Iris-com-redes-neurais.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Curso Formação Cientista de Dados - Fernando Amaral\n",
    "#### Redes Neurais Artificiais com Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importação das bibliotecas\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.utils import np_utils\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregamento da base de dados e criação dos previsores (variáveis independentes - X) e da classe (variável dependente - y)\n",
    "base = datasets.load_iris()\n",
    "previsores = base.data\n",
    "classe = base.target\n",
    "classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformação da classe para o formato \"dummy\", pois temos uma rede neural com 3 neurônios na camada de saída\n",
    "classe_dummy = np_utils.to_categorical(classe)\n",
    "classe_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divisão da base de dados entre treinamento e teste (30% para testar e 70% para treinar)\n",
    "X_treino, X_teste, y_treino, y_teste = train_test_split(previsores, classe_dummy, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criação da estrutura da rede neural com a classe Sequential (sequência de camadas)\n",
    "modelo = Sequential()\n",
    "\n",
    "# Primeira camada oculta, 5 neurônios, 4 neurônios de entrada\n",
    "modelo.add(Dense(units=5, input_dim=4))\n",
    "\n",
    "# Segunda camada oculta\n",
    "modelo.add(Dense(units=4))\n",
    "\n",
    "# Função softmax porque temos um problema de classificação com mais de duas classes (é gerada uma probabilidade em cada neurônio)\n",
    "modelo.add(Dense(units=3, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 5)                 25        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 24        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 15        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 64\n",
      "Trainable params: 64\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Visualização da estrutura da rede neural\n",
    "modelo.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "4/4 [==============================] - 0s 43ms/step - loss: 3.2417 - accuracy: 0.2857 - val_loss: 2.6535 - val_accuracy: 0.3556\n",
      "Epoch 2/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 3.0831 - accuracy: 0.2857 - val_loss: 2.5490 - val_accuracy: 0.3111\n",
      "Epoch 3/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.9424 - accuracy: 0.2571 - val_loss: 2.4482 - val_accuracy: 0.2667\n",
      "Epoch 4/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.7945 - accuracy: 0.2286 - val_loss: 2.3528 - val_accuracy: 0.2000\n",
      "Epoch 5/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 2.6542 - accuracy: 0.2000 - val_loss: 2.2629 - val_accuracy: 0.1778\n",
      "Epoch 6/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 2.5309 - accuracy: 0.2000 - val_loss: 2.1758 - val_accuracy: 0.1556\n",
      "Epoch 7/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.4078 - accuracy: 0.1238 - val_loss: 2.0926 - val_accuracy: 0.1111\n",
      "Epoch 8/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.2885 - accuracy: 0.1048 - val_loss: 2.0142 - val_accuracy: 0.0889\n",
      "Epoch 9/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.1747 - accuracy: 0.0952 - val_loss: 1.9417 - val_accuracy: 0.0444\n",
      "Epoch 10/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.0726 - accuracy: 0.0476 - val_loss: 1.8731 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.9725 - accuracy: 0.0381 - val_loss: 1.8090 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.8790 - accuracy: 0.0286 - val_loss: 1.7499 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.7952 - accuracy: 0.0286 - val_loss: 1.6959 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.7183 - accuracy: 0.0190 - val_loss: 1.6476 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1.6499 - accuracy: 0.0095 - val_loss: 1.6052 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.5832 - accuracy: 0.0095 - val_loss: 1.5681 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.5296 - accuracy: 0.0095 - val_loss: 1.5355 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.4800 - accuracy: 0.0095 - val_loss: 1.5068 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.4344 - accuracy: 0.0095 - val_loss: 1.4813 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.3949 - accuracy: 0.0000e+00 - val_loss: 1.4594 - val_accuracy: 0.0222\n",
      "Epoch 21/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.3613 - accuracy: 0.0286 - val_loss: 1.4412 - val_accuracy: 0.0667\n",
      "Epoch 22/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 1.3313 - accuracy: 0.0476 - val_loss: 1.4261 - val_accuracy: 0.0667\n",
      "Epoch 23/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1.3040 - accuracy: 0.1714 - val_loss: 1.4121 - val_accuracy: 0.1333\n",
      "Epoch 24/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.2789 - accuracy: 0.2190 - val_loss: 1.3974 - val_accuracy: 0.2000\n",
      "Epoch 25/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.2575 - accuracy: 0.2667 - val_loss: 1.3821 - val_accuracy: 0.2222\n",
      "Epoch 26/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.2383 - accuracy: 0.3143 - val_loss: 1.3654 - val_accuracy: 0.2444\n",
      "Epoch 27/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.2208 - accuracy: 0.3333 - val_loss: 1.3489 - val_accuracy: 0.2444\n",
      "Epoch 28/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.2038 - accuracy: 0.3524 - val_loss: 1.3323 - val_accuracy: 0.2444\n",
      "Epoch 29/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.1875 - accuracy: 0.3714 - val_loss: 1.3149 - val_accuracy: 0.2444\n",
      "Epoch 30/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.1718 - accuracy: 0.3714 - val_loss: 1.2977 - val_accuracy: 0.2444\n",
      "Epoch 31/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.1575 - accuracy: 0.3714 - val_loss: 1.2813 - val_accuracy: 0.2444\n",
      "Epoch 32/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.1426 - accuracy: 0.3714 - val_loss: 1.2645 - val_accuracy: 0.2444\n",
      "Epoch 33/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.1281 - accuracy: 0.3714 - val_loss: 1.2474 - val_accuracy: 0.2444\n",
      "Epoch 34/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.1142 - accuracy: 0.3714 - val_loss: 1.2332 - val_accuracy: 0.2444\n",
      "Epoch 35/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.1008 - accuracy: 0.3714 - val_loss: 1.2187 - val_accuracy: 0.2444\n",
      "Epoch 36/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1.0881 - accuracy: 0.3714 - val_loss: 1.2045 - val_accuracy: 0.2444\n",
      "Epoch 37/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.0758 - accuracy: 0.3714 - val_loss: 1.1897 - val_accuracy: 0.2444\n",
      "Epoch 38/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1.0641 - accuracy: 0.3714 - val_loss: 1.1752 - val_accuracy: 0.2444\n",
      "Epoch 39/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 1.0525 - accuracy: 0.3714 - val_loss: 1.1621 - val_accuracy: 0.2444\n",
      "Epoch 40/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.0416 - accuracy: 0.3714 - val_loss: 1.1510 - val_accuracy: 0.2444\n",
      "Epoch 41/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.0302 - accuracy: 0.3714 - val_loss: 1.1413 - val_accuracy: 0.2444\n",
      "Epoch 42/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.0189 - accuracy: 0.3714 - val_loss: 1.1322 - val_accuracy: 0.2444\n",
      "Epoch 43/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.0081 - accuracy: 0.3714 - val_loss: 1.1250 - val_accuracy: 0.2444\n",
      "Epoch 44/1000\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 0.9974 - accuracy: 0.3714 - val_loss: 1.1183 - val_accuracy: 0.2444\n",
      "Epoch 45/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.9881 - accuracy: 0.3714 - val_loss: 1.1113 - val_accuracy: 0.2444\n",
      "Epoch 46/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.9786 - accuracy: 0.3714 - val_loss: 1.1029 - val_accuracy: 0.2444\n",
      "Epoch 47/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9678 - accuracy: 0.3714 - val_loss: 1.0897 - val_accuracy: 0.2444\n",
      "Epoch 48/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.9577 - accuracy: 0.3714 - val_loss: 1.0745 - val_accuracy: 0.2444\n",
      "Epoch 49/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.9477 - accuracy: 0.3714 - val_loss: 1.0612 - val_accuracy: 0.2444\n",
      "Epoch 50/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.9385 - accuracy: 0.3714 - val_loss: 1.0487 - val_accuracy: 0.2444\n",
      "Epoch 51/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.9283 - accuracy: 0.3810 - val_loss: 1.0374 - val_accuracy: 0.2444\n",
      "Epoch 52/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.9190 - accuracy: 0.3810 - val_loss: 1.0284 - val_accuracy: 0.2444\n",
      "Epoch 53/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.9090 - accuracy: 0.3810 - val_loss: 1.0187 - val_accuracy: 0.2444\n",
      "Epoch 54/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8997 - accuracy: 0.3810 - val_loss: 1.0091 - val_accuracy: 0.2444\n",
      "Epoch 55/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8907 - accuracy: 0.4000 - val_loss: 0.9989 - val_accuracy: 0.2444\n",
      "Epoch 56/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8811 - accuracy: 0.4286 - val_loss: 0.9872 - val_accuracy: 0.2667\n",
      "Epoch 57/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.8721 - accuracy: 0.4476 - val_loss: 0.9788 - val_accuracy: 0.2889\n",
      "Epoch 58/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8634 - accuracy: 0.5143 - val_loss: 0.9700 - val_accuracy: 0.3111\n",
      "Epoch 59/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8545 - accuracy: 0.5524 - val_loss: 0.9595 - val_accuracy: 0.4000\n",
      "Epoch 60/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8460 - accuracy: 0.5714 - val_loss: 0.9481 - val_accuracy: 0.4222\n",
      "Epoch 61/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.8373 - accuracy: 0.6095 - val_loss: 0.9380 - val_accuracy: 0.4889\n",
      "Epoch 62/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8288 - accuracy: 0.6286 - val_loss: 0.9285 - val_accuracy: 0.4889\n",
      "Epoch 63/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8202 - accuracy: 0.6381 - val_loss: 0.9201 - val_accuracy: 0.5111\n",
      "Epoch 64/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8122 - accuracy: 0.6476 - val_loss: 0.9083 - val_accuracy: 0.5556\n",
      "Epoch 65/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.8034 - accuracy: 0.6857 - val_loss: 0.8990 - val_accuracy: 0.5778\n",
      "Epoch 66/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7953 - accuracy: 0.6857 - val_loss: 0.8903 - val_accuracy: 0.6000\n",
      "Epoch 67/1000\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.7872 - accuracy: 0.6952 - val_loss: 0.8835 - val_accuracy: 0.6000\n",
      "Epoch 68/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.7790 - accuracy: 0.6952 - val_loss: 0.8762 - val_accuracy: 0.6000\n",
      "Epoch 69/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7709 - accuracy: 0.6952 - val_loss: 0.8668 - val_accuracy: 0.6000\n",
      "Epoch 70/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7628 - accuracy: 0.6952 - val_loss: 0.8606 - val_accuracy: 0.6000\n",
      "Epoch 71/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.7549 - accuracy: 0.6952 - val_loss: 0.8554 - val_accuracy: 0.6000\n",
      "Epoch 72/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.7475 - accuracy: 0.6952 - val_loss: 0.8495 - val_accuracy: 0.6000\n",
      "Epoch 73/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7400 - accuracy: 0.6952 - val_loss: 0.8425 - val_accuracy: 0.6000\n",
      "Epoch 74/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.7323 - accuracy: 0.6952 - val_loss: 0.8320 - val_accuracy: 0.6000\n",
      "Epoch 75/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7247 - accuracy: 0.6952 - val_loss: 0.8196 - val_accuracy: 0.6000\n",
      "Epoch 76/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.7169 - accuracy: 0.7048 - val_loss: 0.8075 - val_accuracy: 0.6000\n",
      "Epoch 77/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.7093 - accuracy: 0.7143 - val_loss: 0.7968 - val_accuracy: 0.6000\n",
      "Epoch 78/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.7025 - accuracy: 0.7333 - val_loss: 0.7873 - val_accuracy: 0.6222\n",
      "Epoch 79/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6949 - accuracy: 0.7429 - val_loss: 0.7812 - val_accuracy: 0.6222\n",
      "Epoch 80/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6876 - accuracy: 0.7429 - val_loss: 0.7761 - val_accuracy: 0.6222\n",
      "Epoch 81/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6804 - accuracy: 0.7429 - val_loss: 0.7692 - val_accuracy: 0.6222\n",
      "Epoch 82/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6732 - accuracy: 0.7429 - val_loss: 0.7623 - val_accuracy: 0.6222\n",
      "Epoch 83/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6663 - accuracy: 0.7429 - val_loss: 0.7543 - val_accuracy: 0.6222\n",
      "Epoch 84/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6593 - accuracy: 0.7429 - val_loss: 0.7462 - val_accuracy: 0.6222\n",
      "Epoch 85/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6525 - accuracy: 0.7524 - val_loss: 0.7361 - val_accuracy: 0.6444\n",
      "Epoch 86/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6459 - accuracy: 0.7810 - val_loss: 0.7247 - val_accuracy: 0.6667\n",
      "Epoch 87/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6394 - accuracy: 0.8000 - val_loss: 0.7150 - val_accuracy: 0.7333\n",
      "Epoch 88/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6336 - accuracy: 0.8286 - val_loss: 0.7042 - val_accuracy: 0.7556\n",
      "Epoch 89/1000\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.6271 - accuracy: 0.8381 - val_loss: 0.6964 - val_accuracy: 0.7778\n",
      "Epoch 90/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.6207 - accuracy: 0.8476 - val_loss: 0.6900 - val_accuracy: 0.8000\n",
      "Epoch 91/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6138 - accuracy: 0.8476 - val_loss: 0.6856 - val_accuracy: 0.7778\n",
      "Epoch 92/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6073 - accuracy: 0.8476 - val_loss: 0.6802 - val_accuracy: 0.7778\n",
      "Epoch 93/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6006 - accuracy: 0.8476 - val_loss: 0.6743 - val_accuracy: 0.7778\n",
      "Epoch 94/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.5944 - accuracy: 0.8476 - val_loss: 0.6694 - val_accuracy: 0.7778\n",
      "Epoch 95/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5880 - accuracy: 0.8476 - val_loss: 0.6626 - val_accuracy: 0.7778\n",
      "Epoch 96/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.5819 - accuracy: 0.8476 - val_loss: 0.6547 - val_accuracy: 0.8000\n",
      "Epoch 97/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5760 - accuracy: 0.8476 - val_loss: 0.6477 - val_accuracy: 0.8000\n",
      "Epoch 98/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.5706 - accuracy: 0.8476 - val_loss: 0.6397 - val_accuracy: 0.8000\n",
      "Epoch 99/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5647 - accuracy: 0.8571 - val_loss: 0.6334 - val_accuracy: 0.8000\n",
      "Epoch 100/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5589 - accuracy: 0.8571 - val_loss: 0.6279 - val_accuracy: 0.8000\n",
      "Epoch 101/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5533 - accuracy: 0.8571 - val_loss: 0.6218 - val_accuracy: 0.8000\n",
      "Epoch 102/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5475 - accuracy: 0.8571 - val_loss: 0.6190 - val_accuracy: 0.8000\n",
      "Epoch 103/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5419 - accuracy: 0.8571 - val_loss: 0.6153 - val_accuracy: 0.8000\n",
      "Epoch 104/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5361 - accuracy: 0.8476 - val_loss: 0.6091 - val_accuracy: 0.8000\n",
      "Epoch 105/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5305 - accuracy: 0.8571 - val_loss: 0.6015 - val_accuracy: 0.8000\n",
      "Epoch 106/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5253 - accuracy: 0.8571 - val_loss: 0.5950 - val_accuracy: 0.8000\n",
      "Epoch 107/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.5203 - accuracy: 0.8571 - val_loss: 0.5874 - val_accuracy: 0.8222\n",
      "Epoch 108/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5150 - accuracy: 0.8667 - val_loss: 0.5793 - val_accuracy: 0.8222\n",
      "Epoch 109/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.5106 - accuracy: 0.8857 - val_loss: 0.5721 - val_accuracy: 0.8222\n",
      "Epoch 110/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.5063 - accuracy: 0.8857 - val_loss: 0.5664 - val_accuracy: 0.8222\n",
      "Epoch 111/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.5009 - accuracy: 0.8857 - val_loss: 0.5650 - val_accuracy: 0.8222\n",
      "Epoch 112/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4955 - accuracy: 0.8762 - val_loss: 0.5608 - val_accuracy: 0.8222\n",
      "Epoch 113/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4911 - accuracy: 0.8762 - val_loss: 0.5605 - val_accuracy: 0.8222\n",
      "Epoch 114/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.4855 - accuracy: 0.8667 - val_loss: 0.5574 - val_accuracy: 0.8222\n",
      "Epoch 115/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4810 - accuracy: 0.8667 - val_loss: 0.5530 - val_accuracy: 0.8222\n",
      "Epoch 116/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4763 - accuracy: 0.8667 - val_loss: 0.5472 - val_accuracy: 0.8222\n",
      "Epoch 117/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4719 - accuracy: 0.8762 - val_loss: 0.5427 - val_accuracy: 0.8222\n",
