HebbMNIST-2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HebbMNIST.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2DY3ioBn2GM"
      },
      "source": [
        "# Iris"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TD0DzIX-n3z3"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.style.use('seaborn')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HU7h81cgpfbU"
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hyrhj-wkphnM"
      },
      "source": [
        "iris = load_iris()\n",
        "X = iris['data']\n",
        "y = iris['target']\n",
        "names = iris['target_names']\n",
        "feature_names = iris['feature_names']\n",
        "\n",
        "# One hot encoding\n",
        "enc = OneHotEncoder()\n",
        "Y = enc.fit_transform(y[:, np.newaxis]).toarray()\n",
        "\n",
        "# Scale data to have mean 0 and variance 1 \n",
        "# which is importance for convergence of the neural network\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data set into training and testing\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(\n",
        "    X_scaled, Y, test_size=0.5, random_state=2)\n",
        "\n",
        "X_val, X_test, Y_val, Y_test = train_test_split(\n",
        "    X_test, Y_test, test_size=0.4, random_state=2)\n",
        "\n",
        "\n",
        "#X_train = X_scaled[0:75]\n",
        "#Y_train = Y[0:75]\n",
        "#X_val  = X_scaled[75:105]\n",
        "#Y_val  = Y[75:105]\n",
        "#X_test  = X_scaled[105:150]\n",
        "#Y_test = Y[105:150]\n",
        "\n",
        "\n",
        "#X_train = np.concatenate((X_scaled[0:30],X_scaled[60:150]),axis=0)\n",
        "\n",
        "\n",
        "n_features = X.shape[1]\n",
        "n_classes = Y.shape[1]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzILC8Pj3aYV",
        "outputId": "b2ce417c-0b45-42e5-f8d1-8f759e29f751"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_val.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(75, 4)\n",
            "(45, 4)\n",
            "(30, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(feature_names[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJskVCqZtAZP",
        "outputId": "df947a40-9f74-4f40-e73c-4e50852fadb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "petal length (cm)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "sUBbEokMplLC",
        "outputId": "4ef8cb7a-dbaa-4c64-8ace-18ae35458888"
      },
      "source": [
        "I = 2\n",
        "J = 0\n",
        "\n",
        "# Visualize the data sets\n",
        "# plt.figure(figsize=(16, 6))\n",
        "# plt.subplot(1, 2, 1)\n",
        "# for target, target_name in enumerate(names):\n",
        "#    X_plot = X[y == target]\n",
        "#    plt.plot(X_plot[:, 0], X_plot[:, 1], linestyle='none', marker='o', label=target_name)\n",
        "#plt.xlabel(feature_names[0])\n",
        "#plt.ylabel(feature_names[1])\n",
        "#plt.axis('equal')\n",
        "#plt.legend();\n",
        "\n",
        "#plt.subplot(1, 2, 2)\n",
        "for target, target_name in enumerate(names):\n",
        "    X_plot = X[y == target]\n",
        "    plt.plot(X_plot[:, J], X_plot[:, I], linestyle='none', marker='o', label=target_name)\n",
        "plt.xlabel('Longitud de sépalo')#feature_names[J])\n",
        "plt.ylabel('Longitud de pétalo') #feature_names[I])\n",
        "plt.axis('equal')\n",
        "plt.legend()\n",
        "plt.savefig('iris.eps');"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eH5MRfN3sgWY",
        "outputId": "509ea597-ec83-4d6d-f977-5644e56739b2"
      },
      "source": [
        "print(X_train[0][2:4],Y_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-1.22655167 -1.3154443 ] [1. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ylt2t_Agr5qe"
      },
      "source": [
        "from time import time\n",
        "\n",
        "def hebb(w1,w2,w3,Xp,yp,s):\n",
        "\tt1 = time()\n",
        "\tfor i in range(len(yp)):\n",
        "\t\tif yp[i][0] == 1:\n",
        "\t\t\tw1[-int(Xp[i][I]*s)+12][int(Xp[i][J]*s)+12] += alpha\t\t\t\n",
        "\t\telif yp[i][1] == 1:\n",
        "\t\t\tw2[-int(Xp[i][I]*s)+12][int(Xp[i][J]*s)+12] += alpha\n",
        "\t\telse:\n",
        "\t\t\tw3[-int(Xp[i][I]*s)+12][int(Xp[i][J]*s)+12] += alpha      \n",
        "\n",
        "\tt2 = time()\n",
        "\treturn w1,w2,w3,t2-t1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dt = 0"
      ],
      "metadata": {
        "id": "x2llloRHQLVV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwarUaIRsMXm"
      },
      "source": [
        "s = 10\n",
        "alpha = 0.1\n",
        "\n",
        "W1 = np.zeros((s*24,s*24))\n",
        "W2 = np.zeros((s*24,s*24))\n",
        "W3 = np.zeros((s*24,s*24))\n",
        "\n",
        "W1,W2,W3,dt = hebb(W1,W2,W3,X_train,Y_train,s)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cif0WFSv0ii",
        "outputId": "c8fb6616-75d1-4813-af29-06ff7eac2f22"
      },
      "source": [
        "# Evaluacion\n",
        "\n",
        "def eval(Xp,Yp):\n",
        "\n",
        "  N = len(Yp)\n",
        "  suma = 0\n",
        "  for i in range(N):\n",
        "    a = W1[-int(Xp[i][I]*s)+12][int(Xp[i][J]*s)+12]\n",
        "    b = W2[-int(Xp[i][I]*s)+12][int(Xp[i][J]*s)+12]\n",
        "    c = W3[-int(Xp[i][I]*s)+12][int(Xp[i][J]*s)+12]\n",
        "\n",
        "    if Yp[i][0] == 1:\n",
        "      if a <= b or a <= c:\n",
        "        suma += 1\n",
        "    elif Yp[i][1] == 1:\n",
        "      if b < a or b <= c:\n",
        "        suma += 1\n",
        "    elif Yp[i][2] == 1:\n",
        "      if c < a or c < b:\n",
        "        suma += 1\n",
        "  \n",
        "  #print(\"Metodo hebbiano\")\n",
        "  print(\"costo=\",suma)\n",
        "  print(\"Efectividad =\",1-float(suma)/(N))\n",
        "\n",
        "Dt += dt\n",
        "print('s = ', s)\n",
        "print(\"Training\")\n",
        "eval(X_train,Y_train)\n",
        "print(\"Validation\")\n",
        "eval(X_val,Y_val)\n",
        "print(\"Testing\")\n",
        "eval(X_test,Y_test)\n",
        "print(\"Dt = \", dt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "s =  10\n",
            "Training\n",
            "costo= 0\n",
            "Efectividad = 1.0\n",
            "Validation\n",
            "costo= 27\n",
            "Efectividad = 0.4\n",
            "Testing\n",
            "costo= 14\n",
            "Efectividad = 0.5333333333333333\n",
            "Dt =  0.0005383491516113281\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Dt = 0.0003921985626220703 + 0.0004267692565917969 + 0.0003895759582519531\n",
        "Dt += 0.0004112720489501953 + 0.0004553794860839844 + 0.0004496574401855469\n",
        "Dt += 0.00041604042053222656 + 0.0035223960876464844 + 0.00036454200744628906\n",
        "Dt += 0.00042057037353515625\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLgKgRrZSrni",
        "outputId": "6e83b574-0978-4cd3-b2c3-f3c378785ca2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0007248401641845703\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Dt/10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gI0JJtJ486N2",
        "outputId": "8ffa0ac4-3ce9-41b3-a4a2-4eddc3aef48a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0010404348373413085\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "s = 1\n",
        "\n",
        "```\n",
        "Training\n",
        "costo= 8\n",
        "Efectividad = 0.8933333333333333\n",
        "Validation\n",
        "costo= 3\n",
        "Efectividad = 0.9333333333333333\n",
        "Testing\n",
        "costo= 3\n",
        "Efectividad = 0.9\n",
        "Dt =  0.0003921985626220703\n",
        "```\n",
        "\n",
        "s = 2\n",
        "\n",
        "```\n",
        "Training\n",
        "costo= 4\n",
        "Efectividad = 0.9466666666666667\n",
        "Validation\n",
        "costo= 2\n",
        "Efectividad = 0.9555555555555556\n",
        "Testing\n",
        "costo= 1\n",
        "Efectividad = 0.9666666666666667\n",
        "Dt =  0.0004267692565917969\n",
        "```\n",
        "\n",
        "\n",
        "s = 3\n",
        "\n",
        "```\n",
        "Training\n",
        "costo= 2\n",
        "Efectividad = 0.9733333333333334\n",
        "Validation\n",
        "costo= 3\n",
        "Efectividad = 0.9333333333333333\n",
        "Testing\n",
        "costo= 0\n",
        "Efectividad = 1.0\n",
        "Dt =  0.0003895759582519531\n",
        "```\n",
        "\n",
        "s = 4 \n",
        "\n",
        "``` \n",
        "Training\n",
        "costo= 1\n",
        "Efectividad = 0.9866666666666667\n",
        "Validation\n",
        "costo= 3\n",
        "Efectividad = 0.9333333333333333\n",
        "Testing\n",
        "costo= 1\n",
        "Efectividad = 0.9666666666666667\n",
        "Dt =  0.0004112720489501953\n",
        "```\n",
        "\n",
        "s = 5\n",
        "\n",
        "``` \n",
        "Training\n",
        "costo= 2\n",
        "Efectividad = 0.9733333333333334\n",
        "Validation\n",
        "costo= 9\n",
        "Efectividad = 0.8\n",
        "Testing\n",
        "costo= 2\n",
        "Efectividad = 0.9333333333333333\n",
        "Dt =  0.0004553794860839844\n",
        "```\n",
        "\n",
        "s = 6\n",
        "\n",
        "``` \n",
        "Training\n",
        "costo= 1\n",
        "Efectividad = 0.9866666666666667\n",
        "Validation\n",
        "costo= 4\n",
        "Efectividad = 0.9111111111111111\n",
        "Testing\n",
        "costo= 4\n",
        "Efectividad = 0.8666666666666667\n",
        "Dt =  0.0004496574401855469\n",
        "```\n",
        "\n",
        "s = 7\n",
        "\n",
        "``` \n",
        "Training\n",
        "costo= 1\n",
        "Efectividad = 0.9866666666666667\n",
        "Validation\n",
        "costo= 6\n",
        "Efectividad = 0.8666666666666667\n",
        "Testing\n",
        "costo= 4\n",
        "Efectividad = 0.8666666666666667\n",
        "Dt =  0.00041604042053222656\n",
        "```\n",
        "\n",
        "s = 8 \n",
        "\n",
        "``` \n",
        "Training\n",
        "costo= 1\n",
        "Efectividad = 0.9866666666666667\n",
        "Validation\n",
        "costo= 9\n",
        "Efectividad = 0.8\n",
        "Testing\n",
        "costo= 11\n",
        "Efectividad = 0.6333333333333333\n",
        "Dt =  0.0035223960876464844\n",
        "```\n",
        "\n",
        "\n",
        "s = 9\n",
        "\n",
        "``` \n",
        "Training\n",
        "costo= 1\n",
        "Efectividad = 0.9866666666666667\n",
        "Validation\n",
        "costo= 11\n",
        "Efectividad = 0.7555555555555555\n",
        "Testing\n",
        "costo= 12\n",
        "Efectividad = 0.6\n",
        "Dt =  0.00036454200744628906\n",
        "```\n",
        "\n",
        "s = 10\n",
        "\n",
        "``` \n",
        "Training\n",
        "costo= 1\n",
        "Efectividad = 0.9866666666666667\n",
        "Validation\n",
        "costo= 11\n",
        "Efectividad = 0.7555555555555555\n",
        "Testing\n",
        "costo= 12\n",
        "Efectividad = 0.6\n",
        "Dt =  0.00042057037353515625\n",
        "```\n"
      ],
      "metadata": {
        "id": "_1qPfxp9NxpU"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxsYfKe4kooW",
        "outputId": "557a9358-945d-4dc0-ab5a-03c53a136316"
      },
      "source": [
        "# In order to ignore FutureWarning\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter(action='ignore', category=DeprecationWarning)\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "def create_custom_model(input_dim, output_dim, nodes, n=1, name='model'):\n",
        "    def create_model():\n",
        "        # Create model\n",
        "        model = Sequential(name=name)\n",
        "        for i in range(n):\n",
        "            model.add(Dense(nodes, input_dim=input_dim, activation='relu'))\n",
        "        model.add(Dense(output_dim, activation='softmax'))\n",
        "\n",
        "        # Compile model\n",
        "        model.compile(loss='categorical_crossentropy', \n",
        "                      optimizer='adam', \n",
        "                      metrics=['accuracy'])\n",
        "        return model\n",
        "    return create_model\n",
        "\n",
        "\n",
        "\n",
        "models = [create_custom_model(n_features, n_classes, 50, i, 'Modelo_{}'.format(i)) \n",
        "          for i in range(1, 4)]\n",
        "\n",
        "for create_model in models:\n",
        "    create_model().summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Modelo_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 50)                250       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 153       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 403\n",
            "Trainable params: 403\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"Modelo_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 50)                250       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 50)                2550      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 3)                 153       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,953\n",
            "Trainable params: 2,953\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"Modelo_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_5 (Dense)             (None, 50)                250       \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 50)                2550      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 50)                2550      \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 3)                 153       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,503\n",
            "Trainable params: 5,503\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dytc9VJSkuB2",
        "outputId": "ebcd6caf-8522-4b59-ac4b-e7650c60a55e"
      },
      "source": [
        "from keras.callbacks import TensorBoard\n",
        "from time import time\n",
        "\n",
        "history_dict = {}\n",
        "\n",
        "# TensorBoard Callback\n",
        "cb = TensorBoard()\n",
        "\n",
        "for create_model in models:\n",
        "    model = create_model()\n",
        "    print('Model name:', model.name)\n",
        "    t1 = time()\n",
        "    history_callback = model.fit(X_train, Y_train,\n",
        "                                 batch_size=5,\n",
        "                                 epochs=15,\n",
        "                                 verbose=0,\n",
        "                                 validation_data=(X_test, Y_test),\n",
        "                                 callbacks=[cb])\n",
        "    t2 = time()\n",
        "    score = model.evaluate(X_test, Y_test, verbose=0)\n",
        "    print('Elapsed time:',t2-t1)\n",
        "    print('Test loss:', score[0])\n",
        "    print('Test accuracy:', score[1])\n",
        "    score2 = model.evaluate(X_train, Y_train, verbose=0)\n",
        "    print('Train loss:', score2[0])\n",
        "    print('Train accuracy:', score2[1])\n",
        "    history_dict[model.name] = [history_callback, model]\n",
        "    score3 = model.evaluate(X_val, Y_val, verbose=0)\n",
        "    print('Val loss:', score3[0])\n",
        "    print('Val accuracy:', score3[1])\n",
        "    print(\"____________________________________________\")    "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model name: Modelo_1\n",
            "Elapsed time: 1.540376901626587\n",
            "Test loss: 0.3009810447692871\n",
            "Test accuracy: 0.8999999761581421\n",
            "Train loss: 0.3439269959926605\n",
            "Train accuracy: 0.8399999737739563\n",
            "Val loss: 0.37444424629211426\n",
            "Val accuracy: 0.8444444537162781\n",
            "____________________________________________\n",
            "Model name: Modelo_2\n",
            "Elapsed time: 1.7802765369415283\n",
            "Test loss: 0.17353922128677368\n",
            "Test accuracy: 0.9666666388511658\n",
            "Train loss: 0.1501399427652359\n",
            "Train accuracy: 0.9733333587646484\n",
            "Val loss: 0.2549203634262085\n",
            "Val accuracy: 0.9111111164093018\n",
            "____________________________________________\n",
            "Model name: Modelo_3\n",
            "Elapsed time: 1.8210175037384033\n",
            "Test loss: 0.20989097654819489\n",
            "Test accuracy: 0.9333333373069763\n",
            "Train loss: 0.06548453867435455\n",
            "Train accuracy: 0.9866666793823242\n",
            "Val loss: 0.4419834017753601\n",
            "Val accuracy: 0.8888888955116272\n",
            "____________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fold 1\n",
        "\n",
        "```\n",
        "Model name: Modelo_1\n",
        "Elapsed time: 1.5819268226623535\n",
        "Test loss: 0.268419086933136\n",
        "Test accuracy: 0.9333333373069763\n",
        "Train loss: 0.301923006772995\n",
        "Train accuracy: 0.9066666960716248\n",
        "Val loss: 0.33547404408454895\n",
        "Val accuracy: 0.8666666746139526\n",
        "____________________________________________\n",
        "Model name: Modelo_2\n",
        "Elapsed time: 1.1914377212524414\n",
        "Test loss: 0.19759227335453033\n",
        "Test accuracy: 0.9333333373069763\n",
        "Train loss: 0.14800545573234558\n",
        "Train accuracy: 0.9733333587646484\n",
        "Val loss: 0.30254480242729187\n",
        "Val accuracy: 0.8888888955116272\n",
        "____________________________________________\n",
        "Model name: Modelo_3\n",
        "Elapsed time: 1.6446101665496826\n",
        "Test loss: 0.18000291287899017\n",
        "Test accuracy: 0.9333333373069763\n",
        "Train loss: 0.061197832226753235\n",
        "Train accuracy: 1.0\n",
        "Val loss: 0.3502825200557709\n",
        "Val accuracy: 0.9111111164093018\n",
        "____________________________________________\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "cxrj66MLx3N9"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "Nbypmz6Ekpju",
        "outputId": "cb0f6ff9-f546-4996-8d94-078912db35eb"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(2, figsize=(8, 6))\n",
        "\n",
        "for model_name in history_dict:\n",
        "    val_accurady = history_dict[model_name][0].history['val_accuracy']\n",
        "    val_loss = history_dict[model_name][0].history['val_loss']\n",
        "    ax1.plot(val_accurady, label=model_name)\n",
        "    ax2.plot(val_loss, label=model_name)\n",
        "    \n",
        "ax1.set_ylabel('Exactitud')\n",
        "ax2.set_ylabel('Pérdida')\n",
        "ax2.set_xlabel('Épocas')\n",
        "ax1.legend()\n",
        "ax2.legend()\n",
        "plt.savefig('accuraciesIris.eps');"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3_4MiDVLqkL"
      },
      "source": [
        "import random\n",
        "import keras\n",
        "\n",
        "# Constants\n",
        "mum = 9\n",
        "M1 = 4\n",
        "T1 = 3\n",
        "k1 = 10\n",
        "\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hihb4_SvTSaR"
      },
      "source": [
        "def geneticCode(V):\n",
        "        model = Sequential()\n",
        "        for v in V:\n",
        "            model.add(Dense(v, input_dim=input_dim, activation='relu'))\n",
        "        model.add(Dense(output_dim, activation='softmax'))\n",
        "\n",
        "        # Compile model\n",
        "        model.compile(loss='categorical_crossentropy', \n",
        "                      optimizer='adam', \n",
        "                      metrics=['accuracy'])\n",
        "\n",
        "        model.fit(X_train, Y_train, epochs=25)\n",
        "\n",
        "\n",
        "        validation_loss, validation_acc = model.evaluate(X_train, Y_train)\n",
        "\n",
        "        print('Validation accuracy:', validation_acc)\n",
        "\n",
        "        return validation_acc\n",
        "\n",
        "def geneticCodeE(V):\n",
        "        model = Sequential()\n",
        "        for v in V:\n",
        "            model.add(Dense(v, input_dim=input_dim, activation='relu'))\n",
        "        model.add(Dense(output_dim, activation='softmax'))\n",
        "\n",
        "        # Compile model\n",
        "        model.compile(loss='categorical_crossentropy', \n",
        "                      optimizer='adam', \n",
        "                      metrics=['accuracy'])\n",
        "\n",
        "        model.fit(X_train, Y_train, epochs=15)\n",
        "        t_loss,t_acc = model.evaluate(X_train, Y_train)\n",
        "        validation_loss, validation_acc = model.evaluate(X_val, Y_val)\n",
        "        test_loss, test_acc = model.evaluate(X_test, Y_test)\n",
        "        print(V)\n",
        "        print('Training accuracy:',t_acc)\n",
        "        print('Validation accuracy:', validation_acc)\n",
        "        print('Test accuracy:', test_acc)\n",
        "        return validation_acc\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJtveVKfNe-8",
        "outputId": "0a2105af-7101-4477-a693-b31141f73f63"
      },
      "source": [
        "V = [50]\n",
        "\n",
        "Nset = [V]\n",
        "\n",
        "input_dim, output_dim = n_features, n_classes\n",
        "\n",
        "ma = geneticCodeE(V)\n",
        "\n",
        "maxis = [ma]\n",
        "\n",
        "# Fision binaria\n",
        "\n",
        "NsetAux = [V]\n",
        "maxisAux= [ma]\n",
        "\n",
        "L = []\n",
        "MAXIS = []\n",
        "\n",
        "r = False"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.2718 - accuracy: 0.0267\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.2233 - accuracy: 0.0800\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1811 - accuracy: 0.0933\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1406 - accuracy: 0.1200\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0987 - accuracy: 0.2933\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0611 - accuracy: 0.4000\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0248 - accuracy: 0.5200\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9898 - accuracy: 0.6933\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9555 - accuracy: 0.7067\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9238 - accuracy: 0.7467\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8927 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8650 - accuracy: 0.7733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8372 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8105 - accuracy: 0.7600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7858 - accuracy: 0.7733\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7671 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7359 - accuracy: 0.7556\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.7483 - accuracy: 0.9000\n",
            "[50]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.7555555701255798\n",
            "Test accuracy: 0.8999999761581421\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGL4hnvBOoRy",
        "outputId": "97f9f9ff-4cd6-4316-97d9-1257985abaff"
      },
      "source": [
        "timeA = time()\n",
        "\n",
        "for t in range(15): #15\n",
        "\n",
        "        # Fision binaria\n",
        "\n",
        "        print(\"___________________________\")\n",
        "        print(\"T=\",t)\n",
        "\n",
        "        # Depredacion\n",
        "        l = len(Nset)\n",
        "        #if l > 5: # PARAMETRO 5\n",
        "\n",
        "        if t == M1:\n",
        "                print(\"Depredacion\")\n",
        "                r = True\n",
        "                maxiscopy = maxis[:]\n",
        "                maxiscopy.sort(reverse=True)\n",
        "                #maxiscopy = np.array(maxiscopy) + 0.0001*np.random.rand(len(maxiscopy))\n",
        "                print(maxiscopy)\n",
        "                print(mum) # PAREMETRO 2\n",
        "                theta = maxiscopy[mum] # VERIFICAR\n",
        "                print(\"theta=\",theta)\n",
        "                Nset2 = Nset[:]\n",
        "                maxis2= maxis[:]\n",
        "                h = 0\n",
        "                for i in range(l):\n",
        "                        if maxis[i] <= theta:\n",
        "                                print(maxis[i])\n",
        "                                print(maxis2[i-h])\n",
        "                                del Nset2[i-h]\n",
        "                                del maxis2[i-h]\n",
        "                                h += 1\n",
        "                Nset = Nset2[:]\n",
        "                maxis= maxis2[:]\n",
        "                NsetAux = Nset2[:]\n",
        "                maxisAux = maxis2[:]\n",
        "                l = len(Nset)\n",
        "                L.append(l)\n",
        "\n",
        "        print(\"__\")\n",
        "        print(Nset)\n",
        "        print(maxis)\n",
        "        for N in Nset: # Reproduccion\n",
        "                #NsetAux.append(N)\n",
        "                N1 = N[:]\n",
        "                alea = random.random()\n",
        "                if alea < 0.7: # Desicion alpha/beta\n",
        "                        #alpha-mutacion\n",
        "                        l = len(N)\n",
        "                        elec = random.randrange(l)\n",
        "                        N1[elec] += k1\n",
        "                else:\n",
        "                        N1.append(k1)\n",
        "\n",
        "                # N1 es el hijo. N se divide en N y N1\n",
        "                #NsetAux.append(N1)\n",
        "\n",
        "                ban = True\n",
        "                for i in range(len(NsetAux)): #NsetAux\n",
        "                        if NsetAux[i] == N1:\n",
        "                                ban = False\n",
        "                                #print(NsetAux)\n",
        "                                #print(maxisAux)\n",
        "                                m1 = maxisAux[i]\n",
        "\n",
        "                NsetAux.append(N1)\n",
        "\n",
        "                if ban:\n",
        "                        m1 = geneticCodeE(N1)\n",
        "                maxisAux.append(m1)\n",
        "        Nset  = NsetAux[:]\n",
        "        maxis = maxisAux[:]\n",
        "        maxis = np.array(maxis) + 0.0001*np.random.rand(len(maxis))\n",
        "        maxis = list(maxis)\n",
        "        print(\"Redes\")\n",
        "        print(Nset)\n",
