuracy: 0.8140\n",
            "Epoch 188/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0781 - accuracy: 0.9702 - val_loss: 1.1235 - val_accuracy: 0.8837\n",
            "Epoch 189/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0663 - accuracy: 0.9702 - val_loss: 1.2734 - val_accuracy: 0.8372\n",
            "Epoch 190/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0734 - accuracy: 0.9762 - val_loss: 1.2861 - val_accuracy: 0.8372\n",
            "Epoch 191/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0683 - accuracy: 0.9702 - val_loss: 1.1574 - val_accuracy: 0.8605\n",
            "Epoch 192/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0857 - accuracy: 0.9702 - val_loss: 1.3517 - val_accuracy: 0.8140\n",
            "Epoch 193/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0743 - accuracy: 0.9762 - val_loss: 1.3211 - val_accuracy: 0.8372\n",
            "Epoch 194/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0947 - accuracy: 0.9643 - val_loss: 1.3128 - val_accuracy: 0.8140\n",
            "Epoch 195/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1689 - accuracy: 0.9345 - val_loss: 1.3936 - val_accuracy: 0.6977\n",
            "Epoch 196/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2004 - accuracy: 0.9048 - val_loss: 1.3021 - val_accuracy: 0.6977\n",
            "Epoch 197/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1532 - accuracy: 0.9345 - val_loss: 1.4379 - val_accuracy: 0.8372\n",
            "Epoch 198/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1472 - accuracy: 0.9345 - val_loss: 1.1543 - val_accuracy: 0.8372\n",
            "Epoch 199/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1360 - accuracy: 0.9464 - val_loss: 1.0235 - val_accuracy: 0.9070\n",
            "Epoch 200/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1508 - accuracy: 0.9107 - val_loss: 1.0466 - val_accuracy: 0.7674\n",
            "Epoch 201/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1135 - accuracy: 0.9524 - val_loss: 1.1358 - val_accuracy: 0.8372\n",
            "Epoch 202/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1197 - accuracy: 0.9524 - val_loss: 1.3288 - val_accuracy: 0.7907\n",
            "Epoch 203/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0924 - accuracy: 0.9524 - val_loss: 1.4387 - val_accuracy: 0.8140\n",
            "Epoch 204/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0968 - accuracy: 0.9524 - val_loss: 1.2113 - val_accuracy: 0.8372\n",
            "Epoch 205/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0896 - accuracy: 0.9643 - val_loss: 1.1333 - val_accuracy: 0.8372\n",
            "Epoch 206/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1004 - accuracy: 0.9702 - val_loss: 1.2908 - val_accuracy: 0.8140\n",
            "Epoch 207/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0840 - accuracy: 0.9643 - val_loss: 1.3298 - val_accuracy: 0.8140\n",
            "Epoch 208/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0787 - accuracy: 0.9643 - val_loss: 1.3453 - val_accuracy: 0.8140\n",
            "Epoch 209/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0705 - accuracy: 0.9702 - val_loss: 1.3457 - val_accuracy: 0.7907\n",
            "Epoch 210/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0778 - accuracy: 0.9762 - val_loss: 1.4694 - val_accuracy: 0.7907\n",
            "Epoch 211/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0716 - accuracy: 0.9821 - val_loss: 1.4522 - val_accuracy: 0.8140\n",
            "Epoch 212/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0950 - accuracy: 0.9583 - val_loss: 1.3936 - val_accuracy: 0.8140\n",
            "Epoch 213/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0860 - accuracy: 0.9643 - val_loss: 1.5757 - val_accuracy: 0.7674\n",
            "Epoch 214/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0857 - accuracy: 0.9643 - val_loss: 1.2310 - val_accuracy: 0.7907\n",
            "Epoch 215/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0973 - accuracy: 0.9524 - val_loss: 1.1863 - val_accuracy: 0.8372\n",
            "Epoch 216/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0742 - accuracy: 0.9762 - val_loss: 1.3953 - val_accuracy: 0.8140\n",
            "Epoch 217/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0720 - accuracy: 0.9821 - val_loss: 1.5015 - val_accuracy: 0.7907\n",
            "Epoch 218/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0827 - accuracy: 0.9643 - val_loss: 1.3521 - val_accuracy: 0.7907\n",
            "Epoch 219/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0743 - accuracy: 0.9762 - val_loss: 1.1854 - val_accuracy: 0.8372\n",
            "Epoch 220/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0748 - accuracy: 0.9762 - val_loss: 1.3970 - val_accuracy: 0.8140\n",
            "Epoch 221/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0619 - accuracy: 0.9821 - val_loss: 1.6255 - val_accuracy: 0.8140\n",
            "Epoch 222/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0705 - accuracy: 0.9762 - val_loss: 1.5585 - val_accuracy: 0.8140\n",
            "Epoch 223/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0625 - accuracy: 0.9821 - val_loss: 1.4227 - val_accuracy: 0.8140\n",
            "Epoch 224/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0628 - accuracy: 0.9821 - val_loss: 1.4657 - val_accuracy: 0.8140\n",
            "Epoch 225/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0606 - accuracy: 0.9821 - val_loss: 1.5272 - val_accuracy: 0.8140\n",
            "Epoch 226/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0582 - accuracy: 0.9821 - val_loss: 1.5546 - val_accuracy: 0.8140\n",
            "Epoch 227/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0603 - accuracy: 0.9821 - val_loss: 1.5949 - val_accuracy: 0.8140\n",
            "Epoch 228/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0677 - accuracy: 0.9821 - val_loss: 1.6718 - val_accuracy: 0.7907\n",
            "Epoch 229/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0654 - accuracy: 0.9762 - val_loss: 1.4348 - val_accuracy: 0.8372\n",
            "Epoch 230/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0761 - accuracy: 0.9583 - val_loss: 1.4470 - val_accuracy: 0.8372\n",
            "Epoch 231/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0581 - accuracy: 0.9821 - val_loss: 1.7186 - val_accuracy: 0.8140\n",
            "Epoch 232/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0741 - accuracy: 0.9643 - val_loss: 1.4683 - val_accuracy: 0.8140\n",
            "Epoch 233/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0605 - accuracy: 0.9821 - val_loss: 1.2486 - val_accuracy: 0.8372\n",
            "Epoch 234/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0614 - accuracy: 0.9821 - val_loss: 1.3725 - val_accuracy: 0.8372\n",
            "Epoch 235/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0620 - accuracy: 0.9762 - val_loss: 1.6248 - val_accuracy: 0.8140\n",
            "Epoch 236/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0562 - accuracy: 0.9881 - val_loss: 1.3466 - val_accuracy: 0.8605\n",
            "Epoch 237/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0800 - accuracy: 0.9702 - val_loss: 1.3304 - val_accuracy: 0.8372\n",
            "Epoch 238/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0733 - accuracy: 0.9643 - val_loss: 1.4343 - val_accuracy: 0.8372\n",
            "Epoch 239/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0622 - accuracy: 0.9821 - val_loss: 1.5686 - val_accuracy: 0.8372\n",
            "Epoch 240/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0599 - accuracy: 0.9821 - val_loss: 1.8860 - val_accuracy: 0.7907\n",
            "Epoch 241/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0881 - accuracy: 0.9583 - val_loss: 1.4687 - val_accuracy: 0.8140\n",
            "Epoch 242/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1040 - accuracy: 0.9643 - val_loss: 1.2793 - val_accuracy: 0.8837\n",
            "Epoch 243/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0978 - accuracy: 0.9643 - val_loss: 1.4571 - val_accuracy: 0.8140\n",
            "Epoch 244/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0962 - accuracy: 0.9464 - val_loss: 1.5410 - val_accuracy: 0.8140\n",
            "Epoch 245/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0768 - accuracy: 0.9702 - val_loss: 1.4737 - val_accuracy: 0.8140\n",
            "Epoch 246/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0667 - accuracy: 0.9821 - val_loss: 1.6042 - val_accuracy: 0.7907\n",
            "Epoch 247/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0666 - accuracy: 0.9762 - val_loss: 1.9350 - val_accuracy: 0.8140\n",
            "Epoch 248/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1150 - accuracy: 0.9464 - val_loss: 2.0202 - val_accuracy: 0.7442\n",
            "Epoch 249/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0890 - accuracy: 0.9643 - val_loss: 1.9931 - val_accuracy: 0.8372\n",
            "Epoch 250/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2170 - accuracy: 0.9464 - val_loss: 1.9659 - val_accuracy: 0.8140\n",
            "Epoch 251/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1106 - accuracy: 0.9583 - val_loss: 1.4992 - val_accuracy: 0.7674\n",
            "Epoch 252/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1080 - accuracy: 0.9524 - val_loss: 1.2572 - val_accuracy: 0.8372\n",
            "Epoch 253/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0761 - accuracy: 0.9643 - val_loss: 1.7402 - val_accuracy: 0.8140\n",
            "Epoch 254/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0928 - accuracy: 0.9702 - val_loss: 1.6403 - val_accuracy: 0.8140\n",
            "Epoch 255/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0709 - accuracy: 0.9702 - val_loss: 1.3453 - val_accuracy: 0.8372\n",
            "Epoch 256/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0836 - accuracy: 0.9643 - val_loss: 1.3542 - val_accuracy: 0.8140\n",
            "Epoch 257/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1281 - accuracy: 0.9643 - val_loss: 1.4429 - val_accuracy: 0.8140\n",
            "Epoch 258/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2272 - accuracy: 0.9107 - val_loss: 1.6134 - val_accuracy: 0.6977\n",
            "Epoch 259/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.3113 - accuracy: 0.8810 - val_loss: 0.9912 - val_accuracy: 0.6279\n",
            "Epoch 260/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2419 - accuracy: 0.8810 - val_loss: 0.9992 - val_accuracy: 0.7442\n",
            "Epoch 261/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1670 - accuracy: 0.9167 - val_loss: 0.9996 - val_accuracy: 0.8372\n",
            "Epoch 262/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1459 - accuracy: 0.9583 - val_loss: 0.8950 - val_accuracy: 0.8140\n",
            "Epoch 263/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1246 - accuracy: 0.9643 - val_loss: 0.6948 - val_accuracy: 0.8372\n",
            "Epoch 264/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1130 - accuracy: 0.9405 - val_loss: 0.7219 - val_accuracy: 0.7907\n",
            "Epoch 265/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1033 - accuracy: 0.9524 - val_loss: 0.8402 - val_accuracy: 0.8372\n",
            "Epoch 266/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0851 - accuracy: 0.9762 - val_loss: 0.9366 - val_accuracy: 0.8140\n",
            "Epoch 267/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0822 - accuracy: 0.9643 - val_loss: 1.0860 - val_accuracy: 0.8140\n",
            "Epoch 268/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0760 - accuracy: 0.9762 - val_loss: 1.0497 - val_accuracy: 0.8140\n",
            "Epoch 269/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0733 - accuracy: 0.9702 - val_loss: 1.0370 - val_accuracy: 0.8140\n",
            "Epoch 270/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0745 - accuracy: 0.9881 - val_loss: 0.8895 - val_accuracy: 0.8140\n",
            "Epoch 271/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0714 - accuracy: 0.9762 - val_loss: 0.9314 - val_accuracy: 0.8140\n",
            "Epoch 272/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0687 - accuracy: 0.9702 - val_loss: 1.1287 - val_accuracy: 0.8140\n",
            "Epoch 273/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0759 - accuracy: 0.9643 - val_loss: 1.1643 - val_accuracy: 0.8140\n",
            "Epoch 274/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0715 - accuracy: 0.9643 - val_loss: 0.9876 - val_accuracy: 0.8372\n",
            "Epoch 275/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0672 - accuracy: 0.9762 - val_loss: 1.1048 - val_accuracy: 0.8140\n",
            "Epoch 276/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0781 - accuracy: 0.9821 - val_loss: 1.2936 - val_accuracy: 0.7907\n",
            "Epoch 277/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0711 - accuracy: 0.9762 - val_loss: 1.0333 - val_accuracy: 0.8372\n",
            "Epoch 278/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0735 - accuracy: 0.9643 - val_loss: 0.9194 - val_accuracy: 0.8372\n",
            "Epoch 279/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0702 - accuracy: 0.9702 - val_loss: 1.0834 - val_accuracy: 0.8140\n",
            "Epoch 280/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0627 - accuracy: 0.9821 - val_loss: 1.1600 - val_accuracy: 0.7907\n",
            "Epoch 281/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0582 - accuracy: 0.9881 - val_loss: 0.9210 - val_accuracy: 0.8372\n",
            "Epoch 282/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0647 - accuracy: 0.9821 - val_loss: 1.0558 - val_accuracy: 0.8372\n",
            "Epoch 283/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9881 - val_loss: 1.1972 - val_accuracy: 0.8140\n",
            "Epoch 284/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0590 - accuracy: 0.9881 - val_loss: 1.1685 - val_accuracy: 0.8140\n",
            "Epoch 285/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0692 - accuracy: 0.9821 - val_loss: 1.1434 - val_accuracy: 0.8140\n",
            "Epoch 286/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0626 - accuracy: 0.9881 - val_loss: 1.1855 - val_accuracy: 0.8140\n",
            "Epoch 287/500\n",
            "6/6 [==============================] - 0s 24ms/step - loss: 0.0584 - accuracy: 0.9821 - val_loss: 1.1865 - val_accuracy: 0.8140\n",
            "Epoch 288/500\n",
            "6/6 [==============================] - 0s 37ms/step - loss: 0.0593 - accuracy: 0.9762 - val_loss: 1.2420 - val_accuracy: 0.8140\n",
            "Epoch 289/500\n",
            "6/6 [==============================] - 0s 27ms/step - loss: 0.0647 - accuracy: 0.9643 - val_loss: 1.4894 - val_accuracy: 0.8140\n",
            "Epoch 290/500\n",
            "6/6 [==============================] - 0s 25ms/step - loss: 0.0625 - accuracy: 0.9821 - val_loss: 1.1104 - val_accuracy: 0.8372\n",
            "Epoch 291/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0824 - accuracy: 0.9643 - val_loss: 1.1615 - val_accuracy: 0.8372\n",
            "Epoch 292/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0691 - accuracy: 0.9702 - val_loss: 1.5672 - val_accuracy: 0.7674\n",
            "Epoch 293/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0809 - accuracy: 0.9524 - val_loss: 1.1170 - val_accuracy: 0.8372\n",
            "Epoch 294/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0539 - accuracy: 0.9821 - val_loss: 0.9361 - val_accuracy: 0.8372\n",
            "Epoch 295/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0640 - accuracy: 0.9762 - val_loss: 1.0601 - val_accuracy: 0.8372\n",
            "Epoch 296/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0526 - accuracy: 0.9821 - val_loss: 1.4488 - val_accuracy: 0.8140\n",
            "Epoch 297/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0550 - accuracy: 0.9821 - val_loss: 1.4296 - val_accuracy: 0.8140\n",
            "Epoch 298/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0598 - accuracy: 0.9821 - val_loss: 1.3230 - val_accuracy: 0.8140\n",
            "Epoch 299/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0581 - accuracy: 0.9762 - val_loss: 1.4407 - val_accuracy: 0.8140\n",
            "Epoch 300/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0479 - accuracy: 0.9821 - val_loss: 1.1681 - val_accuracy: 0.8372\n",
            "Epoch 301/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0502 - accuracy: 0.9821 - val_loss: 1.1542 - val_accuracy: 0.8140\n",
            "Epoch 302/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0522 - accuracy: 0.9881 - val_loss: 1.4151 - val_accuracy: 0.7442\n",
            "Epoch 303/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0922 - accuracy: 0.9583 - val_loss: 1.4330 - val_accuracy: 0.7907\n",
            "Epoch 304/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0852 - accuracy: 0.9643 - val_loss: 1.1321 - val_accuracy: 0.8605\n",
            "Epoch 305/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0681 - accuracy: 0.9702 - val_loss: 1.0547 - val_accuracy: 0.8372\n",
            "Epoch 306/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0710 - accuracy: 0.9762 - val_loss: 1.4592 - val_accuracy: 0.7674\n",
            "Epoch 307/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0824 - accuracy: 0.9702 - val_loss: 1.2700 - val_accuracy: 0.8140\n",
            "Epoch 308/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0605 - accuracy: 0.9762 - val_loss: 1.1618 - val_accuracy: 0.8605\n",
            "Epoch 309/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0726 - accuracy: 0.9762 - val_loss: 1.2783 - val_accuracy: 0.8372\n",
            "Epoch 310/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0503 - accuracy: 0.9881 - val_loss: 1.2618 - val_accuracy: 0.8372\n",