      "Epoch 118/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4674 - accuracy: 0.8762 - val_loss: 0.5396 - val_accuracy: 0.8222\n",
      "Epoch 119/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.4630 - accuracy: 0.8762 - val_loss: 0.5382 - val_accuracy: 0.8222\n",
      "Epoch 120/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4591 - accuracy: 0.8667 - val_loss: 0.5366 - val_accuracy: 0.8000\n",
      "Epoch 121/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4551 - accuracy: 0.8571 - val_loss: 0.5345 - val_accuracy: 0.8000\n",
      "Epoch 122/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.4514 - accuracy: 0.8571 - val_loss: 0.5311 - val_accuracy: 0.8000\n",
      "Epoch 123/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4471 - accuracy: 0.8667 - val_loss: 0.5224 - val_accuracy: 0.8222\n",
      "Epoch 124/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4423 - accuracy: 0.8762 - val_loss: 0.5124 - val_accuracy: 0.8222\n",
      "Epoch 125/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4387 - accuracy: 0.8952 - val_loss: 0.5035 - val_accuracy: 0.8222\n",
      "Epoch 126/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4345 - accuracy: 0.9048 - val_loss: 0.4974 - val_accuracy: 0.8222\n",
      "Epoch 127/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4308 - accuracy: 0.9143 - val_loss: 0.4918 - val_accuracy: 0.8222\n",
      "Epoch 128/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.4270 - accuracy: 0.9238 - val_loss: 0.4884 - val_accuracy: 0.8222\n",
      "Epoch 129/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.4229 - accuracy: 0.9143 - val_loss: 0.4876 - val_accuracy: 0.8222\n",
      "Epoch 130/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.4191 - accuracy: 0.9143 - val_loss: 0.4847 - val_accuracy: 0.8222\n",
      "Epoch 131/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.4153 - accuracy: 0.9048 - val_loss: 0.4816 - val_accuracy: 0.8222\n",
      "Epoch 132/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4121 - accuracy: 0.9048 - val_loss: 0.4792 - val_accuracy: 0.8222\n",
      "Epoch 133/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4085 - accuracy: 0.9143 - val_loss: 0.4724 - val_accuracy: 0.8222\n",
      "Epoch 134/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.4045 - accuracy: 0.9143 - val_loss: 0.4703 - val_accuracy: 0.8222\n",
      "Epoch 135/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.4011 - accuracy: 0.9143 - val_loss: 0.4651 - val_accuracy: 0.8222\n",
      "Epoch 136/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3974 - accuracy: 0.9238 - val_loss: 0.4628 - val_accuracy: 0.8222\n",
      "Epoch 137/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3941 - accuracy: 0.9143 - val_loss: 0.4593 - val_accuracy: 0.8222\n",
      "Epoch 138/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3904 - accuracy: 0.9143 - val_loss: 0.4584 - val_accuracy: 0.8222\n",
      "Epoch 139/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.3872 - accuracy: 0.9143 - val_loss: 0.4565 - val_accuracy: 0.8222\n",
      "Epoch 140/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.3839 - accuracy: 0.9143 - val_loss: 0.4564 - val_accuracy: 0.8222\n",
      "Epoch 141/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3811 - accuracy: 0.9048 - val_loss: 0.4541 - val_accuracy: 0.8222\n",
      "Epoch 142/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3782 - accuracy: 0.9048 - val_loss: 0.4531 - val_accuracy: 0.8222\n",
      "Epoch 143/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3750 - accuracy: 0.9048 - val_loss: 0.4487 - val_accuracy: 0.8222\n",
      "Epoch 144/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3718 - accuracy: 0.9143 - val_loss: 0.4424 - val_accuracy: 0.8222\n",
      "Epoch 145/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3682 - accuracy: 0.9143 - val_loss: 0.4354 - val_accuracy: 0.8222\n",
      "Epoch 146/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3647 - accuracy: 0.9429 - val_loss: 0.4294 - val_accuracy: 0.8444\n",
      "Epoch 147/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3617 - accuracy: 0.9524 - val_loss: 0.4225 - val_accuracy: 0.8444\n",
      "Epoch 148/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3594 - accuracy: 0.9714 - val_loss: 0.4161 - val_accuracy: 0.8667\n",
      "Epoch 149/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3560 - accuracy: 0.9714 - val_loss: 0.4130 - val_accuracy: 0.8667\n",
      "Epoch 150/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.3530 - accuracy: 0.9714 - val_loss: 0.4084 - val_accuracy: 0.8667\n",
      "Epoch 151/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3504 - accuracy: 0.9714 - val_loss: 0.4063 - val_accuracy: 0.8667\n",
      "Epoch 152/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3470 - accuracy: 0.9714 - val_loss: 0.4093 - val_accuracy: 0.8667\n",
      "Epoch 153/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3435 - accuracy: 0.9619 - val_loss: 0.4107 - val_accuracy: 0.8444\n",
      "Epoch 154/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3417 - accuracy: 0.9524 - val_loss: 0.4151 - val_accuracy: 0.8222\n",
      "Epoch 155/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3390 - accuracy: 0.9143 - val_loss: 0.4147 - val_accuracy: 0.8222\n",
      "Epoch 156/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3367 - accuracy: 0.9143 - val_loss: 0.4134 - val_accuracy: 0.8222\n",
      "Epoch 157/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3343 - accuracy: 0.9143 - val_loss: 0.4132 - val_accuracy: 0.8222\n",
      "Epoch 158/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3319 - accuracy: 0.9143 - val_loss: 0.4096 - val_accuracy: 0.8222\n",
      "Epoch 159/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3291 - accuracy: 0.9143 - val_loss: 0.4087 - val_accuracy: 0.8222\n",
      "Epoch 160/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3262 - accuracy: 0.9143 - val_loss: 0.4006 - val_accuracy: 0.8444\n",
      "Epoch 161/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3224 - accuracy: 0.9524 - val_loss: 0.3922 - val_accuracy: 0.8444\n",
      "Epoch 162/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3205 - accuracy: 0.9619 - val_loss: 0.3819 - val_accuracy: 0.8667\n",
      "Epoch 163/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3169 - accuracy: 0.9714 - val_loss: 0.3775 - val_accuracy: 0.9111\n",
      "Epoch 164/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3143 - accuracy: 0.9714 - val_loss: 0.3748 - val_accuracy: 0.9111\n",
      "Epoch 165/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3123 - accuracy: 0.9714 - val_loss: 0.3743 - val_accuracy: 0.9111\n",
      "Epoch 166/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3090 - accuracy: 0.9714 - val_loss: 0.3683 - val_accuracy: 0.9111\n",
      "Epoch 167/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3069 - accuracy: 0.9714 - val_loss: 0.3631 - val_accuracy: 0.9111\n",
      "Epoch 168/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.3050 - accuracy: 0.9714 - val_loss: 0.3574 - val_accuracy: 0.9111\n",
      "Epoch 169/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3027 - accuracy: 0.9714 - val_loss: 0.3553 - val_accuracy: 0.9111\n",
      "Epoch 170/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.3002 - accuracy: 0.9714 - val_loss: 0.3526 - val_accuracy: 0.9111\n",
      "Epoch 171/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2976 - accuracy: 0.9714 - val_loss: 0.3528 - val_accuracy: 0.9111\n",
      "Epoch 172/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2948 - accuracy: 0.9714 - val_loss: 0.3529 - val_accuracy: 0.9111\n",
      "Epoch 173/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2924 - accuracy: 0.9714 - val_loss: 0.3552 - val_accuracy: 0.9111\n",
      "Epoch 174/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.2895 - accuracy: 0.9714 - val_loss: 0.3536 - val_accuracy: 0.9111\n",
      "Epoch 175/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2871 - accuracy: 0.9714 - val_loss: 0.3532 - val_accuracy: 0.9111\n",
      "Epoch 176/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2851 - accuracy: 0.9714 - val_loss: 0.3542 - val_accuracy: 0.8889\n",
      "Epoch 177/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2828 - accuracy: 0.9714 - val_loss: 0.3516 - val_accuracy: 0.9111\n",
      "Epoch 178/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.2804 - accuracy: 0.9714 - val_loss: 0.3477 - val_accuracy: 0.9111\n",
      "Epoch 179/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2782 - accuracy: 0.9714 - val_loss: 0.3440 - val_accuracy: 0.9111\n",
      "Epoch 180/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2757 - accuracy: 0.9714 - val_loss: 0.3405 - val_accuracy: 0.9111\n",
      "Epoch 181/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2738 - accuracy: 0.9714 - val_loss: 0.3364 - val_accuracy: 0.9111\n",
      "Epoch 182/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2713 - accuracy: 0.9714 - val_loss: 0.3351 - val_accuracy: 0.9111\n",
      "Epoch 183/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2691 - accuracy: 0.9714 - val_loss: 0.3338 - val_accuracy: 0.9111\n",
      "Epoch 184/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2670 - accuracy: 0.9714 - val_loss: 0.3336 - val_accuracy: 0.9111\n",
      "Epoch 185/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2649 - accuracy: 0.9714 - val_loss: 0.3294 - val_accuracy: 0.9111\n",
      "Epoch 186/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2627 - accuracy: 0.9714 - val_loss: 0.3260 - val_accuracy: 0.9111\n",
      "Epoch 187/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2607 - accuracy: 0.9714 - val_loss: 0.3254 - val_accuracy: 0.9111\n",
      "Epoch 188/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.2584 - accuracy: 0.9714 - val_loss: 0.3257 - val_accuracy: 0.9111\n",
      "Epoch 189/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2566 - accuracy: 0.9714 - val_loss: 0.3246 - val_accuracy: 0.9111\n",
      "Epoch 190/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2546 - accuracy: 0.9714 - val_loss: 0.3204 - val_accuracy: 0.9111\n",
      "Epoch 191/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2526 - accuracy: 0.9714 - val_loss: 0.3176 - val_accuracy: 0.9111\n",
      "Epoch 192/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.2506 - accuracy: 0.9714 - val_loss: 0.3160 - val_accuracy: 0.9111\n",
      "Epoch 193/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.2487 - accuracy: 0.9714 - val_loss: 0.3128 - val_accuracy: 0.9111\n",
      "Epoch 194/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2467 - accuracy: 0.9714 - val_loss: 0.3125 - val_accuracy: 0.9111\n",
      "Epoch 195/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2448 - accuracy: 0.9714 - val_loss: 0.3116 - val_accuracy: 0.9111\n",
      "Epoch 196/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2429 - accuracy: 0.9714 - val_loss: 0.3105 - val_accuracy: 0.9111\n",
      "Epoch 197/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2411 - accuracy: 0.9714 - val_loss: 0.3090 - val_accuracy: 0.9111\n",
      "Epoch 198/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.2392 - accuracy: 0.9714 - val_loss: 0.3063 - val_accuracy: 0.9111\n",
      "Epoch 199/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2373 - accuracy: 0.9714 - val_loss: 0.3040 - val_accuracy: 0.9111\n",
      "Epoch 200/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2355 - accuracy: 0.9714 - val_loss: 0.3036 - val_accuracy: 0.9111\n",
      "Epoch 201/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2338 - accuracy: 0.9714 - val_loss: 0.3034 - val_accuracy: 0.9111\n",
      "Epoch 202/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2320 - accuracy: 0.9714 - val_loss: 0.3009 - val_accuracy: 0.9111\n",
      "Epoch 203/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2307 - accuracy: 0.9810 - val_loss: 0.2969 - val_accuracy: 0.9111\n",
      "Epoch 204/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2285 - accuracy: 0.9810 - val_loss: 0.2951 - val_accuracy: 0.9111\n",
      "Epoch 205/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2267 - accuracy: 0.9810 - val_loss: 0.2888 - val_accuracy: 0.9111\n",
      "Epoch 206/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.2251 - accuracy: 0.9810 - val_loss: 0.2834 - val_accuracy: 0.9333\n",
      "Epoch 207/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2237 - accuracy: 0.9810 - val_loss: 0.2807 - val_accuracy: 0.9333\n",
      "Epoch 208/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2223 - accuracy: 0.9810 - val_loss: 0.2782 - val_accuracy: 0.9333\n",
      "Epoch 209/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2206 - accuracy: 0.9810 - val_loss: 0.2777 - val_accuracy: 0.9333\n",
      "Epoch 210/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2185 - accuracy: 0.9810 - val_loss: 0.2787 - val_accuracy: 0.9333\n",
      "Epoch 211/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2161 - accuracy: 0.9810 - val_loss: 0.2844 - val_accuracy: 0.9111\n",
      "Epoch 212/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2146 - accuracy: 0.9810 - val_loss: 0.2899 - val_accuracy: 0.9111\n",
      "Epoch 213/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2135 - accuracy: 0.9714 - val_loss: 0.2893 - val_accuracy: 0.9111\n",
      "Epoch 214/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2119 - accuracy: 0.9714 - val_loss: 0.2863 - val_accuracy: 0.9111\n",
      "Epoch 215/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2104 - accuracy: 0.9810 - val_loss: 0.2812 - val_accuracy: 0.9111\n",
      "Epoch 216/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2084 - accuracy: 0.9810 - val_loss: 0.2793 - val_accuracy: 0.9111\n",
      "Epoch 217/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2068 - accuracy: 0.9810 - val_loss: 0.2744 - val_accuracy: 0.9111\n",
      "Epoch 218/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2051 - accuracy: 0.9810 - val_loss: 0.2706 - val_accuracy: 0.9333\n",
      "Epoch 219/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.2038 - accuracy: 0.9810 - val_loss: 0.2683 - val_accuracy: 0.9333\n",
      "Epoch 220/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2025 - accuracy: 0.9810 - val_loss: 0.2678 - val_accuracy: 0.9333\n",
      "Epoch 221/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2007 - accuracy: 0.9810 - val_loss: 0.2658 - val_accuracy: 0.9333\n",
      "Epoch 222/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1993 - accuracy: 0.9810 - val_loss: 0.2630 - val_accuracy: 0.9333\n",
      "Epoch 223/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1980 - accuracy: 0.9810 - val_loss: 0.2643 - val_accuracy: 0.9333\n",
      "Epoch 224/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1962 - accuracy: 0.9810 - val_loss: 0.2653 - val_accuracy: 0.9333\n",
      "Epoch 225/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1952 - accuracy: 0.9810 - val_loss: 0.2675 - val_accuracy: 0.9111\n",
      "Epoch 226/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1936 - accuracy: 0.9810 - val_loss: 0.2666 - val_accuracy: 0.9111\n",
      "Epoch 227/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1924 - accuracy: 0.9810 - val_loss: 0.2633 - val_accuracy: 0.9333\n",
      "Epoch 228/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1909 - accuracy: 0.9810 - val_loss: 0.2599 - val_accuracy: 0.9333\n",
      "Epoch 229/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1905 - accuracy: 0.9810 - val_loss: 0.2555 - val_accuracy: 0.9333\n",
      "Epoch 230/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1882 - accuracy: 0.9810 - val_loss: 0.2548 - val_accuracy: 0.9333\n",
      "Epoch 231/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1867 - accuracy: 0.9810 - val_loss: 0.2560 - val_accuracy: 0.9333\n",
      "Epoch 232/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1855 - accuracy: 0.9810 - val_loss: 0.2576 - val_accuracy: 0.9333\n",
      "Epoch 233/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.1846 - accuracy: 0.9810 - val_loss: 0.2568 - val_accuracy: 0.9333\n",
      "Epoch 234/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1834 - accuracy: 0.9810 - val_loss: 0.2509 - val_accuracy: 0.9333\n",
      "Epoch 235/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1818 - accuracy: 0.9810 - val_loss: 0.2484 - val_accuracy: 0.9333\n",
      "Epoch 236/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1812 - accuracy: 0.9810 - val_loss: 0.2416 - val_accuracy: 0.9333\n",
      "Epoch 237/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1800 - accuracy: 0.9810 - val_loss: 0.2401 - val_accuracy: 0.9333\n",
      "Epoch 238/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1787 - accuracy: 0.9810 - val_loss: 0.2416 - val_accuracy: 0.9333\n",
      "Epoch 239/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1771 - accuracy: 0.9810 - val_loss: 0.2433 - val_accuracy: 0.9333\n",
      "Epoch 240/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1757 - accuracy: 0.9810 - val_loss: 0.2436 - val_accuracy: 0.9333\n",
      "Epoch 241/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1744 - accuracy: 0.9810 - val_loss: 0.2438 - val_accuracy: 0.9333\n",
      "Epoch 242/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1732 - accuracy: 0.9810 - val_loss: 0.2445 - val_accuracy: 0.9333\n",
      "Epoch 243/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1722 - accuracy: 0.9810 - val_loss: 0.2446 - val_accuracy: 0.9333\n",
      "Epoch 244/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1710 - accuracy: 0.9810 - val_loss: 0.2420 - val_accuracy: 0.9333\n",
      "Epoch 245/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1699 - accuracy: 0.9810 - val_loss: 0.2380 - val_accuracy: 0.9333\n",
      "Epoch 246/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1689 - accuracy: 0.9810 - val_loss: 0.2361 - val_accuracy: 0.9333\n",