        "        print(\"Maximos\")\n",
        "        print(maxis)\n",
        "        print\n",
        "        print\n",
        "        maxisAux = maxis[:]\n",
        "\n",
        "        l = len(Nset)\n",
        "        L.append(l)\n",
        "        MAXIS.append(max(maxis))\n",
        "\n",
        "        if r:\n",
        "                M1 += T1\n",
        "                r = False\n",
        "\n",
        "timeB = time()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "___________________________\n",
            "T= 0\n",
            "__\n",
            "[[50]]\n",
            "[0.7555555701255798]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0874 - accuracy: 0.6133\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 1.0548 - accuracy: 0.6400\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0226 - accuracy: 0.6533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9947 - accuracy: 0.6667\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9686 - accuracy: 0.6400\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9440 - accuracy: 0.6533\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9211 - accuracy: 0.6800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8993 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8777 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8576 - accuracy: 0.7867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.8363 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8159 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7954 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7759 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7549 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7398 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.7559 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.7483 - accuracy: 0.8667\n",
            "[50, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.8666666746139526\n",
            "Redes\n",
            "[[50], [50, 10]]\n",
            "Maximos\n",
            "[0.75564761728077, 0.777861041544]\n",
            "___________________________\n",
            "T= 1\n",
            "__\n",
            "[[50], [50, 10]]\n",
            "[0.75564761728077, 0.777861041544]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1763 - accuracy: 0.3200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1203 - accuracy: 0.3200\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0720 - accuracy: 0.4533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0212 - accuracy: 0.6000\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9781 - accuracy: 0.6267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9359 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8967 - accuracy: 0.7333\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8582 - accuracy: 0.7467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8254 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7923 - accuracy: 0.7867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7620 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7334 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7067 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6814 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6586 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6414 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.6679 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.6352 - accuracy: 0.8333\n",
            "[60]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.8333333134651184\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1041 - accuracy: 0.2667\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0788 - accuracy: 0.3067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0569 - accuracy: 0.4533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0344 - accuracy: 0.4933\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0138 - accuracy: 0.5733\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9950 - accuracy: 0.5467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9798 - accuracy: 0.6000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9656 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9523 - accuracy: 0.7733\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9379 - accuracy: 0.8400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9234 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9078 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8920 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8756 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.8588 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8449 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8443 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.8674 - accuracy: 0.9333\n",
            "[50, 10, 10]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.9333333373069763\n",
            "Redes\n",
            "[[50], [50, 10], [60], [50, 10, 10]]\n",
            "Maximos\n",
            "[0.7557114009560256, 0.777869311338249, 0.7778650821824751, 0.8000293431601214]\n",
            "___________________________\n",
            "T= 2\n",
            "__\n",
            "[[50], [50, 10], [60], [50, 10, 10]]\n",
            "[0.7557114009560256, 0.777869311338249, 0.7778650821824751, 0.8000293431601214]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0223 - accuracy: 0.4667\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9742 - accuracy: 0.5600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9294 - accuracy: 0.6800\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8890 - accuracy: 0.7733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8494 - accuracy: 0.8667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8152 - accuracy: 0.8533\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.7829 - accuracy: 0.8400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7524 - accuracy: 0.8400\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7235 - accuracy: 0.8400\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6978 - accuracy: 0.8400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6737 - accuracy: 0.8400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6504 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6298 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6104 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.5926 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.5788 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.6004 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.5508 - accuracy: 0.9000\n",
            "[70]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1329 - accuracy: 0.1200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1022 - accuracy: 0.2000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0733 - accuracy: 0.2800\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0489 - accuracy: 0.4267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0263 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0052 - accuracy: 0.7600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9820 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9597 - accuracy: 0.8267\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9345 - accuracy: 0.8667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9111 - accuracy: 0.8400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8880 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8655 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8434 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8210 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7989 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7831 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.7879 - accuracy: 0.7556\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.7441 - accuracy: 0.9667\n",
            "[50, 20, 10]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.7555555701255798\n",
            "Test accuracy: 0.9666666388511658\n",
            "Redes\n",
            "[[50], [50, 10], [60], [50, 10, 10], [50, 10], [50, 10, 10], [70], [50, 20, 10]]\n",
            "Maximos\n",
            "[0.7557811265027431, 0.7778737114235739, 0.7778851325669462, 0.800047525921597, 0.7779027947661946, 0.8000478272955859, 0.8000863428563456, 0.7556159898820405]\n",
            "___________________________\n",
            "T= 3\n",
            "__\n",
            "[[50], [50, 10], [60], [50, 10, 10], [50, 10], [50, 10, 10], [70], [50, 20, 10]]\n",
            "[0.7557811265027431, 0.7778737114235739, 0.7778851325669462, 0.800047525921597, 0.7779027947661946, 0.8000478272955859, 0.8000863428563456, 0.7556159898820405]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8972 - accuracy: 0.6933\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8657 - accuracy: 0.7867\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8357 - accuracy: 0.8533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8075 - accuracy: 0.9333\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7804 - accuracy: 0.9200\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7529 - accuracy: 0.8800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7271 - accuracy: 0.8800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7025 - accuracy: 0.8267\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6778 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6535 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6297 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6065 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5845 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5632 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5434 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5283 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.5402 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.5161 - accuracy: 0.9333\n",
            "[50, 20]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.9333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.2180 - accuracy: 0.2133\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1576 - accuracy: 0.2133\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0997 - accuracy: 0.2800\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0472 - accuracy: 0.3867\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9960 - accuracy: 0.4533\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9494 - accuracy: 0.5867\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9053 - accuracy: 0.6933\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8627 - accuracy: 0.7467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8251 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7881 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7548 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7220 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6920 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6643 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6387 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6199 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6241 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6276 - accuracy: 0.9000\n",
            "[80]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0743 - accuracy: 0.2533\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0433 - accuracy: 0.5067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0143 - accuracy: 0.6933\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9862 - accuracy: 0.7733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9589 - accuracy: 0.8267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9290 - accuracy: 0.8400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9002 - accuracy: 0.8400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8685 - accuracy: 0.8533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8357 - accuracy: 0.8667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8028 - accuracy: 0.8667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7659 - accuracy: 0.8667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7294 - accuracy: 0.8667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6915 - accuracy: 0.8800\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6541 - accuracy: 0.8667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6181 - accuracy: 0.8667\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5898 - accuracy: 0.8667\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6039 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.5864 - accuracy: 0.9333\n",
            "[50, 20, 20]\n",
            "Training accuracy: 0.8666666746139526\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.9333333373069763\n",
            "Redes\n",
            "[[50], [50, 10], [60], [50, 10, 10], [50, 10], [50, 10, 10], [70], [50, 20, 10], [60], [50, 20], [70], [50, 20, 10], [50, 20], [50, 20, 10], [80], [50, 20, 20]]\n",
            "Maximos\n",
            "[0.755824162728133, 0.7779188956109346, 0.7778874905771835, 0.8000690986122251, 0.7779358107214125, 0.8000975818007622, 0.800161055833969, 0.7556618554288299, 0.7779674300198282, 0.8000911135302164, 0.8001497026623866, 0.7556807006549354, 0.8000610498434967, 0.7556993472868073, 0.8445271774803835, 0.8000224831365568]\n",
            "___________________________\n",
            "T= 4\n",
            "Depredacion\n",
            "[0.8445271774803835, 0.800161055833969, 0.8001497026623866, 0.8000975818007622, 0.8000911135302164, 0.8000690986122251, 0.8000610498434967, 0.8000224831365568, 0.7779674300198282, 0.7779358107214125, 0.7779188956109346, 0.7778874905771835, 0.755824162728133, 0.7556993472868073, 0.7556807006549354, 0.7556618554288299]\n",
            "9\n",
            "theta= 0.7779358107214125\n",
            "0.755824162728133\n",
            "0.755824162728133\n",
            "0.7779188956109346\n",
            "0.7779188956109346\n",
            "0.7778874905771835\n",
            "0.7778874905771835\n",
            "0.7779358107214125\n",
            "0.7779358107214125\n",
            "0.7556618554288299\n",
            "0.7556618554288299\n",
            "0.7556807006549354\n",
            "0.7556807006549354\n",
            "0.7556993472868073\n",
            "0.7556993472868073\n",
            "__\n",
            "[[50, 10, 10], [50, 10, 10], [70], [60], [50, 20], [70], [50, 20], [80], [50, 20, 20]]\n",
            "[0.8000690986122251, 0.8000975818007622, 0.800161055833969, 0.7779674300198282, 0.8000911135302164, 0.8001497026623866, 0.8000610498434967, 0.8445271774803835, 0.8000224831365568]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.3039 - accuracy: 0.1600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1838 - accuracy: 0.2667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1027 - accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0394 - accuracy: 0.5467\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0069 - accuracy: 0.5600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9863 - accuracy: 0.5600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9618 - accuracy: 0.5600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9388 - accuracy: 0.5733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9157 - accuracy: 0.5867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8904 - accuracy: 0.5867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8677 - accuracy: 0.5733\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8490 - accuracy: 0.5867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8306 - accuracy: 0.5867\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8163 - accuracy: 0.5733\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8015 - accuracy: 0.5867\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7926 - accuracy: 0.5867\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8087 - accuracy: 0.5333\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.7044 - accuracy: 0.6667\n",
            "[50, 20, 10]\n",
            "Training accuracy: 0.5866666436195374\n",
            "Validation accuracy: 0.5333333611488342\n",
            "Test accuracy: 0.6666666865348816\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.0920 - accuracy: 0.3867\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0740 - accuracy: 0.5200\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0598 - accuracy: 0.5733\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0470 - accuracy: 0.6133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0336 - accuracy: 0.6533\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0206 - accuracy: 0.6800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0057 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9887 - accuracy: 0.7600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9705 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9503 - accuracy: 0.8267\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9289 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9065 - accuracy: 0.8400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8844 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8602 - accuracy: 0.8400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8341 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8139 - accuracy: 0.8533\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.8082 - accuracy: 0.8889\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.8144 - accuracy: 0.9667\n",
            "[50, 10, 10, 10]\n",
            "Training accuracy: 0.8533333539962769\n",
            "Validation accuracy: 0.8888888955116272\n",
            "Test accuracy: 0.9666666388511658\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0970 - accuracy: 0.2800\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0551 - accuracy: 0.4000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0163 - accuracy: 0.6000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9864 - accuracy: 0.6267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9563 - accuracy: 0.6533\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9223 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.8890 - accuracy: 0.6800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8583 - accuracy: 0.7200\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8269 - accuracy: 0.7333\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7986 - accuracy: 0.7333\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.7708 - accuracy: 0.7333\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7467 - accuracy: 0.7600\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7235 - accuracy: 0.7467\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7018 - accuracy: 0.7733\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6819 - accuracy: 0.7733\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6668 - accuracy: 0.7733\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 0.6692 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.6201 - accuracy: 0.7667\n",
            "[60, 10]\n",
            "Training accuracy: 0.7733333110809326\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.7666666507720947\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1816 - accuracy: 0.1733\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1130 - accuracy: 0.3067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0494 - accuracy: 0.5200\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9960 - accuracy: 0.7600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9450 - accuracy: 0.8000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8981 - accuracy: 0.8000\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8547 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8165 - accuracy: 0.8000\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7803 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7450 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7134 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6823 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6538 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6260 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6007 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5817 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.5728 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.5393 - accuracy: 0.9000\n",
            "[50, 30]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0896 - accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0418 - accuracy: 0.6667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9990 - accuracy: 0.8533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9592 - accuracy: 0.8400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9196 - accuracy: 0.8800\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8807 - accuracy: 0.9333\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8428 - accuracy: 0.8933\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8033 - accuracy: 0.8800\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7655 - accuracy: 0.8800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7303 - accuracy: 0.8667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6939 - accuracy: 0.8667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6608 - accuracy: 0.8667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6274 - accuracy: 0.8667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5972 - accuracy: 0.8667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5672 - accuracy: 0.8533\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5466 - accuracy: 0.8533\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.5518 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.5371 - accuracy: 0.9000\n",
            "[60, 20]\n",
            "Training accuracy: 0.8533333539962769\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1334 - accuracy: 0.2267\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0745 - accuracy: 0.4933\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0214 - accuracy: 0.5867\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9722 - accuracy: 0.6667\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9251 - accuracy: 0.6933\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8797 - accuracy: 0.7333\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8378 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7978 - accuracy: 0.7733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7621 - accuracy: 0.7733\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7274 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6948 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6663 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6396 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6146 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5922 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5761 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.5576 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.5328 - accuracy: 0.9000\n",
            "[90]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.1070 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0660 - accuracy: 0.3067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0310 - accuracy: 0.4267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0035 - accuracy: 0.4933\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9774 - accuracy: 0.6000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9514 - accuracy: 0.6267\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9263 - accuracy: 0.6400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8988 - accuracy: 0.6533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8727 - accuracy: 0.6533\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8489 - accuracy: 0.6533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8238 - accuracy: 0.6533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8022 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7809 - accuracy: 0.6667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7615 - accuracy: 0.6667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7420 - accuracy: 0.6667\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7289 - accuracy: 0.6667\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.7288 - accuracy: 0.6222\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.6548 - accuracy: 0.7000\n",
            "[50, 20, 20, 10]\n",
            "Training accuracy: 0.6666666865348816\n",
            "Validation accuracy: 0.6222222447395325\n",
            "Test accuracy: 0.699999988079071\n",
            "Redes\n",
            "[[50, 10, 10], [50, 10, 10], [70], [60], [50, 20], [70], [50, 20], [80], [50, 20, 20], [50, 20, 10], [50, 10, 10, 10], [80], [60, 10], [50, 30], [80], [60, 20], [90], [50, 20, 20, 10]]\n",
            "Maximos\n",
            "[0.800133871420158, 0.8001180482892243, 0.8002165564046012, 0.7780274284781887, 0.8001815886487219, 0.8002444775744155, 0.8001182311034827, 0.8445418288228487, 0.8000754593348205, 0.533397300545982, 0.8889056207441101, 0.8446045001638136, 0.822300004107406, 0.8222883313095483, 0.8446232515000675, 0.8000459910772795, 0.8222952794456695, 0.6222637962698281]\n",
            "___________________________\n",
            "T= 5\n",
            "__\n",
            "[[50, 10, 10], [50, 10, 10], [70], [60], [50, 20], [70], [50, 20], [80], [50, 20, 20], [50, 20, 10], [50, 10, 10, 10], [80], [60, 10], [50, 30], [80], [60, 20], [90], [50, 20, 20, 10]]\n",
            "[0.800133871420158, 0.8001180482892243, 0.8002165564046012, 0.7780274284781887, 0.8001815886487219, 0.8002444775744155, 0.8001182311034827, 0.8445418288228487, 0.8000754593348205, 0.533397300545982, 0.8889056207441101, 0.8446045001638136, 0.822300004107406, 0.8222883313095483, 0.8446232515000675, 0.8000459910772795, 0.8222952794456695, 0.6222637962698281]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1089 - accuracy: 0.3467\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0868 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0674 - accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0488 - accuracy: 0.3600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0315 - accuracy: 0.3867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0132 - accuracy: 0.5067\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.9972 - accuracy: 0.5467\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9805 - accuracy: 0.5600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9612 - accuracy: 0.6133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9400 - accuracy: 0.6133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9177 - accuracy: 0.6400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8950 - accuracy: 0.6400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8690 - accuracy: 0.6933\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8435 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8184 - accuracy: 0.7867\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7997 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.8289 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.8334 - accuracy: 0.8667\n",