            "Epoch 311/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0502 - accuracy: 0.9821 - val_loss: 1.2949 - val_accuracy: 0.8140\n",
            "Epoch 312/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0495 - accuracy: 0.9821 - val_loss: 1.4588 - val_accuracy: 0.7907\n",
            "Epoch 313/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0498 - accuracy: 0.9821 - val_loss: 1.3342 - val_accuracy: 0.8372\n",
            "Epoch 314/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0455 - accuracy: 0.9881 - val_loss: 1.4162 - val_accuracy: 0.8140\n",
            "Epoch 315/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0444 - accuracy: 0.9881 - val_loss: 1.4765 - val_accuracy: 0.8140\n",
            "Epoch 316/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0437 - accuracy: 0.9881 - val_loss: 1.5396 - val_accuracy: 0.8140\n",
            "Epoch 317/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0422 - accuracy: 0.9821 - val_loss: 1.3040 - val_accuracy: 0.8140\n",
            "Epoch 318/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0451 - accuracy: 0.9821 - val_loss: 1.3199 - val_accuracy: 0.8372\n",
            "Epoch 319/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0454 - accuracy: 0.9881 - val_loss: 1.4841 - val_accuracy: 0.8140\n",
            "Epoch 320/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0423 - accuracy: 0.9821 - val_loss: 1.5658 - val_accuracy: 0.8140\n",
            "Epoch 321/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0446 - accuracy: 0.9821 - val_loss: 1.6458 - val_accuracy: 0.8140\n",
            "Epoch 322/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0464 - accuracy: 0.9821 - val_loss: 1.9097 - val_accuracy: 0.7907\n",
            "Epoch 323/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0444 - accuracy: 0.9821 - val_loss: 1.5961 - val_accuracy: 0.7907\n",
            "Epoch 324/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0522 - accuracy: 0.9821 - val_loss: 1.9450 - val_accuracy: 0.7907\n",
            "Epoch 325/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0459 - accuracy: 0.9881 - val_loss: 1.7335 - val_accuracy: 0.8140\n",
            "Epoch 326/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0421 - accuracy: 0.9881 - val_loss: 1.3153 - val_accuracy: 0.8372\n",
            "Epoch 327/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0556 - accuracy: 0.9762 - val_loss: 1.5189 - val_accuracy: 0.8372\n",
            "Epoch 328/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0494 - accuracy: 0.9762 - val_loss: 1.5998 - val_accuracy: 0.8140\n",
            "Epoch 329/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0593 - accuracy: 0.9821 - val_loss: 1.1892 - val_accuracy: 0.8605\n",
            "Epoch 330/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0827 - accuracy: 0.9702 - val_loss: 1.1632 - val_accuracy: 0.8605\n",
            "Epoch 331/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0874 - accuracy: 0.9643 - val_loss: 1.2436 - val_accuracy: 0.8372\n",
            "Epoch 332/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0895 - accuracy: 0.9524 - val_loss: 1.1482 - val_accuracy: 0.8140\n",
            "Epoch 333/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0651 - accuracy: 0.9762 - val_loss: 1.2514 - val_accuracy: 0.8140\n",
            "Epoch 334/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0585 - accuracy: 0.9762 - val_loss: 1.7658 - val_accuracy: 0.7907\n",
            "Epoch 335/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0833 - accuracy: 0.9643 - val_loss: 1.3891 - val_accuracy: 0.7907\n",
            "Epoch 336/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0657 - accuracy: 0.9643 - val_loss: 1.2005 - val_accuracy: 0.8372\n",
            "Epoch 337/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0479 - accuracy: 0.9821 - val_loss: 1.9553 - val_accuracy: 0.8140\n",
            "Epoch 338/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0484 - accuracy: 0.9762 - val_loss: 2.1121 - val_accuracy: 0.8140\n",
            "Epoch 339/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0671 - accuracy: 0.9643 - val_loss: 2.0034 - val_accuracy: 0.7907\n",
            "Epoch 340/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0555 - accuracy: 0.9762 - val_loss: 2.0171 - val_accuracy: 0.8140\n",
            "Epoch 341/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0496 - accuracy: 0.9821 - val_loss: 1.9116 - val_accuracy: 0.7907\n",
            "Epoch 342/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0433 - accuracy: 0.9881 - val_loss: 1.8432 - val_accuracy: 0.7907\n",
            "Epoch 343/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0402 - accuracy: 0.9821 - val_loss: 1.9217 - val_accuracy: 0.7907\n",
            "Epoch 344/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0367 - accuracy: 0.9881 - val_loss: 2.1084 - val_accuracy: 0.7907\n",
            "Epoch 345/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0380 - accuracy: 0.9821 - val_loss: 2.1627 - val_accuracy: 0.7907\n",
            "Epoch 346/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0359 - accuracy: 0.9881 - val_loss: 1.8845 - val_accuracy: 0.7907\n",
            "Epoch 347/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0409 - accuracy: 0.9821 - val_loss: 2.5311 - val_accuracy: 0.8140\n",
            "Epoch 348/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0578 - accuracy: 0.9702 - val_loss: 1.8084 - val_accuracy: 0.8140\n",
            "Epoch 349/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0568 - accuracy: 0.9702 - val_loss: 1.2563 - val_accuracy: 0.8605\n",
            "Epoch 350/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0561 - accuracy: 0.9762 - val_loss: 1.3309 - val_accuracy: 0.8605\n",
            "Epoch 351/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0542 - accuracy: 0.9702 - val_loss: 1.4038 - val_accuracy: 0.8372\n",
            "Epoch 352/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0475 - accuracy: 0.9821 - val_loss: 1.2957 - val_accuracy: 0.8140\n",
            "Epoch 353/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0480 - accuracy: 0.9821 - val_loss: 1.3401 - val_accuracy: 0.8140\n",
            "Epoch 354/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0420 - accuracy: 0.9821 - val_loss: 1.2970 - val_accuracy: 0.8372\n",
            "Epoch 355/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0475 - accuracy: 0.9762 - val_loss: 1.5254 - val_accuracy: 0.8140\n",
            "Epoch 356/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0408 - accuracy: 0.9821 - val_loss: 1.6464 - val_accuracy: 0.8140\n",
            "Epoch 357/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0557 - accuracy: 0.9762 - val_loss: 1.4070 - val_accuracy: 0.8372\n",
            "Epoch 358/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0376 - accuracy: 0.9821 - val_loss: 1.4191 - val_accuracy: 0.8372\n",
            "Epoch 359/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0357 - accuracy: 0.9821 - val_loss: 1.5757 - val_accuracy: 0.8372\n",
            "Epoch 360/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0350 - accuracy: 0.9821 - val_loss: 1.6916 - val_accuracy: 0.8372\n",
            "Epoch 361/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0540 - accuracy: 0.9702 - val_loss: 1.5825 - val_accuracy: 0.8372\n",
            "Epoch 362/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0654 - accuracy: 0.9821 - val_loss: 1.3021 - val_accuracy: 0.8605\n",
            "Epoch 363/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0547 - accuracy: 0.9762 - val_loss: 1.7920 - val_accuracy: 0.7907\n",
            "Epoch 364/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0620 - accuracy: 0.9702 - val_loss: 1.4580 - val_accuracy: 0.8372\n",
            "Epoch 365/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0522 - accuracy: 0.9821 - val_loss: 1.4426 - val_accuracy: 0.8372\n",
            "Epoch 366/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0378 - accuracy: 0.9881 - val_loss: 1.6122 - val_accuracy: 0.8140\n",
            "Epoch 367/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0358 - accuracy: 0.9881 - val_loss: 1.6926 - val_accuracy: 0.8140\n",
            "Epoch 368/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0344 - accuracy: 0.9821 - val_loss: 1.7600 - val_accuracy: 0.8140\n",
            "Epoch 369/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0323 - accuracy: 0.9881 - val_loss: 1.8030 - val_accuracy: 0.8140\n",
            "Epoch 370/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0310 - accuracy: 0.9881 - val_loss: 1.6343 - val_accuracy: 0.8372\n",
            "Epoch 371/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0350 - accuracy: 0.9821 - val_loss: 1.7196 - val_accuracy: 0.8140\n",
            "Epoch 372/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.0312 - accuracy: 0.9881 - val_loss: 1.8254 - val_accuracy: 0.8140\n",
            "Epoch 373/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0321 - accuracy: 0.9881 - val_loss: 1.7152 - val_accuracy: 0.8140\n",
            "Epoch 374/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0307 - accuracy: 0.9821 - val_loss: 1.6965 - val_accuracy: 0.8140\n",
            "Epoch 375/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0298 - accuracy: 0.9940 - val_loss: 1.7699 - val_accuracy: 0.8140\n",
            "Epoch 376/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0278 - accuracy: 0.9940 - val_loss: 1.7581 - val_accuracy: 0.8140\n",
            "Epoch 377/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0272 - accuracy: 0.9940 - val_loss: 1.6966 - val_accuracy: 0.8140\n",
            "Epoch 378/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0307 - accuracy: 0.9881 - val_loss: 1.7492 - val_accuracy: 0.7907\n",
            "Epoch 379/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0288 - accuracy: 0.9940 - val_loss: 1.9944 - val_accuracy: 0.8140\n",
            "Epoch 380/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0316 - accuracy: 0.9940 - val_loss: 1.7589 - val_accuracy: 0.8140\n",
            "Epoch 381/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0369 - accuracy: 0.9821 - val_loss: 1.6477 - val_accuracy: 0.8372\n",
            "Epoch 382/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0420 - accuracy: 0.9821 - val_loss: 1.8050 - val_accuracy: 0.7907\n",
            "Epoch 383/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0541 - accuracy: 0.9821 - val_loss: 1.5317 - val_accuracy: 0.8372\n",
            "Epoch 384/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0450 - accuracy: 0.9762 - val_loss: 1.5686 - val_accuracy: 0.8372\n",
            "Epoch 385/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0383 - accuracy: 0.9821 - val_loss: 1.8074 - val_accuracy: 0.8140\n",
            "Epoch 386/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0335 - accuracy: 0.9881 - val_loss: 1.6898 - val_accuracy: 0.8372\n",
            "Epoch 387/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0345 - accuracy: 0.9762 - val_loss: 1.7122 - val_accuracy: 0.8372\n",
            "Epoch 388/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0287 - accuracy: 0.9881 - val_loss: 1.7625 - val_accuracy: 0.8140\n",
            "Epoch 389/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0279 - accuracy: 0.9940 - val_loss: 1.7760 - val_accuracy: 0.8372\n",
            "Epoch 390/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0246 - accuracy: 0.9940 - val_loss: 1.7329 - val_accuracy: 0.8372\n",
            "Epoch 391/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0239 - accuracy: 0.9940 - val_loss: 1.7660 - val_accuracy: 0.8372\n",
            "Epoch 392/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0247 - accuracy: 0.9940 - val_loss: 1.9194 - val_accuracy: 0.8140\n",
            "Epoch 393/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0231 - accuracy: 0.9940 - val_loss: 1.8976 - val_accuracy: 0.8140\n",
            "Epoch 394/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0222 - accuracy: 0.9940 - val_loss: 1.8028 - val_accuracy: 0.8140\n",
            "Epoch 395/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0228 - accuracy: 0.9940 - val_loss: 1.8361 - val_accuracy: 0.8372\n",
            "Epoch 396/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0230 - accuracy: 0.9940 - val_loss: 1.8739 - val_accuracy: 0.8372\n",
            "Epoch 397/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0219 - accuracy: 0.9940 - val_loss: 1.8283 - val_accuracy: 0.8372\n",
            "Epoch 398/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0234 - accuracy: 0.9940 - val_loss: 1.8802 - val_accuracy: 0.8140\n",
            "Epoch 399/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0205 - accuracy: 0.9940 - val_loss: 1.9494 - val_accuracy: 0.8140\n",
            "Epoch 400/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0223 - accuracy: 0.9940 - val_loss: 1.8808 - val_accuracy: 0.8372\n",
            "Epoch 401/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0205 - accuracy: 0.9940 - val_loss: 1.8209 - val_accuracy: 0.8372\n",
            "Epoch 402/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0218 - accuracy: 0.9940 - val_loss: 1.9611 - val_accuracy: 0.8140\n",
            "Epoch 403/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0213 - accuracy: 0.9940 - val_loss: 1.9398 - val_accuracy: 0.8140\n",
            "Epoch 404/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0202 - accuracy: 0.9940 - val_loss: 1.8815 - val_accuracy: 0.8372\n",
            "Epoch 405/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0216 - accuracy: 0.9940 - val_loss: 1.9390 - val_accuracy: 0.8140\n",
            "Epoch 406/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0190 - accuracy: 0.9940 - val_loss: 2.0134 - val_accuracy: 0.8140\n",
            "Epoch 407/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0206 - accuracy: 0.9940 - val_loss: 1.9156 - val_accuracy: 0.8372\n",
            "Epoch 408/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0212 - accuracy: 0.9881 - val_loss: 1.9925 - val_accuracy: 0.8140\n",
            "Epoch 409/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0242 - accuracy: 0.9881 - val_loss: 2.0000 - val_accuracy: 0.8140\n",
            "Epoch 410/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0228 - accuracy: 0.9940 - val_loss: 1.9195 - val_accuracy: 0.8140\n",
            "Epoch 411/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0207 - accuracy: 0.9940 - val_loss: 1.9433 - val_accuracy: 0.8372\n",
            "Epoch 412/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0193 - accuracy: 0.9940 - val_loss: 2.0069 - val_accuracy: 0.8140\n",
            "Epoch 413/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0185 - accuracy: 0.9940 - val_loss: 1.9658 - val_accuracy: 0.8372\n",
            "Epoch 414/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0171 - accuracy: 0.9940 - val_loss: 1.9341 - val_accuracy: 0.8372\n",
            "Epoch 415/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0183 - accuracy: 0.9940 - val_loss: 1.9962 - val_accuracy: 0.8140\n",
            "Epoch 416/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.0172 - accuracy: 0.9940 - val_loss: 2.0256 - val_accuracy: 0.8140\n",
            "Epoch 417/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0164 - accuracy: 0.9940 - val_loss: 2.0250 - val_accuracy: 0.8372\n",
            "Epoch 418/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0164 - accuracy: 0.9940 - val_loss: 2.0558 - val_accuracy: 0.8140\n",
            "Epoch 419/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0165 - accuracy: 0.9940 - val_loss: 2.0634 - val_accuracy: 0.8140\n",
            "Epoch 420/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 2.1311 - val_accuracy: 0.8140\n",
            "Epoch 421/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0167 - accuracy: 0.9940 - val_loss: 2.0556 - val_accuracy: 0.8140\n",
            "Epoch 422/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0174 - accuracy: 0.9940 - val_loss: 2.0579 - val_accuracy: 0.8140\n",
            "Epoch 423/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0161 - accuracy: 0.9940 - val_loss: 2.0869 - val_accuracy: 0.8140\n",
            "Epoch 424/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0154 - accuracy: 0.9940 - val_loss: 2.1368 - val_accuracy: 0.8140\n",
            "Epoch 425/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 2.1340 - val_accuracy: 0.8140\n",
            "Epoch 426/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0150 - accuracy: 0.9940 - val_loss: 2.0555 - val_accuracy: 0.8372\n",
            "Epoch 427/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0170 - accuracy: 0.9940 - val_loss: 2.1360 - val_accuracy: 0.8140\n",
            "Epoch 428/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0165 - accuracy: 1.0000 - val_loss: 2.1808 - val_accuracy: 0.8140\n",
            "Epoch 429/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0216 - accuracy: 0.9940 - val_loss: 2.1190 - val_accuracy: 0.8140\n",
            "Epoch 430/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0163 - accuracy: 0.9940 - val_loss: 2.2722 - val_accuracy: 0.8140\n",
            "Epoch 431/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0167 - accuracy: 0.9940 - val_loss: 2.2169 - val_accuracy: 0.8140\n",
            "Epoch 432/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0163 - accuracy: 0.9940 - val_loss: 2.1153 - val_accuracy: 0.8140\n",
            "Epoch 433/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0151 - accuracy: 0.9940 - val_loss: 2.1372 - val_accuracy: 0.8372\n",
            "Epoch 434/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0146 - accuracy: 0.9940 - val_loss: 2.1272 - val_accuracy: 0.8372\n",