      "Epoch 247/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1677 - accuracy: 0.9810 - val_loss: 0.2356 - val_accuracy: 0.9333\n",
      "Epoch 248/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1667 - accuracy: 0.9810 - val_loss: 0.2349 - val_accuracy: 0.9333\n",
      "Epoch 249/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1655 - accuracy: 0.9810 - val_loss: 0.2365 - val_accuracy: 0.9333\n",
      "Epoch 250/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1643 - accuracy: 0.9810 - val_loss: 0.2368 - val_accuracy: 0.9333\n",
      "Epoch 251/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.1632 - accuracy: 0.9810 - val_loss: 0.2392 - val_accuracy: 0.9333\n",
      "Epoch 252/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1627 - accuracy: 0.9810 - val_loss: 0.2407 - val_accuracy: 0.9333\n",
      "Epoch 253/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1618 - accuracy: 0.9810 - val_loss: 0.2419 - val_accuracy: 0.9333\n",
      "Epoch 254/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1606 - accuracy: 0.9810 - val_loss: 0.2376 - val_accuracy: 0.9333\n",
      "Epoch 255/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1593 - accuracy: 0.9810 - val_loss: 0.2306 - val_accuracy: 0.9333\n",
      "Epoch 256/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1581 - accuracy: 0.9810 - val_loss: 0.2263 - val_accuracy: 0.9333\n",
      "Epoch 257/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1571 - accuracy: 0.9810 - val_loss: 0.2255 - val_accuracy: 0.9333\n",
      "Epoch 258/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1560 - accuracy: 0.9810 - val_loss: 0.2239 - val_accuracy: 0.9333\n",
      "Epoch 259/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1552 - accuracy: 0.9810 - val_loss: 0.2253 - val_accuracy: 0.9333\n",
      "Epoch 260/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1541 - accuracy: 0.9810 - val_loss: 0.2283 - val_accuracy: 0.9333\n",
      "Epoch 261/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1533 - accuracy: 0.9810 - val_loss: 0.2294 - val_accuracy: 0.9333\n",
      "Epoch 262/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1521 - accuracy: 0.9810 - val_loss: 0.2245 - val_accuracy: 0.9333\n",
      "Epoch 263/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1511 - accuracy: 0.9810 - val_loss: 0.2183 - val_accuracy: 0.9333\n",
      "Epoch 264/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1506 - accuracy: 0.9810 - val_loss: 0.2153 - val_accuracy: 0.9333\n",
      "Epoch 265/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1497 - accuracy: 0.9810 - val_loss: 0.2154 - val_accuracy: 0.9333\n",
      "Epoch 266/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1488 - accuracy: 0.9810 - val_loss: 0.2169 - val_accuracy: 0.9333\n",
      "Epoch 267/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.1475 - accuracy: 0.9810 - val_loss: 0.2170 - val_accuracy: 0.9333\n",
      "Epoch 268/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1465 - accuracy: 0.9810 - val_loss: 0.2211 - val_accuracy: 0.9333\n",
      "Epoch 269/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1457 - accuracy: 0.9810 - val_loss: 0.2226 - val_accuracy: 0.9333\n",
      "Epoch 270/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.1455 - accuracy: 0.9810 - val_loss: 0.2245 - val_accuracy: 0.9333\n",
      "Epoch 271/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1441 - accuracy: 0.9810 - val_loss: 0.2203 - val_accuracy: 0.9333\n",
      "Epoch 272/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1433 - accuracy: 0.9810 - val_loss: 0.2164 - val_accuracy: 0.9333\n",
      "Epoch 273/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1420 - accuracy: 0.9810 - val_loss: 0.2163 - val_accuracy: 0.9333\n",
      "Epoch 274/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1412 - accuracy: 0.9810 - val_loss: 0.2151 - val_accuracy: 0.9333\n",
      "Epoch 275/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1404 - accuracy: 0.9810 - val_loss: 0.2130 - val_accuracy: 0.9333\n",
      "Epoch 276/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1395 - accuracy: 0.9810 - val_loss: 0.2125 - val_accuracy: 0.9333\n",
      "Epoch 277/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1387 - accuracy: 0.9810 - val_loss: 0.2120 - val_accuracy: 0.9333\n",
      "Epoch 278/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1380 - accuracy: 0.9810 - val_loss: 0.2118 - val_accuracy: 0.9333\n",
      "Epoch 279/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1371 - accuracy: 0.9810 - val_loss: 0.2103 - val_accuracy: 0.9333\n",
      "Epoch 280/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1363 - accuracy: 0.9810 - val_loss: 0.2080 - val_accuracy: 0.9333\n",
      "Epoch 281/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1355 - accuracy: 0.9810 - val_loss: 0.2070 - val_accuracy: 0.9333\n",
      "Epoch 282/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.1350 - accuracy: 0.9810 - val_loss: 0.2053 - val_accuracy: 0.9333\n",
      "Epoch 283/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1339 - accuracy: 0.9810 - val_loss: 0.2067 - val_accuracy: 0.9333\n",
      "Epoch 284/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1331 - accuracy: 0.9810 - val_loss: 0.2075 - val_accuracy: 0.9333\n",
      "Epoch 285/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1327 - accuracy: 0.9810 - val_loss: 0.2113 - val_accuracy: 0.9333\n",
      "Epoch 286/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1319 - accuracy: 0.9810 - val_loss: 0.2110 - val_accuracy: 0.9333\n",
      "Epoch 287/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1313 - accuracy: 0.9810 - val_loss: 0.2118 - val_accuracy: 0.9333\n",
      "Epoch 288/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1305 - accuracy: 0.9810 - val_loss: 0.2092 - val_accuracy: 0.9333\n",
      "Epoch 289/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1297 - accuracy: 0.9810 - val_loss: 0.2057 - val_accuracy: 0.9333\n",
      "Epoch 290/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1292 - accuracy: 0.9810 - val_loss: 0.2022 - val_accuracy: 0.9333\n",
      "Epoch 291/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1280 - accuracy: 0.9810 - val_loss: 0.2006 - val_accuracy: 0.9333\n",
      "Epoch 292/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1274 - accuracy: 0.9810 - val_loss: 0.2002 - val_accuracy: 0.9333\n",
      "Epoch 293/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1266 - accuracy: 0.9810 - val_loss: 0.2017 - val_accuracy: 0.9333\n",
      "Epoch 294/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1262 - accuracy: 0.9810 - val_loss: 0.2037 - val_accuracy: 0.9333\n",
      "Epoch 295/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1254 - accuracy: 0.9810 - val_loss: 0.2059 - val_accuracy: 0.9333\n",
      "Epoch 296/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1249 - accuracy: 0.9810 - val_loss: 0.2106 - val_accuracy: 0.9333\n",
      "Epoch 297/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1250 - accuracy: 0.9810 - val_loss: 0.2127 - val_accuracy: 0.9333\n",
      "Epoch 298/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1249 - accuracy: 0.9810 - val_loss: 0.2106 - val_accuracy: 0.9333\n",
      "Epoch 299/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1237 - accuracy: 0.9810 - val_loss: 0.2047 - val_accuracy: 0.9333\n",
      "Epoch 300/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.1222 - accuracy: 0.9810 - val_loss: 0.1987 - val_accuracy: 0.9333\n",
      "Epoch 301/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1215 - accuracy: 0.9810 - val_loss: 0.1954 - val_accuracy: 0.9333\n",
      "Epoch 302/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1208 - accuracy: 0.9810 - val_loss: 0.1954 - val_accuracy: 0.9333\n",
      "Epoch 303/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1202 - accuracy: 0.9810 - val_loss: 0.1950 - val_accuracy: 0.9333\n",
      "Epoch 304/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1196 - accuracy: 0.9810 - val_loss: 0.1958 - val_accuracy: 0.9333\n",
      "Epoch 305/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1190 - accuracy: 0.9810 - val_loss: 0.1958 - val_accuracy: 0.9333\n",
      "Epoch 306/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1186 - accuracy: 0.9810 - val_loss: 0.1957 - val_accuracy: 0.9333\n",
      "Epoch 307/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1178 - accuracy: 0.9810 - val_loss: 0.1928 - val_accuracy: 0.9333\n",
      "Epoch 308/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1184 - accuracy: 0.9810 - val_loss: 0.1855 - val_accuracy: 0.9333\n",
      "Epoch 309/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1172 - accuracy: 0.9905 - val_loss: 0.1849 - val_accuracy: 0.9333\n",
      "Epoch 310/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1162 - accuracy: 0.9905 - val_loss: 0.1878 - val_accuracy: 0.9333\n",
      "Epoch 311/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.1154 - accuracy: 0.9810 - val_loss: 0.1923 - val_accuracy: 0.9333\n",
      "Epoch 312/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1150 - accuracy: 0.9810 - val_loss: 0.1961 - val_accuracy: 0.9333\n",
      "Epoch 313/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1146 - accuracy: 0.9810 - val_loss: 0.1984 - val_accuracy: 0.9333\n",
      "Epoch 314/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.1142 - accuracy: 0.9810 - val_loss: 0.1963 - val_accuracy: 0.9333\n",
      "Epoch 315/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1135 - accuracy: 0.9810 - val_loss: 0.1900 - val_accuracy: 0.9333\n",
      "Epoch 316/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1126 - accuracy: 0.9810 - val_loss: 0.1887 - val_accuracy: 0.9333\n",
      "Epoch 317/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1123 - accuracy: 0.9810 - val_loss: 0.1875 - val_accuracy: 0.9333\n",
      "Epoch 318/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1115 - accuracy: 0.9810 - val_loss: 0.1892 - val_accuracy: 0.9333\n",
      "Epoch 319/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1113 - accuracy: 0.9810 - val_loss: 0.1929 - val_accuracy: 0.9333\n",
      "Epoch 320/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1110 - accuracy: 0.9810 - val_loss: 0.1926 - val_accuracy: 0.9333\n",
      "Epoch 321/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1106 - accuracy: 0.9810 - val_loss: 0.1854 - val_accuracy: 0.9333\n",
      "Epoch 322/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1094 - accuracy: 0.9810 - val_loss: 0.1829 - val_accuracy: 0.9333\n",
      "Epoch 323/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1093 - accuracy: 0.9905 - val_loss: 0.1766 - val_accuracy: 0.9556\n",
      "Epoch 324/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1095 - accuracy: 0.9905 - val_loss: 0.1742 - val_accuracy: 0.9556\n",
      "Epoch 325/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1093 - accuracy: 0.9905 - val_loss: 0.1736 - val_accuracy: 0.9556\n",
      "Epoch 326/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1088 - accuracy: 0.9905 - val_loss: 0.1747 - val_accuracy: 0.9556\n",
      "Epoch 327/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1080 - accuracy: 0.9905 - val_loss: 0.1756 - val_accuracy: 0.9556\n",
      "Epoch 328/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.1076 - accuracy: 0.9905 - val_loss: 0.1749 - val_accuracy: 0.9556\n",
      "Epoch 329/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1081 - accuracy: 0.9810 - val_loss: 0.1820 - val_accuracy: 0.9333\n",
      "Epoch 330/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1054 - accuracy: 0.9810 - val_loss: 0.1850 - val_accuracy: 0.9333\n",
      "Epoch 331/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1055 - accuracy: 0.9810 - val_loss: 0.1875 - val_accuracy: 0.9333\n",
      "Epoch 332/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1048 - accuracy: 0.9810 - val_loss: 0.1862 - val_accuracy: 0.9333\n",
      "Epoch 333/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1042 - accuracy: 0.9810 - val_loss: 0.1844 - val_accuracy: 0.9333\n",
      "Epoch 334/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1036 - accuracy: 0.9810 - val_loss: 0.1833 - val_accuracy: 0.9333\n",
      "Epoch 335/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1032 - accuracy: 0.9810 - val_loss: 0.1818 - val_accuracy: 0.9333\n",
      "Epoch 336/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1034 - accuracy: 0.9810 - val_loss: 0.1783 - val_accuracy: 0.9333\n",
      "Epoch 337/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1022 - accuracy: 0.9810 - val_loss: 0.1793 - val_accuracy: 0.9333\n",
      "Epoch 338/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1018 - accuracy: 0.9810 - val_loss: 0.1786 - val_accuracy: 0.9333\n",
      "Epoch 339/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1013 - accuracy: 0.9810 - val_loss: 0.1773 - val_accuracy: 0.9333\n",
      "Epoch 340/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1013 - accuracy: 0.9810 - val_loss: 0.1763 - val_accuracy: 0.9333\n",
      "Epoch 341/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1009 - accuracy: 0.9810 - val_loss: 0.1830 - val_accuracy: 0.9333\n",
      "Epoch 342/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.1003 - accuracy: 0.9810 - val_loss: 0.1862 - val_accuracy: 0.9333\n",
      "Epoch 343/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1001 - accuracy: 0.9810 - val_loss: 0.1864 - val_accuracy: 0.9333\n",
      "Epoch 344/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.0998 - accuracy: 0.9810 - val_loss: 0.1859 - val_accuracy: 0.9333\n",
      "Epoch 345/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0994 - accuracy: 0.9810 - val_loss: 0.1837 - val_accuracy: 0.9333\n",
      "Epoch 346/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0994 - accuracy: 0.9810 - val_loss: 0.1755 - val_accuracy: 0.9333\n",
      "Epoch 347/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0979 - accuracy: 0.9810 - val_loss: 0.1734 - val_accuracy: 0.9333\n",
      "Epoch 348/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0977 - accuracy: 0.9905 - val_loss: 0.1689 - val_accuracy: 0.9556\n",
      "Epoch 349/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0976 - accuracy: 0.9905 - val_loss: 0.1691 - val_accuracy: 0.9556\n",
      "Epoch 350/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0972 - accuracy: 0.9905 - val_loss: 0.1730 - val_accuracy: 0.9333\n",
      "Epoch 351/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0962 - accuracy: 0.9810 - val_loss: 0.1767 - val_accuracy: 0.9333\n",
      "Epoch 352/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0961 - accuracy: 0.9810 - val_loss: 0.1790 - val_accuracy: 0.9333\n",
      "Epoch 353/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0959 - accuracy: 0.9810 - val_loss: 0.1749 - val_accuracy: 0.9333\n",
      "Epoch 354/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0952 - accuracy: 0.9810 - val_loss: 0.1729 - val_accuracy: 0.9333\n",
      "Epoch 355/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0950 - accuracy: 0.9810 - val_loss: 0.1704 - val_accuracy: 0.9333\n",
      "Epoch 356/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0945 - accuracy: 0.9810 - val_loss: 0.1704 - val_accuracy: 0.9333\n",
      "Epoch 357/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0945 - accuracy: 0.9810 - val_loss: 0.1717 - val_accuracy: 0.9333\n",
      "Epoch 358/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0938 - accuracy: 0.9810 - val_loss: 0.1690 - val_accuracy: 0.9556\n",
      "Epoch 359/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0938 - accuracy: 0.9810 - val_loss: 0.1701 - val_accuracy: 0.9333\n",
      "Epoch 360/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0932 - accuracy: 0.9810 - val_loss: 0.1708 - val_accuracy: 0.9333\n",
      "Epoch 361/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0928 - accuracy: 0.9810 - val_loss: 0.1701 - val_accuracy: 0.9333\n",
      "Epoch 362/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0924 - accuracy: 0.9810 - val_loss: 0.1655 - val_accuracy: 0.9556\n",
      "Epoch 363/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0922 - accuracy: 0.9905 - val_loss: 0.1601 - val_accuracy: 0.9556\n",
      "Epoch 364/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0937 - accuracy: 0.9905 - val_loss: 0.1583 - val_accuracy: 0.9556\n",
      "Epoch 365/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0929 - accuracy: 0.9905 - val_loss: 0.1625 - val_accuracy: 0.9556\n",
      "Epoch 366/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.0929 - accuracy: 0.9810 - val_loss: 0.1716 - val_accuracy: 0.9333\n",
      "Epoch 367/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0908 - accuracy: 0.9810 - val_loss: 0.1726 - val_accuracy: 0.9333\n",
      "Epoch 368/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0904 - accuracy: 0.9810 - val_loss: 0.1694 - val_accuracy: 0.9333\n",
      "Epoch 369/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0899 - accuracy: 0.9810 - val_loss: 0.1676 - val_accuracy: 0.9556\n",
      "Epoch 370/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0896 - accuracy: 0.9810 - val_loss: 0.1651 - val_accuracy: 0.9556\n",
      "Epoch 371/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0898 - accuracy: 0.9905 - val_loss: 0.1622 - val_accuracy: 0.9556\n",
      "Epoch 372/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0898 - accuracy: 0.9905 - val_loss: 0.1628 - val_accuracy: 0.9556\n",
      "Epoch 373/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0895 - accuracy: 0.9905 - val_loss: 0.1589 - val_accuracy: 0.9556\n",
      "Epoch 374/1000\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0902 - accuracy: 0.9905 - val_loss: 0.1566 - val_accuracy: 0.9556\n",
      "Epoch 375/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0894 - accuracy: 0.9905 - val_loss: 0.1590 - val_accuracy: 0.9556\n",