            "[50, 10, 20]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1643 - accuracy: 0.2000\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1385 - accuracy: 0.2933\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1177 - accuracy: 0.3200\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 1.1011 - accuracy: 0.4133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0835 - accuracy: 0.5067\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0662 - accuracy: 0.5467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0459 - accuracy: 0.6533\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0230 - accuracy: 0.7200\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9997 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9746 - accuracy: 0.7333\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9487 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9209 - accuracy: 0.7467\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8954 - accuracy: 0.7467\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8711 - accuracy: 0.7600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8431 - accuracy: 0.7600\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8224 - accuracy: 0.7600\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.8246 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.8185 - accuracy: 0.7667\n",
            "[80, 10]\n",
            "Training accuracy: 0.7599999904632568\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.7666666507720947\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 8ms/step - loss: 1.1178 - accuracy: 0.1067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0818 - accuracy: 0.3467\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0494 - accuracy: 0.3867\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0198 - accuracy: 0.5733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9943 - accuracy: 0.6267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9705 - accuracy: 0.7200\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9467 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9253 - accuracy: 0.8133\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9057 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8849 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8632 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.8411 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8188 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7955 - accuracy: 0.8400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7726 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7541 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.7486 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.7207 - accuracy: 0.9000\n",
            "[50, 30, 10]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.0871 - accuracy: 0.4000\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0708 - accuracy: 0.5733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0596 - accuracy: 0.6000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0491 - accuracy: 0.6133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0376 - accuracy: 0.6133\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0248 - accuracy: 0.6267\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0118 - accuracy: 0.6533\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9978 - accuracy: 0.7600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9827 - accuracy: 0.8800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9676 - accuracy: 0.9333\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9505 - accuracy: 0.8400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9316 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9112 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8905 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8695 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8520 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8528 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.8875 - accuracy: 0.9000\n",
            "[50, 10, 10, 10, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.0754 - accuracy: 0.3467\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0472 - accuracy: 0.3867\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0200 - accuracy: 0.4667\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9909 - accuracy: 0.5067\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9632 - accuracy: 0.5333\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9338 - accuracy: 0.5733\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9030 - accuracy: 0.6800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8748 - accuracy: 0.7333\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8473 - accuracy: 0.7467\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8200 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7951 - accuracy: 0.7733\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7692 - accuracy: 0.7733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7441 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7189 - accuracy: 0.7733\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6936 - accuracy: 0.7733\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6754 - accuracy: 0.7733\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6660 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.6894 - accuracy: 0.8667\n",
            "[60, 10, 10]\n",
            "Training accuracy: 0.7733333110809326\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 10ms/step - loss: 1.0779 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0115 - accuracy: 0.3333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9593 - accuracy: 0.3467\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9147 - accuracy: 0.5733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8765 - accuracy: 0.7067\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8390 - accuracy: 0.7600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8043 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7696 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7355 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7014 - accuracy: 0.7867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6704 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6400 - accuracy: 0.7867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6110 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5831 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5571 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5381 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 0.5439 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.5290 - accuracy: 0.9000\n",
            "[60, 30]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1507 - accuracy: 0.3200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0764 - accuracy: 0.3333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0054 - accuracy: 0.5733\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9510 - accuracy: 0.6933\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8937 - accuracy: 0.7600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8449 - accuracy: 0.8000\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7994 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7607 - accuracy: 0.8000\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7250 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6933 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6649 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6384 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6153 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5941 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5751 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5614 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.5646 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.5289 - accuracy: 0.8333\n",
            "[100]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8333333134651184\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0874 - accuracy: 0.5333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0699 - accuracy: 0.6267\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0507 - accuracy: 0.6800\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0283 - accuracy: 0.7200\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0008 - accuracy: 0.7867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9694 - accuracy: 0.8133\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9369 - accuracy: 0.8267\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8968 - accuracy: 0.8400\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8603 - accuracy: 0.8400\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8171 - accuracy: 0.8400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7792 - accuracy: 0.8400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7427 - accuracy: 0.8533\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7053 - accuracy: 0.8667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6720 - accuracy: 0.8533\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6413 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6192 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6086 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 0.5630 - accuracy: 0.9667\n",
            "[60, 20, 20, 10]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.9666666388511658\n",
            "Redes\n",
            "[[50, 10, 10], [50, 10, 10], [70], [60], [50, 20], [70], [50, 20], [80], [50, 20, 20], [50, 20, 10], [50, 10, 10, 10], [80], [60, 10], [50, 30], [80], [60, 20], [90], [50, 20, 20, 10], [50, 20, 10], [50, 10, 20], [80], [70], [60, 20], [80], [50, 20, 10], [80, 10], [50, 20, 20, 10], [50, 30, 10], [50, 10, 10, 10, 10], [90], [60, 10, 10], [60, 30], [80, 10], [60, 30], [100], [60, 20, 20, 10]]\n",
            "Maximos\n",
            "[0.8001439255471464, 0.8001654743062491, 0.8002283421907697, 0.778115589010775, 0.8002555306029137, 0.8003072794565281, 0.8001932577085298, 0.8445804009499707, 0.8001463831430944, 0.5334326375278815, 0.8889443028538336, 0.8446732903051365, 0.8223167405282262, 0.8223527107884212, 0.8447045438994178, 0.8001255743047501, 0.8223735317348758, 0.6222766266967887, 0.5334680778876529, 0.7777960047353922, 0.8447057524665355, 0.8003124085604518, 0.8000622330429867, 0.8447204645714851, 0.5333986948514441, 0.8000273379479287, 0.6223167410265339, 0.844462969658279, 0.8222922979733308, 0.8223243068029005, 0.8000312439420564, 0.8222246253713611, 0.800036753220428, 0.8222455284840162, 0.8000493030886362, 0.8222278607702658]\n",
            "___________________________\n",
            "T= 6\n",
            "__\n",
            "[[50, 10, 10], [50, 10, 10], [70], [60], [50, 20], [70], [50, 20], [80], [50, 20, 20], [50, 20, 10], [50, 10, 10, 10], [80], [60, 10], [50, 30], [80], [60, 20], [90], [50, 20, 20, 10], [50, 20, 10], [50, 10, 20], [80], [70], [60, 20], [80], [50, 20, 10], [80, 10], [50, 20, 20, 10], [50, 30, 10], [50, 10, 10, 10, 10], [90], [60, 10, 10], [60, 30], [80, 10], [60, 30], [100], [60, 20, 20, 10]]\n",
            "[0.8001439255471464, 0.8001654743062491, 0.8002283421907697, 0.778115589010775, 0.8002555306029137, 0.8003072794565281, 0.8001932577085298, 0.8445804009499707, 0.8001463831430944, 0.5334326375278815, 0.8889443028538336, 0.8446732903051365, 0.8223167405282262, 0.8223527107884212, 0.8447045438994178, 0.8001255743047501, 0.8223735317348758, 0.6222766266967887, 0.5334680778876529, 0.7777960047353922, 0.8447057524665355, 0.8003124085604518, 0.8000622330429867, 0.8447204645714851, 0.5333986948514441, 0.8000273379479287, 0.6223167410265339, 0.844462969658279, 0.8222922979733308, 0.8223243068029005, 0.8000312439420564, 0.8222246253713611, 0.800036753220428, 0.8222455284840162, 0.8000493030886362, 0.8222278607702658]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.2924 - accuracy: 0.0400\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.2296 - accuracy: 0.1067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1747 - accuracy: 0.2400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1291 - accuracy: 0.2933\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0964 - accuracy: 0.3867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0706 - accuracy: 0.4533\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0466 - accuracy: 0.5867\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0265 - accuracy: 0.6800\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0084 - accuracy: 0.7200\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9910 - accuracy: 0.7867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9737 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9551 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9383 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9183 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9013 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8858 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8590 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.8901 - accuracy: 0.9000\n",
            "[60, 20, 20]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0183 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9730 - accuracy: 0.3467\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9350 - accuracy: 0.4000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9046 - accuracy: 0.4400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8814 - accuracy: 0.5333\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8635 - accuracy: 0.6133\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8461 - accuracy: 0.6800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8300 - accuracy: 0.7467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8166 - accuracy: 0.7733\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8021 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7904 - accuracy: 0.7600\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7795 - accuracy: 0.7200\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7690 - accuracy: 0.7333\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7587 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7493 - accuracy: 0.8533\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7419 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.7418 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.6554 - accuracy: 0.9000\n",
            "[70, 10]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 4ms/step - loss: 1.0475 - accuracy: 0.6400\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 1.0156 - accuracy: 0.6400\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9833 - accuracy: 0.6400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9521 - accuracy: 0.6400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9229 - accuracy: 0.6400\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8916 - accuracy: 0.6400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8597 - accuracy: 0.6400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8258 - accuracy: 0.6933\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7916 - accuracy: 0.7200\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7570 - accuracy: 0.7467\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7210 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6849 - accuracy: 0.7600\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.6504 - accuracy: 0.7600\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6159 - accuracy: 0.7600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5822 - accuracy: 0.7600\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5579 - accuracy: 0.7733\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.5322 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.5282 - accuracy: 0.8667\n",
            "[50, 20, 30, 10]\n",
            "Training accuracy: 0.7733333110809326\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0991 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0822 - accuracy: 0.3333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0664 - accuracy: 0.3867\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0506 - accuracy: 0.4267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0359 - accuracy: 0.4667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0197 - accuracy: 0.5200\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0039 - accuracy: 0.5600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9868 - accuracy: 0.5867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9687 - accuracy: 0.6400\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9501 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9325 - accuracy: 0.7067\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9153 - accuracy: 0.7200\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8960 - accuracy: 0.7467\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8763 - accuracy: 0.7733\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8557 - accuracy: 0.7867\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8392 - accuracy: 0.7867\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7990 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.8630 - accuracy: 0.8667\n",
            "[50, 10, 30]\n",
            "Training accuracy: 0.7866666913032532\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 9ms/step - loss: 1.1215 - accuracy: 0.4800\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0786 - accuracy: 0.4933\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0406 - accuracy: 0.5067\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0085 - accuracy: 0.5067\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9798 - accuracy: 0.5867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9547 - accuracy: 0.7600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9342 - accuracy: 0.8133\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9133 - accuracy: 0.8400\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8935 - accuracy: 0.8267\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8751 - accuracy: 0.8400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8563 - accuracy: 0.8400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8368 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8169 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7967 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7753 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7595 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7621 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.7931 - accuracy: 0.9000\n",
            "[60, 20, 10]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0643 - accuracy: 0.3733\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0316 - accuracy: 0.6133\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0047 - accuracy: 0.7067\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9803 - accuracy: 0.7333\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9543 - accuracy: 0.7200\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9297 - accuracy: 0.7467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9032 - accuracy: 0.7467\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8764 - accuracy: 0.7467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8503 - accuracy: 0.7600\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8245 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7976 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.7714 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7478 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7210 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.6966 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6773 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.7098 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6317 - accuracy: 0.8333\n",
            "[80, 10, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8333333134651184\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.0601 - accuracy: 0.4000\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9937 - accuracy: 0.5467\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9405 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8899 - accuracy: 0.7467\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8497 - accuracy: 0.7733\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8107 - accuracy: 0.7600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7764 - accuracy: 0.7733\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7453 - accuracy: 0.7600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7144 - accuracy: 0.7600\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6865 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6609 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6373 - accuracy: 0.7733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6144 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5929 - accuracy: 0.7733\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5739 - accuracy: 0.7733\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.5602 - accuracy: 0.7733\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.5429 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.5181 - accuracy: 0.8333\n",
            "[50, 40, 10]\n",
            "Training accuracy: 0.7733333110809326\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8333333134651184\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 10ms/step - loss: 1.1061 - accuracy: 0.3467\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0929 - accuracy: 0.3333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0816 - accuracy: 0.3467\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0716 - accuracy: 0.3733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0636 - accuracy: 0.4133\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0563 - accuracy: 0.4267\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0462 - accuracy: 0.4800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0351 - accuracy: 0.5600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0233 - accuracy: 0.6000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0108 - accuracy: 0.6000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9971 - accuracy: 0.5867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9827 - accuracy: 0.5733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9671 - accuracy: 0.5733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.9484 - accuracy: 0.5600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.9288 - accuracy: 0.6933\n",
            "3/3 [==============================] - 1s 9ms/step - loss: 0.9130 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.9578 - accuracy: 0.6889\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.9267 - accuracy: 0.7000\n",
            "[50, 10, 10, 10, 20]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.6888889074325562\n",
            "Test accuracy: 0.699999988079071\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.0066 - accuracy: 0.4933\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9529 - accuracy: 0.6667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9049 - accuracy: 0.7600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8624 - accuracy: 0.8000\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8219 - accuracy: 0.8133\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7824 - accuracy: 0.8133\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7407 - accuracy: 0.8133\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7003 - accuracy: 0.8133\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6608 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6236 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5858 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5526 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5212 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.4921 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.4650 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.4469 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.4400 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.4212 - accuracy: 0.9000\n",
            "[60, 30, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.1185 - accuracy: 0.3200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0725 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0292 - accuracy: 0.5333\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9913 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9552 - accuracy: 0.6533\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9220 - accuracy: 0.7200\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8891 - accuracy: 0.7867\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8574 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8252 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7931 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7611 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7321 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7016 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.6739 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.6459 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6257 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 0.6349 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.6162 - accuracy: 0.8667\n",
            "[80, 20]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.1034 - accuracy: 0.2933\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0325 - accuracy: 0.6267\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9737 - accuracy: 0.6400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9210 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8732 - accuracy: 0.6800\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8283 - accuracy: 0.7467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7888 - accuracy: 0.7467\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7503 - accuracy: 0.7467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7145 - accuracy: 0.7467\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6815 - accuracy: 0.7600\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6485 - accuracy: 0.7600\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6196 - accuracy: 0.7733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5937 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5700 - accuracy: 0.7867\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5482 - accuracy: 0.7867\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.5327 - accuracy: 0.7867\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.4999 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.4901 - accuracy: 0.9000\n",
