binary-class-cardio-desease.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"binary-class-cardio-desease.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPrNxAPieabaiupmQYd2D1d"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"f1cy78QFiv9e"},"source":["# Rede Neural Artificial no suporte ao diagnóstico de doenças cardiovasculares\n","---\n"]},{"cell_type":"markdown","metadata":{"id":"Mob4ysb5kz32"},"source":["Importando a biblioteca `pandas` e criando o nosso dataset"]},{"cell_type":"code","metadata":{"id":"rbYmXoKSkc4B","executionInfo":{"status":"ok","timestamp":1601592269941,"user_tz":180,"elapsed":831,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["import pandas as pd"],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BmtJTmZIkH1k"},"source":["Importando o banco de dados `heart.csv`"]},{"cell_type":"code","metadata":{"id":"-WjVTPz8RoGI","executionInfo":{"status":"ok","timestamp":1601592271967,"user_tz":180,"elapsed":723,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["# Criando o dataset\n","dataset = pd.read_csv('https://raw.githubusercontent.com/franklinthony/dataset/master/heart.csv', sep = ',')"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JDDCZ2m9_t52"},"source":["Descrevendo o dataset \n","\n","|                Coluna                | Descrição |\n","|:------------------------------------:|:---------:|\n","|                  age                 |    Idade (em anos)       |\n","|                  sex                 |       Sexo (0 - feminino; 1 - masculino)    |\n","|            chest pain type (cp)      |    Tipo de dor no peito (0 a 4)       |\n","|           resting blood pressure (trestbps)   |  Pressão arterial em repouso         |\n","| serum cholestoral in mg/dl (chol)    |     Colesterol (em mg/dL)      |\n","|          fasting blood sugar (fps)   |    Açúcar no sangue em jejum - 120 mg/dL (0 - falso; 1 - verdadeiro)       |\n","| resting electrocardiographic results (restcg) |    Resultados eletrocardiográficos em repouso       |\n","|      maximum heart rate achieved (thalach)   |      Frequência cardíaca máxima    |\n","|        exercise induced angina (exang)       |    Estreitamento das artérias durante atividade física (0 - não; 1 - sim)       |\n","|                oldpeak               |       Depressão de ST induzida por exercício em relação ao repouso    |\n","|    the slope of the peak exercise (slope)  |      Inclinação do segmento ST de pico do exercício     |\n","|        number of major vessels (ca)   |     Número de vasos principais (0 a 3)      |\n","|                 thal                 |   3 - normal; 6 - defeito corrigido; 7 - defeito reversível     |\n","| target |  0 - sem problemas cardiovasculares; 1 - com problemas cardiovasculares  |"]},{"cell_type":"code","metadata":{"id":"PJs-prT7UC4u","executionInfo":{"status":"ok","timestamp":1601592274235,"user_tz":180,"elapsed":802,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"58ca3426-c227-40b3-fbc1-23a2201dce60","colab":{"base_uri":"https://localhost:8080/","height":419}},"source":["# Visualizando o dataset\n","dataset"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>145</td>\n","      <td>233</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>150</td>\n","      <td>0</td>\n","      <td>2.3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>37</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>130</td>\n","      <td>250</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>187</td>\n","      <td>0</td>\n","      <td>3.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>204</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>172</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>56</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>120</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>178</td>\n","      <td>0</td>\n","      <td>0.8</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>354</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>163</td>\n","      <td>1</td>\n","      <td>0.6</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>298</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>140</td>\n","      <td>241</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>123</td>\n","      <td>1</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>299</th>\n","      <td>45</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>110</td>\n","      <td>264</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>132</td>\n","      <td>0</td>\n","      <td>1.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>300</th>\n","      <td>68</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>144</td>\n","      <td>193</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>141</td>\n","      <td>0</td>\n","      <td>3.4</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>301</th>\n","      <td>57</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>130</td>\n","      <td>131</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>115</td>\n","      <td>1</td>\n","      <td>1.2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>302</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>174</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>303 rows × 14 columns</p>\n","</div>"],"text/plain":["     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n","0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n","1     37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n","2     41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n","3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n","4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n","..