      "Epoch 376/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0883 - accuracy: 0.9905 - val_loss: 0.1619 - val_accuracy: 0.9556\n",
      "Epoch 377/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0873 - accuracy: 0.9905 - val_loss: 0.1670 - val_accuracy: 0.9333\n",
      "Epoch 378/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0875 - accuracy: 0.9810 - val_loss: 0.1755 - val_accuracy: 0.9333\n",
      "Epoch 379/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0890 - accuracy: 0.9810 - val_loss: 0.1796 - val_accuracy: 0.9333\n",
      "Epoch 380/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0876 - accuracy: 0.9810 - val_loss: 0.1739 - val_accuracy: 0.9333\n",
      "Epoch 381/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0863 - accuracy: 0.9810 - val_loss: 0.1667 - val_accuracy: 0.9333\n",
      "Epoch 382/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0857 - accuracy: 0.9810 - val_loss: 0.1620 - val_accuracy: 0.9556\n",
      "Epoch 383/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0857 - accuracy: 0.9905 - val_loss: 0.1596 - val_accuracy: 0.9556\n",
      "Epoch 384/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0860 - accuracy: 0.9905 - val_loss: 0.1575 - val_accuracy: 0.9556\n",
      "Epoch 385/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0863 - accuracy: 0.9905 - val_loss: 0.1569 - val_accuracy: 0.9556\n",
      "Epoch 386/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0855 - accuracy: 0.9905 - val_loss: 0.1597 - val_accuracy: 0.9556\n",
      "Epoch 387/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0840 - accuracy: 0.9905 - val_loss: 0.1694 - val_accuracy: 0.9333\n",
      "Epoch 388/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0853 - accuracy: 0.9810 - val_loss: 0.1783 - val_accuracy: 0.9333\n",
      "Epoch 389/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0856 - accuracy: 0.9810 - val_loss: 0.1794 - val_accuracy: 0.9333\n",
      "Epoch 390/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0856 - accuracy: 0.9810 - val_loss: 0.1779 - val_accuracy: 0.9333\n",
      "Epoch 391/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0843 - accuracy: 0.9810 - val_loss: 0.1700 - val_accuracy: 0.9333\n",
      "Epoch 392/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0832 - accuracy: 0.9810 - val_loss: 0.1639 - val_accuracy: 0.9556\n",
      "Epoch 393/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0831 - accuracy: 0.9810 - val_loss: 0.1604 - val_accuracy: 0.9556\n",
      "Epoch 394/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0826 - accuracy: 0.9810 - val_loss: 0.1614 - val_accuracy: 0.9556\n",
      "Epoch 395/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0825 - accuracy: 0.9810 - val_loss: 0.1644 - val_accuracy: 0.9333\n",
      "Epoch 396/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0825 - accuracy: 0.9810 - val_loss: 0.1655 - val_accuracy: 0.9333\n",
      "Epoch 397/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0819 - accuracy: 0.9810 - val_loss: 0.1600 - val_accuracy: 0.9556\n",
      "Epoch 398/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0819 - accuracy: 0.9905 - val_loss: 0.1541 - val_accuracy: 0.9556\n",
      "Epoch 399/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0826 - accuracy: 0.9905 - val_loss: 0.1527 - val_accuracy: 0.9556\n",
      "Epoch 400/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0818 - accuracy: 0.9905 - val_loss: 0.1575 - val_accuracy: 0.9556\n",
      "Epoch 401/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0812 - accuracy: 0.9810 - val_loss: 0.1611 - val_accuracy: 0.9556\n",
      "Epoch 402/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0809 - accuracy: 0.9810 - val_loss: 0.1597 - val_accuracy: 0.9556\n",
      "Epoch 403/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0806 - accuracy: 0.9810 - val_loss: 0.1633 - val_accuracy: 0.9556\n",
      "Epoch 404/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0800 - accuracy: 0.9810 - val_loss: 0.1659 - val_accuracy: 0.9333\n",
      "Epoch 405/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0806 - accuracy: 0.9810 - val_loss: 0.1680 - val_accuracy: 0.9333\n",
      "Epoch 406/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0803 - accuracy: 0.9810 - val_loss: 0.1625 - val_accuracy: 0.9556\n",
      "Epoch 407/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0794 - accuracy: 0.9810 - val_loss: 0.1596 - val_accuracy: 0.9556\n",
      "Epoch 408/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0787 - accuracy: 0.9810 - val_loss: 0.1536 - val_accuracy: 0.9556\n",
      "Epoch 409/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0804 - accuracy: 0.9905 - val_loss: 0.1489 - val_accuracy: 0.9556\n",
      "Epoch 410/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0803 - accuracy: 0.9905 - val_loss: 0.1500 - val_accuracy: 0.9556\n",
      "Epoch 411/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0793 - accuracy: 0.9905 - val_loss: 0.1564 - val_accuracy: 0.9556\n",
      "Epoch 412/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0783 - accuracy: 0.9810 - val_loss: 0.1607 - val_accuracy: 0.9556\n",
      "Epoch 413/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0781 - accuracy: 0.9810 - val_loss: 0.1602 - val_accuracy: 0.9556\n",
      "Epoch 414/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0778 - accuracy: 0.9810 - val_loss: 0.1587 - val_accuracy: 0.9556\n",
      "Epoch 415/1000\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0776 - accuracy: 0.9810 - val_loss: 0.1590 - val_accuracy: 0.9556\n",
      "Epoch 416/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0773 - accuracy: 0.9810 - val_loss: 0.1601 - val_accuracy: 0.9556\n",
      "Epoch 417/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0772 - accuracy: 0.9810 - val_loss: 0.1607 - val_accuracy: 0.9556\n",
      "Epoch 418/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0774 - accuracy: 0.9810 - val_loss: 0.1640 - val_accuracy: 0.9333\n",
      "Epoch 419/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0766 - accuracy: 0.9810 - val_loss: 0.1580 - val_accuracy: 0.9556\n",
      "Epoch 420/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0764 - accuracy: 0.9905 - val_loss: 0.1512 - val_accuracy: 0.9556\n",
      "Epoch 421/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0775 - accuracy: 0.9905 - val_loss: 0.1464 - val_accuracy: 0.9556\n",
      "Epoch 422/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0776 - accuracy: 0.9905 - val_loss: 0.1479 - val_accuracy: 0.9556\n",
      "Epoch 423/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0763 - accuracy: 0.9905 - val_loss: 0.1552 - val_accuracy: 0.9556\n",
      "Epoch 424/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0763 - accuracy: 0.9810 - val_loss: 0.1661 - val_accuracy: 0.9333\n",
      "Epoch 425/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0758 - accuracy: 0.9810 - val_loss: 0.1703 - val_accuracy: 0.9333\n",
      "Epoch 426/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0767 - accuracy: 0.9810 - val_loss: 0.1724 - val_accuracy: 0.9333\n",
      "Epoch 427/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.0769 - accuracy: 0.9810 - val_loss: 0.1655 - val_accuracy: 0.9333\n",
      "Epoch 428/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0757 - accuracy: 0.9810 - val_loss: 0.1601 - val_accuracy: 0.9556\n",
      "Epoch 429/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0746 - accuracy: 0.9810 - val_loss: 0.1583 - val_accuracy: 0.9556\n",
      "Epoch 430/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0743 - accuracy: 0.9810 - val_loss: 0.1558 - val_accuracy: 0.9556\n",
      "Epoch 431/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0743 - accuracy: 0.9810 - val_loss: 0.1533 - val_accuracy: 0.9556\n",
      "Epoch 432/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0741 - accuracy: 0.9810 - val_loss: 0.1548 - val_accuracy: 0.9556\n",
      "Epoch 433/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0738 - accuracy: 0.9810 - val_loss: 0.1561 - val_accuracy: 0.9556\n",
      "Epoch 434/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0732 - accuracy: 0.9810 - val_loss: 0.1599 - val_accuracy: 0.9556\n",
      "Epoch 435/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0737 - accuracy: 0.9810 - val_loss: 0.1628 - val_accuracy: 0.9333\n",
      "Epoch 436/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0735 - accuracy: 0.9810 - val_loss: 0.1602 - val_accuracy: 0.9556\n",
      "Epoch 437/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0731 - accuracy: 0.9810 - val_loss: 0.1570 - val_accuracy: 0.9556\n",
      "Epoch 438/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0729 - accuracy: 0.9810 - val_loss: 0.1565 - val_accuracy: 0.9556\n",
      "Epoch 439/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0726 - accuracy: 0.9810 - val_loss: 0.1580 - val_accuracy: 0.9556\n",
      "Epoch 440/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0725 - accuracy: 0.9810 - val_loss: 0.1584 - val_accuracy: 0.9556\n",
      "Epoch 441/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0725 - accuracy: 0.9810 - val_loss: 0.1566 - val_accuracy: 0.9556\n",
      "Epoch 442/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0717 - accuracy: 0.9810 - val_loss: 0.1500 - val_accuracy: 0.9556\n",
      "Epoch 443/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0722 - accuracy: 0.9905 - val_loss: 0.1451 - val_accuracy: 0.9556\n",
      "Epoch 444/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0727 - accuracy: 0.9905 - val_loss: 0.1449 - val_accuracy: 0.9556\n",
      "Epoch 445/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0727 - accuracy: 0.9905 - val_loss: 0.1462 - val_accuracy: 0.9556\n",
      "Epoch 446/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0716 - accuracy: 0.9905 - val_loss: 0.1515 - val_accuracy: 0.9556\n",
      "Epoch 447/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0706 - accuracy: 0.9905 - val_loss: 0.1573 - val_accuracy: 0.9556\n",
      "Epoch 448/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0716 - accuracy: 0.9810 - val_loss: 0.1628 - val_accuracy: 0.9333\n",
      "Epoch 449/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0716 - accuracy: 0.9810 - val_loss: 0.1629 - val_accuracy: 0.9333\n",
      "Epoch 450/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0714 - accuracy: 0.9810 - val_loss: 0.1612 - val_accuracy: 0.9333\n",
      "Epoch 451/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0710 - accuracy: 0.9810 - val_loss: 0.1599 - val_accuracy: 0.9556\n",
      "Epoch 452/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0707 - accuracy: 0.9810 - val_loss: 0.1578 - val_accuracy: 0.9556\n",
      "Epoch 453/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0704 - accuracy: 0.9810 - val_loss: 0.1532 - val_accuracy: 0.9556\n",
      "Epoch 454/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0699 - accuracy: 0.9810 - val_loss: 0.1513 - val_accuracy: 0.9556\n",
      "Epoch 455/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0697 - accuracy: 0.9810 - val_loss: 0.1491 - val_accuracy: 0.9556\n",
      "Epoch 456/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0703 - accuracy: 0.9905 - val_loss: 0.1457 - val_accuracy: 0.9556\n",
      "Epoch 457/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0699 - accuracy: 0.9905 - val_loss: 0.1470 - val_accuracy: 0.9556\n",
      "Epoch 458/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0700 - accuracy: 0.9810 - val_loss: 0.1523 - val_accuracy: 0.9556\n",
      "Epoch 459/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0692 - accuracy: 0.9810 - val_loss: 0.1538 - val_accuracy: 0.9556\n",
      "Epoch 460/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0690 - accuracy: 0.9810 - val_loss: 0.1550 - val_accuracy: 0.9556\n",
      "Epoch 461/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0690 - accuracy: 0.9810 - val_loss: 0.1564 - val_accuracy: 0.9556\n",
      "Epoch 462/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0690 - accuracy: 0.9810 - val_loss: 0.1589 - val_accuracy: 0.9556\n",
      "Epoch 463/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0692 - accuracy: 0.9810 - val_loss: 0.1602 - val_accuracy: 0.9556\n",
      "Epoch 464/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0687 - accuracy: 0.9810 - val_loss: 0.1568 - val_accuracy: 0.9556\n",
      "Epoch 465/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0685 - accuracy: 0.9810 - val_loss: 0.1522 - val_accuracy: 0.9556\n",
      "Epoch 466/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0685 - accuracy: 0.9810 - val_loss: 0.1493 - val_accuracy: 0.9556\n",
      "Epoch 467/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0678 - accuracy: 0.9905 - val_loss: 0.1502 - val_accuracy: 0.9556\n",
      "Epoch 468/1000\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0677 - accuracy: 0.9810 - val_loss: 0.1525 - val_accuracy: 0.9556\n",
      "Epoch 469/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0675 - accuracy: 0.9810 - val_loss: 0.1524 - val_accuracy: 0.9556\n",
      "Epoch 470/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0674 - accuracy: 0.9810 - val_loss: 0.1537 - val_accuracy: 0.9556\n",
      "Epoch 471/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0675 - accuracy: 0.9810 - val_loss: 0.1521 - val_accuracy: 0.9556\n",
      "Epoch 472/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0671 - accuracy: 0.9810 - val_loss: 0.1530 - val_accuracy: 0.9556\n",
      "Epoch 473/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0673 - accuracy: 0.9810 - val_loss: 0.1532 - val_accuracy: 0.9556\n",
      "Epoch 474/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0674 - accuracy: 0.9810 - val_loss: 0.1463 - val_accuracy: 0.9556\n",
      "Epoch 475/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0668 - accuracy: 0.9905 - val_loss: 0.1449 - val_accuracy: 0.9556\n",
      "Epoch 476/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0668 - accuracy: 0.9905 - val_loss: 0.1466 - val_accuracy: 0.9556\n",
      "Epoch 477/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0663 - accuracy: 0.9905 - val_loss: 0.1496 - val_accuracy: 0.9556\n",
      "Epoch 478/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0663 - accuracy: 0.9810 - val_loss: 0.1499 - val_accuracy: 0.9556\n",
      "Epoch 479/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0660 - accuracy: 0.9810 - val_loss: 0.1474 - val_accuracy: 0.9556\n",
      "Epoch 480/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0660 - accuracy: 0.9905 - val_loss: 0.1471 - val_accuracy: 0.9556\n",
      "Epoch 481/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0658 - accuracy: 0.9905 - val_loss: 0.1488 - val_accuracy: 0.9556\n",
      "Epoch 482/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0656 - accuracy: 0.9810 - val_loss: 0.1533 - val_accuracy: 0.9556\n",
      "Epoch 483/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0658 - accuracy: 0.9810 - val_loss: 0.1545 - val_accuracy: 0.9556\n",
      "Epoch 484/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0662 - accuracy: 0.9810 - val_loss: 0.1490 - val_accuracy: 0.9556\n",
      "Epoch 485/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0652 - accuracy: 0.9810 - val_loss: 0.1489 - val_accuracy: 0.9556\n",
      "Epoch 486/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0650 - accuracy: 0.9810 - val_loss: 0.1494 - val_accuracy: 0.9556\n",
      "Epoch 487/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0648 - accuracy: 0.9810 - val_loss: 0.1508 - val_accuracy: 0.9556\n",
      "Epoch 488/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0647 - accuracy: 0.9810 - val_loss: 0.1543 - val_accuracy: 0.9556\n",
      "Epoch 489/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0653 - accuracy: 0.9810 - val_loss: 0.1563 - val_accuracy: 0.9556\n",
      "Epoch 490/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0652 - accuracy: 0.9810 - val_loss: 0.1582 - val_accuracy: 0.9556\n",
      "Epoch 491/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0656 - accuracy: 0.9810 - val_loss: 0.1572 - val_accuracy: 0.9556\n",
      "Epoch 492/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0645 - accuracy: 0.9810 - val_loss: 0.1447 - val_accuracy: 0.9556\n",
      "Epoch 493/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0644 - accuracy: 0.9905 - val_loss: 0.1369 - val_accuracy: 0.9556\n",
      "Epoch 494/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0657 - accuracy: 0.9905 - val_loss: 0.1350 - val_accuracy: 0.9556\n",
      "Epoch 495/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0668 - accuracy: 0.9905 - val_loss: 0.1338 - val_accuracy: 0.9556\n",
      "Epoch 496/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0666 - accuracy: 0.9905 - val_loss: 0.1366 - val_accuracy: 0.9556\n",
      "Epoch 497/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0646 - accuracy: 0.9905 - val_loss: 0.1418 - val_accuracy: 0.9556\n",
      "Epoch 498/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0637 - accuracy: 0.9905 - val_loss: 0.1488 - val_accuracy: 0.9556\n",
      "Epoch 499/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0635 - accuracy: 0.9810 - val_loss: 0.1528 - val_accuracy: 0.9556\n",
      "Epoch 500/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0643 - accuracy: 0.9810 - val_loss: 0.1558 - val_accuracy: 0.9556\n",
      "Epoch 501/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0635 - accuracy: 0.9810 - val_loss: 0.1541 - val_accuracy: 0.9556\n",
      "Epoch 502/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0632 - accuracy: 0.9810 - val_loss: 0.1508 - val_accuracy: 0.9556\n",
      "Epoch 503/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0629 - accuracy: 0.9810 - val_loss: 0.1481 - val_accuracy: 0.9556\n",