            "[60, 40]\n",
            "Training accuracy: 0.7866666913032532\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.1558 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0930 - accuracy: 0.5600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0352 - accuracy: 0.6533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9804 - accuracy: 0.8000\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9308 - accuracy: 0.8533\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8869 - accuracy: 0.8800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8432 - accuracy: 0.8267\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8036 - accuracy: 0.8267\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7677 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7350 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7023 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6739 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.6482 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.6232 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5998 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5836 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.5661 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.5590 - accuracy: 0.8667\n",
            "[110]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1110 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0605 - accuracy: 0.3200\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0207 - accuracy: 0.3733\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9828 - accuracy: 0.6000\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9458 - accuracy: 0.6267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9095 - accuracy: 0.6400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8735 - accuracy: 0.6400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8393 - accuracy: 0.6533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8064 - accuracy: 0.6800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7768 - accuracy: 0.6800\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7491 - accuracy: 0.7067\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7259 - accuracy: 0.7200\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7049 - accuracy: 0.7200\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6868 - accuracy: 0.7333\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6700 - accuracy: 0.7467\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6577 - accuracy: 0.7600\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 0.6375 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 18ms/step - loss: 0.5905 - accuracy: 0.9000\n",
            "[60, 20, 30, 10]\n",
            "Training accuracy: 0.7599999904632568\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8999999761581421\n",
            "Redes\n",
            "[[50, 10, 10], [50, 10, 10], [70], [60], [50, 20], [70], [50, 20], [80], [50, 20, 20], [50, 20, 10], [50, 10, 10, 10], [80], [60, 10], [50, 30], [80], [60, 20], [90], [50, 20, 20, 10], [50, 20, 10], [50, 10, 20], [80], [70], [60, 20], [80], [50, 20, 10], [80, 10], [50, 20, 20, 10], [50, 30, 10], [50, 10, 10, 10, 10], [90], [60, 10, 10], [60, 30], [80, 10], [60, 30], [100], [60, 20, 20, 10], [50, 10, 10, 10], [50, 10, 10, 10], [80], [70], [60, 20], [80], [60, 20], [90], [60, 20, 20], [50, 30, 10], [50, 10, 10, 10, 10], [90], [70, 10], [60, 30], [90], [60, 30], [100], [50, 20, 30, 10], [50, 30, 10], [50, 10, 30], [80, 10], [80], [60, 30], [90], [60, 20, 10], [80, 10, 10], [50, 20, 30, 10], [50, 40, 10], [50, 10, 10, 10, 20], [100], [60, 20, 10], [60, 30, 10], [80, 20], [60, 40], [110], [60, 20, 30, 10]]\n",
            "Maximos\n",
            "[0.8001971158624255, 0.8002426060668152, 0.8002719539973261, 0.778149755591453, 0.8002627456124899, 0.8003775323179017, 0.8002065885295114, 0.8445917857795238, 0.8002034111403044, 0.5335244736022595, 0.889032238417571, 0.8447005250506704, 0.822326277042968, 0.8224084177492589, 0.8447812145958589, 0.8001416555043112, 0.8224227759448965, 0.6223484551403202, 0.5335648034101781, 0.7778058106636078, 0.844739378985225, 0.8003655501925664, 0.8001496458750493, 0.8448047528948658, 0.5334033490098747, 0.8000882976241416, 0.6223637782705937, 0.8444749639064152, 0.8223898243395887, 0.8223472112521868, 0.8000404205888211, 0.8222273665506534, 0.8001202385984074, 0.8223049029748183, 0.800090318176419, 0.8222776084230167, 0.8889939047231984, 0.8890257340300329, 0.8447364779927238, 0.8003152789481348, 0.8000987668423967, 0.8447484876959496, 0.8000692210981096, 0.8223521383399157, 0.8222879753383228, 0.8445526380293634, 0.8223205152888011, 0.8223621717150583, 0.7777937831624211, 0.8222595049345668, 0.8223716590545819, 0.8222560567461796, 0.8001128555963073, 0.8444586550932349, 0.8445057552024838, 0.8222613648801712, 0.8000646686392562, 0.8447780587454149, 0.8222806435550204, 0.8224009301605875, 0.8222551373673265, 0.8222681104691569, 0.8444459713852992, 0.8000565095739424, 0.6889568732112261, 0.800056433808271, 0.8222351465436651, 0.8222411494286344, 0.8000926527529476, 0.8000633924095226, 0.8222768305198367, 0.800045479482669]\n",
            "___________________________\n",
            "T= 7\n",
            "Depredacion\n",
            "[0.889032238417571, 0.8890257340300329, 0.8889939047231984, 0.8448047528948658, 0.8447812145958589, 0.8447780587454149, 0.8447484876959496, 0.844739378985225, 0.8447364779927238, 0.8447005250506704, 0.8445917857795238, 0.8445526380293634, 0.8445057552024838, 0.8444749639064152, 0.8444586550932349, 0.8444459713852992, 0.8224227759448965, 0.8224084177492589, 0.8224009301605875, 0.8223898243395887, 0.8223716590545819, 0.8223621717150583, 0.8223521383399157, 0.8223472112521868, 0.822326277042968, 0.8223205152888011, 0.8223049029748183, 0.8222879753383228, 0.8222806435550204, 0.8222776084230167, 0.8222768305198367, 0.8222681104691569, 0.8222613648801712, 0.8222595049345668, 0.8222560567461796, 0.8222551373673265, 0.8222411494286344, 0.8222351465436651, 0.8222273665506534, 0.8003775323179017, 0.8003655501925664, 0.8003152789481348, 0.8002719539973261, 0.8002627456124899, 0.8002426060668152, 0.8002065885295114, 0.8002034111403044, 0.8001971158624255, 0.8001496458750493, 0.8001416555043112, 0.8001202385984074, 0.8001128555963073, 0.8000987668423967, 0.8000926527529476, 0.800090318176419, 0.8000882976241416, 0.8000692210981096, 0.8000646686392562, 0.8000633924095226, 0.8000565095739424, 0.800056433808271, 0.800045479482669, 0.8000404205888211, 0.778149755591453, 0.7778058106636078, 0.7777937831624211, 0.6889568732112261, 0.6223637782705937, 0.6223484551403202, 0.5335648034101781, 0.5335244736022595, 0.5334033490098747]\n",
            "9\n",
            "theta= 0.8447005250506704\n",
            "0.8001971158624255\n",
            "0.8001971158624255\n",
            "0.8002426060668152\n",
            "0.8002426060668152\n",
            "0.8002719539973261\n",
            "0.8002719539973261\n",
            "0.778149755591453\n",
            "0.778149755591453\n",
            "0.8002627456124899\n",
            "0.8002627456124899\n",
            "0.8003775323179017\n",
            "0.8003775323179017\n",
            "0.8002065885295114\n",
            "0.8002065885295114\n",
            "0.8445917857795238\n",
            "0.8445917857795238\n",
            "0.8002034111403044\n",
            "0.8002034111403044\n",
            "0.5335244736022595\n",
            "0.5335244736022595\n",
            "0.8447005250506704\n",
            "0.8447005250506704\n",
            "0.822326277042968\n",
            "0.822326277042968\n",
            "0.8224084177492589\n",
            "0.8224084177492589\n",
            "0.8001416555043112\n",
            "0.8001416555043112\n",
            "0.8224227759448965\n",
            "0.8224227759448965\n",
            "0.6223484551403202\n",
            "0.6223484551403202\n",
            "0.5335648034101781\n",
            "0.5335648034101781\n",
            "0.7778058106636078\n",
            "0.7778058106636078\n",
            "0.8003655501925664\n",
            "0.8003655501925664\n",
            "0.8001496458750493\n",
            "0.8001496458750493\n",
            "0.5334033490098747\n",
            "0.5334033490098747\n",
            "0.8000882976241416\n",
            "0.8000882976241416\n",
            "0.6223637782705937\n",
            "0.6223637782705937\n",
            "0.8444749639064152\n",
            "0.8444749639064152\n",
            "0.8223898243395887\n",
            "0.8223898243395887\n",
            "0.8223472112521868\n",
            "0.8223472112521868\n",
            "0.8000404205888211\n",
            "0.8000404205888211\n",
            "0.8222273665506534\n",
            "0.8222273665506534\n",
            "0.8001202385984074\n",
            "0.8001202385984074\n",
            "0.8223049029748183\n",
            "0.8223049029748183\n",
            "0.800090318176419\n",
            "0.800090318176419\n",
            "0.8222776084230167\n",
            "0.8222776084230167\n",
            "0.8003152789481348\n",
            "0.8003152789481348\n",
            "0.8000987668423967\n",
            "0.8000987668423967\n",
            "0.8000692210981096\n",
            "0.8000692210981096\n",
            "0.8223521383399157\n",
            "0.8223521383399157\n",
            "0.8222879753383228\n",
            "0.8222879753383228\n",
            "0.8445526380293634\n",
            "0.8445526380293634\n",
            "0.8223205152888011\n",
            "0.8223205152888011\n",
            "0.8223621717150583\n",
            "0.8223621717150583\n",
            "0.7777937831624211\n",
            "0.7777937831624211\n",
            "0.8222595049345668\n",
            "0.8222595049345668\n",
            "0.8223716590545819\n",
            "0.8223716590545819\n",
            "0.8222560567461796\n",
            "0.8222560567461796\n",
            "0.8001128555963073\n",
            "0.8001128555963073\n",
            "0.8444586550932349\n",
            "0.8444586550932349\n",
            "0.8445057552024838\n",
            "0.8445057552024838\n",
            "0.8222613648801712\n",
            "0.8222613648801712\n",
            "0.8000646686392562\n",
            "0.8000646686392562\n",
            "0.8222806435550204\n",
            "0.8222806435550204\n",
            "0.8224009301605875\n",
            "0.8224009301605875\n",
            "0.8222551373673265\n",
            "0.8222551373673265\n",
            "0.8222681104691569\n",
            "0.8222681104691569\n",
            "0.8444459713852992\n",
            "0.8444459713852992\n",
            "0.8000565095739424\n",
            "0.8000565095739424\n",
            "0.6889568732112261\n",
            "0.6889568732112261\n",
            "0.800056433808271\n",
            "0.800056433808271\n",
            "0.8222351465436651\n",
            "0.8222351465436651\n",
            "0.8222411494286344\n",
            "0.8222411494286344\n",
            "0.8000926527529476\n",
            "0.8000926527529476\n",
            "0.8000633924095226\n",
            "0.8000633924095226\n",
            "0.8222768305198367\n",
            "0.8222768305198367\n",
            "0.800045479482669\n",
            "0.800045479482669\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80]]\n",
            "[0.889032238417571, 0.8447812145958589, 0.844739378985225, 0.8448047528948658, 0.8889939047231984, 0.8890257340300329, 0.8447364779927238, 0.8447484876959496, 0.8447780587454149]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 9ms/step - loss: 1.3485 - accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.2597 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1865 - accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1286 - accuracy: 0.3600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0833 - accuracy: 0.3600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0472 - accuracy: 0.3600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0250 - accuracy: 0.3600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0073 - accuracy: 0.3600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9893 - accuracy: 0.3600\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9711 - accuracy: 0.3600\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9523 - accuracy: 0.3600\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9335 - accuracy: 0.3600\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9151 - accuracy: 0.3600\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8977 - accuracy: 0.3600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8794 - accuracy: 0.3600\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8673 - accuracy: 0.3600\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.9232 - accuracy: 0.3111\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.9156 - accuracy: 0.3333\n",
            "[50, 20, 10, 10]\n",
            "Training accuracy: 0.36000001430511475\n",
            "Validation accuracy: 0.31111112236976624\n",
            "Test accuracy: 0.3333333432674408\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 1.0744 - accuracy: 0.5600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0174 - accuracy: 0.6400\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.9647 - accuracy: 0.6533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9138 - accuracy: 0.6400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8711 - accuracy: 0.6400\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8277 - accuracy: 0.6533\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7899 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7546 - accuracy: 0.8133\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7224 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6920 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6642 - accuracy: 0.8533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6388 - accuracy: 0.8667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6149 - accuracy: 0.8533\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5922 - accuracy: 0.8400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5722 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5580 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.5646 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.5246 - accuracy: 0.9333\n",
            "[90]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.9333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1256 - accuracy: 0.0800\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0959 - accuracy: 0.1867\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0695 - accuracy: 0.4000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0392 - accuracy: 0.4800\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0113 - accuracy: 0.6000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9828 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9564 - accuracy: 0.7067\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9299 - accuracy: 0.7067\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9048 - accuracy: 0.7200\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8801 - accuracy: 0.7467\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8547 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8318 - accuracy: 0.7600\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8082 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7856 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7642 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7485 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7620 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.6938 - accuracy: 0.9000\n",
            "[80, 10]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.1005 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0476 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0056 - accuracy: 0.4400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9704 - accuracy: 0.4267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9371 - accuracy: 0.4667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9104 - accuracy: 0.4800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8855 - accuracy: 0.5200\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8625 - accuracy: 0.6933\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8397 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8173 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7949 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7738 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7521 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7310 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.7094 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6930 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6978 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.7327 - accuracy: 0.9000\n",
            "[50, 10, 10, 20]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1018 - accuracy: 0.4533\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0896 - accuracy: 0.5467\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0795 - accuracy: 0.5867\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0698 - accuracy: 0.6400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0593 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0485 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0355 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0208 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0031 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9825 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9589 - accuracy: 0.6667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9324 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.9031 - accuracy: 0.6800\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8734 - accuracy: 0.6933\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8388 - accuracy: 0.6933\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8121 - accuracy: 0.6800\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8420 - accuracy: 0.6222\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.7747 - accuracy: 0.7667\n",
            "[50, 10, 10, 10, 10]\n",
            "Training accuracy: 0.6800000071525574\n",
            "Validation accuracy: 0.6222222447395325\n",
            "Test accuracy: 0.7666666507720947\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [50, 10, 10, 10, 10], [90], [90], [90]]\n",
            "Maximos\n",
            "[0.8890695287139907, 0.844843165362579, 0.8448165050978996, 0.8448472935586634, 0.8890929818112872, 0.8890832492061675, 0.8447931035607092, 0.844827437448049, 0.8447951184289485, 0.3111596622152243, 0.8222926431769069, 0.8445148740416624, 0.8223140330925579, 0.822245550360621, 0.622295898010106, 0.8222695155830139, 0.8222483473388579, 0.8222804882454497]\n",
            "___________________________\n",
            "T= 8\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [50, 10, 10, 10, 10], [90], [90], [90]]\n",
            "[0.8890695287139907, 0.844843165362579, 0.8448165050978996, 0.8448472935586634, 0.8890929818112872, 0.8890832492061675, 0.8447931035607092, 0.844827437448049, 0.8447951184289485, 0.3111596622152243, 0.8222926431769069, 0.8445148740416624, 0.8223140330925579, 0.822245550360621, 0.622295898010106, 0.8222695155830139, 0.8222483473388579, 0.8222804882454497]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.0675 - accuracy: 0.4933\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0546 - accuracy: 0.5867\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0419 - accuracy: 0.6133\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0285 - accuracy: 0.6667\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0132 - accuracy: 0.7867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9947 - accuracy: 0.7867\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9762 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9550 - accuracy: 0.8000\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9320 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9081 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8834 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8575 - accuracy: 0.7867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8342 - accuracy: 0.7867\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8097 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7848 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7651 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.7630 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.6944 - accuracy: 0.9000\n",
            "[60, 10, 10, 10]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0602 - accuracy: 0.5333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0112 - accuracy: 0.7200\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9721 - accuracy: 0.8267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9338 - accuracy: 0.8000\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8918 - accuracy: 0.7867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8510 - accuracy: 0.8000\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.8108 - accuracy: 0.8267\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7727 - accuracy: 0.8400\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7348 - accuracy: 0.8267\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6986 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6651 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6332 - accuracy: 0.8400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6035 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5763 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5506 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.5320 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.5311 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.4869 - accuracy: 0.9333\n",
            "[60, 20, 10, 10]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.9333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1589 - accuracy: 0.1600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0962 - accuracy: 0.3067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0376 - accuracy: 0.4133\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9833 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9318 - accuracy: 0.7067\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8835 - accuracy: 0.7467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8371 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7962 - accuracy: 0.7733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7564 - accuracy: 0.7733\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7215 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6898 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6593 - accuracy: 0.7867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6325 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6072 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5840 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5682 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.5701 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.5460 - accuracy: 0.9000\n",
            "[100]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.2566 - accuracy: 0.1733\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1829 - accuracy: 0.2800\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1192 - accuracy: 0.4000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0606 - accuracy: 0.3600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0070 - accuracy: 0.5200\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9581 - accuracy: 0.7200\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9106 - accuracy: 0.7467\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8639 - accuracy: 0.7733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8210 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7805 - accuracy: 0.7600\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7446 - accuracy: 0.7733\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7082 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6742 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6424 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6126 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5907 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.5791 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.5767 - accuracy: 0.9333\n",
            "[80, 20]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.9333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.1150 - accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0835 - accuracy: 0.4000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0545 - accuracy: 0.4800\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0305 - accuracy: 0.5333\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0092 - accuracy: 0.6267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9861 - accuracy: 0.7467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9622 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9364 - accuracy: 0.8000\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9085 - accuracy: 0.8400\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8770 - accuracy: 0.8800\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8426 - accuracy: 0.8933\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8079 - accuracy: 0.9067\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7698 - accuracy: 0.9067\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7308 - accuracy: 0.9067\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6895 - accuracy: 0.9067\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6578 - accuracy: 0.9200\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7333 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.6995 - accuracy: 0.9000\n",
            "[50, 20, 10, 20]\n",
            "Training accuracy: 0.9200000166893005\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 10ms/step - loss: 1.0681 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0553 - accuracy: 0.4667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0427 - accuracy: 0.5867\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0298 - accuracy: 0.6267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0166 - accuracy: 0.6400\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0003 - accuracy: 0.6400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9854 - accuracy: 0.6400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9683 - accuracy: 0.6533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9521 - accuracy: 0.6533\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9324 - accuracy: 0.6533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9127 - accuracy: 0.6533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8938 - accuracy: 0.6533\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8728 - accuracy: 0.6533\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8517 - accuracy: 0.6667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8307 - accuracy: 0.6667\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8153 - accuracy: 0.6667\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.8223 - accuracy: 0.6000\n",