            "Epoch 435/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0142 - accuracy: 0.9940 - val_loss: 2.1541 - val_accuracy: 0.8140\n",
            "Epoch 436/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0143 - accuracy: 1.0000 - val_loss: 2.2476 - val_accuracy: 0.7907\n",
            "Epoch 437/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 2.2199 - val_accuracy: 0.7907\n",
            "Epoch 438/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0164 - accuracy: 0.9940 - val_loss: 2.1806 - val_accuracy: 0.7907\n",
            "Epoch 439/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0131 - accuracy: 0.9940 - val_loss: 2.1830 - val_accuracy: 0.7907\n",
            "Epoch 440/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0148 - accuracy: 1.0000 - val_loss: 2.1655 - val_accuracy: 0.8140\n",
            "Epoch 441/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0170 - accuracy: 0.9940 - val_loss: 2.0920 - val_accuracy: 0.8140\n",
            "Epoch 442/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0153 - accuracy: 0.9940 - val_loss: 2.1159 - val_accuracy: 0.8140\n",
            "Epoch 443/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0205 - accuracy: 0.9881 - val_loss: 2.1665 - val_accuracy: 0.7907\n",
            "Epoch 444/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0204 - accuracy: 0.9881 - val_loss: 2.4812 - val_accuracy: 0.7907\n",
            "Epoch 445/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0224 - accuracy: 0.9940 - val_loss: 2.0483 - val_accuracy: 0.8140\n",
            "Epoch 446/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0279 - accuracy: 0.9881 - val_loss: 2.1927 - val_accuracy: 0.8140\n",
            "Epoch 447/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0252 - accuracy: 0.9940 - val_loss: 1.8936 - val_accuracy: 0.7907\n",
            "Epoch 448/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0825 - accuracy: 0.9643 - val_loss: 1.7747 - val_accuracy: 0.7907\n",
            "Epoch 449/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0336 - accuracy: 0.9940 - val_loss: 2.0025 - val_accuracy: 0.7907\n",
            "Epoch 450/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0623 - accuracy: 0.9821 - val_loss: 1.8065 - val_accuracy: 0.8605\n",
            "Epoch 451/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0284 - accuracy: 0.9940 - val_loss: 1.9700 - val_accuracy: 0.8140\n",
            "Epoch 452/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0260 - accuracy: 0.9940 - val_loss: 1.9275 - val_accuracy: 0.7907\n",
            "Epoch 453/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0310 - accuracy: 0.9821 - val_loss: 1.9165 - val_accuracy: 0.8372\n",
            "Epoch 454/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0220 - accuracy: 0.9940 - val_loss: 2.0484 - val_accuracy: 0.8372\n",
            "Epoch 455/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0206 - accuracy: 0.9940 - val_loss: 2.1040 - val_accuracy: 0.8372\n",
            "Epoch 456/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0242 - accuracy: 0.9881 - val_loss: 1.9739 - val_accuracy: 0.8372\n",
            "Epoch 457/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0204 - accuracy: 0.9940 - val_loss: 1.8143 - val_accuracy: 0.8140\n",
            "Epoch 458/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0327 - accuracy: 0.9881 - val_loss: 2.0999 - val_accuracy: 0.8140\n",
            "Epoch 459/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0236 - accuracy: 0.9940 - val_loss: 2.2864 - val_accuracy: 0.8140\n",
            "Epoch 460/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0294 - accuracy: 0.9881 - val_loss: 2.0409 - val_accuracy: 0.8140\n",
            "Epoch 461/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2470 - accuracy: 0.9643 - val_loss: 2.2172 - val_accuracy: 0.7442\n",
            "Epoch 462/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2734 - accuracy: 0.9107 - val_loss: 2.1930 - val_accuracy: 0.6279\n",
            "Epoch 463/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1641 - accuracy: 0.9464 - val_loss: 1.9909 - val_accuracy: 0.8605\n",
            "Epoch 464/500\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.0927 - accuracy: 0.9583 - val_loss: 2.0463 - val_accuracy: 0.8140\n",
            "Epoch 465/500\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.0607 - accuracy: 0.9583 - val_loss: 2.0667 - val_accuracy: 0.8140\n",
            "Epoch 466/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0469 - accuracy: 0.9881 - val_loss: 2.0070 - val_accuracy: 0.8140\n",
            "Epoch 467/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0501 - accuracy: 0.9762 - val_loss: 2.3138 - val_accuracy: 0.8372\n",
            "Epoch 468/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.1039 - accuracy: 0.9702 - val_loss: 2.4343 - val_accuracy: 0.8140\n",
            "Epoch 469/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0763 - accuracy: 0.9821 - val_loss: 2.1173 - val_accuracy: 0.8372\n",
            "Epoch 470/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0618 - accuracy: 0.9821 - val_loss: 1.8868 - val_accuracy: 0.8372\n",
            "Epoch 471/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0400 - accuracy: 0.9881 - val_loss: 2.0045 - val_accuracy: 0.8372\n",
            "Epoch 472/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0396 - accuracy: 0.9940 - val_loss: 1.8502 - val_accuracy: 0.8372\n",
            "Epoch 473/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0348 - accuracy: 0.9940 - val_loss: 1.8530 - val_accuracy: 0.8372\n",
            "Epoch 474/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0295 - accuracy: 0.9940 - val_loss: 1.7629 - val_accuracy: 0.8140\n",
            "Epoch 475/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0309 - accuracy: 0.9940 - val_loss: 1.8136 - val_accuracy: 0.8140\n",
            "Epoch 476/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0305 - accuracy: 0.9940 - val_loss: 1.8215 - val_accuracy: 0.8140\n",
            "Epoch 477/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0276 - accuracy: 0.9940 - val_loss: 1.9956 - val_accuracy: 0.7907\n",
            "Epoch 478/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.0436 - accuracy: 0.9881 - val_loss: 1.9548 - val_accuracy: 0.8140\n",
            "Epoch 479/500\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.0671 - accuracy: 0.9821 - val_loss: 1.9767 - val_accuracy: 0.8372\n",
            "Epoch 480/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0630 - accuracy: 0.9702 - val_loss: 2.0848 - val_accuracy: 0.8372\n",
            "Epoch 481/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0409 - accuracy: 0.9762 - val_loss: 2.1274 - val_accuracy: 0.8140\n",
            "Epoch 482/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0302 - accuracy: 0.9881 - val_loss: 1.9719 - val_accuracy: 0.8372\n",
            "Epoch 483/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0328 - accuracy: 0.9881 - val_loss: 2.0442 - val_accuracy: 0.8372\n",
            "Epoch 484/500\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.0284 - accuracy: 0.9881 - val_loss: 2.1394 - val_accuracy: 0.8372\n",
            "Epoch 485/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0644 - accuracy: 0.9762 - val_loss: 1.7767 - val_accuracy: 0.8605\n",
            "Epoch 486/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0537 - accuracy: 0.9821 - val_loss: 2.0492 - val_accuracy: 0.8372\n",
            "Epoch 487/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.0476 - accuracy: 0.9702 - val_loss: 2.0231 - val_accuracy: 0.8372\n",
            "Epoch 488/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0322 - accuracy: 0.9881 - val_loss: 1.7739 - val_accuracy: 0.8605\n",
            "Epoch 489/500\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.0677 - accuracy: 0.9643 - val_loss: 2.0164 - val_accuracy: 0.8605\n",
            "Epoch 490/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0560 - accuracy: 0.9702 - val_loss: 1.6708 - val_accuracy: 0.8372\n",
            "Epoch 491/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.0591 - accuracy: 0.9762 - val_loss: 1.5065 - val_accuracy: 0.8605\n",
            "Epoch 492/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.0892 - accuracy: 0.9583 - val_loss: 3.3210 - val_accuracy: 0.7907\n",
            "Epoch 493/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2452 - accuracy: 0.9583 - val_loss: 1.4966 - val_accuracy: 0.8837\n",
            "Epoch 494/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2476 - accuracy: 0.9405 - val_loss: 1.6015 - val_accuracy: 0.8140\n",
            "Epoch 495/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1946 - accuracy: 0.9345 - val_loss: 3.4330 - val_accuracy: 0.8140\n",
            "Epoch 496/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2430 - accuracy: 0.9107 - val_loss: 2.7624 - val_accuracy: 0.7907\n",
            "Epoch 497/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2118 - accuracy: 0.8869 - val_loss: 2.0396 - val_accuracy: 0.8372\n",
            "Epoch 498/500\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.1863 - accuracy: 0.9286 - val_loss: 1.9245 - val_accuracy: 0.8140\n",
            "Epoch 499/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1524 - accuracy: 0.9643 - val_loss: 2.3726 - val_accuracy: 0.6977\n",
            "Epoch 500/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1931 - accuracy: 0.9226 - val_loss: 2.2673 - val_accuracy: 0.7442\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_decision_regions"
      ],
      "metadata": {
        "id": "onv45VRexAz2"
      },
      "execution_count": null,
      "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": 325
        },
        "id": "EAb_i8vPxd9W",
        "outputId": "e22b7fff-0550-4179-cc2e-c2e0f7cbd354"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "acsg1tSSx0zS",
        "outputId": "c1966173-4911-4651-b840-7c89dac5566f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f42e959fd10>]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "--RJquSDzhS_",
        "outputId": "7299a916-eb67-4827-fd0e-5730b7b8d41d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f42ec12b450>]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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": "1aa3eb2b-4864-4a7b-d36a-069a7e08bfda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "6/6 [==============================] - 1s 78ms/step - loss: 0.5846 - accuracy: 0.7738 - val_loss: 0.6484 - val_accuracy: 0.5814\n",
            "Epoch 2/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.3438 - accuracy: 0.8571 - val_loss: 0.9476 - val_accuracy: 0.5814\n",
            "Epoch 3/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2886 - accuracy: 0.8631 - val_loss: 0.6692 - val_accuracy: 0.6977\n",
            "Epoch 4/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2981 - accuracy: 0.8810 - val_loss: 0.4833 - val_accuracy: 0.7442\n",
            "Epoch 5/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2408 - accuracy: 0.9167 - val_loss: 0.3649 - val_accuracy: 0.8140\n",
            "Epoch 6/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2541 - accuracy: 0.9107 - val_loss: 0.3456 - val_accuracy: 0.8605\n",
            "Epoch 7/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2637 - accuracy: 0.8988 - val_loss: 0.3714 - val_accuracy: 0.8372\n",
            "Epoch 8/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2515 - accuracy: 0.8810 - val_loss: 0.4228 - val_accuracy: 0.8140\n",
            "Epoch 9/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.2342 - accuracy: 0.8929 - val_loss: 0.4598 - val_accuracy: 0.7907\n",
            "Epoch 10/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2535 - accuracy: 0.8929 - val_loss: 0.4082 - val_accuracy: 0.8372\n",
            "Epoch 11/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2290 - accuracy: 0.9286 - val_loss: 0.3471 - val_accuracy: 0.8605\n",
            "Epoch 12/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2100 - accuracy: 0.9226 - val_loss: 0.3436 - val_accuracy: 0.8605\n",
            "Epoch 13/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2578 - accuracy: 0.8929 - val_loss: 0.3524 - val_accuracy: 0.8605\n",
            "Epoch 14/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2351 - accuracy: 0.8929 - val_loss: 0.3733 - val_accuracy: 0.8372\n",
            "Epoch 15/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2520 - accuracy: 0.8988 - val_loss: 0.3792 - val_accuracy: 0.8605\n",
            "Epoch 16/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2449 - accuracy: 0.8869 - val_loss: 0.4230 - val_accuracy: 0.7907\n",
            "Epoch 17/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2230 - accuracy: 0.9107 - val_loss: 0.4280 - val_accuracy: 0.8372\n",
            "Epoch 18/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2269 - accuracy: 0.9167 - val_loss: 0.4135 - val_accuracy: 0.8372\n",
            "Epoch 19/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2146 - accuracy: 0.9167 - val_loss: 0.3629 - val_accuracy: 0.8605\n",
            "Epoch 20/500\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.2195 - accuracy: 0.8988 - val_loss: 0.3568 - val_accuracy: 0.8605\n",
            "Epoch 21/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2201 - accuracy: 0.9107 - val_loss: 0.4109 - val_accuracy: 0.8372\n",
            "Epoch 22/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2246 - accuracy: 0.9286 - val_loss: 0.3891 - val_accuracy: 0.8605\n",
            "Epoch 23/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2233 - accuracy: 0.9226 - val_loss: 0.4098 - val_accuracy: 0.8605\n",
            "Epoch 24/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2355 - accuracy: 0.8988 - val_loss: 0.4258 - val_accuracy: 0.8605\n",
            "Epoch 25/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2185 - accuracy: 0.9167 - val_loss: 0.4370 - val_accuracy: 0.8372\n",
            "Epoch 26/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2336 - accuracy: 0.9226 - val_loss: 0.4027 - val_accuracy: 0.8605\n",
            "Epoch 27/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2089 - accuracy: 0.9167 - val_loss: 0.3827 - val_accuracy: 0.8605\n",
            "Epoch 28/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2192 - accuracy: 0.9226 - val_loss: 0.3848 - val_accuracy: 0.8605\n",
            "Epoch 29/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2108 - accuracy: 0.9286 - val_loss: 0.3867 - val_accuracy: 0.8605\n",
            "Epoch 30/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2206 - accuracy: 0.8988 - val_loss: 0.4086 - val_accuracy: 0.8605\n",
            "Epoch 31/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1968 - accuracy: 0.9167 - val_loss: 0.4289 - val_accuracy: 0.8605\n",
            "Epoch 32/500\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.2127 - accuracy: 0.9167 - val_loss: 0.4435 - val_accuracy: 0.8372\n",
            "Epoch 33/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2201 - accuracy: 0.9226 - val_loss: 0.4023 - val_accuracy: 0.8605\n",
            "Epoch 34/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1984 - accuracy: 0.9226 - val_loss: 0.3802 - val_accuracy: 0.8837\n",
            "Epoch 35/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1738 - accuracy: 0.9405 - val_loss: 0.4382 - val_accuracy: 0.8605\n",
            "Epoch 36/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1971 - accuracy: 0.9345 - val_loss: 0.4938 - val_accuracy: 0.8372\n",
            "Epoch 37/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2081 - accuracy: 0.9048 - val_loss: 0.4277 - val_accuracy: 0.8605\n",
            "Epoch 38/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2666 - accuracy: 0.9048 - val_loss: 0.3945 - val_accuracy: 0.8605\n",
            "Epoch 39/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2258 - accuracy: 0.8988 - val_loss: 0.3723 - val_accuracy: 0.8372\n",
            "Epoch 40/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2645 - accuracy: 0.8810 - val_loss: 0.3469 - val_accuracy: 0.8605\n",
            "Epoch 41/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2322 - accuracy: 0.9048 - val_loss: 0.3571 - val_accuracy: 0.8605\n",
            "Epoch 42/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2329 - accuracy: 0.9048 - val_loss: 0.4235 - val_accuracy: 0.8605\n",
            "Epoch 43/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2238 - accuracy: 0.9167 - val_loss: 0.4410 - val_accuracy: 0.8605\n",
            "Epoch 44/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2122 - accuracy: 0.9107 - val_loss: 0.4033 - val_accuracy: 0.8605\n",
            "Epoch 45/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2173 - accuracy: 0.9048 - val_loss: 0.4051 - val_accuracy: 0.8605\n",
            "Epoch 46/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2023 - accuracy: 0.9226 - val_loss: 0.4049 - val_accuracy: 0.8605\n",
            "Epoch 47/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2139 - accuracy: 0.9286 - val_loss: 0.4242 - val_accuracy: 0.8605\n",
            "Epoch 48/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2574 - accuracy: 0.9226 - val_loss: 0.3861 - val_accuracy: 0.8605\n",
            "Epoch 49/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1861 - accuracy: 0.9405 - val_loss: 0.4337 - val_accuracy: 0.8605\n",
            "Epoch 50/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2249 - accuracy: 0.8988 - val_loss: 0.4201 - val_accuracy: 0.8605\n",
            "Epoch 51/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1903 - accuracy: 0.9405 - val_loss: 0.4099 - val_accuracy: 0.8605\n",
            "Epoch 52/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2023 - accuracy: 0.9107 - val_loss: 0.4692 - val_accuracy: 0.8605\n",