      "Epoch 504/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0628 - accuracy: 0.9810 - val_loss: 0.1453 - val_accuracy: 0.9556\n",
      "Epoch 505/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0624 - accuracy: 0.9905 - val_loss: 0.1402 - val_accuracy: 0.9556\n",
      "Epoch 506/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0642 - accuracy: 0.9905 - val_loss: 0.1346 - val_accuracy: 0.9556\n",
      "Epoch 507/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0650 - accuracy: 0.9905 - val_loss: 0.1341 - val_accuracy: 0.9556\n",
      "Epoch 508/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0647 - accuracy: 0.9905 - val_loss: 0.1388 - val_accuracy: 0.9556\n",
      "Epoch 509/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0633 - accuracy: 0.9905 - val_loss: 0.1405 - val_accuracy: 0.9556\n",
      "Epoch 510/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0628 - accuracy: 0.9905 - val_loss: 0.1384 - val_accuracy: 0.9556\n",
      "Epoch 511/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0634 - accuracy: 0.9905 - val_loss: 0.1424 - val_accuracy: 0.9556\n",
      "Epoch 512/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0620 - accuracy: 0.9905 - val_loss: 0.1437 - val_accuracy: 0.9556\n",
      "Epoch 513/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0619 - accuracy: 0.9905 - val_loss: 0.1456 - val_accuracy: 0.9556\n",
      "Epoch 514/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0617 - accuracy: 0.9810 - val_loss: 0.1470 - val_accuracy: 0.9556\n",
      "Epoch 515/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0617 - accuracy: 0.9810 - val_loss: 0.1489 - val_accuracy: 0.9556\n",
      "Epoch 516/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0624 - accuracy: 0.9810 - val_loss: 0.1522 - val_accuracy: 0.9556\n",
      "Epoch 517/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0615 - accuracy: 0.9810 - val_loss: 0.1487 - val_accuracy: 0.9556\n",
      "Epoch 518/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0615 - accuracy: 0.9810 - val_loss: 0.1446 - val_accuracy: 0.9556\n",
      "Epoch 519/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0611 - accuracy: 0.9810 - val_loss: 0.1461 - val_accuracy: 0.9556\n",
      "Epoch 520/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0610 - accuracy: 0.9810 - val_loss: 0.1504 - val_accuracy: 0.9556\n",
      "Epoch 521/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0608 - accuracy: 0.9810 - val_loss: 0.1531 - val_accuracy: 0.9556\n",
      "Epoch 522/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0615 - accuracy: 0.9810 - val_loss: 0.1556 - val_accuracy: 0.9556\n",
      "Epoch 523/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0611 - accuracy: 0.9810 - val_loss: 0.1510 - val_accuracy: 0.9556\n",
      "Epoch 524/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0608 - accuracy: 0.9810 - val_loss: 0.1454 - val_accuracy: 0.9556\n",
      "Epoch 525/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0604 - accuracy: 0.9810 - val_loss: 0.1430 - val_accuracy: 0.9556\n",
      "Epoch 526/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0605 - accuracy: 0.9905 - val_loss: 0.1422 - val_accuracy: 0.9556\n",
      "Epoch 527/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0604 - accuracy: 0.9905 - val_loss: 0.1439 - val_accuracy: 0.9556\n",
      "Epoch 528/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0600 - accuracy: 0.9905 - val_loss: 0.1455 - val_accuracy: 0.9556\n",
      "Epoch 529/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0602 - accuracy: 0.9810 - val_loss: 0.1497 - val_accuracy: 0.9556\n",
      "Epoch 530/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0607 - accuracy: 0.9810 - val_loss: 0.1506 - val_accuracy: 0.9556\n",
      "Epoch 531/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0599 - accuracy: 0.9810 - val_loss: 0.1446 - val_accuracy: 0.9556\n",
      "Epoch 532/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0596 - accuracy: 0.9810 - val_loss: 0.1425 - val_accuracy: 0.9556\n",
      "Epoch 533/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0600 - accuracy: 0.9810 - val_loss: 0.1428 - val_accuracy: 0.9556\n",
      "Epoch 534/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0601 - accuracy: 0.9905 - val_loss: 0.1399 - val_accuracy: 0.9556\n",
      "Epoch 535/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0593 - accuracy: 0.9905 - val_loss: 0.1430 - val_accuracy: 0.9556\n",
      "Epoch 536/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0595 - accuracy: 0.9810 - val_loss: 0.1498 - val_accuracy: 0.9556\n",
      "Epoch 537/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0595 - accuracy: 0.9810 - val_loss: 0.1516 - val_accuracy: 0.9556\n",
      "Epoch 538/1000\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0597 - accuracy: 0.9810 - val_loss: 0.1505 - val_accuracy: 0.9556\n",
      "Epoch 539/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0589 - accuracy: 0.9810 - val_loss: 0.1448 - val_accuracy: 0.9556\n",
      "Epoch 540/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0587 - accuracy: 0.9810 - val_loss: 0.1421 - val_accuracy: 0.9556\n",
      "Epoch 541/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0588 - accuracy: 0.9905 - val_loss: 0.1398 - val_accuracy: 0.9556\n",
      "Epoch 542/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0589 - accuracy: 0.9905 - val_loss: 0.1407 - val_accuracy: 0.9556\n",
      "Epoch 543/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0584 - accuracy: 0.9905 - val_loss: 0.1435 - val_accuracy: 0.9556\n",
      "Epoch 544/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0586 - accuracy: 0.9810 - val_loss: 0.1465 - val_accuracy: 0.9556\n",
      "Epoch 545/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0583 - accuracy: 0.9810 - val_loss: 0.1466 - val_accuracy: 0.9556\n",
      "Epoch 546/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0583 - accuracy: 0.9810 - val_loss: 0.1460 - val_accuracy: 0.9556\n",
      "Epoch 547/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0581 - accuracy: 0.9810 - val_loss: 0.1443 - val_accuracy: 0.9556\n",
      "Epoch 548/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0584 - accuracy: 0.9810 - val_loss: 0.1404 - val_accuracy: 0.9556\n",
      "Epoch 549/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0581 - accuracy: 0.9905 - val_loss: 0.1392 - val_accuracy: 0.9556\n",
      "Epoch 550/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0580 - accuracy: 0.9905 - val_loss: 0.1358 - val_accuracy: 0.9556\n",
      "Epoch 551/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0585 - accuracy: 0.9905 - val_loss: 0.1372 - val_accuracy: 0.9556\n",
      "Epoch 552/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0580 - accuracy: 0.9905 - val_loss: 0.1398 - val_accuracy: 0.9556\n",
      "Epoch 553/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0579 - accuracy: 0.9905 - val_loss: 0.1440 - val_accuracy: 0.9556\n",
      "Epoch 554/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0578 - accuracy: 0.9810 - val_loss: 0.1466 - val_accuracy: 0.9556\n",
      "Epoch 555/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0574 - accuracy: 0.9810 - val_loss: 0.1462 - val_accuracy: 0.9556\n",
      "Epoch 556/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0573 - accuracy: 0.9810 - val_loss: 0.1464 - val_accuracy: 0.9556\n",
      "Epoch 557/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0572 - accuracy: 0.9810 - val_loss: 0.1483 - val_accuracy: 0.9556\n",
      "Epoch 558/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0572 - accuracy: 0.9810 - val_loss: 0.1512 - val_accuracy: 0.9556\n",
      "Epoch 559/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0582 - accuracy: 0.9810 - val_loss: 0.1554 - val_accuracy: 0.9556\n",
      "Epoch 560/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0580 - accuracy: 0.9810 - val_loss: 0.1539 - val_accuracy: 0.9556\n",
      "Epoch 561/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0574 - accuracy: 0.9810 - val_loss: 0.1488 - val_accuracy: 0.9556\n",
      "Epoch 562/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0565 - accuracy: 0.9810 - val_loss: 0.1438 - val_accuracy: 0.9556\n",
      "Epoch 563/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0565 - accuracy: 0.9905 - val_loss: 0.1388 - val_accuracy: 0.9556\n",
      "Epoch 564/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 0.9905 - val_loss: 0.1330 - val_accuracy: 0.9556\n",
      "Epoch 565/1000\n",
      "4/4 [==============================] - 0s 21ms/step - loss: 0.0579 - accuracy: 0.9905 - val_loss: 0.1320 - val_accuracy: 0.9556\n",
      "Epoch 566/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0587 - accuracy: 0.9905 - val_loss: 0.1339 - val_accuracy: 0.9556\n",
      "Epoch 567/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0572 - accuracy: 0.9905 - val_loss: 0.1472 - val_accuracy: 0.9556\n",
      "Epoch 568/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0557 - accuracy: 0.9810 - val_loss: 0.1562 - val_accuracy: 0.9556\n",
      "Epoch 569/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0574 - accuracy: 0.9810 - val_loss: 0.1629 - val_accuracy: 0.9333\n",
      "Epoch 570/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0591 - accuracy: 0.9810 - val_loss: 0.1636 - val_accuracy: 0.9333\n",
      "Epoch 571/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0593 - accuracy: 0.9810 - val_loss: 0.1511 - val_accuracy: 0.9556\n",
      "Epoch 572/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 0.9810 - val_loss: 0.1450 - val_accuracy: 0.9556\n",
      "Epoch 573/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0553 - accuracy: 0.9905 - val_loss: 0.1380 - val_accuracy: 0.9556\n",
      "Epoch 574/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0560 - accuracy: 0.9905 - val_loss: 0.1352 - val_accuracy: 0.9556\n",
      "Epoch 575/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0564 - accuracy: 0.9905 - val_loss: 0.1361 - val_accuracy: 0.9556\n",
      "Epoch 576/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 0.9905 - val_loss: 0.1384 - val_accuracy: 0.9556\n",
      "Epoch 577/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0556 - accuracy: 0.9905 - val_loss: 0.1400 - val_accuracy: 0.9556\n",
      "Epoch 578/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0558 - accuracy: 0.9905 - val_loss: 0.1400 - val_accuracy: 0.9556\n",
      "Epoch 579/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0553 - accuracy: 0.9905 - val_loss: 0.1420 - val_accuracy: 0.9556\n",
      "Epoch 580/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0551 - accuracy: 0.9810 - val_loss: 0.1441 - val_accuracy: 0.9556\n",
      "Epoch 581/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0552 - accuracy: 0.9810 - val_loss: 0.1483 - val_accuracy: 0.9556\n",
      "Epoch 582/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0553 - accuracy: 0.9810 - val_loss: 0.1500 - val_accuracy: 0.9556\n",
      "Epoch 583/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0557 - accuracy: 0.9810 - val_loss: 0.1506 - val_accuracy: 0.9556\n",
      "Epoch 584/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0557 - accuracy: 0.9810 - val_loss: 0.1471 - val_accuracy: 0.9556\n",
      "Epoch 585/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0552 - accuracy: 0.9810 - val_loss: 0.1449 - val_accuracy: 0.9556\n",
      "Epoch 586/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0553 - accuracy: 0.9810 - val_loss: 0.1360 - val_accuracy: 0.9556\n",
      "Epoch 587/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0561 - accuracy: 0.9905 - val_loss: 0.1325 - val_accuracy: 0.9556\n",
      "Epoch 588/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0558 - accuracy: 0.9905 - val_loss: 0.1333 - val_accuracy: 0.9556\n",
      "Epoch 589/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0552 - accuracy: 0.9905 - val_loss: 0.1369 - val_accuracy: 0.9556\n",
      "Epoch 590/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0564 - accuracy: 0.9810 - val_loss: 0.1436 - val_accuracy: 0.9556\n",
      "Epoch 591/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0544 - accuracy: 0.9810 - val_loss: 0.1405 - val_accuracy: 0.9556\n",
      "Epoch 592/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0543 - accuracy: 0.9905 - val_loss: 0.1401 - val_accuracy: 0.9556\n",
      "Epoch 593/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0543 - accuracy: 0.9905 - val_loss: 0.1407 - val_accuracy: 0.9556\n",
      "Epoch 594/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0541 - accuracy: 0.9810 - val_loss: 0.1449 - val_accuracy: 0.9556\n",
      "Epoch 595/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0541 - accuracy: 0.9810 - val_loss: 0.1467 - val_accuracy: 0.9556\n",
      "Epoch 596/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0540 - accuracy: 0.9810 - val_loss: 0.1501 - val_accuracy: 0.9556\n",
      "Epoch 597/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0551 - accuracy: 0.9810 - val_loss: 0.1527 - val_accuracy: 0.9556\n",
      "Epoch 598/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0548 - accuracy: 0.9810 - val_loss: 0.1488 - val_accuracy: 0.9556\n",
      "Epoch 599/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0541 - accuracy: 0.9810 - val_loss: 0.1459 - val_accuracy: 0.9556\n",
      "Epoch 600/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0541 - accuracy: 0.9810 - val_loss: 0.1418 - val_accuracy: 0.9556\n",
      "Epoch 601/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0536 - accuracy: 0.9810 - val_loss: 0.1404 - val_accuracy: 0.9556\n",
      "Epoch 602/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0540 - accuracy: 0.9905 - val_loss: 0.1387 - val_accuracy: 0.9556\n",
      "Epoch 603/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0536 - accuracy: 0.9905 - val_loss: 0.1415 - val_accuracy: 0.9556\n",
      "Epoch 604/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0534 - accuracy: 0.9810 - val_loss: 0.1436 - val_accuracy: 0.9556\n",
      "Epoch 605/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0535 - accuracy: 0.9810 - val_loss: 0.1438 - val_accuracy: 0.9556\n",
      "Epoch 606/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0533 - accuracy: 0.9810 - val_loss: 0.1414 - val_accuracy: 0.9556\n",
      "Epoch 607/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0534 - accuracy: 0.9905 - val_loss: 0.1410 - val_accuracy: 0.9556\n",
      "Epoch 608/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0542 - accuracy: 0.9810 - val_loss: 0.1477 - val_accuracy: 0.9556\n",
      "Epoch 609/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0536 - accuracy: 0.9810 - val_loss: 0.1465 - val_accuracy: 0.9556\n",
      "Epoch 610/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0535 - accuracy: 0.9810 - val_loss: 0.1445 - val_accuracy: 0.9556\n",
      "Epoch 611/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0530 - accuracy: 0.9810 - val_loss: 0.1425 - val_accuracy: 0.9556\n",
      "Epoch 612/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0531 - accuracy: 0.9810 - val_loss: 0.1437 - val_accuracy: 0.9556\n",
      "Epoch 613/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0533 - accuracy: 0.9810 - val_loss: 0.1438 - val_accuracy: 0.9556\n",
      "Epoch 614/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0522 - accuracy: 0.9810 - val_loss: 0.1376 - val_accuracy: 0.9556\n",
      "Epoch 615/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0526 - accuracy: 0.9905 - val_loss: 0.1358 - val_accuracy: 0.9556\n",
      "Epoch 616/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0528 - accuracy: 0.9905 - val_loss: 0.1359 - val_accuracy: 0.9556\n",
      "Epoch 617/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0526 - accuracy: 0.9905 - val_loss: 0.1376 - val_accuracy: 0.9556\n",
      "Epoch 618/1000\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 0.0525 - accuracy: 0.9905 - val_loss: 0.1414 - val_accuracy: 0.9556\n",
      "Epoch 619/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0524 - accuracy: 0.9810 - val_loss: 0.1405 - val_accuracy: 0.9556\n",
      "Epoch 620/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0522 - accuracy: 0.9810 - val_loss: 0.1402 - val_accuracy: 0.9556\n",
      "Epoch 621/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0523 - accuracy: 0.9905 - val_loss: 0.1386 - val_accuracy: 0.9556\n",
      "Epoch 622/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0521 - accuracy: 0.9905 - val_loss: 0.1388 - val_accuracy: 0.9556\n",
      "Epoch 623/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0522 - accuracy: 0.9905 - val_loss: 0.1395 - val_accuracy: 0.9556\n",
      "Epoch 624/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0519 - accuracy: 0.9905 - val_loss: 0.1421 - val_accuracy: 0.9556\n",
      "Epoch 625/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0522 - accuracy: 0.9810 - val_loss: 0.1465 - val_accuracy: 0.9556\n",
      "Epoch 626/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0522 - accuracy: 0.9810 - val_loss: 0.1472 - val_accuracy: 0.9556\n",
      "Epoch 627/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0522 - accuracy: 0.9810 - val_loss: 0.1454 - val_accuracy: 0.9556\n",
      "Epoch 628/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0521 - accuracy: 0.9810 - val_loss: 0.1397 - val_accuracy: 0.9556\n",
      "Epoch 629/1000\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0515 - accuracy: 0.9905 - val_loss: 0.1366 - val_accuracy: 0.9556\n",
      "Epoch 630/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0520 - accuracy: 0.9905 - val_loss: 0.1333 - val_accuracy: 0.9556\n",
      "Epoch 631/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0528 - accuracy: 0.9905 - val_loss: 0.1302 - val_accuracy: 0.9556\n",
      "Epoch 632/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0528 - accuracy: 0.9905 - val_loss: 0.1316 - val_accuracy: 0.9556\n",