            "1/1 [==============================] - 0s 37ms/step - loss: 0.7471 - accuracy: 0.7000\n",
            "[50, 10, 10, 20, 10]\n",
            "Training accuracy: 0.6666666865348816\n",
            "Validation accuracy: 0.6000000238418579\n",
            "Test accuracy: 0.699999988079071\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.1200 - accuracy: 0.3200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0765 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0401 - accuracy: 0.4133\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0133 - accuracy: 0.4400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9896 - accuracy: 0.6000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9672 - accuracy: 0.6800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9459 - accuracy: 0.7200\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9260 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9057 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8869 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8677 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8488 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8316 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8127 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7957 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7824 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8310 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.8323 - accuracy: 0.8667\n",
            "[90, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8666666746139526\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [50, 10, 10, 10, 10], [90], [90], [90], [50, 20, 10, 10], [90], [90], [90], [60, 10, 10, 10], [50, 20, 10, 10], [90], [90], [80, 10], [60, 20, 10, 10], [100], [80, 20], [100], [50, 20, 10, 20], [50, 10, 10, 20, 10], [90, 10], [100], [100]]\n",
            "Maximos\n",
            "[0.889155102090635, 0.8448722385519492, 0.8448515205840619, 0.8448974115421802, 0.8891271667369145, 0.8891629138418716, 0.8448059626401008, 0.8448450302565428, 0.8448446163273193, 0.3112052442380245, 0.822391816721145, 0.8445547215639333, 0.8223454570684017, 0.8223419043314882, 0.6223714102383011, 0.8223127859584058, 0.8223045488668138, 0.8223279759329761, 0.31118531617375145, 0.8223144161399808, 0.8223524070964083, 0.8223334776072032, 0.8222295493411633, 0.31117059084240456, 0.8223318034100896, 0.8222818826774299, 0.8445578387018098, 0.8000016343387029, 0.8000542374782941, 0.844450916025669, 0.8000224167797545, 0.844461595084324, 0.6000338548810299, 0.8222975062156538, 0.8000538224773103, 0.8000892078826871]\n",
            "___________________________\n",
            "T= 9\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [50, 10, 10, 10, 10], [90], [90], [90], [50, 20, 10, 10], [90], [90], [90], [60, 10, 10, 10], [50, 20, 10, 10], [90], [90], [80, 10], [60, 20, 10, 10], [100], [80, 20], [100], [50, 20, 10, 20], [50, 10, 10, 20, 10], [90, 10], [100], [100]]\n",
            "[0.889155102090635, 0.8448722385519492, 0.8448515205840619, 0.8448974115421802, 0.8891271667369145, 0.8891629138418716, 0.8448059626401008, 0.8448450302565428, 0.8448446163273193, 0.3112052442380245, 0.822391816721145, 0.8445547215639333, 0.8223454570684017, 0.8223419043314882, 0.6223714102383011, 0.8223127859584058, 0.8223045488668138, 0.8223279759329761, 0.31118531617375145, 0.8223144161399808, 0.8223524070964083, 0.8223334776072032, 0.8222295493411633, 0.31117059084240456, 0.8223318034100896, 0.8222818826774299, 0.8445578387018098, 0.8000016343387029, 0.8000542374782941, 0.844450916025669, 0.8000224167797545, 0.844461595084324, 0.6000338548810299, 0.8222975062156538, 0.8000538224773103, 0.8000892078826871]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0947 - accuracy: 0.3733\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0800 - accuracy: 0.5867\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0686 - accuracy: 0.6800\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0578 - accuracy: 0.7333\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0466 - accuracy: 0.7867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0341 - accuracy: 0.8000\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0211 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0065 - accuracy: 0.8000\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9904 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9725 - accuracy: 0.8533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9525 - accuracy: 0.8667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9305 - accuracy: 0.8667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9049 - accuracy: 0.8667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8781 - accuracy: 0.9067\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8477 - accuracy: 0.9200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8243 - accuracy: 0.9467\n",
            "2/2 [==============================] - 0s 15ms/step - loss: 0.8495 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 0.8384 - accuracy: 0.9333\n",
            "[50, 10, 20, 10]\n",
            "Training accuracy: 0.9466666579246521\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.9333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.1272 - accuracy: 0.3467\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0893 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0571 - accuracy: 0.5733\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0312 - accuracy: 0.6133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0078 - accuracy: 0.6400\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9863 - accuracy: 0.6800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9609 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9380 - accuracy: 0.6533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9102 - accuracy: 0.6533\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8837 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8535 - accuracy: 0.6667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8248 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.7927 - accuracy: 0.6533\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7611 - accuracy: 0.6533\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.7326 - accuracy: 0.6533\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7103 - accuracy: 0.6533\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.6799 - accuracy: 0.7556\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.6800 - accuracy: 0.7333\n",
            "[50, 20, 20, 10]\n",
            "Training accuracy: 0.653333306312561\n",
            "Validation accuracy: 0.7555555701255798\n",
            "Test accuracy: 0.7333333492279053\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 11ms/step - loss: 1.0876 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0748 - accuracy: 0.3067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0640 - accuracy: 0.4667\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0540 - accuracy: 0.6400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0422 - accuracy: 0.7467\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0296 - accuracy: 0.8400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0157 - accuracy: 0.8133\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9989 - accuracy: 0.7467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9789 - accuracy: 0.7333\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9560 - accuracy: 0.7467\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9298 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9043 - accuracy: 0.7467\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8768 - accuracy: 0.7467\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8498 - accuracy: 0.7600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8228 - accuracy: 0.7867\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8043 - accuracy: 0.7733\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.8002 - accuracy: 0.7333\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.7369 - accuracy: 0.8000\n",
            "[50, 10, 10, 10, 20]\n",
            "Training accuracy: 0.7733333110809326\n",
            "Validation accuracy: 0.7333333492279053\n",
            "Test accuracy: 0.800000011920929\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 8ms/step - loss: 1.0912 - accuracy: 0.3867\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0772 - accuracy: 0.3733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0656 - accuracy: 0.3467\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0547 - accuracy: 0.3867\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0460 - accuracy: 0.4133\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0371 - accuracy: 0.4533\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0267 - accuracy: 0.6533\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0159 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0048 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.9924 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.9797 - accuracy: 0.6667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9662 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9524 - accuracy: 0.6667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9368 - accuracy: 0.6667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9215 - accuracy: 0.6667\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9093 - accuracy: 0.6667\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.9649 - accuracy: 0.6000\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.9173 - accuracy: 0.7333\n",
            "[60, 10, 10, 10, 10]\n",
            "Training accuracy: 0.6666666865348816\n",
            "Validation accuracy: 0.6000000238418579\n",
            "Test accuracy: 0.7333333492279053\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 11ms/step - loss: 1.0911 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0636 - accuracy: 0.3067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0402 - accuracy: 0.3333\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0162 - accuracy: 0.4133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9930 - accuracy: 0.5600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9693 - accuracy: 0.6267\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9440 - accuracy: 0.7867\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9199 - accuracy: 0.8400\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8953 - accuracy: 0.8533\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8710 - accuracy: 0.9067\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8486 - accuracy: 0.9333\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8261 - accuracy: 0.9467\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.8041 - accuracy: 0.9467\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7827 - accuracy: 0.9467\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7632 - accuracy: 0.9467\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7482 - accuracy: 0.9467\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.8109 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.7008 - accuracy: 0.9000\n",
            "[50, 20, 10, 10, 10]\n",
            "Training accuracy: 0.9466666579246521\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.0786 - accuracy: 0.4133\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0537 - accuracy: 0.5200\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0294 - accuracy: 0.6400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0046 - accuracy: 0.6933\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9785 - accuracy: 0.6800\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9503 - accuracy: 0.6933\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9168 - accuracy: 0.6933\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8832 - accuracy: 0.6933\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8475 - accuracy: 0.6800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8078 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7714 - accuracy: 0.6933\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7316 - accuracy: 0.6933\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6922 - accuracy: 0.7067\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6568 - accuracy: 0.7333\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6214 - accuracy: 0.7467\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5978 - accuracy: 0.7467\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.5612 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5537 - accuracy: 0.7667\n",
            "[60, 20, 10, 10, 10]\n",
            "Training accuracy: 0.746666669845581\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.7666666507720947\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.0389 - accuracy: 0.6000\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9915 - accuracy: 0.6667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9528 - accuracy: 0.6667\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9174 - accuracy: 0.6667\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8835 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8514 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8185 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7882 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7574 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7292 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7024 - accuracy: 0.6667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6760 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6521 - accuracy: 0.6800\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.6297 - accuracy: 0.7600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.6092 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5944 - accuracy: 0.8533\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.6305 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.5612 - accuracy: 0.8000\n",
            "[100, 10]\n",
            "Training accuracy: 0.8533333539962769\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.800000011920929\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 4ms/step - loss: 1.1483 - accuracy: 0.2267\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0931 - accuracy: 0.3733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0572 - accuracy: 0.6667\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0283 - accuracy: 0.7600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0002 - accuracy: 0.8133\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9724 - accuracy: 0.8133\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9465 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9196 - accuracy: 0.8133\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8906 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8609 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8323 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8015 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7727 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7436 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7148 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6923 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6939 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.6912 - accuracy: 0.8667\n",
            "[80, 20, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 8ms/step - loss: 1.0947 - accuracy: 0.3200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0537 - accuracy: 0.3733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0177 - accuracy: 0.4000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9850 - accuracy: 0.4400\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9545 - accuracy: 0.5600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9212 - accuracy: 0.6133\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8919 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8603 - accuracy: 0.6933\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8328 - accuracy: 0.7200\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8046 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7791 - accuracy: 0.8533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7565 - accuracy: 0.8667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7356 - accuracy: 0.8400\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7158 - accuracy: 0.8933\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6973 - accuracy: 0.9200\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.6827 - accuracy: 0.9200\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.6793 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 0.6089 - accuracy: 0.9333\n",
            "[50, 20, 10, 30]\n",
            "Training accuracy: 0.9200000166893005\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.9333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0943 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0778 - accuracy: 0.4800\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0632 - accuracy: 0.5600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0473 - accuracy: 0.6000\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0319 - accuracy: 0.6000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0126 - accuracy: 0.6400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9903 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9637 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9368 - accuracy: 0.6533\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9068 - accuracy: 0.6533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8752 - accuracy: 0.6533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8406 - accuracy: 0.6400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8055 - accuracy: 0.6400\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7704 - accuracy: 0.6533\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7379 - accuracy: 0.6533\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7116 - accuracy: 0.6533\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 0.6852 - accuracy: 0.7111\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6440 - accuracy: 0.6667\n",
            "[50, 10, 10, 20, 20]\n",
            "Training accuracy: 0.653333306312561\n",
            "Validation accuracy: 0.7111111283302307\n",
            "Test accuracy: 0.6666666865348816\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0563 - accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0176 - accuracy: 0.4533\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9863 - accuracy: 0.5067\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9616 - accuracy: 0.5333\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9379 - accuracy: 0.6000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9145 - accuracy: 0.6267\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8916 - accuracy: 0.6400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8694 - accuracy: 0.6400\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8499 - accuracy: 0.6400\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8299 - accuracy: 0.6400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8123 - accuracy: 0.6400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7964 - accuracy: 0.6533\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7803 - accuracy: 0.6533\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7663 - accuracy: 0.6533\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7544 - accuracy: 0.6533\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7463 - accuracy: 0.6533\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.7528 - accuracy: 0.5778\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.6661 - accuracy: 0.7000\n",
            "[90, 10, 10]\n",
            "Training accuracy: 0.653333306312561\n",
            "Validation accuracy: 0.5777778029441833\n",
            "Test accuracy: 0.699999988079071\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0399 - accuracy: 0.5067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9821 - accuracy: 0.6667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9268 - accuracy: 0.8000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8777 - accuracy: 0.8133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8317 - accuracy: 0.7733\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7888 - accuracy: 0.8400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7489 - accuracy: 0.8267\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7126 - accuracy: 0.8267\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6796 - accuracy: 0.8400\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6497 - accuracy: 0.8400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.6221 - accuracy: 0.8400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5972 - accuracy: 0.8400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5733 - accuracy: 0.8400\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5520 - accuracy: 0.8400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5324 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.5181 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.5170 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 26ms/step - loss: 0.4892 - accuracy: 0.9333\n",
            "[110]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.9333333373069763\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [50, 10, 10, 10, 10], [90], [90], [90], [50, 20, 10, 10], [90], [90], [90], [60, 10, 10, 10], [50, 20, 10, 10], [90], [90], [80, 10], [60, 20, 10, 10], [100], [80, 20], [100], [50, 20, 10, 20], [50, 10, 10, 20, 10], [90, 10], [100], [100], [50, 10, 20, 10], [90], [90], [90], [50, 10, 20, 10], [50, 10, 20, 10], [90], [90], [90], [50, 20, 20, 10], [100], [80, 20], [100], [50, 20, 10, 20], [50, 10, 10, 10, 20], [100], [100], [100], [60, 20, 10, 10], [100], [90, 10], [90, 10], [60, 10, 10, 10, 10], [50, 20, 10, 10, 10], [100], [90, 10], [80, 20], [60, 20, 10, 10, 10], [100, 10], [80, 20, 10], [100, 10], [50, 20, 10, 30], [50, 10, 10, 20, 20], [90, 10, 10], [110], [100, 10]]\n",
            "Maximos\n",
            "[0.8892507349659573, 0.8449279165869699, 0.8448968284644612, 0.8449687828472742, 0.8891961308742912, 0.8892043570571486, 0.8448747363861148, 0.8448740768044467, 0.84487392639713, 0.311248281630139, 0.8224049894697107, 0.8446203260842776, 0.8223614820529632, 0.8224007325717302, 0.6223820916742356, 0.8223822111979194, 0.8223806231730598, 0.8223388961445736, 0.31121302485593955, 0.8223561629484207, 0.822420254188172, 0.8223544766544268, 0.8222950168597142, 0.3112478917678312, 0.8223667512101677, 0.8223519995748175, 0.8445720727975317, 0.8000608174070537, 0.8001522668390697, 0.8445209863078167, 0.8001000586564694, 0.8445462743642705, 0.6000898667034306, 0.8223263935704915, 0.8001214154561961, 0.8001072389167165, 0.8444979185988282, 0.8223088353114373, 0.8222981113330277, 0.8223385613182583, 0.8444515454420959, 0.8444593031672608, 0.8223453681101126, 0.822333505404293, 0.8223371311937647, 0.7555597348443589, 0.8001482990238612, 0.8445284555665434, 0.8001744470458316, 0.8445311707465337, 0.7333972643309412, 0.8001846672664866, 0.8001144055267992, 0.8001534830260556, 0.8000460430877239, 0.800160646712934, 0.8223125798129157, 0.8223644280025317, 0.6000616300607752, 0.8445054197700529, 0.8001414743837193, 0.8223006963688185, 0.8444592546449987, 0.7777978127075178, 0.7778347941464939, 0.8000194737037069, 0.7777912471656113, 0.8000606232480951, 0.711190664610653, 0.5777964971486398, 0.8000591362499947, 0.7778284418467244]\n",
            "___________________________\n",
            "T= 10\n",
            "Depredacion\n",
            "[0.8892507349659573, 0.8892043570571486, 0.8891961308742912, 0.8449687828472742, 0.8449279165869699, 0.8448968284644612, 0.8448747363861148, 0.8448740768044467, 0.84487392639713, 0.8446203260842776, 0.8445720727975317, 0.8445462743642705, 0.8445311707465337, 0.8445284555665434, 0.8445209863078167, 0.8445054197700529, 0.8444979185988282, 0.8444593031672608, 0.8444592546449987, 0.8444515454420959, 0.822420254188172, 0.8224049894697107, 0.8224007325717302, 0.8223822111979194, 0.8223806231730598, 0.8223667512101677, 0.8223644280025317, 0.8223614820529632, 0.8223561629484207, 0.8223544766544268, 0.8223519995748175, 0.8223453681101126, 0.8223388961445736, 0.8223385613182583, 0.8223371311937647, 0.822333505404293, 0.8223263935704915, 0.8223125798129157, 0.8223088353114373, 0.8223006963688185, 0.8222981113330277, 0.8222950168597142, 0.8001846672664866, 0.8001744470458316, 0.800160646712934, 0.8001534830260556, 0.8001522668390697, 0.8001482990238612, 0.8001414743837193, 0.8001214154561961, 0.8001144055267992, 0.8001072389167165, 0.8001000586564694, 0.8000608174070537, 0.8000606232480951, 0.8000591362499947, 0.8000460430877239, 0.8000194737037069, 0.7778347941464939, 0.7778284418467244, 0.7777978127075178, 0.7777912471656113, 0.7555597348443589, 0.7333972643309412, 0.711190664610653, 0.6223820916742356, 0.6000898667034306, 0.6000616300607752, 0.5777964971486398, 0.311248281630139, 0.3112478917678312, 0.31121302485593955]\n",
            "9\n",
            "theta= 0.8446203260842776\n",
            "0.311248281630139\n",
            "0.311248281630139\n",
            "0.8224049894697107\n",
            "0.8224049894697107\n",
            "0.8446203260842776\n",
            "0.8446203260842776\n",
            "0.8223614820529632\n",
            "0.8223614820529632\n",
            "0.8224007325717302\n",
            "0.8224007325717302\n",
            "0.6223820916742356\n",
            "0.6223820916742356\n",
            "0.8223822111979194\n",
            "0.8223822111979194\n",
            "0.8223806231730598\n",
            "0.8223806231730598\n",
            "0.8223388961445736\n",
            "0.8223388961445736\n",
            "0.31121302485593955\n",
            "0.31121302485593955\n",
            "0.8223561629484207\n",
            "0.8223561629484207\n",
            "0.822420254188172\n",
            "0.822420254188172\n",
            "0.8223544766544268\n",
            "0.8223544766544268\n",
            "0.8222950168597142\n",
            "0.8222950168597142\n",