            "Epoch 53/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2309 - accuracy: 0.9048 - val_loss: 0.4725 - val_accuracy: 0.8605\n",
            "Epoch 54/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2424 - accuracy: 0.8869 - val_loss: 0.4236 - val_accuracy: 0.8372\n",
            "Epoch 55/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2157 - accuracy: 0.9226 - val_loss: 0.3890 - val_accuracy: 0.8605\n",
            "Epoch 56/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1924 - accuracy: 0.9405 - val_loss: 0.4235 - val_accuracy: 0.8605\n",
            "Epoch 57/500\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.2351 - accuracy: 0.9107 - val_loss: 0.4419 - val_accuracy: 0.8605\n",
            "Epoch 58/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2196 - accuracy: 0.9286 - val_loss: 0.3629 - val_accuracy: 0.8605\n",
            "Epoch 59/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2021 - accuracy: 0.9226 - val_loss: 0.3742 - val_accuracy: 0.8605\n",
            "Epoch 60/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1830 - accuracy: 0.9464 - val_loss: 0.3988 - val_accuracy: 0.8605\n",
            "Epoch 61/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1927 - accuracy: 0.9286 - val_loss: 0.4106 - val_accuracy: 0.8605\n",
            "Epoch 62/500\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.1969 - accuracy: 0.9405 - val_loss: 0.4624 - val_accuracy: 0.8372\n",
            "Epoch 63/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2320 - accuracy: 0.9167 - val_loss: 0.4383 - val_accuracy: 0.8605\n",
            "Epoch 64/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1993 - accuracy: 0.9048 - val_loss: 0.3665 - val_accuracy: 0.8605\n",
            "Epoch 65/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1997 - accuracy: 0.9345 - val_loss: 0.3709 - val_accuracy: 0.8605\n",
            "Epoch 66/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2002 - accuracy: 0.9226 - val_loss: 0.3804 - val_accuracy: 0.8605\n",
            "Epoch 67/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2030 - accuracy: 0.9167 - val_loss: 0.4170 - val_accuracy: 0.8605\n",
            "Epoch 68/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2083 - accuracy: 0.8988 - val_loss: 0.4422 - val_accuracy: 0.8605\n",
            "Epoch 69/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2118 - accuracy: 0.9345 - val_loss: 0.4184 - val_accuracy: 0.8605\n",
            "Epoch 70/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1858 - accuracy: 0.9107 - val_loss: 0.4140 - val_accuracy: 0.8372\n",
            "Epoch 71/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1733 - accuracy: 0.9226 - val_loss: 0.4046 - val_accuracy: 0.8605\n",
            "Epoch 72/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1892 - accuracy: 0.9345 - val_loss: 0.4111 - val_accuracy: 0.8605\n",
            "Epoch 73/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1839 - accuracy: 0.9286 - val_loss: 0.3990 - val_accuracy: 0.8605\n",
            "Epoch 74/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1690 - accuracy: 0.9286 - val_loss: 0.3925 - val_accuracy: 0.8605\n",
            "Epoch 75/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1726 - accuracy: 0.9226 - val_loss: 0.4351 - val_accuracy: 0.8605\n",
            "Epoch 76/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2138 - accuracy: 0.9226 - val_loss: 0.4747 - val_accuracy: 0.8605\n",
            "Epoch 77/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1947 - accuracy: 0.9226 - val_loss: 0.3630 - val_accuracy: 0.8837\n",
            "Epoch 78/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1966 - accuracy: 0.9286 - val_loss: 0.3626 - val_accuracy: 0.8605\n",
            "Epoch 79/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1964 - accuracy: 0.9286 - val_loss: 0.3845 - val_accuracy: 0.8605\n",
            "Epoch 80/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2022 - accuracy: 0.9226 - val_loss: 0.4245 - val_accuracy: 0.8605\n",
            "Epoch 81/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2086 - accuracy: 0.9286 - val_loss: 0.4502 - val_accuracy: 0.8605\n",
            "Epoch 82/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1967 - accuracy: 0.9226 - val_loss: 0.4318 - val_accuracy: 0.8605\n",
            "Epoch 83/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1985 - accuracy: 0.9107 - val_loss: 0.3647 - val_accuracy: 0.8837\n",
            "Epoch 84/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1911 - accuracy: 0.9286 - val_loss: 0.3785 - val_accuracy: 0.8605\n",
            "Epoch 85/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2204 - accuracy: 0.9405 - val_loss: 0.3833 - val_accuracy: 0.8837\n",
            "Epoch 86/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1933 - accuracy: 0.9226 - val_loss: 0.4468 - val_accuracy: 0.8605\n",
            "Epoch 87/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1973 - accuracy: 0.9345 - val_loss: 0.5250 - val_accuracy: 0.8605\n",
            "Epoch 88/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1686 - accuracy: 0.9286 - val_loss: 0.4370 - val_accuracy: 0.8605\n",
            "Epoch 89/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1948 - accuracy: 0.9345 - val_loss: 0.4196 - val_accuracy: 0.8605\n",
            "Epoch 90/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2169 - accuracy: 0.9048 - val_loss: 0.4628 - val_accuracy: 0.8605\n",
            "Epoch 91/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2000 - accuracy: 0.9286 - val_loss: 0.4408 - val_accuracy: 0.8605\n",
            "Epoch 92/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2028 - accuracy: 0.9226 - val_loss: 0.4274 - val_accuracy: 0.8605\n",
            "Epoch 93/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1777 - accuracy: 0.9345 - val_loss: 0.4738 - val_accuracy: 0.8605\n",
            "Epoch 94/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1834 - accuracy: 0.9286 - val_loss: 0.5070 - val_accuracy: 0.8605\n",
            "Epoch 95/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1779 - accuracy: 0.9345 - val_loss: 0.4526 - val_accuracy: 0.8605\n",
            "Epoch 96/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1814 - accuracy: 0.9286 - val_loss: 0.4410 - val_accuracy: 0.8605\n",
            "Epoch 97/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1922 - accuracy: 0.9345 - val_loss: 0.5132 - val_accuracy: 0.8605\n",
            "Epoch 98/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1884 - accuracy: 0.9286 - val_loss: 0.4966 - val_accuracy: 0.8605\n",
            "Epoch 99/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1841 - accuracy: 0.9405 - val_loss: 0.4181 - val_accuracy: 0.8605\n",
            "Epoch 100/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1714 - accuracy: 0.9107 - val_loss: 0.4092 - val_accuracy: 0.8605\n",
            "Epoch 101/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1611 - accuracy: 0.9524 - val_loss: 0.4194 - val_accuracy: 0.8605\n",
            "Epoch 102/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1935 - accuracy: 0.9226 - val_loss: 0.3958 - val_accuracy: 0.8605\n",
            "Epoch 103/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1693 - accuracy: 0.9405 - val_loss: 0.4149 - val_accuracy: 0.8605\n",
            "Epoch 104/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1759 - accuracy: 0.9405 - val_loss: 0.4627 - val_accuracy: 0.8605\n",
            "Epoch 105/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1596 - accuracy: 0.9405 - val_loss: 0.4943 - val_accuracy: 0.8605\n",
            "Epoch 106/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1649 - accuracy: 0.9345 - val_loss: 0.4918 - val_accuracy: 0.8605\n",
            "Epoch 107/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1872 - accuracy: 0.9345 - val_loss: 0.4511 - val_accuracy: 0.8605\n",
            "Epoch 108/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2019 - accuracy: 0.9226 - val_loss: 0.4274 - val_accuracy: 0.8605\n",
            "Epoch 109/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1878 - accuracy: 0.9345 - val_loss: 0.4453 - val_accuracy: 0.8605\n",
            "Epoch 110/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1775 - accuracy: 0.9345 - val_loss: 0.4756 - val_accuracy: 0.8605\n",
            "Epoch 111/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.2018 - accuracy: 0.9286 - val_loss: 0.4685 - val_accuracy: 0.8605\n",
            "Epoch 112/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1751 - accuracy: 0.9405 - val_loss: 0.5356 - val_accuracy: 0.8605\n",
            "Epoch 113/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2132 - accuracy: 0.9286 - val_loss: 0.5353 - val_accuracy: 0.8605\n",
            "Epoch 114/500\n",
            "6/6 [==============================] - 0s 5ms/step - loss: 0.1844 - accuracy: 0.9345 - val_loss: 0.4586 - val_accuracy: 0.8605\n",
            "Epoch 115/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1891 - accuracy: 0.9167 - val_loss: 0.4298 - val_accuracy: 0.8605\n",
            "Epoch 116/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1685 - accuracy: 0.9226 - val_loss: 0.4663 - val_accuracy: 0.8605\n",
            "Epoch 117/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1960 - accuracy: 0.8988 - val_loss: 0.4782 - val_accuracy: 0.8605\n",
            "Epoch 118/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1943 - accuracy: 0.9167 - val_loss: 0.4773 - val_accuracy: 0.8605\n",
            "Epoch 119/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2270 - accuracy: 0.9107 - val_loss: 0.4655 - val_accuracy: 0.8605\n",
            "Epoch 120/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1541 - accuracy: 0.9405 - val_loss: 0.4822 - val_accuracy: 0.8605\n",
            "Epoch 121/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1619 - accuracy: 0.9524 - val_loss: 0.4698 - val_accuracy: 0.8605\n",
            "Epoch 122/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1708 - accuracy: 0.9286 - val_loss: 0.4834 - val_accuracy: 0.8605\n",
            "Epoch 123/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1847 - accuracy: 0.9167 - val_loss: 0.4842 - val_accuracy: 0.8605\n",
            "Epoch 124/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1680 - accuracy: 0.9226 - val_loss: 0.4873 - val_accuracy: 0.8605\n",
            "Epoch 125/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1785 - accuracy: 0.9405 - val_loss: 0.4313 - val_accuracy: 0.8837\n",
            "Epoch 126/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1960 - accuracy: 0.9107 - val_loss: 0.4847 - val_accuracy: 0.8605\n",
            "Epoch 127/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1706 - accuracy: 0.9286 - val_loss: 0.5123 - val_accuracy: 0.8605\n",
            "Epoch 128/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1753 - accuracy: 0.9167 - val_loss: 0.4996 - val_accuracy: 0.8605\n",
            "Epoch 129/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1779 - accuracy: 0.9405 - val_loss: 0.4445 - val_accuracy: 0.8605\n",
            "Epoch 130/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1553 - accuracy: 0.9405 - val_loss: 0.4760 - val_accuracy: 0.8605\n",
            "Epoch 131/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1850 - accuracy: 0.9226 - val_loss: 0.4464 - val_accuracy: 0.8605\n",
            "Epoch 132/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1820 - accuracy: 0.9345 - val_loss: 0.4478 - val_accuracy: 0.8605\n",
            "Epoch 133/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2005 - accuracy: 0.9405 - val_loss: 0.4365 - val_accuracy: 0.8605\n",
            "Epoch 134/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1527 - accuracy: 0.9524 - val_loss: 0.4518 - val_accuracy: 0.8605\n",
            "Epoch 135/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1751 - accuracy: 0.9464 - val_loss: 0.4856 - val_accuracy: 0.8605\n",
            "Epoch 136/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1941 - accuracy: 0.9226 - val_loss: 0.4610 - val_accuracy: 0.8605\n",
            "Epoch 137/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2094 - accuracy: 0.9226 - val_loss: 0.4607 - val_accuracy: 0.8605\n",
            "Epoch 138/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1871 - accuracy: 0.9345 - val_loss: 0.5123 - val_accuracy: 0.8605\n",
            "Epoch 139/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1697 - accuracy: 0.9405 - val_loss: 0.4534 - val_accuracy: 0.8605\n",
            "Epoch 140/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1852 - accuracy: 0.9345 - val_loss: 0.4409 - val_accuracy: 0.8605\n",
            "Epoch 141/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1754 - accuracy: 0.9345 - val_loss: 0.3880 - val_accuracy: 0.8837\n",
            "Epoch 142/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1867 - accuracy: 0.9345 - val_loss: 0.4059 - val_accuracy: 0.8605\n",
            "Epoch 143/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2000 - accuracy: 0.9226 - val_loss: 0.4652 - val_accuracy: 0.8605\n",
            "Epoch 144/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1891 - accuracy: 0.9345 - val_loss: 0.5053 - val_accuracy: 0.8605\n",
            "Epoch 145/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2013 - accuracy: 0.9226 - val_loss: 0.4686 - val_accuracy: 0.8605\n",
            "Epoch 146/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1897 - accuracy: 0.9226 - val_loss: 0.4300 - val_accuracy: 0.8605\n",
            "Epoch 147/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1730 - accuracy: 0.9286 - val_loss: 0.4452 - val_accuracy: 0.8837\n",
            "Epoch 148/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1819 - accuracy: 0.9167 - val_loss: 0.4503 - val_accuracy: 0.8605\n",
            "Epoch 149/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2048 - accuracy: 0.9226 - val_loss: 0.5489 - val_accuracy: 0.8605\n",
            "Epoch 150/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1671 - accuracy: 0.9345 - val_loss: 0.5703 - val_accuracy: 0.8605\n",
            "Epoch 151/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1999 - accuracy: 0.9405 - val_loss: 0.5646 - val_accuracy: 0.8605\n",
            "Epoch 152/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1844 - accuracy: 0.9345 - val_loss: 0.5252 - val_accuracy: 0.8605\n",
            "Epoch 153/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1880 - accuracy: 0.9345 - val_loss: 0.5379 - val_accuracy: 0.8605\n",
            "Epoch 154/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1429 - accuracy: 0.9345 - val_loss: 0.5512 - val_accuracy: 0.8605\n",
            "Epoch 155/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1544 - accuracy: 0.9286 - val_loss: 0.5679 - val_accuracy: 0.8605\n",
            "Epoch 156/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1991 - accuracy: 0.9226 - val_loss: 0.5762 - val_accuracy: 0.8605\n",
            "Epoch 157/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1840 - accuracy: 0.9107 - val_loss: 0.5103 - val_accuracy: 0.8605\n",
            "Epoch 158/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1956 - accuracy: 0.9345 - val_loss: 0.4575 - val_accuracy: 0.8605\n",
            "Epoch 159/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1582 - accuracy: 0.9524 - val_loss: 0.4866 - val_accuracy: 0.8605\n",
            "Epoch 160/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1739 - accuracy: 0.9286 - val_loss: 0.5549 - val_accuracy: 0.8605\n",
            "Epoch 161/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1817 - accuracy: 0.9226 - val_loss: 0.5758 - val_accuracy: 0.8605\n",
            "Epoch 162/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1857 - accuracy: 0.9226 - val_loss: 0.5672 - val_accuracy: 0.8605\n",
            "Epoch 163/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1643 - accuracy: 0.9345 - val_loss: 0.6258 - val_accuracy: 0.8605\n",
            "Epoch 164/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1785 - accuracy: 0.9167 - val_loss: 0.6575 - val_accuracy: 0.8605\n",
            "Epoch 165/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1819 - accuracy: 0.9107 - val_loss: 0.6108 - val_accuracy: 0.8605\n",
            "Epoch 166/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1680 - accuracy: 0.9345 - val_loss: 0.5102 - val_accuracy: 0.8372\n",
            "Epoch 167/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2084 - accuracy: 0.9286 - val_loss: 0.4959 - val_accuracy: 0.8372\n",
            "Epoch 168/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1867 - accuracy: 0.9226 - val_loss: 0.5032 - val_accuracy: 0.8605\n",
            "Epoch 169/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2119 - accuracy: 0.9286 - val_loss: 0.5168 - val_accuracy: 0.8605\n",
            "Epoch 170/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1892 - accuracy: 0.9167 - val_loss: 0.5424 - val_accuracy: 0.8605\n",
            "Epoch 171/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1564 - accuracy: 0.9286 - val_loss: 0.5269 - val_accuracy: 0.8605\n",
            "Epoch 172/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1881 - accuracy: 0.9226 - val_loss: 0.4748 - val_accuracy: 0.8605\n",
            "Epoch 173/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1624 - accuracy: 0.9405 - val_loss: 0.4598 - val_accuracy: 0.8605\n",
            "Epoch 174/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1666 - accuracy: 0.9345 - val_loss: 0.4758 - val_accuracy: 0.8605\n",
            "Epoch 175/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1700 - accuracy: 0.9286 - val_loss: 0.5700 - val_accuracy: 0.8605\n",
            "Epoch 176/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1916 - accuracy: 0.9286 - val_loss: 0.5669 - val_accuracy: 0.8605\n",