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n","298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n","299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n","300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n","301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n","302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n","\n","[303 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"xxHf8duYWSG6","executionInfo":{"status":"ok","timestamp":1601592277198,"user_tz":180,"elapsed":826,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"6996a8e1-a4df-411e-b373-4908c8ca3866","colab":{"base_uri":"https://localhost:8080/","height":272}},"source":["# Tratando valores nulos\n","dataset.isnull().sum()"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age         0\n","sex         0\n","cp          0\n","trestbps    0\n","chol        0\n","fbs         0\n","restecg     0\n","thalach     0\n","exang       0\n","oldpeak     0\n","slope       0\n","ca          0\n","thal        0\n","target      0\n","dtype: int64"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"rmN_XE90brgt"},"source":["Importando a função `train_test_split` para realizarmos a subdivisão do dataset entre treinamento e teste"]},{"cell_type":"code","metadata":{"id":"IAuSpyBJEQ9I","executionInfo":{"status":"ok","timestamp":1601592279818,"user_tz":180,"elapsed":756,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["from sklearn.model_selection import train_test_split"],"execution_count":21,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TfEkRN2tcYHK"},"source":["Criando as variáveis que irão receber os atributos previsores e a classe"]},{"cell_type":"code","metadata":{"id":"MXVb5D0SFw7q","executionInfo":{"status":"ok","timestamp":1601592280856,"user_tz":180,"elapsed":616,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["previsores = dataset.iloc[:, 0:13].values\n","classe = dataset.iloc[:, 13].values"],"execution_count":22,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ambFX6EGdiJk"},"source":["Realizando a subdivisão do dataset entre treinamento e teste. O `test_size = 0.25` indica que 25% dos dados corresponderão à base de dados de teste, ou seja, 75% de todo o dataset será para treinamento da nossa rede neural"]},{"cell_type":"code","metadata":{"id":"OBeXGreRIQvT","executionInfo":{"status":"ok","timestamp":1601592282663,"user_tz":180,"elapsed":773,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["previsores_treinamento, previsores_teste, classe_treinamento, classe_teste = train_test_split(previsores, classe, test_size = 0.25)"],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Hf64C4difuIW"},"source":["Importando a biblioteca `keras` para aplicações envolvendo redes neurais profundas"]},{"cell_type":"code","metadata":{"id":"mH4P177GIWSw","executionInfo":{"status":"ok","timestamp":1601592284259,"user_tz":180,"elapsed":752,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["import keras\n","from keras.models import Sequential\n","from keras.layers import Dense"],"execution_count":24,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"U3Mrtz1MmoeU"},"source":["Criando a nossa rede neural"]},{"cell_type":"code","metadata":{"id":"cUf7FHJ3IbfN","executionInfo":{"status":"ok","timestamp":1601592287113,"user_tz":180,"elapsed":786,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["# 'Sequential()' significa que a rede será totalmente conectada\n","model = Sequential()\n","# Criando a camada de entrada e a primeira camada oculta/densa\n","model.add(Dense(units = 8, activation = 'relu', kernel_initializer = 'random_uniform', input_dim = 13))\n","# Mais uma camada oculta/densa\n","model.add(Dense(units = 8, activation = 'relu', kernel_initializer = 'random_uniform'))\n","# Criando a camada de saída\n","model.add(Dense(units = 1, activation = 'sigmoid'))"],"execution_count":25,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hj1nysgQW5o0"},"source":["![rna-cardio-desease.png](data:image/png;base64,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Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["# Compilando o modelo\n","model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['binary_accuracy'])"],"execution_count":26,"outputs":[]},{"cell_type":"code","metadata":{"id":"ft8hciQpIjjb","executionInfo":{"status":"ok","timestamp":1601592352507,"user_tz":180,"elapsed":7903,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"457a1d20-e7cf-4ae9-a6d9-b72d4df5721a","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Treinamento do modelo\n","model.fit(previsores_treinamento, classe_treinamento, batch_size = 50, epochs = 500)"],"execution_count":27,"outputs":[{"output_type":"stream","text":["Epoch 1/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6962 - binary_accuracy: 0.5374\n","Epoch 2/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6883 - binary_accuracy: 0.5551\n","Epoch 3/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6859 - binary_accuracy: 0.5507\n","Epoch 4/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6854 - binary_accuracy: 0.5507\n","Epoch 5/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6825 - binary_accuracy: 0.5507\n","Epoch 6/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6827 - binary_accuracy: 0.5507\n","Epoch 7/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6776 - binary_accuracy: 0.5551\n","Epoch 8/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6747 - binary_accuracy: 0.5507\n","Epoch 9/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6725 - binary_accuracy: 0.5507\n","Epoch 10/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6701 - binary_accuracy: 0.5551\n","Epoch 11/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6662 - binary_accuracy: 0.5551\n","Epoch 12/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6611 - binary_accuracy: 0.5683\n","Epoch 13/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.6573 - binary_accuracy: 0.6123\n","Epoch 14/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6538 - binary_accuracy: 0.6079\n","Epoch 15/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6489 - binary_accuracy: 0.6035\n","Epoch 16/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6459 - binary_accuracy: 0.6256\n","Epoch 17/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.6395 - binary_accuracy: 0.6256\n","Epoch 18/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6325 - binary_accuracy: 0.6432\n","Epoch 19/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6280 - binary_accuracy: 0.6696\n","Epoch 20/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6224 - binary_accuracy: 0.6696\n","Epoch 21/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6190 - binary_accuracy: 0.6564\n","Epoch 22/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6123 - binary_accuracy: 0.6564\n","Epoch 23/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.6053 - binary_accuracy: 