            "0.3112478917678312\n",
            "0.3112478917678312\n",
            "0.8223667512101677\n",
            "0.8223667512101677\n",
            "0.8223519995748175\n",
            "0.8223519995748175\n",
            "0.8445720727975317\n",
            "0.8445720727975317\n",
            "0.8000608174070537\n",
            "0.8000608174070537\n",
            "0.8001522668390697\n",
            "0.8001522668390697\n",
            "0.8445209863078167\n",
            "0.8445209863078167\n",
            "0.8001000586564694\n",
            "0.8001000586564694\n",
            "0.8445462743642705\n",
            "0.8445462743642705\n",
            "0.6000898667034306\n",
            "0.6000898667034306\n",
            "0.8223263935704915\n",
            "0.8223263935704915\n",
            "0.8001214154561961\n",
            "0.8001214154561961\n",
            "0.8001072389167165\n",
            "0.8001072389167165\n",
            "0.8444979185988282\n",
            "0.8444979185988282\n",
            "0.8223088353114373\n",
            "0.8223088353114373\n",
            "0.8222981113330277\n",
            "0.8222981113330277\n",
            "0.8223385613182583\n",
            "0.8223385613182583\n",
            "0.8444515454420959\n",
            "0.8444515454420959\n",
            "0.8444593031672608\n",
            "0.8444593031672608\n",
            "0.8223453681101126\n",
            "0.8223453681101126\n",
            "0.822333505404293\n",
            "0.822333505404293\n",
            "0.8223371311937647\n",
            "0.8223371311937647\n",
            "0.7555597348443589\n",
            "0.7555597348443589\n",
            "0.8001482990238612\n",
            "0.8001482990238612\n",
            "0.8445284555665434\n",
            "0.8445284555665434\n",
            "0.8001744470458316\n",
            "0.8001744470458316\n",
            "0.8445311707465337\n",
            "0.8445311707465337\n",
            "0.7333972643309412\n",
            "0.7333972643309412\n",
            "0.8001846672664866\n",
            "0.8001846672664866\n",
            "0.8001144055267992\n",
            "0.8001144055267992\n",
            "0.8001534830260556\n",
            "0.8001534830260556\n",
            "0.8000460430877239\n",
            "0.8000460430877239\n",
            "0.800160646712934\n",
            "0.800160646712934\n",
            "0.8223125798129157\n",
            "0.8223125798129157\n",
            "0.8223644280025317\n",
            "0.8223644280025317\n",
            "0.6000616300607752\n",
            "0.6000616300607752\n",
            "0.8445054197700529\n",
            "0.8445054197700529\n",
            "0.8001414743837193\n",
            "0.8001414743837193\n",
            "0.8223006963688185\n",
            "0.8223006963688185\n",
            "0.8444592546449987\n",
            "0.8444592546449987\n",
            "0.7777978127075178\n",
            "0.7777978127075178\n",
            "0.7778347941464939\n",
            "0.7778347941464939\n",
            "0.8000194737037069\n",
            "0.8000194737037069\n",
            "0.7777912471656113\n",
            "0.7777912471656113\n",
            "0.8000606232480951\n",
            "0.8000606232480951\n",
            "0.711190664610653\n",
            "0.711190664610653\n",
            "0.5777964971486398\n",
            "0.5777964971486398\n",
            "0.8000591362499947\n",
            "0.8000591362499947\n",
            "0.7778284418467244\n",
            "0.7778284418467244\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80]]\n",
            "[0.8892507349659573, 0.8449279165869699, 0.8448968284644612, 0.8449687828472742, 0.8891961308742912, 0.8892043570571486, 0.8448747363861148, 0.8448740768044467, 0.84487392639713]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0879 - accuracy: 0.3467\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0784 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0693 - accuracy: 0.4000\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 1.0599 - accuracy: 0.4933\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0501 - accuracy: 0.5200\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0377 - accuracy: 0.5867\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0245 - accuracy: 0.6533\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0110 - accuracy: 0.6533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9956 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9808 - accuracy: 0.6933\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9636 - accuracy: 0.7333\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9444 - accuracy: 0.7467\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9241 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9026 - accuracy: 0.8800\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8803 - accuracy: 0.8800\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8628 - accuracy: 0.8800\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.8629 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.8094 - accuracy: 0.8667\n",
            "[50, 10, 10, 10, 10]\n",
            "Training accuracy: 0.8799999952316284\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0064 - accuracy: 0.4800\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9489 - accuracy: 0.5733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8946 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8462 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8015 - accuracy: 0.7067\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7600 - accuracy: 0.7467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7253 - accuracy: 0.7733\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6929 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6632 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6379 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6140 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5935 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5740 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5568 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5402 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.5290 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.5123 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.4888 - accuracy: 0.9000\n",
            "[90]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0200 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9724 - accuracy: 0.4133\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9288 - accuracy: 0.5067\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8872 - accuracy: 0.7067\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8497 - accuracy: 0.8000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8158 - accuracy: 0.8133\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7867 - accuracy: 0.8267\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7595 - accuracy: 0.8667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7359 - accuracy: 0.8933\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7129 - accuracy: 0.8933\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6920 - accuracy: 0.9067\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6720 - accuracy: 0.9067\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6535 - accuracy: 0.9067\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6356 - accuracy: 0.9067\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6184 - accuracy: 0.8933\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6056 - accuracy: 0.8933\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.6379 - accuracy: 0.8667\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 0.5564 - accuracy: 0.9000\n",
            "[80, 10]\n",
            "Training accuracy: 0.8933333158493042\n",
            "Validation accuracy: 0.8666666746139526\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0998 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0808 - accuracy: 0.3333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0636 - accuracy: 0.3333\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0484 - accuracy: 0.3333\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0353 - accuracy: 0.3600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0216 - accuracy: 0.4400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0083 - accuracy: 0.5867\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9980 - accuracy: 0.6133\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9859 - accuracy: 0.6267\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9749 - accuracy: 0.6533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9646 - accuracy: 0.6533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9534 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9415 - accuracy: 0.6667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9302 - accuracy: 0.7200\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9185 - accuracy: 0.7200\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9085 - accuracy: 0.7333\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.8675 - accuracy: 0.7333\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.9146 - accuracy: 0.7000\n",
            "[50, 10, 20, 10]\n",
            "Training accuracy: 0.7333333492279053\n",
            "Validation accuracy: 0.7333333492279053\n",
            "Test accuracy: 0.699999988079071\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 10ms/step - loss: 1.0624 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0323 - accuracy: 0.3333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9975 - accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9679 - accuracy: 0.3733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9368 - accuracy: 0.4400\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9054 - accuracy: 0.4800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8800 - accuracy: 0.5333\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8525 - accuracy: 0.5867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8297 - accuracy: 0.6800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8104 - accuracy: 0.7200\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7914 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7755 - accuracy: 0.7733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7620 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7507 - accuracy: 0.7867\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7406 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7333 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.7260 - accuracy: 0.7556\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.6487 - accuracy: 0.8000\n",
            "[50, 20, 10, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.7555555701255798\n",
            "Test accuracy: 0.800000011920929\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10, 10], [90], [80, 10], [90], [50, 10, 20, 10], [50, 20, 10, 10], [90], [80, 10], [90]]\n",
            "Maximos\n",
            "[0.8892954904832808, 0.8449683076995032, 0.8449891827921168, 0.8450205667591228, 0.8892106755694138, 0.8892516206186132, 0.8449021488941708, 0.8448938319577329, 0.8449368598590644, 0.77783279869053, 0.8222567200495668, 0.8667556749888814, 0.8222593165999899, 0.7334071459710374, 0.7556085587449891, 0.8222862848090011, 0.8667602835056, 0.8222956883370219]\n",
            "___________________________\n",
            "T= 11\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10, 10], [90], [80, 10], [90], [50, 10, 20, 10], [50, 20, 10, 10], [90], [80, 10], [90]]\n",
            "[0.8892954904832808, 0.8449683076995032, 0.8449891827921168, 0.8450205667591228, 0.8892106755694138, 0.8892516206186132, 0.8449021488941708, 0.8448938319577329, 0.8449368598590644, 0.77783279869053, 0.8222567200495668, 0.8667556749888814, 0.8222593165999899, 0.7334071459710374, 0.7556085587449891, 0.8222862848090011, 0.8667602835056, 0.8222956883370219]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.1100 - accuracy: 0.3467\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0939 - accuracy: 0.4000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0809 - accuracy: 0.4533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0684 - accuracy: 0.4800\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0564 - accuracy: 0.5467\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0448 - accuracy: 0.6133\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0314 - accuracy: 0.6400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0172 - accuracy: 0.6533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0017 - accuracy: 0.6533\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9856 - accuracy: 0.6800\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9666 - accuracy: 0.7200\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.9457 - accuracy: 0.7467\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9222 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8964 - accuracy: 0.8533\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8654 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8409 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8576 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.8730 - accuracy: 0.8333\n",
            "[50, 10, 10, 20]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8333333134651184\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 13ms/step - loss: 1.1008 - accuracy: 0.2133\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0982 - accuracy: 0.4533\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0971 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0957 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0941 - accuracy: 0.4933\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0922 - accuracy: 0.4800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0897 - accuracy: 0.5067\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0876 - accuracy: 0.5467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0846 - accuracy: 0.6267\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0813 - accuracy: 0.6533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0758 - accuracy: 0.7067\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0672 - accuracy: 0.7867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0561 - accuracy: 0.7867\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0417 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0254 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0107 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 1.0016 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.9942 - accuracy: 0.8667\n",
            "[50, 10, 10, 10, 10, 10]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 1.1414 - accuracy: 0.1200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0843 - accuracy: 0.2800\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0273 - accuracy: 0.6400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9743 - accuracy: 0.7200\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9256 - accuracy: 0.7600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8791 - accuracy: 0.7867\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8367 - accuracy: 0.7867\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7958 - accuracy: 0.8000\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7600 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7258 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6934 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6652 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6392 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6150 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5920 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5761 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.5651 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.5463 - accuracy: 0.9000\n",
            "[100]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.0350 - accuracy: 0.5067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9881 - accuracy: 0.7867\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9440 - accuracy: 0.8533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9022 - accuracy: 0.8267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.8603 - accuracy: 0.8267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.8221 - accuracy: 0.8000\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7836 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7470 - accuracy: 0.8133\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7119 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.6777 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6444 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6144 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5841 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5561 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5303 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5112 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.5358 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.4898 - accuracy: 0.9000\n",
            "[80, 10, 10]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 9ms/step - loss: 1.1226 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1003 - accuracy: 0.4133\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0812 - accuracy: 0.5333\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0658 - accuracy: 0.6267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0511 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0341 - accuracy: 0.6933\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0170 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9987 - accuracy: 0.7733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9770 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9530 - accuracy: 0.7867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9292 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9032 - accuracy: 0.7467\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8744 - accuracy: 0.6800\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8446 - accuracy: 0.6933\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8144 - accuracy: 0.6933\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7932 - accuracy: 0.6933\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.8365 - accuracy: 0.6667\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.7489 - accuracy: 0.7000\n",
            "[50, 10, 20, 10, 10]\n",
            "Training accuracy: 0.6933333277702332\n",
            "Validation accuracy: 0.6666666865348816\n",
            "Test accuracy: 0.699999988079071\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.0899 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0721 - accuracy: 0.3333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0564 - accuracy: 0.3333\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0415 - accuracy: 0.3333\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0277 - accuracy: 0.3333\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0136 - accuracy: 0.3467\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9998 - accuracy: 0.3467\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9847 - accuracy: 0.3467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9694 - accuracy: 0.3867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9539 - accuracy: 0.3867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9366 - accuracy: 0.3867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9208 - accuracy: 0.4133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9034 - accuracy: 0.4267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8855 - accuracy: 0.4267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8684 - accuracy: 0.4267\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8548 - accuracy: 0.4400\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.8264 - accuracy: 0.4444\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.8878 - accuracy: 0.3333\n",
            "[50, 20, 10, 20]\n",
            "Training accuracy: 0.4399999976158142\n",
            "Validation accuracy: 0.4444444477558136\n",
            "Test accuracy: 0.3333333432674408\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 8ms/step - loss: 0.9967 - accuracy: 0.7200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9535 - accuracy: 0.7733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9137 - accuracy: 0.8400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.8744 - accuracy: 0.8533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8330 - accuracy: 0.8800\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7940 - accuracy: 0.8800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7552 - accuracy: 0.8800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7165 - accuracy: 0.8800\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6794 - accuracy: 0.8933\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6442 - accuracy: 0.8800\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6103 - accuracy: 0.8800\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5761 - accuracy: 0.8667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5424 - accuracy: 0.8800\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5126 - accuracy: 0.8800\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.4842 - accuracy: 0.8800\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.4635 - accuracy: 0.8800\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 0.4939 - accuracy: 0.8444\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4557 - accuracy: 0.9333\n",
            "[80, 20]\n",
            "Training accuracy: 0.8799999952316284\n",
            "Validation accuracy: 0.8444444537162781\n",
            "Test accuracy: 0.9333333373069763\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10, 10], [90], [80, 10], [90], [50, 10, 20, 10], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [90], [90], [80, 10], [50, 10, 20, 10], [50, 10, 10, 10, 10], [80, 10], [90], [90], [50, 10, 10, 10, 10, 10], [100], [80, 10, 10], [100], [50, 10, 20, 10, 10], [50, 20, 10, 20], [100], [80, 20], [100]]\n",
            "Maximos\n",
            "[0.889391194658079, 0.845024666042038, 0.845046454888146, 0.8451153602468175, 0.8892301755382405, 0.88928657117976, 0.8449447806733292, 0.8449833714984665, 0.8449673269941307, 0.7778712012301999, 0.8222914347710749, 0.866822184408852, 0.8222608680735028, 0.7334739196710349, 0.7556495491438376, 0.8222942893182996, 0.866845940549735, 0.8223142571254652, 0.8000939647670295, 0.8223527376721786, 0.8223013257844374, 0.8668265140234979, 0.7334763518304367, 0.777929258791999, 0.8667866072489809, 0.8223289814033574, 0.822354279637718, 0.8222958040392948, 0.8445235440352508, 0.8445048718738132, 0.8444675024632405, 0.6666852685226953, 0.4445393381299158, 0.8445052952080065, 0.8444519865672959, 0.844521712110893]\n",
            "___________________________\n",
            "T= 12\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10, 10], [90], [80, 10], [90], [50, 10, 20, 10], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [90], [90], [80, 10], [50, 10, 20, 10], [50, 10, 10, 10, 10], [80, 10], [90], [90], [50, 10, 10, 10, 10, 10], [100], [80, 10, 10], [100], [50, 10, 20, 10, 10], [50, 20, 10, 20], [100], [80, 20], [100]]\n",
            "[0.889391194658079, 0.845024666042038, 0.845046454888146, 0.8451153602468175, 0.8892301755382405, 0.88928657117976, 0.8449447806733292, 0.8449833714984665, 0.8449673269941307, 0.7778712012301999, 0.8222914347710749, 0.866822184408852, 0.8222608680735028, 0.7334739196710349, 0.7556495491438376, 0.8222942893182996, 0.866845940549735, 0.8223142571254652, 0.8000939647670295, 0.8223527376721786, 0.8223013257844374, 0.8668265140234979, 0.7334763518304367, 0.777929258791999, 0.8667866072489809, 0.8223289814033574, 0.822354279637718, 0.8222958040392948, 0.8445235440352508, 0.8445048718738132, 0.8444675024632405, 0.6666852685226953, 0.4445393381299158, 0.8445052952080065, 0.8444519865672959, 0.844521712110893]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1026 - accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0926 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0855 - accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0797 - accuracy: 0.4000\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0754 - accuracy: 0.3733\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0705 - accuracy: 0.3867\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0648 - accuracy: 0.3867\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0577 - accuracy: 0.3867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0505 - accuracy: 0.3867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0428 - accuracy: 0.3867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0335 - accuracy: 0.3867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0245 - accuracy: 0.4000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0136 - accuracy: 0.4000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0023 - accuracy: 0.4000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9901 - accuracy: 0.4000\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9802 - accuracy: 0.4133\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.0023 - accuracy: 0.3111\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 1.0020 - accuracy: 0.4333\n",
            "[50, 10, 10, 10, 20]\n",
            "Training accuracy: 0.41333332657814026\n",
            "Validation accuracy: 0.31111112236976624\n",
            "Test accuracy: 0.4333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.1003 - accuracy: 0.3200\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0628 - accuracy: 0.6667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0311 - accuracy: 0.7867\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0001 - accuracy: 0.7600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9688 - accuracy: 0.7600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9349 - accuracy: 0.7600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9012 - accuracy: 0.7733\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8675 - accuracy: 0.7733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8316 - accuracy: 0.7733\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7924 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7516 - accuracy: 0.7733\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7102 - accuracy: 0.7867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6704 - accuracy: 0.7867\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6321 - accuracy: 0.7867\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5939 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5681 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.5626 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.5410 - accuracy: 0.9000\n",