      "Epoch 633/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0526 - accuracy: 0.9905 - val_loss: 0.1335 - val_accuracy: 0.9556\n",
      "Epoch 634/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0522 - accuracy: 0.9905 - val_loss: 0.1321 - val_accuracy: 0.9556\n",
      "Epoch 635/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0519 - accuracy: 0.9905 - val_loss: 0.1373 - val_accuracy: 0.9556\n",
      "Epoch 636/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0517 - accuracy: 0.9905 - val_loss: 0.1423 - val_accuracy: 0.9556\n",
      "Epoch 637/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0512 - accuracy: 0.9810 - val_loss: 0.1422 - val_accuracy: 0.9556\n",
      "Epoch 638/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0511 - accuracy: 0.9810 - val_loss: 0.1424 - val_accuracy: 0.9556\n",
      "Epoch 639/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0511 - accuracy: 0.9810 - val_loss: 0.1416 - val_accuracy: 0.9556\n",
      "Epoch 640/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0512 - accuracy: 0.9810 - val_loss: 0.1399 - val_accuracy: 0.9556\n",
      "Epoch 641/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0512 - accuracy: 0.9810 - val_loss: 0.1428 - val_accuracy: 0.9556\n",
      "Epoch 642/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0513 - accuracy: 0.9810 - val_loss: 0.1412 - val_accuracy: 0.9556\n",
      "Epoch 643/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0508 - accuracy: 0.9810 - val_loss: 0.1436 - val_accuracy: 0.9556\n",
      "Epoch 644/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0513 - accuracy: 0.9810 - val_loss: 0.1440 - val_accuracy: 0.9556\n",
      "Epoch 645/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.0515 - accuracy: 0.9810 - val_loss: 0.1369 - val_accuracy: 0.9556\n",
      "Epoch 646/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0515 - accuracy: 0.9905 - val_loss: 0.1342 - val_accuracy: 0.9556\n",
      "Epoch 647/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0507 - accuracy: 0.9905 - val_loss: 0.1365 - val_accuracy: 0.9556\n",
      "Epoch 648/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0508 - accuracy: 0.9810 - val_loss: 0.1389 - val_accuracy: 0.9556\n",
      "Epoch 649/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0505 - accuracy: 0.9905 - val_loss: 0.1368 - val_accuracy: 0.9556\n",
      "Epoch 650/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0504 - accuracy: 0.9905 - val_loss: 0.1359 - val_accuracy: 0.9556\n",
      "Epoch 651/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0504 - accuracy: 0.9905 - val_loss: 0.1363 - val_accuracy: 0.9556\n",
      "Epoch 652/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0504 - accuracy: 0.9905 - val_loss: 0.1379 - val_accuracy: 0.9556\n",
      "Epoch 653/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0502 - accuracy: 0.9905 - val_loss: 0.1401 - val_accuracy: 0.9556\n",
      "Epoch 654/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0502 - accuracy: 0.9810 - val_loss: 0.1407 - val_accuracy: 0.9556\n",
      "Epoch 655/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0501 - accuracy: 0.9810 - val_loss: 0.1409 - val_accuracy: 0.9556\n",
      "Epoch 656/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0504 - accuracy: 0.9810 - val_loss: 0.1399 - val_accuracy: 0.9556\n",
      "Epoch 657/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0501 - accuracy: 0.9810 - val_loss: 0.1410 - val_accuracy: 0.9556\n",
      "Epoch 658/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0501 - accuracy: 0.9810 - val_loss: 0.1448 - val_accuracy: 0.9556\n",
      "Epoch 659/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0501 - accuracy: 0.9810 - val_loss: 0.1452 - val_accuracy: 0.9556\n",
      "Epoch 660/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0506 - accuracy: 0.9810 - val_loss: 0.1450 - val_accuracy: 0.9556\n",
      "Epoch 661/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0494 - accuracy: 0.9810 - val_loss: 0.1376 - val_accuracy: 0.9556\n",
      "Epoch 662/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.0499 - accuracy: 0.9905 - val_loss: 0.1317 - val_accuracy: 0.9556\n",
      "Epoch 663/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0503 - accuracy: 0.9905 - val_loss: 0.1289 - val_accuracy: 0.9556\n",
      "Epoch 664/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0516 - accuracy: 0.9905 - val_loss: 0.1257 - val_accuracy: 0.9556\n",
      "Epoch 665/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0533 - accuracy: 0.9905 - val_loss: 0.1270 - val_accuracy: 0.9556\n",
      "Epoch 666/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0520 - accuracy: 0.9905 - val_loss: 0.1278 - val_accuracy: 0.9556\n",
      "Epoch 667/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0512 - accuracy: 0.9905 - val_loss: 0.1303 - val_accuracy: 0.9556\n",
      "Epoch 668/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0519 - accuracy: 0.9905 - val_loss: 0.1385 - val_accuracy: 0.9556\n",
      "Epoch 669/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0491 - accuracy: 0.9905 - val_loss: 0.1420 - val_accuracy: 0.9556\n",
      "Epoch 670/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0493 - accuracy: 0.9810 - val_loss: 0.1458 - val_accuracy: 0.9556\n",
      "Epoch 671/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0499 - accuracy: 0.9810 - val_loss: 0.1461 - val_accuracy: 0.9556\n",
      "Epoch 672/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0498 - accuracy: 0.9810 - val_loss: 0.1502 - val_accuracy: 0.9556\n",
      "Epoch 673/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0504 - accuracy: 0.9810 - val_loss: 0.1512 - val_accuracy: 0.9556\n",
      "Epoch 674/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0503 - accuracy: 0.9810 - val_loss: 0.1485 - val_accuracy: 0.9556\n",
      "Epoch 675/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0498 - accuracy: 0.9810 - val_loss: 0.1453 - val_accuracy: 0.9556\n",
      "Epoch 676/1000\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0493 - accuracy: 0.9810 - val_loss: 0.1450 - val_accuracy: 0.9556\n",
      "Epoch 677/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0493 - accuracy: 0.9810 - val_loss: 0.1422 - val_accuracy: 0.9556\n",
      "Epoch 678/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0489 - accuracy: 0.9905 - val_loss: 0.1332 - val_accuracy: 0.9556\n",
      "Epoch 679/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0497 - accuracy: 0.9905 - val_loss: 0.1297 - val_accuracy: 0.9556\n",
      "Epoch 680/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0499 - accuracy: 0.9905 - val_loss: 0.1303 - val_accuracy: 0.9556\n",
      "Epoch 681/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0511 - accuracy: 0.9905 - val_loss: 0.1314 - val_accuracy: 0.9556\n",
      "Epoch 682/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0509 - accuracy: 0.9905 - val_loss: 0.1259 - val_accuracy: 0.9556\n",
      "Epoch 683/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0521 - accuracy: 0.9905 - val_loss: 0.1258 - val_accuracy: 0.9556\n",
      "Epoch 684/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0514 - accuracy: 0.9905 - val_loss: 0.1278 - val_accuracy: 0.9556\n",
      "Epoch 685/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0499 - accuracy: 0.9905 - val_loss: 0.1316 - val_accuracy: 0.9556\n",
      "Epoch 686/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0485 - accuracy: 0.9905 - val_loss: 0.1364 - val_accuracy: 0.9556\n",
      "Epoch 687/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0486 - accuracy: 0.9810 - val_loss: 0.1413 - val_accuracy: 0.9556\n",
      "Epoch 688/1000\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 0.0486 - accuracy: 0.9810 - val_loss: 0.1393 - val_accuracy: 0.9556\n",
      "Epoch 689/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0489 - accuracy: 0.9810 - val_loss: 0.1391 - val_accuracy: 0.9556\n",
      "Epoch 690/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0482 - accuracy: 0.9905 - val_loss: 0.1328 - val_accuracy: 0.9556\n",
      "Epoch 691/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0485 - accuracy: 0.9905 - val_loss: 0.1314 - val_accuracy: 0.9556\n",
      "Epoch 692/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0489 - accuracy: 0.9905 - val_loss: 0.1302 - val_accuracy: 0.9556\n",
      "Epoch 693/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0494 - accuracy: 0.9905 - val_loss: 0.1304 - val_accuracy: 0.9556\n",
      "Epoch 694/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0486 - accuracy: 0.9905 - val_loss: 0.1355 - val_accuracy: 0.9556\n",
      "Epoch 695/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0480 - accuracy: 0.9905 - val_loss: 0.1404 - val_accuracy: 0.9556\n",
      "Epoch 696/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0481 - accuracy: 0.9810 - val_loss: 0.1417 - val_accuracy: 0.9556\n",
      "Epoch 697/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0482 - accuracy: 0.9810 - val_loss: 0.1439 - val_accuracy: 0.9556\n",
      "Epoch 698/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0479 - accuracy: 0.9810 - val_loss: 0.1475 - val_accuracy: 0.9556\n",
      "Epoch 699/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0485 - accuracy: 0.9810 - val_loss: 0.1511 - val_accuracy: 0.9556\n",
      "Epoch 700/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0496 - accuracy: 0.9810 - val_loss: 0.1531 - val_accuracy: 0.9556\n",
      "Epoch 701/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.0492 - accuracy: 0.9810 - val_loss: 0.1475 - val_accuracy: 0.9556\n",
      "Epoch 702/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0482 - accuracy: 0.9810 - val_loss: 0.1409 - val_accuracy: 0.9556\n",
      "Epoch 703/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0484 - accuracy: 0.9810 - val_loss: 0.1359 - val_accuracy: 0.9556\n",
      "Epoch 704/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0480 - accuracy: 0.9905 - val_loss: 0.1370 - val_accuracy: 0.9556\n",
      "Epoch 705/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0472 - accuracy: 0.9810 - val_loss: 0.1464 - val_accuracy: 0.9556\n",
      "Epoch 706/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0489 - accuracy: 0.9810 - val_loss: 0.1560 - val_accuracy: 0.9556\n",
      "Epoch 707/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0503 - accuracy: 0.9810 - val_loss: 0.1574 - val_accuracy: 0.9556\n",
      "Epoch 708/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0504 - accuracy: 0.9810 - val_loss: 0.1453 - val_accuracy: 0.9556\n",
      "Epoch 709/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0484 - accuracy: 0.9810 - val_loss: 0.1373 - val_accuracy: 0.9556\n",
      "Epoch 710/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0471 - accuracy: 0.9905 - val_loss: 0.1348 - val_accuracy: 0.9556\n",
      "Epoch 711/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0473 - accuracy: 0.9905 - val_loss: 0.1328 - val_accuracy: 0.9556\n",
      "Epoch 712/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0476 - accuracy: 0.9905 - val_loss: 0.1319 - val_accuracy: 0.9556\n",
      "Epoch 713/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0477 - accuracy: 0.9905 - val_loss: 0.1308 - val_accuracy: 0.9556\n",
      "Epoch 714/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0478 - accuracy: 0.9905 - val_loss: 0.1335 - val_accuracy: 0.9556\n",
      "Epoch 715/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0471 - accuracy: 0.9905 - val_loss: 0.1362 - val_accuracy: 0.9556\n",
      "Epoch 716/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0468 - accuracy: 0.9905 - val_loss: 0.1395 - val_accuracy: 0.9556\n",
      "Epoch 717/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0467 - accuracy: 0.9810 - val_loss: 0.1432 - val_accuracy: 0.9556\n",
      "Epoch 718/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0475 - accuracy: 0.9810 - val_loss: 0.1475 - val_accuracy: 0.9556\n",
      "Epoch 719/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.0477 - accuracy: 0.9810 - val_loss: 0.1476 - val_accuracy: 0.9556\n",
      "Epoch 720/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0475 - accuracy: 0.9810 - val_loss: 0.1447 - val_accuracy: 0.9556\n",
      "Epoch 721/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0468 - accuracy: 0.9810 - val_loss: 0.1404 - val_accuracy: 0.9556\n",
      "Epoch 722/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0466 - accuracy: 0.9810 - val_loss: 0.1350 - val_accuracy: 0.9556\n",
      "Epoch 723/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0464 - accuracy: 0.9905 - val_loss: 0.1308 - val_accuracy: 0.9556\n",
      "Epoch 724/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0469 - accuracy: 0.9905 - val_loss: 0.1273 - val_accuracy: 0.9556\n",
      "Epoch 725/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0514 - accuracy: 0.9905 - val_loss: 0.1236 - val_accuracy: 0.9778\n",
      "Epoch 726/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0509 - accuracy: 0.9905 - val_loss: 0.1257 - val_accuracy: 0.9556\n",
      "Epoch 727/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0479 - accuracy: 0.9905 - val_loss: 0.1336 - val_accuracy: 0.9556\n",
      "Epoch 728/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0457 - accuracy: 0.9905 - val_loss: 0.1460 - val_accuracy: 0.9556\n",
      "Epoch 729/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0490 - accuracy: 0.9810 - val_loss: 0.1565 - val_accuracy: 0.9556\n",
      "Epoch 730/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0492 - accuracy: 0.9810 - val_loss: 0.1585 - val_accuracy: 0.9556\n",
      "Epoch 731/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0493 - accuracy: 0.9810 - val_loss: 0.1540 - val_accuracy: 0.9556\n",
      "Epoch 732/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0490 - accuracy: 0.9810 - val_loss: 0.1447 - val_accuracy: 0.9556\n",
      "Epoch 733/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0464 - accuracy: 0.9810 - val_loss: 0.1398 - val_accuracy: 0.9556\n",
      "Epoch 734/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0462 - accuracy: 0.9810 - val_loss: 0.1366 - val_accuracy: 0.9556\n",
      "Epoch 735/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0463 - accuracy: 0.9905 - val_loss: 0.1367 - val_accuracy: 0.9556\n",
      "Epoch 736/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0461 - accuracy: 0.9905 - val_loss: 0.1382 - val_accuracy: 0.9556\n",
      "Epoch 737/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0460 - accuracy: 0.9810 - val_loss: 0.1391 - val_accuracy: 0.9556\n",
      "Epoch 738/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0460 - accuracy: 0.9810 - val_loss: 0.1391 - val_accuracy: 0.9556\n",
      "Epoch 739/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0460 - accuracy: 0.9810 - val_loss: 0.1376 - val_accuracy: 0.9556\n",
      "Epoch 740/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0458 - accuracy: 0.9905 - val_loss: 0.1347 - val_accuracy: 0.9556\n",
      "Epoch 741/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0460 - accuracy: 0.9905 - val_loss: 0.1352 - val_accuracy: 0.9556\n",
      "Epoch 742/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0462 - accuracy: 0.9905 - val_loss: 0.1354 - val_accuracy: 0.9556\n",
      "Epoch 743/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0460 - accuracy: 0.9905 - val_loss: 0.1357 - val_accuracy: 0.9556\n",
      "Epoch 744/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0457 - accuracy: 0.9905 - val_loss: 0.1378 - val_accuracy: 0.9556\n",
      "Epoch 745/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0455 - accuracy: 0.9810 - val_loss: 0.1442 - val_accuracy: 0.9556\n",
      "Epoch 746/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0466 - accuracy: 0.9810 - val_loss: 0.1480 - val_accuracy: 0.9556\n",
      "Epoch 747/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0466 - accuracy: 0.9810 - val_loss: 0.1469 - val_accuracy: 0.9556\n",
      "Epoch 748/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0463 - accuracy: 0.9810 - val_loss: 0.1436 - val_accuracy: 0.9556\n",
      "Epoch 749/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0457 - accuracy: 0.9810 - val_loss: 0.1393 - val_accuracy: 0.9556\n",
      "Epoch 750/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0450 - accuracy: 0.9905 - val_loss: 0.1333 - val_accuracy: 0.9556\n",
      "Epoch 751/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0458 - accuracy: 0.9905 - val_loss: 0.1279 - val_accuracy: 0.9556\n",
      "Epoch 752/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.0472 - accuracy: 0.9905 - val_loss: 0.1268 - val_accuracy: 0.9556\n",
      "Epoch 753/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0474 - accuracy: 0.9905 - val_loss: 0.1288 - val_accuracy: 0.9556\n",
      "Epoch 754/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0468 - accuracy: 0.9905 - val_loss: 0.1356 - val_accuracy: 0.9556\n",
      "Epoch 755/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0453 - accuracy: 0.9905 - val_loss: 0.1369 - val_accuracy: 0.9556\n",
      "Epoch 756/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0454 - accuracy: 0.9905 - val_loss: 0.1379 - val_accuracy: 0.9556\n",
      "Epoch 757/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0459 - accuracy: 0.9810 - val_loss: 0.1429 - val_accuracy: 0.9556\n",
      "Epoch 758/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0456 - accuracy: 0.9810 - val_loss: 0.1443 - val_accuracy: 0.9556\n",
      "Epoch 759/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0457 - accuracy: 0.9810 - val_loss: 0.1432 - val_accuracy: 0.9556\n",
      "Epoch 760/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0454 - accuracy: 0.9810 - val_loss: 0.1395 - val_accuracy: 0.9556\n",
      "Epoch 761/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0461 - accuracy: 0.9905 - val_loss: 0.1299 - val_accuracy: 0.9556\n",