            "Epoch 177/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1722 - accuracy: 0.9405 - val_loss: 0.5407 - val_accuracy: 0.8605\n",
            "Epoch 178/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1693 - accuracy: 0.9345 - val_loss: 0.5450 - val_accuracy: 0.8605\n",
            "Epoch 179/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1501 - accuracy: 0.9464 - val_loss: 0.6399 - val_accuracy: 0.8605\n",
            "Epoch 180/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1715 - accuracy: 0.9048 - val_loss: 0.5456 - val_accuracy: 0.8605\n",
            "Epoch 181/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2119 - accuracy: 0.9345 - val_loss: 0.5515 - val_accuracy: 0.8605\n",
            "Epoch 182/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1537 - accuracy: 0.9405 - val_loss: 0.6111 - val_accuracy: 0.8605\n",
            "Epoch 183/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1601 - accuracy: 0.9405 - val_loss: 0.5605 - val_accuracy: 0.8605\n",
            "Epoch 184/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1720 - accuracy: 0.9286 - val_loss: 0.5484 - val_accuracy: 0.8605\n",
            "Epoch 185/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2046 - accuracy: 0.9048 - val_loss: 0.5444 - val_accuracy: 0.8605\n",
            "Epoch 186/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1771 - accuracy: 0.9345 - val_loss: 0.6254 - val_accuracy: 0.8605\n",
            "Epoch 187/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2002 - accuracy: 0.9107 - val_loss: 0.6676 - val_accuracy: 0.8605\n",
            "Epoch 188/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1571 - accuracy: 0.9345 - val_loss: 0.5730 - val_accuracy: 0.8605\n",
            "Epoch 189/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1739 - accuracy: 0.9226 - val_loss: 0.6804 - val_accuracy: 0.8605\n",
            "Epoch 190/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1641 - accuracy: 0.9345 - val_loss: 0.8414 - val_accuracy: 0.8605\n",
            "Epoch 191/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1729 - accuracy: 0.9405 - val_loss: 0.8589 - val_accuracy: 0.8605\n",
            "Epoch 192/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1951 - accuracy: 0.9167 - val_loss: 0.6572 - val_accuracy: 0.8605\n",
            "Epoch 193/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2167 - accuracy: 0.8988 - val_loss: 0.5631 - val_accuracy: 0.8605\n",
            "Epoch 194/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1549 - accuracy: 0.9345 - val_loss: 0.6584 - val_accuracy: 0.8605\n",
            "Epoch 195/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2355 - accuracy: 0.9048 - val_loss: 0.5926 - val_accuracy: 0.8605\n",
            "Epoch 196/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1842 - accuracy: 0.9167 - val_loss: 0.6028 - val_accuracy: 0.8605\n",
            "Epoch 197/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1714 - accuracy: 0.9286 - val_loss: 0.7000 - val_accuracy: 0.8605\n",
            "Epoch 198/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1855 - accuracy: 0.9286 - val_loss: 0.6599 - val_accuracy: 0.8605\n",
            "Epoch 199/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1657 - accuracy: 0.9345 - val_loss: 0.5170 - val_accuracy: 0.9070\n",
            "Epoch 200/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1722 - accuracy: 0.9286 - val_loss: 0.4380 - val_accuracy: 0.9302\n",
            "Epoch 201/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2017 - accuracy: 0.9107 - val_loss: 0.4246 - val_accuracy: 0.9070\n",
            "Epoch 202/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2648 - accuracy: 0.9226 - val_loss: 0.5120 - val_accuracy: 0.8372\n",
            "Epoch 203/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1796 - accuracy: 0.9167 - val_loss: 0.5415 - val_accuracy: 0.8372\n",
            "Epoch 204/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1893 - accuracy: 0.9286 - val_loss: 0.4397 - val_accuracy: 0.8605\n",
            "Epoch 205/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1890 - accuracy: 0.9345 - val_loss: 0.4185 - val_accuracy: 0.8837\n",
            "Epoch 206/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1720 - accuracy: 0.9345 - val_loss: 0.4475 - val_accuracy: 0.8837\n",
            "Epoch 207/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1780 - accuracy: 0.9107 - val_loss: 0.5505 - val_accuracy: 0.8372\n",
            "Epoch 208/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1969 - accuracy: 0.9167 - val_loss: 0.5654 - val_accuracy: 0.8372\n",
            "Epoch 209/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.2038 - accuracy: 0.9107 - val_loss: 0.4679 - val_accuracy: 0.8372\n",
            "Epoch 210/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1834 - accuracy: 0.9345 - val_loss: 0.4425 - val_accuracy: 0.8837\n",
            "Epoch 211/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1806 - accuracy: 0.9286 - val_loss: 0.4552 - val_accuracy: 0.8837\n",
            "Epoch 212/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1697 - accuracy: 0.9345 - val_loss: 0.5289 - val_accuracy: 0.8605\n",
            "Epoch 213/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1748 - accuracy: 0.9286 - val_loss: 0.5723 - val_accuracy: 0.8605\n",
            "Epoch 214/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1926 - accuracy: 0.9167 - val_loss: 0.5031 - val_accuracy: 0.8837\n",
            "Epoch 215/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1708 - accuracy: 0.9226 - val_loss: 0.4604 - val_accuracy: 0.8837\n",
            "Epoch 216/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1747 - accuracy: 0.9405 - val_loss: 0.4901 - val_accuracy: 0.8605\n",
            "Epoch 217/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1637 - accuracy: 0.9524 - val_loss: 0.4784 - val_accuracy: 0.8605\n",
            "Epoch 218/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1620 - accuracy: 0.9464 - val_loss: 0.4494 - val_accuracy: 0.8837\n",
            "Epoch 219/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1606 - accuracy: 0.9286 - val_loss: 0.4646 - val_accuracy: 0.8837\n",
            "Epoch 220/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1582 - accuracy: 0.9405 - val_loss: 0.5462 - val_accuracy: 0.8605\n",
            "Epoch 221/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1740 - accuracy: 0.9345 - val_loss: 0.5872 - val_accuracy: 0.8605\n",
            "Epoch 222/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1570 - accuracy: 0.9405 - val_loss: 0.5592 - val_accuracy: 0.8605\n",
            "Epoch 223/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1485 - accuracy: 0.9345 - val_loss: 0.5530 - val_accuracy: 0.8605\n",
            "Epoch 224/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1641 - accuracy: 0.9226 - val_loss: 0.5555 - val_accuracy: 0.8605\n",
            "Epoch 225/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1750 - accuracy: 0.9345 - val_loss: 0.5270 - val_accuracy: 0.8605\n",
            "Epoch 226/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1841 - accuracy: 0.9226 - val_loss: 0.5436 - val_accuracy: 0.8605\n",
            "Epoch 227/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1831 - accuracy: 0.9345 - val_loss: 0.6428 - val_accuracy: 0.8605\n",
            "Epoch 228/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1700 - accuracy: 0.9226 - val_loss: 0.7154 - val_accuracy: 0.8605\n",
            "Epoch 229/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1685 - accuracy: 0.9345 - val_loss: 0.6878 - val_accuracy: 0.8605\n",
            "Epoch 230/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1768 - accuracy: 0.9226 - val_loss: 0.5235 - val_accuracy: 0.9070\n",
            "Epoch 231/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1841 - accuracy: 0.9286 - val_loss: 0.5933 - val_accuracy: 0.8605\n",
            "Epoch 232/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1476 - accuracy: 0.9583 - val_loss: 0.7628 - val_accuracy: 0.8605\n",
            "Epoch 233/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1797 - accuracy: 0.9464 - val_loss: 0.7982 - val_accuracy: 0.8605\n",
            "Epoch 234/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1684 - accuracy: 0.9345 - val_loss: 0.7502 - val_accuracy: 0.8605\n",
            "Epoch 235/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1855 - accuracy: 0.9226 - val_loss: 0.6797 - val_accuracy: 0.8605\n",
            "Epoch 236/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1567 - accuracy: 0.9345 - val_loss: 0.6878 - val_accuracy: 0.8605\n",
            "Epoch 237/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1615 - accuracy: 0.9345 - val_loss: 0.7657 - val_accuracy: 0.8605\n",
            "Epoch 238/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1984 - accuracy: 0.9107 - val_loss: 0.7881 - val_accuracy: 0.8605\n",
            "Epoch 239/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1715 - accuracy: 0.9345 - val_loss: 0.8257 - val_accuracy: 0.8605\n",
            "Epoch 240/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1325 - accuracy: 0.9286 - val_loss: 0.8968 - val_accuracy: 0.8605\n",
            "Epoch 241/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1347 - accuracy: 0.9524 - val_loss: 0.8790 - val_accuracy: 0.8605\n",
            "Epoch 242/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1670 - accuracy: 0.9524 - val_loss: 0.7921 - val_accuracy: 0.8605\n",
            "Epoch 243/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1397 - accuracy: 0.9464 - val_loss: 0.7032 - val_accuracy: 0.8605\n",
            "Epoch 244/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1735 - accuracy: 0.9226 - val_loss: 0.6976 - val_accuracy: 0.8605\n",
            "Epoch 245/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1777 - accuracy: 0.9286 - val_loss: 0.7224 - val_accuracy: 0.8605\n",
            "Epoch 246/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1916 - accuracy: 0.9167 - val_loss: 0.7427 - val_accuracy: 0.8372\n",
            "Epoch 247/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1675 - accuracy: 0.9226 - val_loss: 0.6497 - val_accuracy: 0.8605\n",
            "Epoch 248/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1690 - accuracy: 0.9345 - val_loss: 0.6033 - val_accuracy: 0.8837\n",
            "Epoch 249/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1941 - accuracy: 0.9107 - val_loss: 0.5933 - val_accuracy: 0.8605\n",
            "Epoch 250/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1641 - accuracy: 0.9405 - val_loss: 0.7441 - val_accuracy: 0.8605\n",
            "Epoch 251/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1623 - accuracy: 0.9405 - val_loss: 0.6951 - val_accuracy: 0.8605\n",
            "Epoch 252/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1907 - accuracy: 0.9405 - val_loss: 0.4673 - val_accuracy: 0.9070\n",
            "Epoch 253/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1766 - accuracy: 0.9167 - val_loss: 0.5004 - val_accuracy: 0.9070\n",
            "Epoch 254/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1518 - accuracy: 0.9226 - val_loss: 0.7981 - val_accuracy: 0.8605\n",
            "Epoch 255/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1883 - accuracy: 0.9286 - val_loss: 0.6799 - val_accuracy: 0.8372\n",
            "Epoch 256/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1890 - accuracy: 0.9167 - val_loss: 0.5484 - val_accuracy: 0.8372\n",
            "Epoch 257/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1814 - accuracy: 0.9345 - val_loss: 0.6023 - val_accuracy: 0.8372\n",
            "Epoch 258/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1713 - accuracy: 0.9345 - val_loss: 0.6388 - val_accuracy: 0.8372\n",
            "Epoch 259/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1670 - accuracy: 0.9167 - val_loss: 0.6717 - val_accuracy: 0.8605\n",
            "Epoch 260/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1446 - accuracy: 0.9405 - val_loss: 0.6787 - val_accuracy: 0.8605\n",
            "Epoch 261/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1817 - accuracy: 0.9226 - val_loss: 0.7216 - val_accuracy: 0.8605\n",
            "Epoch 262/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1561 - accuracy: 0.9405 - val_loss: 0.8061 - val_accuracy: 0.8605\n",
            "Epoch 263/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1602 - accuracy: 0.9286 - val_loss: 0.7726 - val_accuracy: 0.8605\n",
            "Epoch 264/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1502 - accuracy: 0.9405 - val_loss: 0.7446 - val_accuracy: 0.8605\n",
            "Epoch 265/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1574 - accuracy: 0.9345 - val_loss: 0.6769 - val_accuracy: 0.8605\n",
            "Epoch 266/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1483 - accuracy: 0.9464 - val_loss: 0.7337 - val_accuracy: 0.8605\n",
            "Epoch 267/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1854 - accuracy: 0.9286 - val_loss: 0.6822 - val_accuracy: 0.8605\n",
            "Epoch 268/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1694 - accuracy: 0.9345 - val_loss: 0.3975 - val_accuracy: 0.9070\n",
            "Epoch 269/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1883 - accuracy: 0.9167 - val_loss: 0.3866 - val_accuracy: 0.9070\n",
            "Epoch 270/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1701 - accuracy: 0.9226 - val_loss: 0.5030 - val_accuracy: 0.8605\n",
            "Epoch 271/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1710 - accuracy: 0.9226 - val_loss: 0.5884 - val_accuracy: 0.8605\n",
            "Epoch 272/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1498 - accuracy: 0.9286 - val_loss: 0.6313 - val_accuracy: 0.8605\n",
            "Epoch 273/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1721 - accuracy: 0.9345 - val_loss: 0.6096 - val_accuracy: 0.8372\n",
            "Epoch 274/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.2045 - accuracy: 0.9226 - val_loss: 0.5631 - val_accuracy: 0.8605\n",
            "Epoch 275/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1766 - accuracy: 0.9226 - val_loss: 0.4817 - val_accuracy: 0.8837\n",
            "Epoch 276/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.2105 - accuracy: 0.9286 - val_loss: 0.4972 - val_accuracy: 0.8837\n",
            "Epoch 277/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1915 - accuracy: 0.9048 - val_loss: 0.5239 - val_accuracy: 0.8837\n",
            "Epoch 278/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1892 - accuracy: 0.9464 - val_loss: 0.5341 - val_accuracy: 0.8837\n",
            "Epoch 279/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1419 - accuracy: 0.9464 - val_loss: 0.5656 - val_accuracy: 0.8605\n",
            "Epoch 280/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1883 - accuracy: 0.9167 - val_loss: 0.6043 - val_accuracy: 0.8605\n",
            "Epoch 281/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1649 - accuracy: 0.9405 - val_loss: 0.6987 - val_accuracy: 0.8605\n",
            "Epoch 282/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1663 - accuracy: 0.9405 - val_loss: 0.8001 - val_accuracy: 0.8605\n",
            "Epoch 283/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1625 - accuracy: 0.9345 - val_loss: 0.7657 - val_accuracy: 0.8605\n",
            "Epoch 284/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1819 - accuracy: 0.9167 - val_loss: 0.7586 - val_accuracy: 0.8605\n",
            "Epoch 285/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1682 - accuracy: 0.9345 - val_loss: 0.6770 - val_accuracy: 0.8605\n",
            "Epoch 286/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1665 - accuracy: 0.9226 - val_loss: 0.6969 - val_accuracy: 0.8605\n",
            "Epoch 287/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1651 - accuracy: 0.9345 - val_loss: 0.7201 - val_accuracy: 0.8372\n",
            "Epoch 288/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1718 - accuracy: 0.9167 - val_loss: 0.7024 - val_accuracy: 0.8372\n",
            "Epoch 289/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1639 - accuracy: 0.9405 - val_loss: 0.6091 - val_accuracy: 0.8605\n",
            "Epoch 290/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1715 - accuracy: 0.9167 - val_loss: 0.4465 - val_accuracy: 0.8837\n",
            "Epoch 291/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1675 - accuracy: 0.9405 - val_loss: 0.4120 - val_accuracy: 0.8837\n",
            "Epoch 292/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1590 - accuracy: 0.9464 - val_loss: 0.4338 - val_accuracy: 0.8837\n",
            "Epoch 293/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1547 - accuracy: 0.9405 - val_loss: 0.6375 - val_accuracy: 0.8605\n",
            "Epoch 294/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1747 - accuracy: 0.9405 - val_loss: 0.6701 - val_accuracy: 0.8372\n",
            "Epoch 295/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1466 - accuracy: 0.9464 - val_loss: 0.6248 - val_accuracy: 0.8605\n",
            "Epoch 296/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1821 - accuracy: 0.9286 - val_loss: 0.5616 - val_accuracy: 0.8837\n",
            "Epoch 297/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1641 - accuracy: 0.9286 - val_loss: 0.5553 - val_accuracy: 0.8837\n",
            "Epoch 298/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1590 - accuracy: 0.9167 - val_loss: 0.6400 - val_accuracy: 0.8605\n",
            "Epoch 299/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1549 - accuracy: 0.9345 - val_loss: 0.6638 - val_accuracy: 0.8605\n",
            "Epoch 300/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1304 - accuracy: 0.9524 - val_loss: 0.6738 - val_accuracy: 0.8372\n",