            "[50, 20, 20, 10]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 8ms/step - loss: 1.0463 - accuracy: 0.4133\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0032 - accuracy: 0.4800\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9639 - accuracy: 0.7200\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9327 - accuracy: 0.7067\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9007 - accuracy: 0.7067\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8721 - accuracy: 0.7200\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8461 - accuracy: 0.7200\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8214 - accuracy: 0.7333\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7977 - accuracy: 0.7333\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7740 - accuracy: 0.7600\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7506 - accuracy: 0.7733\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7262 - accuracy: 0.7867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6996 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6705 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6424 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6209 - accuracy: 0.8133\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.6147 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5757 - accuracy: 0.8667\n",
            "[90, 10]\n",
            "Training accuracy: 0.8133333325386047\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.1127 - accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1046 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0968 - accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0894 - accuracy: 0.3600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0835 - accuracy: 0.3600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0778 - accuracy: 0.3600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0720 - accuracy: 0.3600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0660 - accuracy: 0.3600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0593 - accuracy: 0.3600\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0534 - accuracy: 0.3600\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0464 - accuracy: 0.3600\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0393 - accuracy: 0.3600\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0309 - accuracy: 0.3600\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0213 - accuracy: 0.3733\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0133 - accuracy: 0.4000\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 1.0050 - accuracy: 0.4933\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.0480 - accuracy: 0.4000\n",
            "1/1 [==============================] - 0s 36ms/step - loss: 1.0223 - accuracy: 0.4667\n",
            "[60, 10, 20, 10]\n",
            "Training accuracy: 0.4933333396911621\n",
            "Validation accuracy: 0.4000000059604645\n",
            "Test accuracy: 0.46666666865348816\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1002 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0915 - accuracy: 0.5333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0851 - accuracy: 0.6133\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0763 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0664 - accuracy: 0.6933\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0553 - accuracy: 0.6933\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0420 - accuracy: 0.6800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0259 - accuracy: 0.6800\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0060 - accuracy: 0.6800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9833 - accuracy: 0.6800\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9564 - accuracy: 0.6533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9244 - accuracy: 0.6400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8889 - accuracy: 0.6400\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8492 - accuracy: 0.6400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8062 - accuracy: 0.6400\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7718 - accuracy: 0.6400\n",
            "2/2 [==============================] - 0s 4ms/step - loss: 0.7612 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.7466 - accuracy: 0.6667\n",
            "[50, 10, 10, 10, 10, 10, 10]\n",
            "Training accuracy: 0.6399999856948853\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.6666666865348816\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0860 - accuracy: 0.4533\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0254 - accuracy: 0.5067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9672 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9149 - accuracy: 0.7067\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8652 - accuracy: 0.7467\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8196 - accuracy: 0.7867\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7772 - accuracy: 0.7733\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7384 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7041 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6719 - accuracy: 0.8000\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6436 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6173 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5934 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5725 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5520 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5382 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.5332 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.5036 - accuracy: 0.8667\n",
            "[110]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 11ms/step - loss: 1.1121 - accuracy: 0.0533\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0982 - accuracy: 0.2667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0864 - accuracy: 0.5067\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0760 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0662 - accuracy: 0.7467\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0552 - accuracy: 0.7600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0415 - accuracy: 0.7733\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0259 - accuracy: 0.7600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0094 - accuracy: 0.7733\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9915 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9713 - accuracy: 0.7733\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9504 - accuracy: 0.7733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9277 - accuracy: 0.7733\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9016 - accuracy: 0.7733\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8746 - accuracy: 0.7733\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8529 - accuracy: 0.7733\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.8339 - accuracy: 0.7778\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.8503 - accuracy: 0.8667\n",
            "[90, 10, 10]\n",
            "Training accuracy: 0.7733333110809326\n",
            "Validation accuracy: 0.7777777910232544\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0945 - accuracy: 0.2800\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0312 - accuracy: 0.5733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9771 - accuracy: 0.6400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9255 - accuracy: 0.6667\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8774 - accuracy: 0.6800\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8378 - accuracy: 0.7067\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8008 - accuracy: 0.7200\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7700 - accuracy: 0.7333\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7420 - accuracy: 0.7333\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7160 - accuracy: 0.7333\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6937 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.6710 - accuracy: 0.7467\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6517 - accuracy: 0.7467\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6337 - accuracy: 0.7333\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6166 - accuracy: 0.7333\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6046 - accuracy: 0.7333\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.5688 - accuracy: 0.7111\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.5494 - accuracy: 0.7333\n",
            "[100, 10]\n",
            "Training accuracy: 0.7333333492279053\n",
            "Validation accuracy: 0.7111111283302307\n",
            "Test accuracy: 0.7333333492279053\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 9ms/step - loss: 1.1054 - accuracy: 0.3733\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0893 - accuracy: 0.5733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0761 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0627 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0450 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0257 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0053 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9835 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9609 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9358 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9081 - accuracy: 0.6667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8793 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8498 - accuracy: 0.6667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8213 - accuracy: 0.6667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7909 - accuracy: 0.6667\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7707 - accuracy: 0.6667\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.7782 - accuracy: 0.6222\n",
            "1/1 [==============================] - 0s 22ms/step - loss: 0.7052 - accuracy: 0.7333\n",
            "[50, 10, 20, 20, 10]\n",
            "Training accuracy: 0.6666666865348816\n",
            "Validation accuracy: 0.6222222447395325\n",
            "Test accuracy: 0.7333333492279053\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0922 - accuracy: 0.2533\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0758 - accuracy: 0.4000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 1.0628 - accuracy: 0.5333\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0494 - accuracy: 0.5733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0341 - accuracy: 0.5867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0158 - accuracy: 0.6000\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9959 - accuracy: 0.6533\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9738 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9500 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9229 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8961 - accuracy: 0.6667\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8642 - accuracy: 0.6667\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8334 - accuracy: 0.6667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7986 - accuracy: 0.6667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7650 - accuracy: 0.6667\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7384 - accuracy: 0.6667\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.7578 - accuracy: 0.6222\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.6996 - accuracy: 0.7333\n",
            "[50, 20, 10, 30]\n",
            "Training accuracy: 0.6666666865348816\n",
            "Validation accuracy: 0.6222222447395325\n",
            "Test accuracy: 0.7333333492279053\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0710 - accuracy: 0.4267\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0089 - accuracy: 0.6000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.9544 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9030 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8538 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8094 - accuracy: 0.6933\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7646 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7264 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.6890 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6551 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6223 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.5916 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5624 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5343 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.5081 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.4881 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.4961 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.4647 - accuracy: 0.9000\n",
            "[80, 30]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80], [80], [80], [50, 10, 10, 10, 10], [90], [80, 10], [90], [50, 10, 20, 10], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 10, 20], [90], [90], [80, 10], [50, 10, 20, 10], [50, 10, 10, 10, 10], [80, 10], [90], [90], [50, 10, 10, 10, 10, 10], [100], [80, 10, 10], [100], [50, 10, 20, 10, 10], [50, 20, 10, 20], [100], [80, 20], [100], [50, 20, 10, 10], [90], [80, 10], [90], [50, 10, 20, 10], [50, 20, 10, 10], [90], [90], [90], [50, 10, 10, 10, 20], [100], [80, 20], [100], [50, 20, 20, 10], [50, 20, 10, 20], [100], [80, 20], [90, 10], [50, 20, 10, 20], [100], [100], [90, 10], [60, 10, 20, 10], [50, 10, 10, 10, 10, 10], [90, 10], [100], [100], [50, 10, 10, 10, 10, 10, 10], [110], [90, 10, 10], [100, 10], [50, 10, 20, 20, 10], [50, 20, 10, 30], [100, 10], [80, 30], [110]]\n",
            "Maximos\n",
            "[0.8894818334432981, 0.8450867213034489, 0.8451133043380462, 0.8452030768649407, 0.8892447629873085, 0.8893426020564204, 0.8449704324987513, 0.8450332253781005, 0.8449720014519855, 0.777893632365719, 0.8223796173253897, 0.8668653480040914, 0.822318152158028, 0.7335415987168734, 0.755665350036305, 0.8223859794359091, 0.8669024811802212, 0.8223992267754775, 0.800171865760338, 0.8224360935585039, 0.8223893412571839, 0.8668560298741187, 0.7335018735951068, 0.7780286789821523, 0.8667884201372659, 0.8224269670697423, 0.8224466056964246, 0.8223704850769002, 0.844539310806829, 0.8446013981941826, 0.844516542223799, 0.666734952528911, 0.44458030567968976, 0.8445491516541294, 0.8444583375217999, 0.8445823590819997, 0.755684270840638, 0.8224451470453604, 0.8668666145757331, 0.8223656486178577, 0.7335145042340725, 0.7556988595008981, 0.8223848707434528, 0.822413372904737, 0.8223697902575876, 0.31112577836316363, 0.8445831200568461, 0.8444599748268843, 0.8446123830134732, 0.8223177702814545, 0.4445710736199747, 0.8445847893270033, 0.8444875499471496, 0.8000682265260697, 0.4445724930204173, 0.8445723140372241, 0.844604177856822, 0.8000482293097033, 0.4000303852053188, 0.8223228920441494, 0.8000943357907476, 0.8446029386917512, 0.8445603383948592, 0.777856465314516, 0.822240861626193, 0.7778718212522157, 0.7111804044265704, 0.6222763592027888, 0.6222436574062679, 0.7111211703943897, 0.8222401659748215, 0.8222994443372736]\n",
            "___________________________\n",
            "T= 13\n",
            "Depredacion\n",
            "[0.8894818334432981, 0.8893426020564204, 0.8892447629873085, 0.8669024811802212, 0.8668666145757331, 0.8668653480040914, 0.8668560298741187, 0.8667884201372659, 0.8452030768649407, 0.8451133043380462, 0.8450867213034489, 0.8450332253781005, 0.8449720014519855, 0.8449704324987513, 0.8446123830134732, 0.844604177856822, 0.8446029386917512, 0.8446013981941826, 0.8445847893270033, 0.8445831200568461, 0.8445823590819997, 0.8445723140372241, 0.8445603383948592, 0.8445491516541294, 0.844539310806829, 0.844516542223799, 0.8444875499471496, 0.8444599748268843, 0.8444583375217999, 0.8224466056964246, 0.8224451470453604, 0.8224360935585039, 0.8224269670697423, 0.822413372904737, 0.8223992267754775, 0.8223893412571839, 0.8223859794359091, 0.8223848707434528, 0.8223796173253897, 0.8223704850769002, 0.8223697902575876, 0.8223656486178577, 0.8223228920441494, 0.822318152158028, 0.8223177702814545, 0.8222994443372736, 0.822240861626193, 0.8222401659748215, 0.800171865760338, 0.8000943357907476, 0.8000682265260697, 0.8000482293097033, 0.7780286789821523, 0.777893632365719, 0.7778718212522157, 0.777856465314516, 0.7556988595008981, 0.755684270840638, 0.755665350036305, 0.7335415987168734, 0.7335145042340725, 0.7335018735951068, 0.7111804044265704, 0.7111211703943897, 0.666734952528911, 0.6222763592027888, 0.6222436574062679, 0.44458030567968976, 0.4445724930204173, 0.4445710736199747, 0.4000303852053188, 0.31112577836316363]\n",
            "9\n",
            "theta= 0.8451133043380462\n",
            "0.8450867213034489\n",
            "0.8450867213034489\n",
            "0.8451133043380462\n",
            "0.8451133043380462\n",
            "0.8449704324987513\n",
            "0.8449704324987513\n",
            "0.8450332253781005\n",
            "0.8450332253781005\n",
            "0.8449720014519855\n",
            "0.8449720014519855\n",
            "0.777893632365719\n",
            "0.777893632365719\n",
            "0.8223796173253897\n",
            "0.8223796173253897\n",
            "0.822318152158028\n",
            "0.822318152158028\n",
            "0.7335415987168734\n",
            "0.7335415987168734\n",
            "0.755665350036305\n",
            "0.755665350036305\n",
            "0.8223859794359091\n",
            "0.8223859794359091\n",
            "0.8223992267754775\n",
            "0.8223992267754775\n",
            "0.800171865760338\n",
            "0.800171865760338\n",
            "0.8224360935585039\n",
            "0.8224360935585039\n",
            "0.8223893412571839\n",
            "0.8223893412571839\n",
            "0.7335018735951068\n",
            "0.7335018735951068\n",
            "0.7780286789821523\n",
            "0.7780286789821523\n",
            "0.8224269670697423\n",
            "0.8224269670697423\n",
            "0.8224466056964246\n",
            "0.8224466056964246\n",
            "0.8223704850769002\n",
            "0.8223704850769002\n",
            "0.844539310806829\n",
            "0.844539310806829\n",
            "0.8446013981941826\n",
            "0.8446013981941826\n",
            "0.844516542223799\n",
            "0.844516542223799\n",
            "0.666734952528911\n",
            "0.666734952528911\n",
            "0.44458030567968976\n",
            "0.44458030567968976\n",
            "0.8445491516541294\n",
            "0.8445491516541294\n",
            "0.8444583375217999\n",
            "0.8444583375217999\n",
            "0.8445823590819997\n",
            "0.8445823590819997\n",
            "0.755684270840638\n",
            "0.755684270840638\n",
            "0.8224451470453604\n",
            "0.8224451470453604\n",
            "0.8223656486178577\n",
            "0.8223656486178577\n",
            "0.7335145042340725\n",
            "0.7335145042340725\n",
            "0.7556988595008981\n",
            "0.7556988595008981\n",
            "0.8223848707434528\n",
            "0.8223848707434528\n",
            "0.822413372904737\n",
            "0.822413372904737\n",
            "0.8223697902575876\n",
            "0.8223697902575876\n",
            "0.31112577836316363\n",
            "0.31112577836316363\n",
            "0.8445831200568461\n",
            "0.8445831200568461\n",
            "0.8444599748268843\n",
            "0.8444599748268843\n",
            "0.8446123830134732\n",
            "0.8446123830134732\n",
            "0.8223177702814545\n",
            "0.8223177702814545\n",
            "0.4445710736199747\n",
            "0.4445710736199747\n",
            "0.8445847893270033\n",
            "0.8445847893270033\n",
            "0.8444875499471496\n",
            "0.8444875499471496\n",
            "0.8000682265260697\n",
            "0.8000682265260697\n",
            "0.4445724930204173\n",
            "0.4445724930204173\n",
            "0.8445723140372241\n",
            "0.8445723140372241\n",
            "0.844604177856822\n",
            "0.844604177856822\n",
            "0.8000482293097033\n",
            "0.8000482293097033\n",
            "0.4000303852053188\n",
            "0.4000303852053188\n",
            "0.8223228920441494\n",
            "0.8223228920441494\n",
            "0.8000943357907476\n",
            "0.8000943357907476\n",
            "0.8446029386917512\n",
            "0.8446029386917512\n",
            "0.8445603383948592\n",
            "0.8445603383948592\n",
            "0.777856465314516\n",
            "0.777856465314516\n",
            "0.822240861626193\n",
            "0.822240861626193\n",
            "0.7778718212522157\n",
            "0.7778718212522157\n",
            "0.7111804044265704\n",
            "0.7111804044265704\n",
            "0.6222763592027888\n",
            "0.6222763592027888\n",
            "0.6222436574062679\n",
            "0.6222436574062679\n",
            "0.7111211703943897\n",
            "0.7111211703943897\n",
            "0.8222401659748215\n",
            "0.8222401659748215\n",
            "0.8222994443372736\n",
            "0.8222994443372736\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80, 10], [80, 10], [80, 10], [80, 10], [80, 10]]\n",
            "[0.8894818334432981, 0.8452030768649407, 0.8892447629873085, 0.8893426020564204, 0.8668653480040914, 0.8669024811802212, 0.8668560298741187, 0.8667884201372659, 0.8668666145757331]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.1060 - accuracy: 0.3067\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0655 - accuracy: 0.3067\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0315 - accuracy: 0.3067\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0021 - accuracy: 0.3067\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9735 - accuracy: 0.3333\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9474 - accuracy: 0.3733\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9195 - accuracy: 0.4667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8947 - accuracy: 0.5733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8698 - accuracy: 0.6267\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8491 - accuracy: 0.6400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8278 - accuracy: 0.6400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.8068 - accuracy: 0.6400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7863 - accuracy: 0.6400\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7664 - accuracy: 0.6400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7478 - accuracy: 0.6400\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7329 - accuracy: 0.6400\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.7137 - accuracy: 0.7111\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.6529 - accuracy: 0.6667\n",
            "[50, 20, 10, 10]\n",
            "Training accuracy: 0.6399999856948853\n",
            "Validation accuracy: 0.7111111283302307\n",
            "Test accuracy: 0.6666666865348816\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0236 - accuracy: 0.4267\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9653 - accuracy: 0.6000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9127 - accuracy: 0.7733\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8637 - accuracy: 0.8133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8209 - accuracy: 0.8267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7784 - accuracy: 0.8400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7411 - accuracy: 0.8267\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7076 - accuracy: 0.8400\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6770 - accuracy: 0.8267\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6483 - accuracy: 0.8267\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6245 - accuracy: 0.8267\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6013 - accuracy: 0.8267\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5808 - accuracy: 0.8267\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5620 - accuracy: 0.8267\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5441 - accuracy: 0.8267\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.5319 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.5232 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 32ms/step - loss: 0.5084 - accuracy: 0.9000\n",
            "[90]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0644 - accuracy: 0.5333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0392 - accuracy: 0.6667\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0144 - accuracy: 0.7467\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9930 - accuracy: 0.7467\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9665 - accuracy: 0.7467\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9406 - accuracy: 0.7733\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9146 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8886 - accuracy: 0.7600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8636 - accuracy: 0.7600\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8392 - accuracy: 0.7467\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8151 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7877 - accuracy: 0.7600\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7607 - accuracy: 0.7600\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7331 - accuracy: 0.7600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7063 - accuracy: 0.7600\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6839 - accuracy: 0.7600\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.6756 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.6820 - accuracy: 0.8000\n",
            "[50, 10, 20, 10]\n",
            "Training accuracy: 0.7599999904632568\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.800000011920929\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.0605 - accuracy: 0.5333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0287 - accuracy: 0.6133\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0041 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9793 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9567 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9330 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9073 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8816 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8524 - accuracy: 0.6800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8231 - accuracy: 0.8933\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7923 - accuracy: 0.8400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7604 - accuracy: 0.8400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7276 - accuracy: 0.8400\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6961 - accuracy: 0.8400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6634 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.6400 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.6430 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.6149 - accuracy: 0.9000\n",