      "Epoch 762/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0460 - accuracy: 0.9905 - val_loss: 0.1261 - val_accuracy: 0.9778\n",
      "Epoch 763/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0476 - accuracy: 0.9905 - val_loss: 0.1266 - val_accuracy: 0.9556\n",
      "Epoch 764/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0469 - accuracy: 0.9905 - val_loss: 0.1294 - val_accuracy: 0.9556\n",
      "Epoch 765/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0466 - accuracy: 0.9905 - val_loss: 0.1337 - val_accuracy: 0.9556\n",
      "Epoch 766/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0451 - accuracy: 0.9905 - val_loss: 0.1339 - val_accuracy: 0.9556\n",
      "Epoch 767/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0449 - accuracy: 0.9905 - val_loss: 0.1360 - val_accuracy: 0.9556\n",
      "Epoch 768/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0447 - accuracy: 0.9905 - val_loss: 0.1387 - val_accuracy: 0.9556\n",
      "Epoch 769/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0451 - accuracy: 0.9810 - val_loss: 0.1406 - val_accuracy: 0.9556\n",
      "Epoch 770/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0455 - accuracy: 0.9810 - val_loss: 0.1364 - val_accuracy: 0.9556\n",
      "Epoch 771/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0448 - accuracy: 0.9905 - val_loss: 0.1361 - val_accuracy: 0.9556\n",
      "Epoch 772/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0450 - accuracy: 0.9905 - val_loss: 0.1333 - val_accuracy: 0.9556\n",
      "Epoch 773/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0450 - accuracy: 0.9905 - val_loss: 0.1339 - val_accuracy: 0.9556\n",
      "Epoch 774/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0447 - accuracy: 0.9905 - val_loss: 0.1359 - val_accuracy: 0.9556\n",
      "Epoch 775/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0446 - accuracy: 0.9905 - val_loss: 0.1366 - val_accuracy: 0.9556\n",
      "Epoch 776/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0446 - accuracy: 0.9905 - val_loss: 0.1370 - val_accuracy: 0.9556\n",
      "Epoch 777/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0448 - accuracy: 0.9810 - val_loss: 0.1381 - val_accuracy: 0.9556\n",
      "Epoch 778/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.1386 - val_accuracy: 0.9556\n",
      "Epoch 779/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.1396 - val_accuracy: 0.9556\n",
      "Epoch 780/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.1395 - val_accuracy: 0.9556\n",
      "Epoch 781/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0446 - accuracy: 0.9810 - val_loss: 0.1395 - val_accuracy: 0.9556\n",
      "Epoch 782/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.1400 - val_accuracy: 0.9556\n",
      "Epoch 783/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0442 - accuracy: 0.9810 - val_loss: 0.1362 - val_accuracy: 0.9556\n",
      "Epoch 784/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0446 - accuracy: 0.9905 - val_loss: 0.1326 - val_accuracy: 0.9556\n",
      "Epoch 785/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 0.9905 - val_loss: 0.1334 - val_accuracy: 0.9556\n",
      "Epoch 786/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0446 - accuracy: 0.9905 - val_loss: 0.1359 - val_accuracy: 0.9556\n",
      "Epoch 787/1000\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0439 - accuracy: 0.9905 - val_loss: 0.1409 - val_accuracy: 0.9556\n",
      "Epoch 788/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0442 - accuracy: 0.9810 - val_loss: 0.1438 - val_accuracy: 0.9556\n",
      "Epoch 789/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0449 - accuracy: 0.9810 - val_loss: 0.1444 - val_accuracy: 0.9556\n",
      "Epoch 790/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.1416 - val_accuracy: 0.9556\n",
      "Epoch 791/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0452 - accuracy: 0.9810 - val_loss: 0.1333 - val_accuracy: 0.9556\n",
      "Epoch 792/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0442 - accuracy: 0.9905 - val_loss: 0.1335 - val_accuracy: 0.9556\n",
      "Epoch 793/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0441 - accuracy: 0.9905 - val_loss: 0.1369 - val_accuracy: 0.9556\n",
      "Epoch 794/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0446 - accuracy: 0.9905 - val_loss: 0.1343 - val_accuracy: 0.9556\n",
      "Epoch 795/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0439 - accuracy: 0.9905 - val_loss: 0.1346 - val_accuracy: 0.9556\n",
      "Epoch 796/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0439 - accuracy: 0.9905 - val_loss: 0.1357 - val_accuracy: 0.9556\n",
      "Epoch 797/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0438 - accuracy: 0.9905 - val_loss: 0.1335 - val_accuracy: 0.9556\n",
      "Epoch 798/1000\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0443 - accuracy: 0.9905 - val_loss: 0.1322 - val_accuracy: 0.9556\n",
      "Epoch 799/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0439 - accuracy: 0.9905 - val_loss: 0.1340 - val_accuracy: 0.9556\n",
      "Epoch 800/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0433 - accuracy: 0.9905 - val_loss: 0.1381 - val_accuracy: 0.9556\n",
      "Epoch 801/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0442 - accuracy: 0.9810 - val_loss: 0.1422 - val_accuracy: 0.9556\n",
      "Epoch 802/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0440 - accuracy: 0.9810 - val_loss: 0.1424 - val_accuracy: 0.9556\n",
      "Epoch 803/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0439 - accuracy: 0.9810 - val_loss: 0.1366 - val_accuracy: 0.9556\n",
      "Epoch 804/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0435 - accuracy: 0.9905 - val_loss: 0.1337 - val_accuracy: 0.9556\n",
      "Epoch 805/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0436 - accuracy: 0.9905 - val_loss: 0.1331 - val_accuracy: 0.9556\n",
      "Epoch 806/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0437 - accuracy: 0.9905 - val_loss: 0.1318 - val_accuracy: 0.9556\n",
      "Epoch 807/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0445 - accuracy: 0.9905 - val_loss: 0.1290 - val_accuracy: 0.9556\n",
      "Epoch 808/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0441 - accuracy: 0.9905 - val_loss: 0.1318 - val_accuracy: 0.9556\n",
      "Epoch 809/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.0439 - accuracy: 0.9905 - val_loss: 0.1374 - val_accuracy: 0.9556\n",
      "Epoch 810/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0441 - accuracy: 0.9810 - val_loss: 0.1385 - val_accuracy: 0.9556\n",
      "Epoch 811/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0431 - accuracy: 0.9905 - val_loss: 0.1337 - val_accuracy: 0.9556\n",
      "Epoch 812/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0434 - accuracy: 0.9905 - val_loss: 0.1327 - val_accuracy: 0.9556\n",
      "Epoch 813/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0440 - accuracy: 0.9905 - val_loss: 0.1332 - val_accuracy: 0.9556\n",
      "Epoch 814/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0432 - accuracy: 0.9905 - val_loss: 0.1394 - val_accuracy: 0.9556\n",
      "Epoch 815/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0436 - accuracy: 0.9810 - val_loss: 0.1463 - val_accuracy: 0.9556\n",
      "Epoch 816/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0439 - accuracy: 0.9810 - val_loss: 0.1491 - val_accuracy: 0.9556\n",
      "Epoch 817/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.1493 - val_accuracy: 0.9556\n",
      "Epoch 818/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0447 - accuracy: 0.9810 - val_loss: 0.1490 - val_accuracy: 0.9556\n",
      "Epoch 819/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0435 - accuracy: 0.9810 - val_loss: 0.1417 - val_accuracy: 0.9556\n",
      "Epoch 820/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0426 - accuracy: 0.9810 - val_loss: 0.1328 - val_accuracy: 0.9556\n",
      "Epoch 821/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0460 - accuracy: 0.9905 - val_loss: 0.1251 - val_accuracy: 0.9778\n",
      "Epoch 822/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0481 - accuracy: 0.9810 - val_loss: 0.1241 - val_accuracy: 0.9778\n",
      "Epoch 823/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0470 - accuracy: 0.9905 - val_loss: 0.1272 - val_accuracy: 0.9778\n",
      "Epoch 824/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0439 - accuracy: 0.9905 - val_loss: 0.1316 - val_accuracy: 0.9556\n",
      "Epoch 825/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 0.9905 - val_loss: 0.1391 - val_accuracy: 0.9556\n",
      "Epoch 826/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0429 - accuracy: 0.9810 - val_loss: 0.1428 - val_accuracy: 0.9556\n",
      "Epoch 827/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.0431 - accuracy: 0.9810 - val_loss: 0.1432 - val_accuracy: 0.9556\n",
      "Epoch 828/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0432 - accuracy: 0.9810 - val_loss: 0.1418 - val_accuracy: 0.9556\n",
      "Epoch 829/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0429 - accuracy: 0.9810 - val_loss: 0.1407 - val_accuracy: 0.9556\n",
      "Epoch 830/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0428 - accuracy: 0.9810 - val_loss: 0.1403 - val_accuracy: 0.9556\n",
      "Epoch 831/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0431 - accuracy: 0.9810 - val_loss: 0.1371 - val_accuracy: 0.9556\n",
      "Epoch 832/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0426 - accuracy: 0.9905 - val_loss: 0.1364 - val_accuracy: 0.9556\n",
      "Epoch 833/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0427 - accuracy: 0.9905 - val_loss: 0.1373 - val_accuracy: 0.9556\n",
      "Epoch 834/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0426 - accuracy: 0.9905 - val_loss: 0.1378 - val_accuracy: 0.9556\n",
      "Epoch 835/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0427 - accuracy: 0.9810 - val_loss: 0.1412 - val_accuracy: 0.9556\n",
      "Epoch 836/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0431 - accuracy: 0.9810 - val_loss: 0.1405 - val_accuracy: 0.9556\n",
      "Epoch 837/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0430 - accuracy: 0.9810 - val_loss: 0.1435 - val_accuracy: 0.9556\n",
      "Epoch 838/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0429 - accuracy: 0.9810 - val_loss: 0.1416 - val_accuracy: 0.9556\n",
      "Epoch 839/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.1338 - val_accuracy: 0.9556\n",
      "Epoch 840/1000\n",
      "4/4 [==============================] - 0s 11ms/step - loss: 0.0424 - accuracy: 0.9905 - val_loss: 0.1346 - val_accuracy: 0.9556\n",
      "Epoch 841/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0426 - accuracy: 0.9905 - val_loss: 0.1344 - val_accuracy: 0.9556\n",
      "Epoch 842/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0424 - accuracy: 0.9905 - val_loss: 0.1359 - val_accuracy: 0.9556\n",
      "Epoch 843/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0424 - accuracy: 0.9905 - val_loss: 0.1358 - val_accuracy: 0.9556\n",
      "Epoch 844/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0424 - accuracy: 0.9905 - val_loss: 0.1357 - val_accuracy: 0.9556\n",
      "Epoch 845/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0425 - accuracy: 0.9905 - val_loss: 0.1324 - val_accuracy: 0.9556\n",
      "Epoch 846/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0431 - accuracy: 0.9905 - val_loss: 0.1328 - val_accuracy: 0.9556\n",
      "Epoch 847/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0435 - accuracy: 0.9905 - val_loss: 0.1286 - val_accuracy: 0.9778\n",
      "Epoch 848/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0438 - accuracy: 0.9905 - val_loss: 0.1287 - val_accuracy: 0.9778\n",
      "Epoch 849/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0431 - accuracy: 0.9905 - val_loss: 0.1313 - val_accuracy: 0.9556\n",
      "Epoch 850/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0429 - accuracy: 0.9905 - val_loss: 0.1364 - val_accuracy: 0.9556\n",
      "Epoch 851/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0420 - accuracy: 0.9905 - val_loss: 0.1426 - val_accuracy: 0.9556\n",
      "Epoch 852/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0430 - accuracy: 0.9810 - val_loss: 0.1481 - val_accuracy: 0.9556\n",
      "Epoch 853/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0433 - accuracy: 0.9810 - val_loss: 0.1487 - val_accuracy: 0.9556\n",
      "Epoch 854/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0430 - accuracy: 0.9810 - val_loss: 0.1460 - val_accuracy: 0.9556\n",
      "Epoch 855/1000\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.0424 - accuracy: 0.9810 - val_loss: 0.1426 - val_accuracy: 0.9556\n",
      "Epoch 856/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0422 - accuracy: 0.9810 - val_loss: 0.1402 - val_accuracy: 0.9556\n",
      "Epoch 857/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0420 - accuracy: 0.9810 - val_loss: 0.1398 - val_accuracy: 0.9556\n",
      "Epoch 858/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0420 - accuracy: 0.9810 - val_loss: 0.1410 - val_accuracy: 0.9556\n",
      "Epoch 859/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0424 - accuracy: 0.9810 - val_loss: 0.1439 - val_accuracy: 0.9556\n",
      "Epoch 860/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0425 - accuracy: 0.9810 - val_loss: 0.1426 - val_accuracy: 0.9556\n",
      "Epoch 861/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0423 - accuracy: 0.9810 - val_loss: 0.1440 - val_accuracy: 0.9556\n",
      "Epoch 862/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0422 - accuracy: 0.9810 - val_loss: 0.1439 - val_accuracy: 0.9556\n",
      "Epoch 863/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0421 - accuracy: 0.9810 - val_loss: 0.1439 - val_accuracy: 0.9556\n",
      "Epoch 864/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0420 - accuracy: 0.9810 - val_loss: 0.1440 - val_accuracy: 0.9556\n",
      "Epoch 865/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0423 - accuracy: 0.9810 - val_loss: 0.1448 - val_accuracy: 0.9556\n",
      "Epoch 866/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0422 - accuracy: 0.9810 - val_loss: 0.1428 - val_accuracy: 0.9556\n",
      "Epoch 867/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0418 - accuracy: 0.9810 - val_loss: 0.1410 - val_accuracy: 0.9556\n",
      "Epoch 868/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0423 - accuracy: 0.9810 - val_loss: 0.1374 - val_accuracy: 0.9556\n",
      "Epoch 869/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0417 - accuracy: 0.9810 - val_loss: 0.1386 - val_accuracy: 0.9556\n",
      "Epoch 870/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.0414 - accuracy: 0.9905 - val_loss: 0.1372 - val_accuracy: 0.9556\n",
      "Epoch 871/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0417 - accuracy: 0.9905 - val_loss: 0.1364 - val_accuracy: 0.9556\n",
      "Epoch 872/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0414 - accuracy: 0.9905 - val_loss: 0.1388 - val_accuracy: 0.9556\n",
      "Epoch 873/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0418 - accuracy: 0.9810 - val_loss: 0.1401 - val_accuracy: 0.9556\n",
      "Epoch 874/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0411 - accuracy: 0.9905 - val_loss: 0.1361 - val_accuracy: 0.9556\n",
      "Epoch 875/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0411 - accuracy: 0.9905 - val_loss: 0.1329 - val_accuracy: 0.9556\n",
      "Epoch 876/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0415 - accuracy: 0.9905 - val_loss: 0.1315 - val_accuracy: 0.9556\n",
      "Epoch 877/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0418 - accuracy: 0.9905 - val_loss: 0.1318 - val_accuracy: 0.9556\n",
      "Epoch 878/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0422 - accuracy: 0.9905 - val_loss: 0.1323 - val_accuracy: 0.9556\n",
      "Epoch 879/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0417 - accuracy: 0.9905 - val_loss: 0.1291 - val_accuracy: 0.9778\n",
      "Epoch 880/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0428 - accuracy: 0.9905 - val_loss: 0.1296 - val_accuracy: 0.9778\n",
      "Epoch 881/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0423 - accuracy: 0.9905 - val_loss: 0.1311 - val_accuracy: 0.9556\n",
      "Epoch 882/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0415 - accuracy: 0.9905 - val_loss: 0.1360 - val_accuracy: 0.9556\n",
      "Epoch 883/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0432 - accuracy: 0.9810 - val_loss: 0.1433 - val_accuracy: 0.9556\n",
      "Epoch 884/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0414 - accuracy: 0.9810 - val_loss: 0.1430 - val_accuracy: 0.9556\n",
      "Epoch 885/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0418 - accuracy: 0.9810 - val_loss: 0.1389 - val_accuracy: 0.9556\n",
      "Epoch 886/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0427 - accuracy: 0.9810 - val_loss: 0.1272 - val_accuracy: 0.9778\n",
      "Epoch 887/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0446 - accuracy: 0.9905 - val_loss: 0.1249 - val_accuracy: 0.9778\n",
      "Epoch 888/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0453 - accuracy: 0.9905 - val_loss: 0.1261 - val_accuracy: 0.9778\n",
      "Epoch 889/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0434 - accuracy: 0.9905 - val_loss: 0.1312 - val_accuracy: 0.9556\n",
      "Epoch 890/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0415 - accuracy: 0.9905 - val_loss: 0.1404 - val_accuracy: 0.9556\n",
      "Epoch 891/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0431 - accuracy: 0.9810 - val_loss: 0.1466 - val_accuracy: 0.9556\n",
      "Epoch 892/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0417 - accuracy: 0.9810 - val_loss: 0.1385 - val_accuracy: 0.9556\n",