            "Epoch 301/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1640 - accuracy: 0.9345 - val_loss: 0.6854 - val_accuracy: 0.8372\n",
            "Epoch 302/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1875 - accuracy: 0.9345 - val_loss: 0.6840 - val_accuracy: 0.8605\n",
            "Epoch 303/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1399 - accuracy: 0.9524 - val_loss: 0.6704 - val_accuracy: 0.8605\n",
            "Epoch 304/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1539 - accuracy: 0.9405 - val_loss: 0.6791 - val_accuracy: 0.8605\n",
            "Epoch 305/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1773 - accuracy: 0.9226 - val_loss: 0.7701 - val_accuracy: 0.8372\n",
            "Epoch 306/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1480 - accuracy: 0.9464 - val_loss: 0.8124 - val_accuracy: 0.8372\n",
            "Epoch 307/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1518 - accuracy: 0.9345 - val_loss: 0.6882 - val_accuracy: 0.8605\n",
            "Epoch 308/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1975 - accuracy: 0.9286 - val_loss: 0.6348 - val_accuracy: 0.8837\n",
            "Epoch 309/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1535 - accuracy: 0.9405 - val_loss: 0.6089 - val_accuracy: 0.8837\n",
            "Epoch 310/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2075 - accuracy: 0.9167 - val_loss: 0.5789 - val_accuracy: 0.9070\n",
            "Epoch 311/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1589 - accuracy: 0.9345 - val_loss: 0.6122 - val_accuracy: 0.8837\n",
            "Epoch 312/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1674 - accuracy: 0.9286 - val_loss: 0.6952 - val_accuracy: 0.8605\n",
            "Epoch 313/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1491 - accuracy: 0.9286 - val_loss: 0.7527 - val_accuracy: 0.8605\n",
            "Epoch 314/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1483 - accuracy: 0.9583 - val_loss: 0.7682 - val_accuracy: 0.8605\n",
            "Epoch 315/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1642 - accuracy: 0.9286 - val_loss: 0.8140 - val_accuracy: 0.8605\n",
            "Epoch 316/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1610 - accuracy: 0.9345 - val_loss: 0.8087 - val_accuracy: 0.8605\n",
            "Epoch 317/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1615 - accuracy: 0.9345 - val_loss: 0.7311 - val_accuracy: 0.8605\n",
            "Epoch 318/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1556 - accuracy: 0.9464 - val_loss: 0.5947 - val_accuracy: 0.8837\n",
            "Epoch 319/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1481 - accuracy: 0.9405 - val_loss: 0.6388 - val_accuracy: 0.8605\n",
            "Epoch 320/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1490 - accuracy: 0.9226 - val_loss: 0.7322 - val_accuracy: 0.8605\n",
            "Epoch 321/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1345 - accuracy: 0.9524 - val_loss: 0.7204 - val_accuracy: 0.8605\n",
            "Epoch 322/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1662 - accuracy: 0.9286 - val_loss: 0.7270 - val_accuracy: 0.8605\n",
            "Epoch 323/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1489 - accuracy: 0.9405 - val_loss: 0.6970 - val_accuracy: 0.8605\n",
            "Epoch 324/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1692 - accuracy: 0.9464 - val_loss: 0.6625 - val_accuracy: 0.8605\n",
            "Epoch 325/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1501 - accuracy: 0.9286 - val_loss: 0.7084 - val_accuracy: 0.8605\n",
            "Epoch 326/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1409 - accuracy: 0.9524 - val_loss: 0.8717 - val_accuracy: 0.8605\n",
            "Epoch 327/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1681 - accuracy: 0.9345 - val_loss: 0.8808 - val_accuracy: 0.8605\n",
            "Epoch 328/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.2020 - accuracy: 0.9464 - val_loss: 0.8534 - val_accuracy: 0.8605\n",
            "Epoch 329/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1559 - accuracy: 0.9226 - val_loss: 0.7237 - val_accuracy: 0.8605\n",
            "Epoch 330/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1582 - accuracy: 0.9286 - val_loss: 0.7600 - val_accuracy: 0.8605\n",
            "Epoch 331/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1674 - accuracy: 0.9345 - val_loss: 0.7435 - val_accuracy: 0.8605\n",
            "Epoch 332/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1534 - accuracy: 0.9345 - val_loss: 0.7579 - val_accuracy: 0.8605\n",
            "Epoch 333/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1453 - accuracy: 0.9464 - val_loss: 0.8095 - val_accuracy: 0.7907\n",
            "Epoch 334/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1835 - accuracy: 0.9226 - val_loss: 0.7724 - val_accuracy: 0.7674\n",
            "Epoch 335/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1373 - accuracy: 0.9405 - val_loss: 0.7387 - val_accuracy: 0.8372\n",
            "Epoch 336/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1443 - accuracy: 0.9405 - val_loss: 0.7855 - val_accuracy: 0.8605\n",
            "Epoch 337/500\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.1810 - accuracy: 0.9345 - val_loss: 0.8411 - val_accuracy: 0.8605\n",
            "Epoch 338/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1729 - accuracy: 0.9226 - val_loss: 0.6959 - val_accuracy: 0.8605\n",
            "Epoch 339/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1480 - accuracy: 0.9405 - val_loss: 0.5704 - val_accuracy: 0.9070\n",
            "Epoch 340/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1630 - accuracy: 0.9345 - val_loss: 0.5913 - val_accuracy: 0.8837\n",
            "Epoch 341/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1787 - accuracy: 0.9464 - val_loss: 0.6947 - val_accuracy: 0.8605\n",
            "Epoch 342/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2114 - accuracy: 0.9405 - val_loss: 0.7650 - val_accuracy: 0.8605\n",
            "Epoch 343/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1681 - accuracy: 0.9405 - val_loss: 0.7758 - val_accuracy: 0.8605\n",
            "Epoch 344/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1855 - accuracy: 0.9286 - val_loss: 0.7940 - val_accuracy: 0.8605\n",
            "Epoch 345/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1475 - accuracy: 0.9345 - val_loss: 0.7226 - val_accuracy: 0.8605\n",
            "Epoch 346/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1391 - accuracy: 0.9405 - val_loss: 0.6986 - val_accuracy: 0.8605\n",
            "Epoch 347/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1563 - accuracy: 0.9345 - val_loss: 0.6985 - val_accuracy: 0.8605\n",
            "Epoch 348/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1673 - accuracy: 0.9286 - val_loss: 0.7459 - val_accuracy: 0.8605\n",
            "Epoch 349/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1746 - accuracy: 0.9345 - val_loss: 0.7755 - val_accuracy: 0.8605\n",
            "Epoch 350/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1485 - accuracy: 0.9464 - val_loss: 0.8213 - val_accuracy: 0.8605\n",
            "Epoch 351/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1663 - accuracy: 0.9405 - val_loss: 0.7918 - val_accuracy: 0.8605\n",
            "Epoch 352/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1653 - accuracy: 0.9226 - val_loss: 0.7888 - val_accuracy: 0.8605\n",
            "Epoch 353/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1472 - accuracy: 0.9345 - val_loss: 0.8594 - val_accuracy: 0.8605\n",
            "Epoch 354/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1426 - accuracy: 0.9464 - val_loss: 1.0199 - val_accuracy: 0.8605\n",
            "Epoch 355/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1904 - accuracy: 0.9345 - val_loss: 0.8852 - val_accuracy: 0.8605\n",
            "Epoch 356/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1619 - accuracy: 0.9286 - val_loss: 0.7824 - val_accuracy: 0.8605\n",
            "Epoch 357/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2004 - accuracy: 0.9286 - val_loss: 0.5982 - val_accuracy: 0.8837\n",
            "Epoch 358/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1706 - accuracy: 0.9345 - val_loss: 0.6034 - val_accuracy: 0.9070\n",
            "Epoch 359/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1879 - accuracy: 0.9345 - val_loss: 0.7022 - val_accuracy: 0.8605\n",
            "Epoch 360/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1459 - accuracy: 0.9345 - val_loss: 0.8362 - val_accuracy: 0.8605\n",
            "Epoch 361/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1361 - accuracy: 0.9464 - val_loss: 0.8665 - val_accuracy: 0.8605\n",
            "Epoch 362/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1487 - accuracy: 0.9405 - val_loss: 0.8905 - val_accuracy: 0.8605\n",
            "Epoch 363/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1697 - accuracy: 0.9405 - val_loss: 0.8554 - val_accuracy: 0.8605\n",
            "Epoch 364/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1515 - accuracy: 0.9405 - val_loss: 0.8403 - val_accuracy: 0.8605\n",
            "Epoch 365/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1443 - accuracy: 0.9405 - val_loss: 0.8929 - val_accuracy: 0.8605\n",
            "Epoch 366/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1455 - accuracy: 0.9524 - val_loss: 0.9278 - val_accuracy: 0.8605\n",
            "Epoch 367/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1459 - accuracy: 0.9345 - val_loss: 0.8416 - val_accuracy: 0.8605\n",
            "Epoch 368/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1394 - accuracy: 0.9405 - val_loss: 0.7558 - val_accuracy: 0.8605\n",
            "Epoch 369/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1245 - accuracy: 0.9583 - val_loss: 0.8068 - val_accuracy: 0.8605\n",
            "Epoch 370/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1581 - accuracy: 0.9167 - val_loss: 0.8644 - val_accuracy: 0.8605\n",
            "Epoch 371/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1711 - accuracy: 0.9345 - val_loss: 0.9715 - val_accuracy: 0.8605\n",
            "Epoch 372/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1879 - accuracy: 0.9226 - val_loss: 0.9514 - val_accuracy: 0.8372\n",
            "Epoch 373/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1255 - accuracy: 0.9524 - val_loss: 0.8647 - val_accuracy: 0.8605\n",
            "Epoch 374/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1723 - accuracy: 0.9167 - val_loss: 0.8519 - val_accuracy: 0.8605\n",
            "Epoch 375/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1527 - accuracy: 0.9405 - val_loss: 0.9696 - val_accuracy: 0.8605\n",
            "Epoch 376/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1620 - accuracy: 0.9405 - val_loss: 1.0498 - val_accuracy: 0.8605\n",
            "Epoch 377/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1502 - accuracy: 0.9405 - val_loss: 0.9670 - val_accuracy: 0.8605\n",
            "Epoch 378/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1367 - accuracy: 0.9345 - val_loss: 0.8736 - val_accuracy: 0.8605\n",
            "Epoch 379/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1435 - accuracy: 0.9405 - val_loss: 0.8443 - val_accuracy: 0.8605\n",
            "Epoch 380/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1440 - accuracy: 0.9405 - val_loss: 0.8327 - val_accuracy: 0.8605\n",
            "Epoch 381/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1620 - accuracy: 0.9405 - val_loss: 0.8283 - val_accuracy: 0.8605\n",
            "Epoch 382/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1358 - accuracy: 0.9524 - val_loss: 0.7936 - val_accuracy: 0.8605\n",
            "Epoch 383/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1443 - accuracy: 0.9464 - val_loss: 0.8679 - val_accuracy: 0.8605\n",
            "Epoch 384/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1544 - accuracy: 0.9405 - val_loss: 0.8133 - val_accuracy: 0.8605\n",
            "Epoch 385/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1524 - accuracy: 0.9405 - val_loss: 0.8770 - val_accuracy: 0.8372\n",
            "Epoch 386/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1142 - accuracy: 0.9643 - val_loss: 0.9240 - val_accuracy: 0.8372\n",
            "Epoch 387/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1406 - accuracy: 0.9405 - val_loss: 0.7351 - val_accuracy: 0.8605\n",
            "Epoch 388/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1709 - accuracy: 0.9107 - val_loss: 0.6201 - val_accuracy: 0.9070\n",
            "Epoch 389/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1624 - accuracy: 0.9464 - val_loss: 0.5940 - val_accuracy: 0.9070\n",
            "Epoch 390/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1952 - accuracy: 0.9226 - val_loss: 0.7024 - val_accuracy: 0.8372\n",
            "Epoch 391/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1686 - accuracy: 0.9286 - val_loss: 0.6065 - val_accuracy: 0.9070\n",
            "Epoch 392/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1763 - accuracy: 0.9167 - val_loss: 0.6736 - val_accuracy: 0.8605\n",
            "Epoch 393/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1940 - accuracy: 0.9286 - val_loss: 0.7623 - val_accuracy: 0.8372\n",
            "Epoch 394/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1638 - accuracy: 0.9286 - val_loss: 0.5222 - val_accuracy: 0.9070\n",
            "Epoch 395/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1981 - accuracy: 0.9345 - val_loss: 0.5337 - val_accuracy: 0.8837\n",
            "Epoch 396/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1619 - accuracy: 0.9405 - val_loss: 0.6753 - val_accuracy: 0.8372\n",
            "Epoch 397/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1440 - accuracy: 0.9345 - val_loss: 0.6637 - val_accuracy: 0.8605\n",
            "Epoch 398/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1546 - accuracy: 0.9345 - val_loss: 0.6615 - val_accuracy: 0.8837\n",
            "Epoch 399/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1713 - accuracy: 0.9226 - val_loss: 0.7083 - val_accuracy: 0.8605\n",
            "Epoch 400/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1511 - accuracy: 0.9464 - val_loss: 0.8506 - val_accuracy: 0.8605\n",
            "Epoch 401/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1345 - accuracy: 0.9405 - val_loss: 0.6685 - val_accuracy: 0.8605\n",
            "Epoch 402/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1772 - accuracy: 0.9405 - val_loss: 0.5899 - val_accuracy: 0.8837\n",
            "Epoch 403/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1695 - accuracy: 0.9226 - val_loss: 0.6337 - val_accuracy: 0.9070\n",
            "Epoch 404/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1633 - accuracy: 0.9583 - val_loss: 0.7427 - val_accuracy: 0.8605\n",
            "Epoch 405/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1712 - accuracy: 0.9107 - val_loss: 0.9203 - val_accuracy: 0.8372\n",
            "Epoch 406/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1668 - accuracy: 0.9286 - val_loss: 1.0902 - val_accuracy: 0.8605\n",
            "Epoch 407/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1479 - accuracy: 0.9524 - val_loss: 1.1763 - val_accuracy: 0.8605\n",
            "Epoch 408/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1313 - accuracy: 0.9524 - val_loss: 1.0213 - val_accuracy: 0.8605\n",
            "Epoch 409/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1482 - accuracy: 0.9405 - val_loss: 0.9329 - val_accuracy: 0.8605\n",
            "Epoch 410/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1344 - accuracy: 0.9524 - val_loss: 0.8374 - val_accuracy: 0.8605\n",
            "Epoch 411/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1672 - accuracy: 0.9405 - val_loss: 0.7630 - val_accuracy: 0.8605\n",
            "Epoch 412/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1499 - accuracy: 0.9286 - val_loss: 0.8219 - val_accuracy: 0.8605\n",
            "Epoch 413/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1612 - accuracy: 0.9286 - val_loss: 0.8296 - val_accuracy: 0.8605\n",
            "Epoch 414/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1469 - accuracy: 0.9405 - val_loss: 0.6555 - val_accuracy: 0.9070\n",
            "Epoch 415/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1690 - accuracy: 0.9286 - val_loss: 0.6460 - val_accuracy: 0.9070\n",
            "Epoch 416/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1548 - accuracy: 0.9405 - val_loss: 0.6951 - val_accuracy: 0.8837\n",
            "Epoch 417/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1484 - accuracy: 0.9345 - val_loss: 0.7825 - val_accuracy: 0.8605\n",
            "Epoch 418/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1435 - accuracy: 0.9345 - val_loss: 0.9641 - val_accuracy: 0.8605\n",
            "Epoch 419/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1567 - accuracy: 0.9345 - val_loss: 0.9522 - val_accuracy: 0.8372\n",
            "Epoch 420/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1416 - accuracy: 0.9405 - val_loss: 0.8403 - val_accuracy: 0.8372\n",
            "Epoch 421/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1364 - accuracy: 0.9405 - val_loss: 0.7989 - val_accuracy: 0.8605\n",
            "Epoch 422/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1572 - accuracy: 0.9405 - val_loss: 0.8221 - val_accuracy: 0.8605\n",
            "Epoch 423/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1753 - accuracy: 0.9286 - val_loss: 0.7483 - val_accuracy: 0.8837\n",
            "Epoch 424/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1626 - accuracy: 0.9524 - val_loss: 0.6968 - val_accuracy: 0.8837\n",