            "[50, 10, 10, 20]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0862 - accuracy: 0.4933\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0288 - accuracy: 0.5600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9847 - accuracy: 0.5867\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9474 - accuracy: 0.6133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9118 - accuracy: 0.7200\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8793 - accuracy: 0.7733\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8486 - accuracy: 0.7867\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8195 - accuracy: 0.8133\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7915 - accuracy: 0.8000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7627 - accuracy: 0.8400\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7346 - accuracy: 0.8533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.7089 - accuracy: 0.8533\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6840 - accuracy: 0.8667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.6582 - accuracy: 0.8667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6345 - accuracy: 0.8533\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.8533\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.6202 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.6234 - accuracy: 0.9333\n",
            "[90, 10]\n",
            "Training accuracy: 0.8533333539962769\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.9333333373069763\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1355 - accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1084 - accuracy: 0.3467\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0879 - accuracy: 0.3467\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0683 - accuracy: 0.3733\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0499 - accuracy: 0.4000\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0350 - accuracy: 0.4400\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0218 - accuracy: 0.4800\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0084 - accuracy: 0.4800\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9963 - accuracy: 0.4800\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9824 - accuracy: 0.4933\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9699 - accuracy: 0.5200\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9549 - accuracy: 0.5200\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9399 - accuracy: 0.5333\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9238 - accuracy: 0.6000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9067 - accuracy: 0.6667\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8925 - accuracy: 0.7067\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.9138 - accuracy: 0.6222\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.9208 - accuracy: 0.6667\n",
            "[80, 10, 10]\n",
            "Training accuracy: 0.7066666483879089\n",
            "Validation accuracy: 0.6222222447395325\n",
            "Test accuracy: 0.6666666865348816\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80, 10], [80, 10], [80, 10], [80, 10], [80, 10], [50, 20, 10, 10], [90], [50, 10, 20, 10], [50, 10, 10, 20], [90, 10], [80, 10, 10], [80, 10, 10], [80, 10, 10], [90, 10]]\n",
            "Maximos\n",
            "[0.8894954076367861, 0.8452854810176499, 0.889301360179757, 0.8894243489473777, 0.8668942511619262, 0.8669828407186635, 0.8668966060908496, 0.8668830693680393, 0.8668967666806514, 0.7111257436712315, 0.8222529334631942, 0.8000148062498641, 0.8222714821721875, 0.8223037525536067, 0.62224004196266, 0.6222385628159367, 0.6223064082313692, 0.8222896452016266]\n",
            "___________________________\n",
            "T= 14\n",
            "__\n",
            "[[50, 10, 10, 10], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80, 10], [80, 10], [80, 10], [80, 10], [80, 10], [50, 20, 10, 10], [90], [50, 10, 20, 10], [50, 10, 10, 20], [90, 10], [80, 10, 10], [80, 10, 10], [80, 10, 10], [90, 10]]\n",
            "[0.8894954076367861, 0.8452854810176499, 0.889301360179757, 0.8894243489473777, 0.8668942511619262, 0.8669828407186635, 0.8668966060908496, 0.8668830693680393, 0.8668967666806514, 0.7111257436712315, 0.8222529334631942, 0.8000148062498641, 0.8222714821721875, 0.8223037525536067, 0.62224004196266, 0.6222385628159367, 0.6223064082313692, 0.8222896452016266]\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1109 - accuracy: 0.1467\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1041 - accuracy: 0.1467\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0980 - accuracy: 0.2400\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0924 - accuracy: 0.4267\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0873 - accuracy: 0.5600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0804 - accuracy: 0.7067\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0727 - accuracy: 0.8000\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0647 - accuracy: 0.7867\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0559 - accuracy: 0.8133\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0460 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0348 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0209 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0050 - accuracy: 0.8533\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9868 - accuracy: 0.8667\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9661 - accuracy: 0.8667\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9491 - accuracy: 0.8667\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 0.9500 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 27ms/step - loss: 0.9280 - accuracy: 0.9667\n",
            "[50, 10, 10, 10, 10]\n",
            "Training accuracy: 0.8666666746139526\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.9666666388511658\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 7ms/step - loss: 1.3257 - accuracy: 0.2933\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.2565 - accuracy: 0.3733\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.1985 - accuracy: 0.4533\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.1433 - accuracy: 0.4667\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0980 - accuracy: 0.4933\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0513 - accuracy: 0.4933\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0073 - accuracy: 0.4933\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9703 - accuracy: 0.5467\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9352 - accuracy: 0.7333\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8983 - accuracy: 0.8133\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8621 - accuracy: 0.8133\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8250 - accuracy: 0.8133\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7888 - accuracy: 0.8133\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7548 - accuracy: 0.8133\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7206 - accuracy: 0.8133\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.6955 - accuracy: 0.8267\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.6974 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.6997 - accuracy: 0.9000\n",
            "[80, 20]\n",
            "Training accuracy: 0.8266666531562805\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.1150 - accuracy: 0.4133\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0942 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0744 - accuracy: 0.4267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0594 - accuracy: 0.5867\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0492 - accuracy: 0.5867\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0364 - accuracy: 0.6800\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0219 - accuracy: 0.7600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0067 - accuracy: 0.7733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9913 - accuracy: 0.7200\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9766 - accuracy: 0.7333\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9605 - accuracy: 0.7467\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9418 - accuracy: 0.7733\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9242 - accuracy: 0.7867\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9065 - accuracy: 0.7867\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8878 - accuracy: 0.7867\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8729 - accuracy: 0.7867\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.8614 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.9065 - accuracy: 0.8333\n",
            "[60, 20, 10, 10]\n",
            "Training accuracy: 0.7866666913032532\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8333333134651184\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 11ms/step - loss: 0.9723 - accuracy: 0.6533\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9122 - accuracy: 0.7867\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8551 - accuracy: 0.8267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8055 - accuracy: 0.8133\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7602 - accuracy: 0.8267\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7178 - accuracy: 0.8267\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6822 - accuracy: 0.8400\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6481 - accuracy: 0.8533\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6182 - accuracy: 0.8533\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5911 - accuracy: 0.8533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5669 - accuracy: 0.8400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5462 - accuracy: 0.8400\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5272 - accuracy: 0.8400\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.5097 - accuracy: 0.8400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.4943 - accuracy: 0.8400\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.4833 - accuracy: 0.8400\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.4636 - accuracy: 0.8222\n",
            "1/1 [==============================] - 0s 33ms/step - loss: 0.4452 - accuracy: 0.8667\n",
            "[100]\n",
            "Training accuracy: 0.8399999737739563\n",
            "Validation accuracy: 0.8222222328186035\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 6ms/step - loss: 1.0777 - accuracy: 0.5867\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0652 - accuracy: 0.7333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0509 - accuracy: 0.7067\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0375 - accuracy: 0.7200\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0228 - accuracy: 0.6800\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0066 - accuracy: 0.7067\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9887 - accuracy: 0.7067\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9692 - accuracy: 0.6933\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9467 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9235 - accuracy: 0.6667\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8975 - accuracy: 0.6533\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8699 - accuracy: 0.6533\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8392 - accuracy: 0.6533\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8061 - accuracy: 0.6400\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7716 - accuracy: 0.6400\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7441 - accuracy: 0.6400\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.7399 - accuracy: 0.6889\n",
            "1/1 [==============================] - 0s 34ms/step - loss: 0.7414 - accuracy: 0.6667\n",
            "[50, 10, 30, 10]\n",
            "Training accuracy: 0.6399999856948853\n",
            "Validation accuracy: 0.6888889074325562\n",
            "Test accuracy: 0.6666666865348816\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 8ms/step - loss: 1.1057 - accuracy: 0.5333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0858 - accuracy: 0.6000\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0671 - accuracy: 0.6267\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0481 - accuracy: 0.6667\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0320 - accuracy: 0.6667\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0145 - accuracy: 0.6667\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9993 - accuracy: 0.6667\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9828 - accuracy: 0.6667\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9644 - accuracy: 0.6667\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9457 - accuracy: 0.6533\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.9237 - accuracy: 0.6400\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9013 - accuracy: 0.6533\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8778 - accuracy: 0.6667\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8528 - accuracy: 0.6933\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8267 - accuracy: 0.7200\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8067 - accuracy: 0.7600\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.8625 - accuracy: 0.7333\n",
            "1/1 [==============================] - 0s 28ms/step - loss: 0.8022 - accuracy: 0.7333\n",
            "[60, 10, 10, 20]\n",
            "Training accuracy: 0.7599999904632568\n",
            "Validation accuracy: 0.7333333492279053\n",
            "Test accuracy: 0.7333333492279053\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0304 - accuracy: 0.4933\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9988 - accuracy: 0.5333\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9714 - accuracy: 0.5467\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.9425 - accuracy: 0.6533\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9141 - accuracy: 0.6933\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.8863 - accuracy: 0.7067\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8588 - accuracy: 0.7467\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8327 - accuracy: 0.7733\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.8067 - accuracy: 0.7867\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7806 - accuracy: 0.7867\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7568 - accuracy: 0.7867\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.7329 - accuracy: 0.7867\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7102 - accuracy: 0.7867\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6871 - accuracy: 0.7867\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6650 - accuracy: 0.7867\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 0.6490 - accuracy: 0.7867\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 0.6380 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 31ms/step - loss: 0.6594 - accuracy: 0.8667\n",
            "[100, 10]\n",
            "Training accuracy: 0.7866666913032532\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8666666746139526\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 4ms/step - loss: 1.1265 - accuracy: 0.4400\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0770 - accuracy: 0.4800\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0441 - accuracy: 0.4933\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0219 - accuracy: 0.4933\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 1.0021 - accuracy: 0.5067\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9826 - accuracy: 0.5067\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9621 - accuracy: 0.5067\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9417 - accuracy: 0.5067\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9182 - accuracy: 0.6000\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8933 - accuracy: 0.7067\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8670 - accuracy: 0.8000\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8397 - accuracy: 0.8000\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8107 - accuracy: 0.8000\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7797 - accuracy: 0.8000\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.7520 - accuracy: 0.8000\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7268 - accuracy: 0.8000\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.7201 - accuracy: 0.8000\n",
            "1/1 [==============================] - 0s 29ms/step - loss: 0.7199 - accuracy: 0.9000\n",
            "[80, 10, 20]\n",
            "Training accuracy: 0.800000011920929\n",
            "Validation accuracy: 0.800000011920929\n",
            "Test accuracy: 0.8999999761581421\n",
            "Epoch 1/15\n",
            "3/3 [==============================] - 1s 10ms/step - loss: 1.0896 - accuracy: 0.3333\n",
            "Epoch 2/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0779 - accuracy: 0.3600\n",
            "Epoch 3/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0687 - accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0597 - accuracy: 0.3600\n",
            "Epoch 5/15\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0517 - accuracy: 0.3600\n",
            "Epoch 6/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0428 - accuracy: 0.3600\n",
            "Epoch 7/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0335 - accuracy: 0.3600\n",
            "Epoch 8/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0246 - accuracy: 0.3600\n",
            "Epoch 9/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0148 - accuracy: 0.3600\n",
            "Epoch 10/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0042 - accuracy: 0.3600\n",
            "Epoch 11/15\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9937 - accuracy: 0.3600\n",
            "Epoch 12/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9816 - accuracy: 0.3600\n",
            "Epoch 13/15\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9690 - accuracy: 0.3600\n",
            "Epoch 14/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9553 - accuracy: 0.3600\n",
            "Epoch 15/15\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9407 - accuracy: 0.3600\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9286 - accuracy: 0.3600\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.9635 - accuracy: 0.2889\n",
            "1/1 [==============================] - 0s 30ms/step - loss: 0.9372 - accuracy: 0.3333\n",
            "[90, 10, 10]\n",
            "Training accuracy: 0.36000001430511475\n",
            "Validation accuracy: 0.2888889014720917\n",
            "Test accuracy: 0.3333333432674408\n",
            "Redes\n",
            "[[50, 10, 10, 10], [80], [50, 10, 10, 10], [50, 10, 10, 10], [80, 10], [80, 10], [80, 10], [80, 10], [80, 10], [50, 20, 10, 10], [90], [50, 10, 20, 10], [50, 10, 10, 20], [90, 10], [80, 10, 10], [80, 10, 10], [80, 10, 10], [90, 10], [50, 10, 10, 10, 10], [80, 10], [50, 20, 10, 10], [50, 10, 10, 20], [80, 20], [90, 10], [90, 10], [80, 10, 10], [80, 20], [60, 20, 10, 10], [100], [50, 10, 30, 10], [60, 10, 10, 20], [100, 10], [80, 10, 20], [90, 10, 10], [90, 10, 10], [100, 10]]\n",
            "Maximos\n",
            "[0.889571100310389, 0.8453579897630817, 0.8893272633371904, 0.8894494471913263, 0.8669218091780606, 0.8670719365974762, 0.8669038962547823, 0.8669484374141121, 0.8668996284243754, 0.7111442849600317, 0.8223197092648966, 0.8000290551829313, 0.8222825579686441, 0.8223541402177152, 0.6223255895768733, 0.6222871080295808, 0.6223685250675983, 0.8223257940356424, 0.8222524830951353, 0.8669824981625212, 0.7111796981854025, 0.8223711115392164, 0.8223016986136689, 0.8223225993298579, 0.8223254798862301, 0.6223884629712563, 0.8222624714196414, 0.8000280684582954, 0.8223064596585059, 0.6889727940414134, 0.7334250135407264, 0.8000487827004199, 0.8000659665839702, 0.2889710491595115, 0.28890832054198584, 0.8000913426363566]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MB1_utYbcjV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad8cb52a-048c-4f37-9f37-1ff0fba50bdd"
      },
      "source": [
        "print(max(maxis))\n",
        "for i in range(len(maxis)):\n",
        "  if maxis[i] > 0.86:\n",
        "    print(i,maxis[i],Nset[i])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.889571100310389\n",
            "0 0.889571100310389 [50, 10, 10, 10]\n",
            "2 0.8893272633371904 [50, 10, 10, 10]\n",
            "3 0.8894494471913263 [50, 10, 10, 10]\n",
            "4 0.8669218091780606 [80, 10]\n",
            "5 0.8670719365974762 [80, 10]\n",
            "6 0.8669038962547823 [80, 10]\n",
            "7 0.8669484374141121 [80, 10]\n",
            "8 0.8668996284243754 [80, 10]\n",
            "19 0.8669824981625212 [80, 10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rsJk6PyaV5Wg",
        "outputId": "4ccbe82f-2d23-4f3c-f216-3bf2d54402a7"
      },
      "source": [
        "V = [70, 10, 10, 10, 10, 10]\n",
        "\n",
        "geneticCode(V)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "3/3 [==============================] - 1s 5ms/step - loss: 1.0956 - accuracy: 0.3067\n",
            "Epoch 2/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0891 - accuracy: 0.3333\n",
            "Epoch 3/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0826 - accuracy: 0.3333\n",
            "Epoch 4/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0762 - accuracy: 0.3333\n",
            "Epoch 5/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0697 - accuracy: 0.3333\n",
            "Epoch 6/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0619 - accuracy: 0.3333\n",
            "Epoch 7/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 1.0527 - accuracy: 0.3333\n",
            "Epoch 8/25\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 1.0443 - accuracy: 0.4000\n",
            "Epoch 9/25\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0341 - accuracy: 0.4133\n",
            "Epoch 10/25\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 1.0234 - accuracy: 0.4267\n",
            "Epoch 11/25\n",
            "3/3 [==============================] - 0s 4ms/step - loss: 1.0104 - accuracy: 0.4533\n",
            "Epoch 12/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9978 - accuracy: 0.4800\n",
            "Epoch 13/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.9819 - accuracy: 0.4933\n",
            "Epoch 14/25\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9659 - accuracy: 0.4933\n",
            "Epoch 15/25\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9484 - accuracy: 0.6000\n",
            "Epoch 16/25\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.9301 - accuracy: 0.7467\n",
            "Epoch 17/25\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.9087 - accuracy: 0.7733\n",
            "Epoch 18/25\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.8874 - accuracy: 0.7867\n",
            "Epoch 19/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8624 - accuracy: 0.7867\n",
            "Epoch 20/25\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.8369 - accuracy: 0.7867\n",
            "Epoch 21/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.8077 - accuracy: 0.7867\n",
            "Epoch 22/25\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7801 - accuracy: 0.7867\n",
            "Epoch 23/25\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.7514 - accuracy: 0.7867\n",
            "Epoch 24/25\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.7248 - accuracy: 0.7867\n",
            "Epoch 25/25\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6952 - accuracy: 0.7867\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.6749 - accuracy: 0.7867\n",
            "Validation accuracy: 0.7866666913032532\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7866666913032532"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "oi3PpTBjaxwX",
        "outputId": "d6870044-0abd-4b3d-8c93-4b46119fccf0"
      },
      "source": [
        "fig,ax = plt.subplots()\n",
        "\n",
        "ax.plot(MAXIS, color = (0.4,0.7,0.3))\n",
        "ax.set_title(u\"Exactitud (validación)\")\n",
        "ax.spines['left'].set_position(('outward',10))\n",
        "ax.spines['bottom'].set_position(('outward',10))\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['top'].set_visible(False)\n",
        "#ax.set_yticks(range(10),minor=True)\n",
        "#ax.set_ylim([0,100])\n",
        "ax.set_xlabel('Generación')\n",
        "ax.set_ylabel('Exactitud máxima')\n",
        "ax.legend(framealpha=1)\n",
        "#ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
        "ax.legend()#loc=\"upper left\", bbox_to_anchor=(0.8,0.2))\n",
        "ax.yaxis.set_ticks_position('left')\n",
        "ax.xaxis.set_ticks_position('bottom')\n",
        "plt.legend()\n",
        "plt.savefig('accuraciesG.png');\n",
        "#plt.plot(MAXIS)\n",
        "#plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots()\n",
        "ax.plot(L, color = (0.4,0.3,0.7))\n",
        "ax.spines['left'].set_position(('outward',10))\n",
        "ax.spines['bottom'].set_position(('outward',10))\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['top'].set_visible(False)\n",
        "#ax.set_yticks(range(10),minor=True)\n",
        "#ax.set_ylim([0,100])\n",
        "ax.set_title(u\"Número de redes por generación\")\n",
        "ax.set_xlabel('Número de redes')\n",
        "ax.set_ylabel('Generación')\n",
        "ax.legend(framealpha=1)\n",
        "#ax.legend(bbox_to_anchor=(1.1, 1.05))\n",
        "ax.legend()#loc=\"upper left\", bbox_to_anchor=(0.8,0.2))\n",
        "ax.yaxis.set_ticks_position('left')\n",
        "ax.xaxis.set_ticks_position('bottom')\n",
        "plt.savefig('generaciones.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "j2j1A0KPAXXw",
        "outputId": "86629eba-fa8e-4c2d-efb2-28ff793639ec"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No handles with labels found to put in legend.\n",
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}