      "Epoch 893/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0411 - accuracy: 0.9905 - val_loss: 0.1340 - val_accuracy: 0.9556\n",
      "Epoch 894/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0410 - accuracy: 0.9905 - val_loss: 0.1341 - val_accuracy: 0.9556\n",
      "Epoch 895/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0417 - accuracy: 0.9905 - val_loss: 0.1335 - val_accuracy: 0.9556\n",
      "Epoch 896/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0405 - accuracy: 0.9905 - val_loss: 0.1274 - val_accuracy: 0.9778\n",
      "Epoch 897/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0436 - accuracy: 0.9905 - val_loss: 0.1262 - val_accuracy: 0.9778\n",
      "Epoch 898/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0434 - accuracy: 0.9905 - val_loss: 0.1284 - val_accuracy: 0.9778\n",
      "Epoch 899/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0415 - accuracy: 0.9905 - val_loss: 0.1326 - val_accuracy: 0.9556\n",
      "Epoch 900/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0415 - accuracy: 0.9905 - val_loss: 0.1391 - val_accuracy: 0.9556\n",
      "Epoch 901/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0406 - accuracy: 0.9810 - val_loss: 0.1418 - val_accuracy: 0.9556\n",
      "Epoch 902/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0413 - accuracy: 0.9810 - val_loss: 0.1409 - val_accuracy: 0.9556\n",
      "Epoch 903/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0414 - accuracy: 0.9810 - val_loss: 0.1454 - val_accuracy: 0.9556\n",
      "Epoch 904/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0415 - accuracy: 0.9810 - val_loss: 0.1461 - val_accuracy: 0.9556\n",
      "Epoch 905/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0413 - accuracy: 0.9810 - val_loss: 0.1438 - val_accuracy: 0.9556\n",
      "Epoch 906/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0409 - accuracy: 0.9810 - val_loss: 0.1430 - val_accuracy: 0.9556\n",
      "Epoch 907/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0409 - accuracy: 0.9810 - val_loss: 0.1417 - val_accuracy: 0.9556\n",
      "Epoch 908/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0413 - accuracy: 0.9810 - val_loss: 0.1391 - val_accuracy: 0.9556\n",
      "Epoch 909/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0404 - accuracy: 0.9810 - val_loss: 0.1424 - val_accuracy: 0.9556\n",
      "Epoch 910/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0407 - accuracy: 0.9810 - val_loss: 0.1421 - val_accuracy: 0.9556\n",
      "Epoch 911/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0407 - accuracy: 0.9810 - val_loss: 0.1423 - val_accuracy: 0.9556\n",
      "Epoch 912/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0406 - accuracy: 0.9810 - val_loss: 0.1403 - val_accuracy: 0.9556\n",
      "Epoch 913/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0404 - accuracy: 0.9810 - val_loss: 0.1378 - val_accuracy: 0.9556\n",
      "Epoch 914/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0404 - accuracy: 0.9905 - val_loss: 0.1349 - val_accuracy: 0.9556\n",
      "Epoch 915/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0398 - accuracy: 0.9905 - val_loss: 0.1306 - val_accuracy: 0.9778\n",
      "Epoch 916/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0414 - accuracy: 0.9905 - val_loss: 0.1294 - val_accuracy: 0.9778\n",
      "Epoch 917/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0410 - accuracy: 0.9905 - val_loss: 0.1325 - val_accuracy: 0.9556\n",
      "Epoch 918/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0395 - accuracy: 0.9905 - val_loss: 0.1387 - val_accuracy: 0.9556\n",
      "Epoch 919/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0401 - accuracy: 0.9810 - val_loss: 0.1478 - val_accuracy: 0.9556\n",
      "Epoch 920/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0428 - accuracy: 0.9810 - val_loss: 0.1510 - val_accuracy: 0.9556\n",
      "Epoch 921/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0421 - accuracy: 0.9810 - val_loss: 0.1392 - val_accuracy: 0.9556\n",
      "Epoch 922/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0409 - accuracy: 0.9810 - val_loss: 0.1340 - val_accuracy: 0.9556\n",
      "Epoch 923/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0405 - accuracy: 0.9905 - val_loss: 0.1339 - val_accuracy: 0.9556\n",
      "Epoch 924/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0412 - accuracy: 0.9905 - val_loss: 0.1400 - val_accuracy: 0.9556\n",
      "Epoch 925/1000\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.0402 - accuracy: 0.9810 - val_loss: 0.1431 - val_accuracy: 0.9556\n",
      "Epoch 926/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0403 - accuracy: 0.9810 - val_loss: 0.1426 - val_accuracy: 0.9556\n",
      "Epoch 927/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0411 - accuracy: 0.9810 - val_loss: 0.1445 - val_accuracy: 0.9556\n",
      "Epoch 928/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0404 - accuracy: 0.9810 - val_loss: 0.1418 - val_accuracy: 0.9556\n",
      "Epoch 929/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0402 - accuracy: 0.9810 - val_loss: 0.1405 - val_accuracy: 0.9556\n",
      "Epoch 930/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0397 - accuracy: 0.9810 - val_loss: 0.1374 - val_accuracy: 0.9556\n",
      "Epoch 931/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0395 - accuracy: 0.9905 - val_loss: 0.1340 - val_accuracy: 0.9556\n",
      "Epoch 932/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0397 - accuracy: 0.9905 - val_loss: 0.1311 - val_accuracy: 0.9778\n",
      "Epoch 933/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0412 - accuracy: 0.9905 - val_loss: 0.1298 - val_accuracy: 0.9778\n",
      "Epoch 934/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0404 - accuracy: 0.9905 - val_loss: 0.1343 - val_accuracy: 0.9556\n",
      "Epoch 935/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0399 - accuracy: 0.9905 - val_loss: 0.1417 - val_accuracy: 0.9556\n",
      "Epoch 936/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0412 - accuracy: 0.9810 - val_loss: 0.1502 - val_accuracy: 0.9556\n",
      "Epoch 937/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0420 - accuracy: 0.9810 - val_loss: 0.1500 - val_accuracy: 0.9556\n",
      "Epoch 938/1000\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0400 - accuracy: 0.9810 - val_loss: 0.1376 - val_accuracy: 0.9556\n",
      "Epoch 939/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0397 - accuracy: 0.9905 - val_loss: 0.1306 - val_accuracy: 0.9778\n",
      "Epoch 940/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0418 - accuracy: 0.9905 - val_loss: 0.1269 - val_accuracy: 0.9778\n",
      "Epoch 941/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0429 - accuracy: 0.9905 - val_loss: 0.1277 - val_accuracy: 0.9778\n",
      "Epoch 942/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0413 - accuracy: 0.9905 - val_loss: 0.1318 - val_accuracy: 0.9778\n",
      "Epoch 943/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0390 - accuracy: 0.9905 - val_loss: 0.1391 - val_accuracy: 0.9556\n",
      "Epoch 944/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0400 - accuracy: 0.9810 - val_loss: 0.1473 - val_accuracy: 0.9556\n",
      "Epoch 945/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0426 - accuracy: 0.9810 - val_loss: 0.1544 - val_accuracy: 0.9556\n",
      "Epoch 946/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0419 - accuracy: 0.9810 - val_loss: 0.1507 - val_accuracy: 0.9556\n",
      "Epoch 947/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0417 - accuracy: 0.9810 - val_loss: 0.1438 - val_accuracy: 0.9556\n",
      "Epoch 948/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0397 - accuracy: 0.9810 - val_loss: 0.1406 - val_accuracy: 0.9556\n",
      "Epoch 949/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0404 - accuracy: 0.9810 - val_loss: 0.1367 - val_accuracy: 0.9556\n",
      "Epoch 950/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0402 - accuracy: 0.9810 - val_loss: 0.1408 - val_accuracy: 0.9556\n",
      "Epoch 951/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0394 - accuracy: 0.9810 - val_loss: 0.1413 - val_accuracy: 0.9556\n",
      "Epoch 952/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0395 - accuracy: 0.9810 - val_loss: 0.1405 - val_accuracy: 0.9556\n",
      "Epoch 953/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0396 - accuracy: 0.9810 - val_loss: 0.1405 - val_accuracy: 0.9556\n",
      "Epoch 954/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0405 - accuracy: 0.9810 - val_loss: 0.1366 - val_accuracy: 0.9556\n",
      "Epoch 955/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9905 - val_loss: 0.1364 - val_accuracy: 0.9556\n",
      "Epoch 956/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0393 - accuracy: 0.9905 - val_loss: 0.1365 - val_accuracy: 0.9556\n",
      "Epoch 957/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0393 - accuracy: 0.9905 - val_loss: 0.1400 - val_accuracy: 0.9556\n",
      "Epoch 958/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0393 - accuracy: 0.9810 - val_loss: 0.1400 - val_accuracy: 0.9556\n",
      "Epoch 959/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0391 - accuracy: 0.9810 - val_loss: 0.1415 - val_accuracy: 0.9556\n",
      "Epoch 960/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0393 - accuracy: 0.9810 - val_loss: 0.1425 - val_accuracy: 0.9556\n",
      "Epoch 961/1000\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0399 - accuracy: 0.9810 - val_loss: 0.1445 - val_accuracy: 0.9556\n",
      "Epoch 962/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0398 - accuracy: 0.9810 - val_loss: 0.1425 - val_accuracy: 0.9556\n",
      "Epoch 963/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0397 - accuracy: 0.9810 - val_loss: 0.1400 - val_accuracy: 0.9556\n",
      "Epoch 964/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0389 - accuracy: 0.9810 - val_loss: 0.1379 - val_accuracy: 0.9556\n",
      "Epoch 965/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0399 - accuracy: 0.9905 - val_loss: 0.1346 - val_accuracy: 0.9556\n",
      "Epoch 966/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0394 - accuracy: 0.9905 - val_loss: 0.1395 - val_accuracy: 0.9556\n",
      "Epoch 967/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0392 - accuracy: 0.9810 - val_loss: 0.1444 - val_accuracy: 0.9556\n",
      "Epoch 968/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0410 - accuracy: 0.9810 - val_loss: 0.1516 - val_accuracy: 0.9556\n",
      "Epoch 969/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0407 - accuracy: 0.9810 - val_loss: 0.1489 - val_accuracy: 0.9556\n",
      "Epoch 970/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0406 - accuracy: 0.9810 - val_loss: 0.1462 - val_accuracy: 0.9556\n",
      "Epoch 971/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0400 - accuracy: 0.9810 - val_loss: 0.1485 - val_accuracy: 0.9556\n",
      "Epoch 972/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0404 - accuracy: 0.9810 - val_loss: 0.1450 - val_accuracy: 0.9556\n",
      "Epoch 973/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0395 - accuracy: 0.9810 - val_loss: 0.1432 - val_accuracy: 0.9556\n",
      "Epoch 974/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0393 - accuracy: 0.9810 - val_loss: 0.1382 - val_accuracy: 0.9556\n",
      "Epoch 975/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0388 - accuracy: 0.9905 - val_loss: 0.1350 - val_accuracy: 0.9556\n",
      "Epoch 976/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0391 - accuracy: 0.9905 - val_loss: 0.1352 - val_accuracy: 0.9556\n",
      "Epoch 977/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0387 - accuracy: 0.9905 - val_loss: 0.1348 - val_accuracy: 0.9556\n",
      "Epoch 978/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0388 - accuracy: 0.9905 - val_loss: 0.1347 - val_accuracy: 0.9556\n",
      "Epoch 979/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0396 - accuracy: 0.9905 - val_loss: 0.1356 - val_accuracy: 0.9556\n",
      "Epoch 980/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0392 - accuracy: 0.9905 - val_loss: 0.1308 - val_accuracy: 0.9778\n",
      "Epoch 981/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0401 - accuracy: 0.9905 - val_loss: 0.1302 - val_accuracy: 0.9778\n",
      "Epoch 982/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0400 - accuracy: 0.9905 - val_loss: 0.1320 - val_accuracy: 0.9778\n",
      "Epoch 983/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0392 - accuracy: 0.9905 - val_loss: 0.1333 - val_accuracy: 0.9778\n",
      "Epoch 984/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0392 - accuracy: 0.9905 - val_loss: 0.1381 - val_accuracy: 0.9556\n",
      "Epoch 985/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0382 - accuracy: 0.9810 - val_loss: 0.1424 - val_accuracy: 0.9556\n",
      "Epoch 986/1000\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.0391 - accuracy: 0.9810 - val_loss: 0.1449 - val_accuracy: 0.9556\n",
      "Epoch 987/1000\n",
      "4/4 [==============================] - 0s 9ms/step - loss: 0.0392 - accuracy: 0.9810 - val_loss: 0.1461 - val_accuracy: 0.9556\n",
      "Epoch 988/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0395 - accuracy: 0.9810 - val_loss: 0.1472 - val_accuracy: 0.9556\n",
      "Epoch 989/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0393 - accuracy: 0.9810 - val_loss: 0.1455 - val_accuracy: 0.9556\n",
      "Epoch 990/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0392 - accuracy: 0.9810 - val_loss: 0.1446 - val_accuracy: 0.9556\n",
      "Epoch 991/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0388 - accuracy: 0.9810 - val_loss: 0.1427 - val_accuracy: 0.9556\n",
      "Epoch 992/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0388 - accuracy: 0.9810 - val_loss: 0.1409 - val_accuracy: 0.9556\n",
      "Epoch 993/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0390 - accuracy: 0.9810 - val_loss: 0.1382 - val_accuracy: 0.9556\n",
      "Epoch 994/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0387 - accuracy: 0.9905 - val_loss: 0.1290 - val_accuracy: 0.9778\n",
      "Epoch 995/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0422 - accuracy: 0.9905 - val_loss: 0.1271 - val_accuracy: 0.9778\n",
      "Epoch 996/1000\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.0427 - accuracy: 0.9905 - val_loss: 0.1281 - val_accuracy: 0.9778\n",
      "Epoch 997/1000\n",
      "4/4 [==============================] - 0s 10ms/step - loss: 0.0410 - accuracy: 0.9905 - val_loss: 0.1321 - val_accuracy: 0.9778\n",
      "Epoch 998/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0402 - accuracy: 0.9905 - val_loss: 0.1401 - val_accuracy: 0.9556\n",
      "Epoch 999/1000\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0382 - accuracy: 0.9810 - val_loss: 0.1447 - val_accuracy: 0.9556\n",
      "Epoch 1000/1000\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0388 - accuracy: 0.9810 - val_loss: 0.1456 - val_accuracy: 0.9556\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1f78553c3a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configuração dos parâmetros da rede neural (adam = algoritmo para atualizar os pesos e loss = cálculo do erro)\n",
    "modelo.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Treinamento, dividindo a base de treinamento em uma porção para validação (validation_data)\n",
    "modelo.fit(X_treino, y_treino, epochs=1000, validation_data=(X_teste, y_teste))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False,  True],\n",
       "       [False,  True, False],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [ True, False, False],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [False, False,  True],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [ True, False, False],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [ True, False, False],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [False,  True, False],\n",
       "       [ True, False, False],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [ True, False, False],\n",
       "       [ True, False, False],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [False, False,  True],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [False, False,  True],\n",
       "       [False,  True, False],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [False,  True, False],\n",
       "       [False,  True, False],\n",
       "       [False, False,  True],\n",
       "       [ True, False, False],\n",
       "       [False, False,  True],\n",
       "       [ True, False, False],\n",
       "       [ True, False, False]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previsões e mudar a variável para True ou False de acordo com o threshold 0.5\n",
    "previsoes = modelo.predict(X_teste)\n",
    "previsoes = (previsoes > 0.5)\n",
    "previsoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Como é um problema com três saídas, precisamos buscar a posição que possui o maior valor (são retornados 3 valores)\n",
    "y_teste_matrix = [np.argmax(t) for t in y_teste]\n",
    "y_previsao_matrix = [np.argmax(t) for t in previsoes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0],\n",
       "       [ 0, 16,  2],\n",
       "       [ 0,  0, 11]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Geração da matriz de confusão\n",
    "confusao = confusion_matrix(y_teste_matrix, y_previsao_matrix)\n",
    "confusao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxa de acerto: 95.56%\n"
     ]
    }
   ],
   "source": [
    "# Taxa de acerto\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "taxa_acerto = accuracy_score(y_teste, previsoes) * 100\n",
    "print(f'Taxa de acerto: {taxa_acerto:.2f}%')"
   ]
  }
 ],
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