            "Epoch 425/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1495 - accuracy: 0.9405 - val_loss: 0.6304 - val_accuracy: 0.8837\n",
            "Epoch 426/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1748 - accuracy: 0.9524 - val_loss: 0.6597 - val_accuracy: 0.8837\n",
            "Epoch 427/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1468 - accuracy: 0.9405 - val_loss: 0.8261 - val_accuracy: 0.8605\n",
            "Epoch 428/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1760 - accuracy: 0.9345 - val_loss: 1.1163 - val_accuracy: 0.8605\n",
            "Epoch 429/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1641 - accuracy: 0.9226 - val_loss: 0.9333 - val_accuracy: 0.8605\n",
            "Epoch 430/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1865 - accuracy: 0.9286 - val_loss: 0.8321 - val_accuracy: 0.8605\n",
            "Epoch 431/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.2126 - accuracy: 0.9286 - val_loss: 0.9185 - val_accuracy: 0.8372\n",
            "Epoch 432/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1781 - accuracy: 0.9226 - val_loss: 0.8075 - val_accuracy: 0.8372\n",
            "Epoch 433/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1596 - accuracy: 0.9286 - val_loss: 0.6815 - val_accuracy: 0.8605\n",
            "Epoch 434/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1658 - accuracy: 0.9167 - val_loss: 0.7130 - val_accuracy: 0.8605\n",
            "Epoch 435/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1760 - accuracy: 0.9345 - val_loss: 0.8917 - val_accuracy: 0.8605\n",
            "Epoch 436/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1638 - accuracy: 0.9524 - val_loss: 1.0711 - val_accuracy: 0.8605\n",
            "Epoch 437/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1851 - accuracy: 0.9345 - val_loss: 0.8902 - val_accuracy: 0.8605\n",
            "Epoch 438/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1661 - accuracy: 0.9286 - val_loss: 0.7785 - val_accuracy: 0.8605\n",
            "Epoch 439/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1754 - accuracy: 0.9286 - val_loss: 0.9045 - val_accuracy: 0.8605\n",
            "Epoch 440/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1891 - accuracy: 0.9167 - val_loss: 1.0197 - val_accuracy: 0.8372\n",
            "Epoch 441/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1620 - accuracy: 0.9167 - val_loss: 0.9668 - val_accuracy: 0.8605\n",
            "Epoch 442/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1547 - accuracy: 0.9286 - val_loss: 0.8228 - val_accuracy: 0.8605\n",
            "Epoch 443/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1365 - accuracy: 0.9405 - val_loss: 0.7995 - val_accuracy: 0.8605\n",
            "Epoch 444/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1544 - accuracy: 0.9405 - val_loss: 0.8286 - val_accuracy: 0.8605\n",
            "Epoch 445/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1615 - accuracy: 0.9345 - val_loss: 0.9092 - val_accuracy: 0.8605\n",
            "Epoch 446/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1323 - accuracy: 0.9464 - val_loss: 0.8921 - val_accuracy: 0.8605\n",
            "Epoch 447/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1509 - accuracy: 0.9464 - val_loss: 0.8824 - val_accuracy: 0.8605\n",
            "Epoch 448/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1362 - accuracy: 0.9524 - val_loss: 0.8878 - val_accuracy: 0.8605\n",
            "Epoch 449/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1724 - accuracy: 0.9167 - val_loss: 0.9413 - val_accuracy: 0.8605\n",
            "Epoch 450/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1284 - accuracy: 0.9405 - val_loss: 1.0645 - val_accuracy: 0.8605\n",
            "Epoch 451/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1561 - accuracy: 0.9345 - val_loss: 1.0870 - val_accuracy: 0.8605\n",
            "Epoch 452/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1335 - accuracy: 0.9464 - val_loss: 0.9693 - val_accuracy: 0.8605\n",
            "Epoch 453/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1295 - accuracy: 0.9583 - val_loss: 0.9729 - val_accuracy: 0.8605\n",
            "Epoch 454/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1462 - accuracy: 0.9464 - val_loss: 1.0186 - val_accuracy: 0.8605\n",
            "Epoch 455/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1304 - accuracy: 0.9345 - val_loss: 1.0989 - val_accuracy: 0.8372\n",
            "Epoch 456/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1442 - accuracy: 0.9464 - val_loss: 1.0327 - val_accuracy: 0.8372\n",
            "Epoch 457/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1577 - accuracy: 0.9345 - val_loss: 0.9942 - val_accuracy: 0.8372\n",
            "Epoch 458/500\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.1357 - accuracy: 0.9524 - val_loss: 1.0725 - val_accuracy: 0.8372\n",
            "Epoch 459/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1580 - accuracy: 0.9226 - val_loss: 0.8606 - val_accuracy: 0.8605\n",
            "Epoch 460/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1487 - accuracy: 0.9345 - val_loss: 0.7322 - val_accuracy: 0.8837\n",
            "Epoch 461/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1535 - accuracy: 0.9286 - val_loss: 0.6954 - val_accuracy: 0.8837\n",
            "Epoch 462/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1691 - accuracy: 0.9464 - val_loss: 0.7329 - val_accuracy: 0.8605\n",
            "Epoch 463/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1465 - accuracy: 0.9464 - val_loss: 0.7566 - val_accuracy: 0.8605\n",
            "Epoch 464/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1603 - accuracy: 0.9286 - val_loss: 0.6811 - val_accuracy: 0.8837\n",
            "Epoch 465/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1604 - accuracy: 0.9167 - val_loss: 0.6846 - val_accuracy: 0.8837\n",
            "Epoch 466/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1452 - accuracy: 0.9345 - val_loss: 0.7388 - val_accuracy: 0.8605\n",
            "Epoch 467/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1854 - accuracy: 0.9167 - val_loss: 0.8409 - val_accuracy: 0.8605\n",
            "Epoch 468/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1438 - accuracy: 0.9405 - val_loss: 0.8602 - val_accuracy: 0.8372\n",
            "Epoch 469/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1845 - accuracy: 0.9345 - val_loss: 0.9210 - val_accuracy: 0.8605\n",
            "Epoch 470/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1511 - accuracy: 0.9405 - val_loss: 1.0847 - val_accuracy: 0.8605\n",
            "Epoch 471/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1700 - accuracy: 0.9405 - val_loss: 1.0432 - val_accuracy: 0.8605\n",
            "Epoch 472/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1583 - accuracy: 0.9345 - val_loss: 0.9314 - val_accuracy: 0.8605\n",
            "Epoch 473/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1518 - accuracy: 0.9286 - val_loss: 0.9343 - val_accuracy: 0.8605\n",
            "Epoch 474/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1786 - accuracy: 0.9167 - val_loss: 1.0167 - val_accuracy: 0.8605\n",
            "Epoch 475/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1451 - accuracy: 0.9167 - val_loss: 1.0459 - val_accuracy: 0.8605\n",
            "Epoch 476/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1588 - accuracy: 0.9226 - val_loss: 1.0826 - val_accuracy: 0.8605\n",
            "Epoch 477/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1400 - accuracy: 0.9464 - val_loss: 1.0102 - val_accuracy: 0.8605\n",
            "Epoch 478/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1574 - accuracy: 0.9107 - val_loss: 1.0334 - val_accuracy: 0.8605\n",
            "Epoch 479/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1620 - accuracy: 0.9286 - val_loss: 1.0137 - val_accuracy: 0.8605\n",
            "Epoch 480/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1414 - accuracy: 0.9345 - val_loss: 1.0087 - val_accuracy: 0.8605\n",
            "Epoch 481/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1529 - accuracy: 0.9464 - val_loss: 0.8958 - val_accuracy: 0.8605\n",
            "Epoch 482/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1596 - accuracy: 0.9226 - val_loss: 0.9616 - val_accuracy: 0.8605\n",
            "Epoch 483/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1796 - accuracy: 0.9107 - val_loss: 1.1606 - val_accuracy: 0.8605\n",
            "Epoch 484/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1655 - accuracy: 0.9345 - val_loss: 1.0039 - val_accuracy: 0.8605\n",
            "Epoch 485/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1374 - accuracy: 0.9405 - val_loss: 0.9663 - val_accuracy: 0.8605\n",
            "Epoch 486/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1506 - accuracy: 0.9345 - val_loss: 1.0564 - val_accuracy: 0.8605\n",
            "Epoch 487/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1545 - accuracy: 0.9286 - val_loss: 1.1322 - val_accuracy: 0.8372\n",
            "Epoch 488/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1551 - accuracy: 0.9167 - val_loss: 1.0671 - val_accuracy: 0.8372\n",
            "Epoch 489/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1430 - accuracy: 0.9405 - val_loss: 0.8638 - val_accuracy: 0.8605\n",
            "Epoch 490/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1669 - accuracy: 0.9524 - val_loss: 0.8280 - val_accuracy: 0.8605\n",
            "Epoch 491/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1292 - accuracy: 0.9405 - val_loss: 0.9376 - val_accuracy: 0.8605\n",
            "Epoch 492/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1717 - accuracy: 0.9345 - val_loss: 0.7126 - val_accuracy: 0.9070\n",
            "Epoch 493/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1533 - accuracy: 0.9405 - val_loss: 0.7031 - val_accuracy: 0.8837\n",
            "Epoch 494/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1661 - accuracy: 0.9226 - val_loss: 0.7795 - val_accuracy: 0.8372\n",
            "Epoch 495/500\n",
            "6/6 [==============================] - 0s 11ms/step - loss: 0.1691 - accuracy: 0.9226 - val_loss: 0.6838 - val_accuracy: 0.8837\n",
            "Epoch 496/500\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.1634 - accuracy: 0.9405 - val_loss: 0.7152 - val_accuracy: 0.9070\n",
            "Epoch 497/500\n",
            "6/6 [==============================] - 0s 8ms/step - loss: 0.1751 - accuracy: 0.9286 - val_loss: 0.8513 - val_accuracy: 0.8605\n",
            "Epoch 498/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1638 - accuracy: 0.9286 - val_loss: 0.9196 - val_accuracy: 0.8605\n",
            "Epoch 499/500\n",
            "6/6 [==============================] - 0s 9ms/step - loss: 0.1319 - accuracy: 0.9405 - val_loss: 0.9255 - val_accuracy: 0.8372\n",
            "Epoch 500/500\n",
            "6/6 [==============================] - 0s 10ms/step - loss: 0.1585 - accuracy: 0.9286 - val_loss: 0.9864 - val_accuracy: 0.8372\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": 337
        },
        "id": "IfZwa5IVz1xS",
        "outputId": "81f5f053-aa88-4ea0-ffc3-193b0fa393f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "k61Sm-2W0DQi",
        "outputId": "fbb9ae62-4234-47e9-9961-62eab4dace89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f533edb4e50>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "di2HOJBZ0Ib-",
        "outputId": "709c15ea-2c57-4422-ecce-28cc3170b1ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f533ed27390>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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imDGi1Px9+Ymj8fHZwzG4SHZW8ycMwfGjByXdv749lCBs6ttDSBZerneSkWgM4Whi+23r6YWHgDkjy8yyXY2dCPri4sZuLmrt7kV9ewifOmGkpTxVx67aaX0SQZ/n96Ag6DPftYvnymO32cxECq/HWZira07W4ehcvyAeWDG8NB8XHx+PRFdRWiMHxTvAg+09GDukABMqpPA/oqabjxrhaMzR9nn931ahqTOMJ798qlmmXuS61h78fcVuXHXyGHNduxb2Vd/eg7N+/gamDi3G2r0tqHOIJz7r529gZ2Mnjh9dhnuumIuL73sHXzlzAu54Nu7WGFaaZw4H9x7qMmO7dZLZoIvz/DjYFsK1f12J0nw/nvv66Y7bTb7teXx+/lj8Zfkus2xHQycmff958/fn/7ISs0aU4ndXHY/Tfvo6/vHFk3DqxHJz/U2PrsEHe1swuarInEqvmHLb8whFYpg3bjB2NHTigjnD8MMLZjjWBZB+jmRamxKshUEvPAQcgnU6/ISKIpw4bjAeWrE7qdBQx1YavdJaiYBJlUWWbatK81DX2oPK4iAqS/Is6+55LXGyS317CGUFgYRyO69vqcftz2zAbefHJ527NcuU5vvNiKCAIUwLAuq++MxoIyfq23oSBOX/OlyHQgnIR1fuwXeeXI/ZI+OdzHefWo8zJpXjf1+rAREQ1RSBznDUjEoBrKa1kjwf6lq60RWOYoLtfju1b0XI8Ne0JAnZzfN7URSMX7s6dmt3r6PQbu3qxXaHMNTucBT17T1mpE0qKoqCGDU4H3ubu1FVkmfpGNWoXo9W29PUhcriPEyqKsL2hk74fQOje7NGb6Br350hZ0H50qaDCRNJdI1Nd8wBVidRfVsIOxs78fyGAwlCPhYTaO3qNV+C9/e0YNvBDtS3h8wwPoU+zE5makoWMqY0+oNtIWw92IGOJCYeABYhn4z1ta14YYOMOHh23X7Luqc+qMXOxk68trke/7bdF/WSvbezGY0dobTn6ghFHbW2125egMKAfJELgz4ENYHW1hPBFfNG4RefnoOZhtafDDUBSkXV9PTG8KkTRuL+q6sx3Ga6qSqWwn1iZRGKNO39SwvGm6MLHbu2nIzXPqzH3ubupHMU7rhgOv58TbXjupI8H5Z943T83xdPMstU3QoCvgR/jKV+7SE0OIyWku2iBJdq6+u0jJYPv7cHv3x5KwApyO0jy3Hlhbju9HHm70/MGY77PnM8pg0rMc0mlcVB/PGz1bjzwhmYUFFotnc1V0IR8HoQjsTM+3vTwskJ4b75Aa/Z4ZXm+831rd29CPVGUVbgxx+uPgE3L5wMQLYZp5QFPb1RvL3NXbbTknw/7rl8Lr63ZCoWzxqKSq1OavT+iTnDcfxoOdrZ1diJipIgfvDx6fj1Zcehekzy0Vd/YEFv0KQ9YCdTQ3uPs9bQ2t1rmgc21rYmrFMkG14CQEc4MUxOvUCpZki+m0TQJ9Poi2xmhXW1/U87+4Fhix41ON9xfW9UJPUZuKE034/WrrAlhwsATB1ajPEVRXGNPuCDfUrBpSeMwuyRZRg7pNDsEJLVEYA5ASkcjWHkoAIsnF6V4BxTE2wmVFg1z6+cOdHRROI20dYG4/nb28npk+QoaWhpHs62mYoUpfl+jCjLx3xtRFVgXK+TaUWnvj2E+vYQyouso45kczvUaEeZDO30aG3P7tAtDPpw2YlxU9jQ0jwsmTUMpfl+U8BWFudh4fQqfPaUsRYzmP05KKVFTa6aOaIkwZSW7/eabb6yOGgeo7W7Fz2RGGaPLMO5M4bia2dPQkHAi9bu3gRT29ghBejujVom1KWiOOjD3NGDcP0ZE1CS50e5w1yT4jyfOfJvD0VQWRxEnt+Li+aOyNq8GDtsujHQXzAVj73lQDu++o/38ej1J1tC6hb/+t947uunw+shtHVHUJLvx6wRpVi6tg5NHSEMKYprDoB8sKle+P/+1yY8+b5Vc1+1W8YTpxKSHaEIhpXmoTcq0BHqNTX5UCSGD/Ycwo3/+AAXzR2OtXtb8eH+toT45mseeC/tfdEZbpgtdFTc889e2IIRZfkozffjIS3CwS2n/uRV/PxTcxLKh5XmYa3R6c0eWWrRIAEp4AHDdNNhfUmmDZNRTh4PYcbwUry3qzllHVq6enHVn2VuFhX+areZqglRdhNDab4fBcHEzuTmx9fipy9sRn7Ai5gQCBj7d4WjKAr6MKwsH3/87Amm49Ueijt6sLTnphIATpEauhPQCZ+HMLGyCE8YUSxzRpWZqQWA+MjFTndvDL94aUtSk4qKogFgTloaUZaP2pZuFAa8FmejMinp9a8sCSasz/N7MHJQgaV+xXk+NHWGcd3fVsn9ivMsPgC1v+rwKkuCKAx45TvbIzX6oCaES/P9eG7dfvzZNvksz+/FM2vqkOd3pxN7bEOh4rxEEVsQ8JkjDVX3gYYFvYE+jVqZbh58Zxdq6jvw7No6y9TkzQfa0dETQWmB35zNNmOEHLZvrGvDGZMrLMecO3qQGRfsxGOr5MtWmu/HFfNG4/dvbnc9pX7skEJ85uTRaO4M43YjH3dPbxSb6tpQ29KNB5fvSuvwPWncYPzk4lm461+b8LoRb/zZU8ZgWGk+fvrCZnO7cRWFCYJeH+q+saUBZQV+vNqHSWB1rT14ySFz3/CyfHOW4a8vOw63Pb3BYrLKN17kUYMLEoSP/jLdvGgy1te2WsIh7WzTsh/mKyFU4MfnTh2LB43kWD+8YAbmjCrDgknyGf/jupNMLbdIO98X5o8zk6ylGs1tq+/ASxsPalEmcttffGoOKoqDmDu6DMPL8k1t9ckvn2J+AORnL25BVzjqOG3+mwsnozTf7xjye8KYQbhy3mhEhcC3n1gHQDptPzFnOBZOq8LTa2px5UmjMXpwAbbVd5htwOch9PRGsdLoMD85d4RpWvx09UizHS+eMRTXnDoW04eVYPLQYiyYXIG/r9iNy04cZRHq6h5fdfIY+LweDC70Y6I2UlLrK4vzcN9njseza+vwk+dlXdQ1t/VEkO/3YurQ4gRBn68J+gkVRSAilOT50Nrdi3AkZvFdlOb7zXamo7JcTqkqNhWOMyZXYPWuZnSGo1gwuQLnzx5m3kc7U6qKcdv50zBrRCkuu38FAl4PAj6PZXRdmeEM877AphsD3QuvTDfjjTCobfUdCS9rSEuoVJrvx4zh0im1QTPBKI1+/oQhSUOzdMYMKcC3z50CAGZ613RUlgTx8dnDscDoXGTdYmaUSjIhf+NZE83lz88fh/EVRfiWcW4AuPPCmfjymda0RGOHFCIV9e2Jjr1McJrlWWU4PD9z0miMryjCd8+bZlmvRkozhpeCENem7Gaqk8YPwSXHWyM6dM6fNczi8NWFgG7GqCoJ4oYFE0zN7dQJ5aZJpcA457RhJbj9gumuU0SosFcgLujPmV6FMyZXoDjPj6+eNdG0s58wZjA+N38cPjd/XLwzchD0BQEfbvzYJMeUDJccPxKXnDASl2r3o7I4iGtPG4fRQwrw9bMnIc/vxTnTq3DqhCHmNsV5PnSHo+jujeHMKRWYpUX73PGJGSgzop6+cc4knDJhCEoL/LhhwQRMG1aCH31yFmaPLLMEOeQbWvKcUWX4ycWzcMu5Uy0acVzQBzG8LB/XnR5/Drqm/LWzJ8JnCFCd/IDXHAVMHVpi3qtWYyKa3jEkyzGjlD7d5HTTwsnmuX588Sx8unqU476AHIl98fTxOHHsYBQFfeaoTx/96aOYgSKnBX1HKIKvPfxB0tC8e17dZk5P7g7HBfFnH3gPD63Ybb5c2+o7Epxq3b1RbKhtxZtbG1CSJ6MeRg8uwMbaNvz0hc14fXO9KehPnVAON5Tk+eHxEII+j5n9Lx1KG9DtmaGIs/NSR5kEACBovHB24WgnraBvC6HBdp8mVxUl2TqRjQ7ZJ9WQWQ1v1TNRL6my3U+oKLTY6J2GzPbQR51Lq62dQH5AF0jx5VQmFBXhocJOlelirBZP7sTbNY2ms1GZbuzaqROqM8o0JE/FcetC1R5BpAjYhOGBth6s3duCfL/XIqACXg9mDi9FwOfBxEp3zzw/hd9EX6/Oo9dXv2bVbgPeRI1etY/xKnwx349n19ahrrXHKugd7qHfS6ZvTte6g754p+LmOam6Tx9eYpoarRo9m276xbJ1+/Hs2joEfR5H+6+KENh19/kJk2geeHun2VNvr+9I0M66e6P4lxFpcqkR+ztzRAnW1bbgxY0HsO1gB8qLAigr8GPqsGKk4/zZw0xtPj/gTRqz+8j1J+O5dfuxrb4dK3Y0m41kSGEAFx03HE+vqUNPr4xGmFBRmJAmdcHkCgwrzcOS2cNwqCuM9/ccwmxDM9NNHYrfX3U8VuxoRp7fi1GDUwus+vaQJda+MODFtxZNwfUPrU7YdkpVMSZWFSHg9SDo82DkoHz8/KWtCdsps8gQw1k4ZWgxPj9/LK4xkkz96JMz8fB7ezF7ZJk5axdwFvS60PrD1SdgxY4mnD21Ch/sOZQQ7aAL9/wU4Yk66v6pCC4lqCZWFpkT666YNxpCCDyiafEAMHNEKdbvazVztLgRIOr4mcymvOaUMThxbKLTOJn5QL9nFx43Ave8uk2e2+81256H5Neorj1tHM6YXJ42sVtZgR8tXb0pwz7lceXzHOOgYOiCWQlPu0ZfVZKHm08ejYdW7Daf78hB+aafZ8msYea2IwdZgwm+/rGJWDh9KC75vczvb+nUfB48dO1JePL9fRhiTK772SWzUeTQ5nSuP328aZIt0Do5+7kHgpwW9HnGzXSKorFP9rEL+t1NnWYq0abOMLba7Hfd4Sg21rVi1ohSXDRXToqYMbzUTCi1sa4VQ4oCmDWiNG3DH1GWj3uvPN78ne/3ogWJGv3Fx4/AyeOH4OTxQ3DTY2sAxAUaEeGHF8zA02vqEOqVcb+Tq4rR0xuz2PuL8ny4+5LZAIAvLbCaZpw0+sUzh2HxTPlC2EPMBhcGLNEw9lmnP7t0DhZOd44U+d8r52JyVbwDfFKb2q6jcn+runk9ZIm5n1hZjB98fLpxD+L7FafRcs+eWolzjVmMpxmRLcnivN1qbaqOMVvbmlhZjFc+lH6Ln1wsM4LMHV2G7zy53txm1ohS7GnqQlNnGEGfx1X0RSrTTTL+68KZjuXJtEpdSz59Ujmeen8f9h3qRl7Aa3YOSsCeNbUyaXZMnQkVRVi9+1DaPD0q1FQ3ESn0jrzQGEnZBf2skSUYX1FkaS/qHT1l/BBLlNJM2zluWiSVLmVy1e9PwOvBqIoCixnx0ycmN98oztHeBf1dSzXSzBY5bbpRObSf33AAv3hpC17ZdBAPLt+J1bubcee/4pOQfvvatgRBHhPAH/69w/y9rb7DMjW+OxzF+tpWzBwRj53WG8v+1h58uL/dtN0nw0NIiL92q0ECVuGmTDB3PLsJ2xs6UVkctNQPSD6NG0DayIJCW1SJkxaoH78k35dUYBXYhu3JhJWaHevmZUin0evYJ3EB1uegp1CwR1IkQ12TfVapkynDbiqYObzUtBO77VhUO8nGtPlkdmK9Ln5v3Imom27sJpN0qFmg6T6zt8uYbe40P0HvyFXbsAt6ZZfXUX43+8SkaWlG3Xr4qdvnkwqn0fNAktMavZ62NtVsP91kcP7sYVg0vQrfeERqzD4PYe7oMjR3hnHG5Apzcs/2hg60dPVaBLm9QUZjIkHQ2rnsxFE4c4pVC7IPac+dUYXmzjC+cfYks+yb50zGgdYeLJ4RH37aNaRRgwswf2I5XtwYj2ZJlYyMiPCJOcOxZNZQx/W6FnLb+dNw2qRy/PT5zWakjh0V4vc/l87GLbaoBPvowckZdtv507BgcgW6w1GLUzAZSh4HfB7cdv40x22+cuYEDCtzHipfcvxIHGzrAYHwsanOI5FUFCbR6MuLArjk+JFYPDN+X1XHVhT04bhRZVgwpQL/NCaZOcVeO5GXgenm7otnmTlfdP58TTX+tW5/UjNKwCLoyezMZESLD0W2iWpu+H/nTMa+Q92W++HEry87Dn97d7fFn6TQOzclNNVtn1BRiPEVRQmzmgHg9MkVWDC5IqF9TKkqxuIZQ9HYETJH6ADw+6tOwPMb9lsUA3uH0hcCPg8unklyKsgAACAASURBVDsC588eln7jLJDTgr4rwxwrfi+ZJpQdDZ34zavbUJLvx+M3yJQH2w62m4J+5S45Q1bX4suLghhWmmeZvTrTQaPXs9r95OLZCevtTqphpfn4w9XWWZGjBhfgH9edbCmzz4CcMbwUp0wYgiWzhpompXTRP/dcMTfpOhVVUl4UwBeNCIi/fH4ext76nOP248rli/ap6lH467u7LJNs7BqNXVgVBLzmOR6+3nqdSTE0+oevOxkTK501tG8vnupYDrg3PSRDCXp7it/iPD9+8Wmrj0h1bJXFQfzdmNE6c0QJ3t3RhKlD0/t0gHjUSjozFQBcPm80LncoP3taVdKJWIBVedCdkKajtDjoKgeMzvCy/IS268SpE8staTV0nJyxKvjhnOlVCdFZ+rZ//cK8hHKf14PfX31CQvnimUMTOqRMUkOn4peXHZeV47ghZ003z6ypzfj7mbpWU+Tw0uqCdOnaOngICS+l0vAri4MoDvpMbUQf3qYzQ+hhZXL7vjWs6Q5DXjdhnunqlSyFrR39fnlsJhy7VpSN9KzqdH35PGM2UMnm7Kf3OZh+nMxkKt1COielIt/vRXEwdYqD/hKwmW7Uc1R1rCgOZsWUkSm6aU6NMpSgL8tPn1uoP2RDoz/cHHs1dkF7Ty++8cgaSw5qnWT2W902ruJcdUE/rNQ65J9cVZzwUp43cyhOnTAEl54wEh+fM8y07+q5OlJNxwfiL9HIQfmoHjPIkgXQLfPGDjaF5xfmx/OLfFWLn8+UkjwfCgJefH+JVVu67fxpqCwOmgJn2rAS3GBz9CqzU8DnccznYbcz//CC6QnbpOPrH5PnmFTlTiPOhFPGD8HlaRxuSuh9e7F05H3znMnwecgM7dMZUVaAgM+DW7S5C+fOGIrBhQHL80rFCWMG4YwpFek37AdeD2Hq0GJUlQRRXhR/xupdOWNyBeaNGzygdXCiSgsHVUrZhcfJyWHnzxoYc8h/GXMFBrJjHShy0nSj58wuzfdj7Q8X4bevbcPPX9qK0YML8O9vn+VobtBNJqrx6B8Pzg94seG/zsXMH74IALj/6sQkU5ecMBKXzBoErH4QWPRlszzg85iTl9Jq9FqiroeuPSnltsl47IZT5ELjNlR3rMOuuy/J7ADNO4C9K4E5l5lFPq8Hm+5cnLDpF08fjy+ePh7zfvQKWrp7sezrpyU4Yc+eVpXyIxx6J7v5rsWutVqds6ZWZu17tHbcmI/y/F5s+e/zzN9nT6tCzY+XxDfYtRwQUWDYHOS/9QtsveokYGpcKA0vy8f7P1jouk5XnzI24VumGVHzCrD9deC0bwKFyed6vPD/zjCX1XNVKSL6ozj0Bz0FhXqfZgwvHbDnDwDXnDoW15w6dsCOP5DkpKDv0GzzSntWNtFUkSUWjd6wIUdsGfjytGFb0hltr94J/Of3QOlIYPqFAGAJsSxIa7rxJNSnz9x7khQuMzMU9KsfBN75rUXQp6OyJAi/111ooB19n74I+WOCBw2hf/nDwPLfAP5C4Pt1qfcZSJZ9G2jeDlROB+Z+xtUuamCalbbZD/T38Fg0pRxuXN0hIlpMRFuIqIaIbnVYP4aIXiWidUT0BhGN1NZdQ0TbjL9rsln5ZHRpGr3q7UtNQS8b6FfPmpCwny5glF3cbo/Wve9JBVK3kcq4Nx6/rk/fLgp6MbQkL6nTTb1EbpxsdgYXBswUqACkkO8LoXa5b8z9/rNGlKaNMkrFoAK/mScop4kaYYW9mfmQsk6vkRsonJiDPRnKbOH1HFnh6vN6zImKTHrSavRE5AVwL4CFAPYBWElES20f+f45gL8JIf5KRB8D8BMAVxPRYAA/BFAN+ZGi1ca+1qTuWUbX6JX2rLz0ynH0rUVTcMu5U9He04uvPfwB3tjSYNFSCrMR56p55a47YzyCfg9uf2YjCgI+rPje2Ul3azfqny6214n3f7DQ2RkZiwKeDLSwkPHyR0JAIPWMWIVTBFEmZGK2OKaJ9P07wQNCKDGZVzLUyMseWXQk+J9LZ+N/Lu1fm/uo4KZbngegRgixQwgRBvAIgAtt20wH8Jqx/Lq2/lwALwshmg3h/jKARCNvltG/6G5+YcYYcqrQKNVgi408NYDVRj8Qs9WU+SZdTpkdRtoCp6gZNziaTqIZChel5WW6Xz8gogHLx31UcRjvaUoixsgiE42eVGTRkRf0H5n2kgXcCPoRAPTEHPuMMp21AC42lj8JoJiIhrjcF0R0PRGtIqJVDQ3Ok28yoUP7QpTSzEcZ32l0mpKvf3bN3C9NSOP48tQJvpxQIZb2WaF21OSg6cP6bgZJIFPhorS8o0Uo5RJHyz1VAj7kXtAr09o4h0iiw0F/TIMfZbKltn4LwG+J6HMA/g2gFoBr464Q4n4A9wNAdXV1v1UFfaKUSkY2sbII733vbMfUsTFjGKrbzFNp9OvuWAR/H2yUatp1Oo3+poWT8YXTxrn61qhrMjUXhA378dEilI51dF/H0XBPI+F4PcLufQVXzBuFc6ZXHpaMi048ccOpGU/QYtwJ+loAegDxSKPMRAhRB0OjJ6IiAJcIIVqIqBbAmbZ93+hHfV2h2+j1BpksFavKqaFr0AUpogpcp4W1TxIyQhbS5bnweT0oL8pyjupjwHST0+jmkaPhnur1CWdmoz9SQh6QARA5G5U1gLhRS1cCmERE44goAOByAEv1DYionIjUsb4L4AFj+UUAi4hoEBENArDIKBtQ9KgbN0n9Jxk506dpgl5F1zhlznONsGoeykbf15mu/SKa4QdBTGfsUSCUcgHdPKLf0yNl69YFfQamG+bYJK1GL4SIENGNkALaC+ABIcRGIroTwCohxFJIrf0nRCQgTTdfNfZtJqK7IDsLALhTCJH6w51ZoNOi0acX9LedPx2fOWkMhtq+s/nqzQv695mvqDXVsIr3PRxpSdPVJS2s0WcX3Tyi39NYBPD2P/1DxujCPQNnLHNs4kriCCGWAVhmK7tdW34CwBNJ9n0AcQ3/sKBnrRxUmN7Onef3WrR5xYQK919HcsQmJOMa/ZEQ9BkIbCFY0Gcb3Tyij66i4SMj6NXz9RewRv8RICenlHVqUTflhQP/Pcak2ISkigDKRgKvjMnEBNPbHTc7saDPDhYNWvuIeaTv39jtF0rQF1Vl5Ixljk1yUtA3doQwsbIIf/nciRid5nudfab9AFC/Of47EgZ2v2PdRn+Jw12YKbbgvs8cj9OSpF7NCo01QGttYnnzdpm/xg36UP5ICaJktOwFmrb3bd+9K61CVqexBvjg7/Lvw2eBPf+RHV7rPllW82ry43YfAtY+ArTVAT1tcvmDvwN1H8S3CScxldhNau0HgQ/+D9jq0pVV+z7QY8szv/sd63MTAtj5b6s/QHU8xUOB1j3yvMkIdwHrHkt+3/eu7FtnEeoA1j4KHNolfzfvBA45JyLE9tflX7bZ9op8VhuflvXZa1iZd70NbH0J6GiQz2Pzc+79KY01wLrH4zPju1tkm2itBXa8Kc+X7DoHiJzMdVPfFsL48sJ+5RZPy69mArFe4A7jJXv1v4B3fwvc8HZ8G/0lXvsP0LJvY8l3dsXz6Q4EvzVyat9he/mfus653Al9pmSmtv2B5tfGp/DcXIdORwPw53OAGRcDn/pL4vplNwM73rCWzblSjmzWPSJ/f2cXkJ+YeRPv/Ql4/b+BEz4n88Y8/21ZPmgs8I21clnvYEJJzDgA8NbPgfful8vf3CjzJSUjFgX+eBYw+hTgCy/IsvrNwF/OA+Z9CVjyM1m24UngyWuBC34j6wjE0x9UTgf2vAu8/SvgvLudz7PpGeDpG4BRJwHXvmRd19ko7+v0i4BP/zV5XZ1Y9wjw3M3ApEXAZx4H7jHys9ufbagdeOgiufzdfUAwS9lJO5uA/9NyQJWOAlr3AjdvBR40kqPNvgxY96hcvnE1UO4iidtTX5Sd/CV/BmZdCqz+C/DKHbLtbXpatqm+3K9+kFMa/dK1dTj5x69if2u3Y7x8VonZBOAB4/ufnY3SwQZYzR6djTJ3jJb/5qjlaAsFzAbKRl6b+KFyAEBXMzBuAXCp5k6q+wDo1mIHuluc91XbdDXHhfiMT9ocsProLoVG36WdL52WrI6z511t/yb5/0D8e7RoM0Z4jdviZUrjP/0mIFBkvU475vU1Ja9D7fup6+pEl5EJxT4isaOvT7dtJqi6zzYS97Uaczs7tNFNizbfM9U90lG5rrqarf/b98dNom6PlSVySqO//ZkNaOmSL86RjPU1haP+ch9LM011e3KmYZlHK+k62HAHUD4ZGGTLBe8mOkU923BHXHAXVlqftb6cLALHfo50bcXRiepgXvAaSk/E5gRW6waNTe2QVeucTBfJTGFuUJ1vOvOgXrdsOo7VPSgbbS3vrI8v6wLZbU4gdb/DWrsA4h0AcNgd4Dml0edpn/hyEz+fFexOzmhvvEzX1o6lmaYWQXSUmW76inlNSeys4U4gWJRoFgh3aC9uEg1blYc7Zcfo8QO+oLVt6MuhFD4Q/RzpHOhO9Yn0JJapqB6njsfrlxp9qklTSlA5Hbs/jlz9vqXcTu9ss+g4Vvcg3/bhFN1fkckIS6GmFNmvry/HyhI5Jej1pGT9in/PBLuWF26PN6CIw3B9IAW9G8epG4eS/tIfbc5YRSzDafDptLFQhxR4AVsOl3AHUFwV38YJPWdMtFcKeW8ghUafwnQTapf72vdxPK/DNTnV0Tyedi51bF9QXnMqwZMqJ04Gs2oTUMdLF8ffx1m8aTEFvc3v0nEgvtzdLDtuez3cHDdku29qdODxH/a5Czkl6PVvVw52ET+fFewPLKQN3/UXS0/7O1C4GQ668RFYTDdH6Qgk0xcl1faxmMwNHyiSfzqhDqDY+ApUMiFjCqx2+Xy9filc9Xz+TmY8e7mqZ8EQ53XJzmvf345KT60fT40WvAE5knFjugm3JyoK/TFBJOtAUp0jmyYPdQ8K7Bq9JuhjkfjzcHtuM4eQdt/UsQB5PDbd9B09B8Zhi1W3P7BwR/yFcnLADaQpRBdEQjh/NMSNgDxanbHR+ES4jAV9qhdLHSvoIOjDHTIEMdUx1H0PdRgToIKAz6aVW8x4HXEt0X5/Qx1xU0K6tuJ0D5zqaNbBNsIgj+wEAsWp76daJ2KJikJ/NFPdt6ELd/voIlloan9R98NfAPg0n177fut2qiNwO5owNXqtXdiP59RpDiA5JejzD5egtzRK9RCNMvWyA9YX63A4Y/UG1dvtfC43DqWjVaPXX7RMNSLzOTmEtqp1gSJAz0oqojIM0dTokzljNRNEtFdqyXbziz3tQdDoUOzCPNwRFyx9ccaa90hro+ocEZugV76HtBq9dt+dRrB9RX93dOGecI525+X+ohQxb8DawesaPQAESwDyurerJ2j0dkE/xLnTHEByStDr34MtGUhB36tFGqiH2NsT/53SGTuAphv7y+IkKNw0VovGeRQ5Y1MJg7T72jpkp+MmOGKN8qLK1OfUHe3hjrjpBoi3BbtjNWCcSzflxWLyWMpmnM7M51QfJ6e/Oo5do1d1VM7YZBpmqvveHw3bctwky/ZzZNUZa7RtXyDe8QKJk8d8LsxbOupZJ3M2q+d7GB2yOSXodWes/jHurONkM9R775TO2MNkugm1O0dtuDXd5JXKof3R5IztTyKuUIr7r7REuyO20/gITv4gKRRTOWM9RqRy96G4MxZw1ugBTaPXynu7AAhNo++H6cZpVGZ3xirzUqAwtYapX5+TqRJInFfihlCH1JT14wCJWnuoA3IkRgNjurFr9MoZq65Zre+PM9ajRbJnagrKAjkl6PXwygHFyWaoP1TTGesQUne4nLHJNHo3WkmoQwoie+TIkaY/qXVTRo5ophsdde2B4uQvesxm3ulqlhq9zzCLOPlrgHin4hSN0xdnrPLHOJkLnOZ1RDSNXo1kkmmYFod0EtNNX0w44fb4cfXJaAmjhk5Zx2DxwDhjvUHrs1f3q6jKut6N2SgWi3d6apQUbgeKhsa3UT6Yw+iQzakJU0HDdBNAL1D/IVA+RdpcOxqsIVN5pfFJEr09QJMxY3DQ2HjejVQ0btWWt8kcIKp3bt8vIzgA2XgPrJcTcVTjbdpmnbXohMcvQ/pa96WvS7J6HVgPFFYkbtPwIVAyLPkxiofLugaKAW+zvB6n+voLpO2y40DiOiD5vRw8QQpG0+FFwKAxMg9I+WSgZXdyzengBut1lBnfwykol0Is1Yuo8v+EO4D966wfhTlofOc+mCRbabBI/rXuMwR5ADi0U9ZdDcOLh8qZlV1Nsm2p2PUDG+TLn2C60TT65h2yHSqtTwmC5h1y/4opcnTVsEX6DRQtWr6UfStl59FWJ3/3tMnn5vHH49+7mmWZL1/eL1VHVZfGrc7PM9QGVE6T13doNzDsOJk7CYi3UeVQ7ai3Tjiy3McS+azVtYU6gCGTgLZ9QFNNfLtDu6RgFFGgZLgMS1R1bN0rryF/MFBqfJU03Gm0n0nyubbVxWfxlo50TlsB2OYS2EZzAW1Ohdcvn7/5Dm+3mm8VQyZahXfLHtkmRUy2jzbjXimN/sD6eDssHiZn/RIBg8c717cf5JSgVybGH/geAu57BTj/F8CJXwTuOylx+vY31slG9/wtwPt/6/tJ3/6l/FPseiu+fHA98PvTgLlXw7QNv3qn/Btonvmqc/nLt8u/ZJSNBsrGyIafVwqsf1z+ZYsZn5QJw9rrEtedcqPMF+SGPt9HAfzhdOdVSpNOKC+Xf1tfAP68CBg8Dthmy/lSNkYK2+5D8oVX2vJjV8eP7fHHtT0lRPatBJ79hlyeq7Y1BMHbv5J/Z9wiz//Cd5Jf1gPnWn+H22XbU/UHpHDWy9T1qg7uwSXJj18+Rdb16Rvkfdj0tG0DITuE31bL/8n42vtS+N2/QP6umALUrgL+eX18G6e2Wz5ZjpI2/0v+eXzAt7bJe/XYZ4GaV4DL/g6MPxP4zXHx0cvQ2cANbyUeD7A6Y+2dQcGQeOdimm46Zdt9YJHz8WZ9Kv6u+Aulwvfi9+TvYk2jLxluXOdX4mXFw2Vn2tMCXPea8/H7QU4J+qjx7dfBZDS0tv1Sm+pqAmZeIoXMgfXAmz+V+SwGjZHbDB4PgOSLUDICOO+n6U+mBGHt+8Cyb8myEz4PTDxbHmvIRKm9P3+rVVsBgI/9QDZwJ2JR4PFr5PL4s4ATr83sJhQNldqGetl8ecCjVwORbmDqx4E5lyffd91jwJbnDSEwGPjME/HRjk77gfg1z74MmHaBdf3zt0rtZfB4YKEmjN+4W2pb7XUywdPMi4HnvhXXIvetkv8X3plcq8krldek8pHseBNY+Ue5nOq+AvKYLXvi8cw6BUPio7xv1cgXrqlGnmvECcClfwZe/L7MAukLyrLmnVLb9AaB4z8LbHgiril7bRP2uppkWgSl7SoHr54RUrUT+0zNtv1GfH7AmosHkCOntjqrCWjIRFm3aFi2pa5GWX7Z3+V9e+5mWabMJrrZ4qQvA2PnW89BXmDc6bJ+e/8jR2NVs4AzjY5nzwrZQbcflO3uuKuAKYutx6j/EHj9R7LtqLa5+G553+o/BOrS5MoJFAGX/Amo3xQ/X1eTbKfqvrXtl6OWaAiYd70cNexfm/yYpjM2KOsy61NylNi8Q3YsKjmdLyC36WmN5w0672dxgQ0Ar/3Imhfn9JuA1+6K100X9MPnAp9dGr8PG54CNv5Tjj7s5sMskVuC3lDpR5b4gE5Y7dRVM6VAKqyQgl6P4S0xhoDN2+ULaBdcqSgeHhd6FVOt+1ZOlRqZPS530kJg2Jzkx3zS0Pwqp2VWl2T486WgH3dG6uM1bAE+XCqFV9loWf/KqYnbtdbGr3nECYnHfOuXUtCXjLCuW/MP+aICsrFPu8DodA1Br/5POtf5vE5Ew3FBn+6+AkDVjPTHLKqQf+WT4mWDxwNDZwFblhmmjJOleaS7WXbu+qQbX9D5YyIFg+OCvqBcClC9bahl+wSecLsR+VHs/PyGzkosq5wm/6u2VGi06/YDUtADmjNWEy5j5ydvI+MXAHtXSPPE4HHx7VS+G/X8Rp2YeIzi4VLQhzvi5o2J50iFadKiuKAvnwI0bkk8d6AQGDJB/nl8UtDbTXXh9rh5Zcyp8jnsWu58LUDcX+b1y3uuOifVRnSN3huQHYM6/pQlcdMhIK0Cuum0YAhQMS3+THVB7w3Ke6lo3gFsfEq2jaqZyevbD1w5Y4loMRFtIaIaIrrVYf1oInqdiD4gonVEtMQoH0tE3US0xvj7fbYvQCcaExgzpABzhubLglCHdXgGxB+e7pxTjh59vVt0u66TjTdQlBiXm+4cyl6Yrd5dOQbTfclI3YP2A8nt1YB1nVMd1Xr7Ov1emNtoIY32dW7Q9x8gbSjh+B31cbu9KtfPrTtjdfTwTTU5S10zeeLLeWXx7cgj26hK0ZApQU1Y6degl6V7nub2RvvpPmTdTu2v6p+qTYTa4/4stZ1+fnsnZ+7v8JztkWwhrRMJFMu/SLd1op2OntjNCVPQq5QWofjx7W3U/p6rGceqrFjzi9nbRkC7f0dKoyciL4B7ASwEsA/ASiJaKoTYpG12G4DHhBC/I6LpkJ8dHGus2y6EOC671XYmGhPwEmkTFtqtwzNAa3Ad8W0sjTbDXNd+zYnj2MCLE5NBpTuHmrKeicBLhXpB1YudDFX/SI9VgCbbDkjSuRU7rwsWxe+F0zbmugyuO9iPZ5cpQf3+FFkFlX5ufcIUIDV3EbUKlIDRUbQZaXeLh8XNAvo1FQ+TAs0X7Nv1BYqlYDYdr4WQoYoi0RkLpD6HuqbuZufOQWmvTsfQhbNdWOrnT+YncWpz9oifcEe8E9E74nAHkK91ngolG5IpQGYn6Y9HoIW1jsS+rf6eK7u+KtOjbuznU/cr0pPoFM4SbjT6eQBqhBA7hBBhAI8AuNC2jQCgPrpaCsDB0zbwxISAx0PxBxjutA7PgPgDsmj0Rc6Nzg36TMpkGr2bMh3lVc5W764ETDLNRWF5eVM0OI8WxpqpRp9um2RlyQg4CJyBQn+57aNAi6YctAp6da2+gHV/fR89QkovL6oyNNX2vl2feW7j2RNZNVV9G/u57Zjpjnv6odF3xN89pSTpHYPFKapFRlnqqL3DKnxRHdvU6IusnYsTUcPvQUk+BBTQ7p1PM9348gCvTUe2C377JCyVGA9IfA/TKU5ZwI2gHwFA8zJgn1GmcweAq4hoH6Q2/zVt3TjDpPMmETmGOxDR9US0iohWNTQ0uK+9jbhGbwh3PR2BurlKgOnxxoFCzVzSjx7VSQt2Op4/3+XxsiXoDQGTznSj19Vtg3MU1EnupUUoJ9nG6SVKeX5tf7f3ta/o5woUaqPEYrlsTrDxWwW9buvVy9Q99hcAedrH6fXzFA81NNXOvrVNtY/+7M0yB3NOqnNYjuEgeE1B79Tm1XvXKf/8hXElyaLRJzHdONUx3CE7HfUxD3Wf1DYB7ZxOqHQVydDNXt6AVBpDHc7XZy/zBqzyIK8snk/H/h5a2tXAjEqzNWHqCgAPCiFGAlgC4CEi8gDYD2C0EGIugJsA/IOISuw7CyHuF0JUCyGqKyocYr9dEo3BptF3JA7P1AsZ6pC2O2WmUDe4P8LCqQE4CcxkGoSbffuCfu2p6Iu926mOfuM7vfYhvJPmaH/RMvaRaOdwe1/7SkL9Kb5MFH/+dmeso6AvjJcHirT2V2AdMRVVGQKso2/twa6969fhc1AAUp3D53AMIH7dKhLKyXTj9cn4/XC7HJ0kGz3aI47s16GfW9fggfh9UnVQ9Ug2MSkSSq38mPdOmW4MjT7ViEXhtWn0wSJ5neS1Pl/7vkdQo68FoLmXMdIo07kWwGMAIIR4F0AegHIhREgI0WSUrwawHcDk/lY6GTEh4PUgbq4JtSc6Y9XQ1W7P86vsdf0QFqns1X3hsDtj+2DvTlXHZE6nVMfPtKEPtLkm2bmcXk71rL0BZ6FoN+eoe5DKdJhXojlj+9CWnM6drJPVr8EJp2Po51A2+mTPROWLsQtLN85Y8/2ENjrQ3mEgbuJSdTBNN0km0umJ3ZxI5oxN5YNQ2NMqqNnVTsqWRcE6cjb6lQAmEdE4IgoAuBzAUts2ewCcDQBENA1S0DcQUYXhzAURjQcwCcCObFXeTqIzVjPdWF684kR7Xn8EvMLpJelPD501jV51cmlSRPTF3p2yQ7DdUze24EyF2UCba3QskR/ass+og915Z27rJGw1G73dsasTKJYCpvtQHzV6bXan/TqcOv5UZrNkmr8eNWJfZ6lLUdwZ62RzB5I7Y/W25PHIYyXT6D0+w3mtaf5O6IndnHB0xibxldjfA4tGT/K+Boud77lbH0k/SCvohRARADcCeBHAh5DRNRuJ6E4i+oSx2c0AriOitQAeBvA5IYQAcAaAdUS0BsATAG4QQgzYV3GdnbHaNGdFoMiIuVVZC7N0c906Y92SLXudasxOE4V07OF/bsjIcZrq+JTZec3dBthco5NMC1V1MG3fdmescd0+m0avtg8WJ9fozUif7uw4Y/VzpHPO27FEDWnP0uOVJqfeLgAUN9051cXU6JO0hWSmGzvmO2wI8aKh1jBU3emc1Bkbtj6ThHOo56Z9X6C7pe9BF4Ei53ven6g/l7jyegkhlkE6WfWy27XlTQDmO+z3JIAn+1lH10SiAj6P5oyN9MRzUlgaqfHptGTJrPqK41C4P87dLA3j3H6azuIUcnlPUr0oqY7vt12benEHaOiaFezOWDtezURmt8cDiWWmcNfs9QnmrjTndFvndPVxg9Mx9N+9XXEh61iXorjWrYcbWmz0DmGQjsdS77ChrBVXyZmp4U7rSAlILehT3QP9PpmhpYfkZLFk2+rHThD0hellxJGKoz+WiAoBD2kaPRD/8rp+g/NKgO2vAdtfl7+DJXHbYGF53yvg1MBVNIWKpXbD4HFAr2QdUgAAFJBJREFUbXP2zBJqBl86Z6zFvJXgM3ePChW05w/RI0tUxIXKEDhojExalkwbPBqwaF4l8Ukw6l6ZUTT58fY2eHx8vS8PKBkpZw37C+PlgSLrNjr6c8jrwzNR++tpctVx/HmJ26fCPiKxn6ezIXUdgyXAthfl8izNVad3+ur5e4NydrZKweHUljY8KdMHAEDpKJnuYO0/gMrpxvkM7fi5m4Flt8T3rb5WztDevVzO0E5GXqn878uLP8+uJueRtv26vYF4mZIteaXO91x3zh5Jjf5YIRYTCPiMHOr5g+XEDpXMTG+kZ31fTt0H5I0dWQ2MmifzzBx3ZeYn/tr7RjZDB8bMB865Q+YGCRTEG08qrngU2P12csdUpiy8UyZ3mnB2+m0vfUDmLNGn/zuR6ppP+ap8MedeZS2vnAGc+2NrjpDTvimzaU44G/jgIWDq+enraOfzz/evY3KLxwNc/EepPAwaC5z9A5lqYNJCuf5jtwEjqmVeJa9P5pYZUS21zMJyYPangZO+JLNnejyyrcUiMgdRUZXMpTLpHHmsL74mFYfySTKHj4gB0y/KvM5zrpDapX5fT/6KPN/sy+JlX3rLOSOjTjJnLCBzv+xdEX+vnDjzVmDYbLmsX4t+X8tGyxxLQyZKAbvnXelgtb+XC+8Cdr4pl/MHA1OXyNQFIibTHwDyfbvwPms7Xf+ETLfQsFU+m7NTJPirnAZceC8w+VyZvgOQ98jJdDN0NrDov6WSI2JSnoQ75fHVPVnwbaCz0flcn3pQ5ixSnVSWIXEYv1vohurqarFq1ao+7fvJ+5ajKODFQ/sWy5wYTTXAgluBN+8GvrIinv+DYZjMqV0N/PFjcvmW7f0b/R4pHr1a5qRp3iEVknPucLff+w8BS2+Uy6d/S3byRxlEtFoIUe20Lqc+PBKLCfhIABBxp0634fvN1B7JMIwVewqHYxEVcZfOPm8n2RyCY4ScEvRRIRAkwz6vzB5dLOgZJivoYbrp/D1HK4FCzW+XwXelk80KPkbILUEfAwIwHJ4qHpc1eobJDkrYBVNE1hztBIriX4DLJLw0lX/iGCCnBH0sJhBQGr3y0js5YxmGyRylxQ9QPpbDgm52yUT5c0ohcQyRU4I+KgTySGn0ynTjEF7JMEzmOOWvP9awzGjORNCz6eaoIRoT8JMx+zPBGXuM2hQZ5mjBKdvlsUZfNXp7CpVjjJwT9EEYppu8Uvl1nnAHAErMGMcwTGaYgv4onr2cDr3uGZluHNI8H0PknKD3w9DofUFrMqlj1XnEMEcLpunm2NNoTQJ9tdGzM/aoISYE/CrqxqsJ+mM1FIxhjiY8HplK4RgUdCb2Tz66hZ2xRw/RmBZH7/Vb04wyDNN/7B/UONbos0Z/bDtjcyvXjRDwmRq9H5h3PbDtJZl3gmGY/nPmrcCok490LfqO3kllEnVTOgo47iqgYNAxqTjmlKCPxgS8ML4fSV5g3nXyj2GY7DD/G0e6Bv2jzxq9D7jo3uzX5zCRc6YbmesGHGXDMEwifRX0xzg5KOiVRp9Tl8YwTDbwaR8RYUFvhYgWE9EWIqoholsd1o8moteJ6AMiWkdES7R13zX220JE52az8naiQhP0npyySjEMky1SfRw9R0kr6I2Pe98L4DwA0wFcQUT27Pi3QX5Ldi7kx8PvM/adbvyeAWAxgPvUx8IHglgMcRs9m24YhnHCDLtmQa8zD0CNEGKHECIM4BEAF9q2EQDUJ35KAdQZyxcCeEQIERJC7ARQYxxvQIgKmzOWYRjGTpA1eidGANir/d5nlOncAeAqItoH+RHxr2WwL4joeiJaRUSrGhoaXFY9EUvUDWv0DMM4waabPnMFgAeFECMBLAHwEJF7b6gQ4n4hRLUQorqioqJPFYjFZLSNl52xDMOk4iOo0bvxWNYCGKX9HmmU6VwLaYOHEOJdIsoDUO5y36wQNb59G9fo2RnLMIwDrNE7shLAJCIaR0QBSOfqUts2ewCcDQBENA1AHoAGY7vLiShIROMATALwXrYqrxON2QU9m24YhnHgIyjo06q9QogIEd0I4EUAXgAPCCE2EtGdAFYJIZYCuBnAH4nom5CO2c8JIQSAjUT0GIBNACIAviqEiA7EhcQMjT4eR8+CnmEYB4JFcsTv+eiYd13ZN4QQyyCdrHrZ7dryJgDzk+z7IwA/6kcdXRFhjZ5hGDeMOwPo7HvQx7FIzhiylTPWI1ijZxgmBdMukH8fIXJm7BK1R92wRs8wDAMghwR9nt+LG8+aiOElhoOFBT3DMAyAHBL0hUEfvnXuFIwsNQQ9m24YhmEA5JCgN1FBPazRMwzDAMhFQR8zBD1r9AzDMAByWdCzRs8wDAMgFwW9iMo8N0RHuiYMwzBHBbkn6GNRNtswDMNo5J6gF1E22zAMw2jknqBnjZ5hGMZCbgp6TlHMMAxjknuCXkQ/UlnpGIZh0pF7EpFNNwzDMBZyUNBH2BnLMAyjkXuCXrBGzzAMo5N7gj4WY2cswzCMhitBT0SLiWgLEdUQ0a0O639FRGuMv61E1KKti2rr7N+azT7sjGUYhrGQVvUlIi+AewEsBLAPwEoiWmp8PhAAIIT4prb91wDM1Q7RLYQ4LntVTgM7YxmGYSy4UX3nAagRQuwQQoQBPALgwhTbXwHg4WxUrk+wM5ZhGMaCG0E/AsBe7fc+oywBIhoDYByA17TiPCJaRUQriOiiJPtdb2yzqqGhnx/tZWcswzCMhWwbsy8H8IQQ6usfAIAxQohqAFcC+DURTbDvJIS4XwhRLYSorqio6F8N2BnLMAxjwY2grwUwSvs90ihz4nLYzDZCiFrj/w4Ab8Bqv88+7IxlGIax4EYirgQwiYjGEVEAUpgnRM8Q0VQAgwC8q5UNIqKgsVwOYD6ATfZ9s0oswqYbhmEYjbQ2DiFEhIhuBPAiAC+AB4QQG4noTgCrhBBK6F8O4BEhhNB2nwbgD0QUg+xU7tajdQaEGKcpZhiG0XFlzBZCLAOwzFZ2u+33HQ77vQNgVj/qlznsjGUYhrGQe8ZsdsYyDMNYyD1Bz85YhmEYC7knEdkZyzAMYyEHBT07YxmGYXRyT9CzM5ZhGMZC7gn6WIw1eoZhGI3cE/SCTTcMwzA6uSfo2RnLMAxjIQcFPWv0DMMwOrkn6NkZyzAMYyH3BH0sCnj9R7oWDMMwRw05KOj5C1MMwzA6OSroOdcNwzCMIvcEfbSXBT3DMIxG7gn6WBTwsI2eYRhGkYOCnm30DMMwOjkq6Nl0wzAMo8gtQS8EEGMbPcMwjI4rQU9Ei4loCxHVENGtDut/RURrjL+tRNSirbuGiLYZf9dks/IJiJj8z3H0DMMwJmlVXyLyArgXwEIA+wCsJKKl+ke+hRDf1Lb/GoC5xvJgAD8EUA1AAFht7Hsoq1ehiEXkf7bRMwzDmLjR6OcBqBFC7BBChAE8AuDCFNtfAeBhY/lcAC8LIZoN4f4ygMX9qXBKor3yP5tuGIZhTNwI+hEA9mq/9xllCRDRGADjALyWyb5EdD0RrSKiVQ0NDW7q7Yyp0bOgZxiGUWTbGXs5gCeEENFMdhJC3C+EqBZCVFdUVPT97DHjtBxHzzAMY+JG0NcCGKX9HmmUOXE54mabTPftP2yjZxiGScCNoF8JYBIRjSOiAKQwX2rfiIimAhgE4F2t+EUAi4hoEBENArDIKBsYYmyjZxiGsZNWIgohIkR0I6SA9gJ4QAixkYjuBLBKCKGE/uUAHhFCCG3fZiK6C7KzAIA7hRDN2b0EDbbRMwzDJOBKIgohlgFYZiu73fb7jiT7PgDggT7WLzOUjZ7j6BmGYUxya2Ys2+gZhmESyC1Bz3H0DMMwCeSWoDc1ejbdMAzDKHJM0Ks4etboGYZhFDkm6NlGzzAMYyfHBD3b6BmGYezkmKA3NHoOr2QYhjHJTUHPGj3DMIxJjgl65YxlGz3DMIwitwQ9x9EzDMMkkFuCnuPoGYZhEshRQc8aPcMwjCLHBD3b6BmGYezkmKBnGz3DMIydHBP0HEfPMAxjJzcFPWv0DMMwJrkl6KMs6BmGYey4EvREtJiIthBRDRHdmmSbTxPRJiLaSET/0MqjRLTG+Ev41mxW4aRmDMMwCaRVfYnIC+BeAAsB7AOwkoiWCiE2adtMAvBdAPOFEIeIqFI7RLcQ4rgs19sZjqNnGIZJwI1GPw9AjRBihxAiDOARABfatrkOwL1CiEMAIISoz241XcI2eoZhmATcCPoRAPZqv/cZZTqTAUwmouVEtIKIFmvr8oholVF+kdMJiOh6Y5tVDQ0NGV2ABRb0DMMwCWRLIvoATAJwJoCRAP5NRLOEEC0AxgghaoloPIDXiGi9EGK7vrMQ4n4A9wNAdXW16HMtYhEABHhyy8fMMAzTH9xIxFoAo7TfI40ynX0AlgoheoUQOwFshRT8EELUGv93AHgDwNx+1jk5sQjH0DMMw9hwI+hXAphEROOIKADgcgD26JmnIbV5EFE5pClnBxENIqKgVj4fwCYMFLEIm20YhmFspJWKQogIEd0I4EUAXgAPCCE2EtGdAFYJIZYa6xYR0SYAUQC3CCGaiOhUAH8gohhkp3K3Hq2TdaIs6BmGYey4kopCiGUAltnKbteWBYCbjD99m3cAzOp/NV3CGj3DMEwCueW1ZEHPMAyTAAt6hmGYHIcFPcMwTI6Te4Ley4KeYRhGJ/cEPWv0DMMwFljQMwzD5Di5JeijEU5RzDAMYyO3BH0swimKGYZhbOSgoGfTDcMwjA4LeoZhmBwnBwU92+gZhmF0ck/Qc5pihmEYC7kn6Nl0wzAMYyG3BD2nKWYYhkkgtwQ92+gZhmESyEFBzzZ6hmEYnRwU9Gy6YRiG0XEl6IloMRFtIaIaIro1yTafJqJNRLSRiP6hlV9DRNuMv2uyVXFHWNAzDMMkkFYqEpEXwL0AFgLYB2AlES3Vv/1KRJMAfBfAfCHEISKqNMoHA/ghgGoAAsBqY99D2b8UsI2eYRjGATca/TwANUKIHUKIMIBHAFxo2+Y6APcqAS6EqDfKzwXwshCi2Vj3MoDF2am6AxxHzzAMk4AbQT8CwF7t9z6jTGcygMlEtJyIVhDR4gz2BRFdT0SriGhVQ0OD+9rbYdMNwzBMAtlyxvoATAJwJoArAPyRiMrc7iyEuF8IUS2EqK6oqOh7LTiOnmEYJgE3gr4WwCjt90ijTGcfgKVCiF4hxE4AWyEFv5t9swdr9AzDMAm4EfQrAUwionFEFABwOYCltm2ehtTmQUTlkKacHQBeBLCIiAYR0SAAi4yygYEFPcMwTAJppaIQIkJEN0IKaC+AB4QQG4noTgCrhBBLERfomwBEAdwihGgCACK6C7KzAIA7hRDNA3EhAFjQMwzDOOBKKgohlgFYZiu7XVsWAG4y/uz7PgDggf5V0wWxKADBgp5hGMZG7syMjUXkfy8LeoZhGJ3cE/Ss0TMMw1hgQc8wDJPj5I6gj7KgZxiGcSJ3BL3HC0y/CBgy4UjXhGEY5qgid9Tf/DLg03890rVgGIY56sgdjZ5hGIZxhAU9wzBMjsOCnmEYJsdhQc8wDJPjsKBnGIbJcVjQMwzD5Dgs6BmGYXIcFvQMwzA5DskMw0cPRNQAYHc/DlEOoDFL1TlW4Gv+aMDX/NGgr9c8Rgjh+C3Wo07Q9xciWiWEqD7S9Tic8DV/NOBr/mgwENfMphuGYZgchwU9wzBMjpOLgv7+I12BIwBf80cDvuaPBlm/5pyz0TMMwzBWclGjZxiGYTRY0DMMw+Q4OSPoiWgxEW0hohoiuvVI1ydbENEDRFRPRBu0ssFE9DIRbTP+DzLKiYjuMe7BOiI6/sjVvO8Q0Sgiep2INhHRRiL6hlGes9dNRHlE9B4RrTWu+b+M8nFE9B/j2h4looBRHjR+1xjrxx7J+vcHIvIS0QdE9C/jd05fMxHtIqL1RLSGiFYZZQPatnNC0BORF8C9AM4DMB3AFUQ0/cjWKms8CGCxrexWAK8KISYBeNX4Dcjrn2T8XQ/gd4epjtkmAuBmIcR0ACcD+KrxPHP5ukMAPiaEmAPgOACLiehkAD8F8Csh/n975/JSVRTF4e8HvR8kWYlkIFLQKAqikhxYUAOJRg6KIAdC0xoFEvQn9Bg2aBgFUZE4McvGFZaVYZaCUFJdCLVpj9XgrCsHqUl5PNzt+mBz9157D9bvsO66+6x97r22HZgGun19NzDt9su+rlY5C4zmxktB8yEz2517Xr7Y2Dazmm9AK9CfG/cAPWX7tYD6moGR3HgMaPR+IzDm/WvAyT+tq+UG3AeOLBXdwBrgObCf7BuSy9w+F+dAP9Dq/WW+TmX7/g9amzyxHQb6AC0BzZPApnm2QmM7iR09sBX4kBt/dFuqNJjZJ+9/Bhq8n9x18NvzPcATEtftJYxhoAIMABPAjJn98CV5XXOafX4WqF9cjxeEK8B54JeP60lfswEPJA1JOuO2QmM7nT8HX6KYmUlK8hlZSeuAO8A5M/smaW4uRd1m9hPYLakOuAfsLNmlQpF0DKiY2ZCk9rL9WUTazGxK0hZgQNLb/GQRsZ3Kjn4K2JYbN7ktVb5IagTw14rbk7kOkpaTJfkbZnbXzcnrBjCzGeAxWdmiTlJ1Q5bXNafZ5zcAXxfZ1f/lIHBc0iRwi6x8c5W0NWNmU/5aIftA30fBsZ1Kon8G7PDT+hXACaC3ZJ+KpBfo8n4XWQ27aj/tJ/UHgNnc7WDNoGzrfh0YNbNLualkdUva7Dt5JK0mO5MYJUv4nb5svubqtegEBs2LuLWCmfWYWZOZNZO9ZwfN7BQJa5a0VtL6ah84CoxQdGyXfTCxgAccHcA7srrmhbL9WUBdN4FPwHey+lw3WV3yEfAeeAhs9LUie/poAngN7C3b/3/U3EZWx3wFDHvrSFk3sAt44ZpHgItubwGeAuPAbWCl21f5eNznW8rW8J/624G+1DW7tpfe3lRzVdGxHT+BEARBkDiplG6CIAiCvxCJPgiCIHEi0QdBECROJPogCILEiUQfBEGQOJHogyAIEicSfRAEQeL8BnJPEb5sU6dRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "poeo3a1J0LiH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}