0.6960\n","Epoch 24/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.6000 - binary_accuracy: 0.6784\n","Epoch 25/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5978 - binary_accuracy: 0.6828\n","Epoch 26/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5891 - binary_accuracy: 0.7137\n","Epoch 27/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5824 - binary_accuracy: 0.7093\n","Epoch 28/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5815 - binary_accuracy: 0.7048\n","Epoch 29/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5867 - binary_accuracy: 0.6740\n","Epoch 30/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5781 - binary_accuracy: 0.7048\n","Epoch 31/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5679 - binary_accuracy: 0.6960\n","Epoch 32/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5644 - binary_accuracy: 0.7181\n","Epoch 33/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5594 - binary_accuracy: 0.7048\n","Epoch 34/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5546 - binary_accuracy: 0.7137\n","Epoch 35/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5497 - binary_accuracy: 0.7181\n","Epoch 36/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5469 - binary_accuracy: 0.7225\n","Epoch 37/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5453 - binary_accuracy: 0.7225\n","Epoch 38/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5413 - binary_accuracy: 0.7181\n","Epoch 39/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5343 - binary_accuracy: 0.7225\n","Epoch 40/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5364 - binary_accuracy: 0.7093\n","Epoch 41/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5299 - binary_accuracy: 0.7269\n","Epoch 42/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5250 - binary_accuracy: 0.7401\n","Epoch 43/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5225 - binary_accuracy: 0.7269\n","Epoch 44/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5178 - binary_accuracy: 0.7401\n","Epoch 45/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5179 - binary_accuracy: 0.7313\n","Epoch 46/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5177 - binary_accuracy: 0.7225\n","Epoch 47/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5085 - binary_accuracy: 0.7093\n","Epoch 48/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.5163 - binary_accuracy: 0.7489\n","Epoch 49/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5035 - binary_accuracy: 0.7313\n","Epoch 50/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.5010 - binary_accuracy: 0.7313\n","Epoch 51/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4996 - binary_accuracy: 0.7445\n","Epoch 52/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4944 - binary_accuracy: 0.7445\n","Epoch 53/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4932 - binary_accuracy: 0.7401\n","Epoch 54/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4884 - binary_accuracy: 0.7357\n","Epoch 55/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4884 - binary_accuracy: 0.7445\n","Epoch 56/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4853 - binary_accuracy: 0.7533\n","Epoch 57/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4798 - binary_accuracy: 0.7577\n","Epoch 58/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4765 - binary_accuracy: 0.7665\n","Epoch 59/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4751 - binary_accuracy: 0.7577\n","Epoch 60/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4741 - binary_accuracy: 0.7621\n","Epoch 61/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4689 - binary_accuracy: 0.7753\n","Epoch 62/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4740 - binary_accuracy: 0.7709\n","Epoch 63/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4862 - binary_accuracy: 0.7489\n","Epoch 64/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4823 - binary_accuracy: 0.7357\n","Epoch 65/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4712 - binary_accuracy: 0.7533\n","Epoch 66/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4635 - binary_accuracy: 0.7974\n","Epoch 67/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4611 - binary_accuracy: 0.7753\n","Epoch 68/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4549 - binary_accuracy: 0.7797\n","Epoch 69/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4528 - binary_accuracy: 0.7797\n","Epoch 70/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4484 - binary_accuracy: 0.7709\n","Epoch 71/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4426 - binary_accuracy: 0.7841\n","Epoch 72/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4408 - binary_accuracy: 0.7930\n","Epoch 73/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4508 - binary_accuracy: 0.7797\n","Epoch 74/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4431 - binary_accuracy: 0.7930\n","Epoch 75/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4320 - binary_accuracy: 0.8018\n","Epoch 76/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4407 - binary_accuracy: 0.7885\n","Epoch 77/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4390 - binary_accuracy: 0.7885\n","Epoch 78/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4445 - binary_accuracy: 0.7930\n","Epoch 79/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4355 - binary_accuracy: 0.8106\n","Epoch 80/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4347 - binary_accuracy: 0.7797\n","Epoch 81/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4247 - binary_accuracy: 0.8062\n","Epoch 82/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4309 - binary_accuracy: 0.7841\n","Epoch 83/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4133 - binary_accuracy: 0.8018\n","Epoch 84/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4441 - binary_accuracy: 0.7974\n","Epoch 85/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4481 - binary_accuracy: 0.7930\n","Epoch 86/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4089 - binary_accuracy: 0.8150\n","Epoch 87/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4202 - binary_accuracy: 0.7885\n","Epoch 88/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4376 - binary_accuracy: 0.7974\n","Epoch 89/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4100 - binary_accuracy: 0.8150\n","Epoch 90/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4026 - binary_accuracy: 0.8194\n","Epoch 91/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4240 - binary_accuracy: 0.7974\n","Epoch 92/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4241 - binary_accuracy: 0.7709\n","Epoch 93/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3997 - binary_accuracy: 0.8062\n","Epoch 94/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4026 - binary_accuracy: 0.8150\n","Epoch 95/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.4046 - binary_accuracy: 0.8238\n","Epoch 96/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3968 - binary_accuracy: 0.8106\n","Epoch 97/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3961 - binary_accuracy: 0.8194\n","Epoch 98/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3958 - binary_accuracy: 0.8194\n","Epoch 99/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3966 - binary_accuracy: 0.8194\n","Epoch 100/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3976 - binary_accuracy: 0.8282\n","Epoch 101/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4084 - binary_accuracy: 0.7974\n","Epoch 102/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3891 - binary_accuracy: 0.8150\n","Epoch 103/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4064 - binary_accuracy: 0.7930\n","Epoch 104/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3874 - binary_accuracy: 0.8238\n","Epoch 105/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4021 - binary_accuracy: 0.8018\n","Epoch 106/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3850 - binary_accuracy: 0.8370\n","Epoch 107/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3897 - binary_accuracy: 0.8282\n","Epoch 108/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3842 - binary_accuracy: 0.8370\n","Epoch 109/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3804 - binary_accuracy: 0.8282\n","Epoch 110/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3814 - binary_accuracy: 0.8326\n","Epoch 111/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3796 - binary_accuracy: 0.8414\n","Epoch 112/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3814 - binary_accuracy: 0.8414\n","Epoch 113/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3772 - binary_accuracy: 0.8458\n","Epoch 114/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3802 - binary_accuracy: 0.8326\n","Epoch 115/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3790 - binary_accuracy: 0.8370\n","Epoch 116/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3793 - binary_accuracy: 0.8370\n","Epoch 117/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3739 - binary_accuracy: 0.8414\n","Epoch 118/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3704 - binary_accuracy: 0.8414\n","Epoch 119/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3739 - binary_accuracy: 0.8458\n","Epoch 120/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3701 - binary_accuracy: 0.8458\n","Epoch 121/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3731 - binary_accuracy: 0.8458\n","Epoch 122/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3685 - binary_accuracy: 0.8458\n","Epoch 123/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3703 - binary_accuracy: 0.8458\n","Epoch 124/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3744 - binary_accuracy: 0.8502\n","Epoch 125/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3923 - binary_accuracy: 0.8282\n","Epoch 126/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3814 - binary_accuracy: 0.8238\n","Epoch 127/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3968 - binary_accuracy: 0.8018\n","Epoch 128/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3807 - binary_accuracy: 0.8238\n","Epoch 129/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3687 - binary_accuracy: 0.8414\n","Epoch 130/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3754 - binary_accuracy: 0.8194\n","Epoch 131/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3805 - binary_accuracy: 0.8282\n","Epoch 132/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3673 - binary_accuracy: 0.8458\n","Epoch 133/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3646 - binary_accuracy: 0.8546\n","Epoch 134/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3696 - binary_accuracy: 0.8414\n","Epoch 135/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3637 - binary_accuracy: 0.8502\n","Epoch 136/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3673 - binary_accuracy: 0.8546\n","Epoch 137/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3940 - binary_accuracy: 0.8282\n","Epoch 138/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3598 - binary_accuracy: 0.8458\n","Epoch 139/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3679 - binary_accuracy: 0.8546\n","Epoch 140/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3670 - binary_accuracy: 0.8458\n","Epoch 141/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3610 - binary_accuracy: 0.8502\n","Epoch 142/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3638 - binary_accuracy: 0.8502\n","Epoch 143/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3615 - binary_accuracy: 0.8458\n","Epoch 144/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3626 - binary_accuracy: 0.8502\n","Epoch 145/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3620 - binary_accuracy: 0.8502\n","Epoch 146/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3775 - binary_accuracy: 0.8282\n","Epoch 147/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3812 - binary_accuracy: 0.8194\n","Epoch 148/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3760 - binary_accuracy: 0.8370\n","Epoch 149/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3774 - binary_accuracy: 0.8238\n","Epoch 150/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3684 - binary_accuracy: 0.8458\n","Epoch 151/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3610 - binary_accuracy: 0.8326\n","Epoch 152/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3967 - binary_accuracy: 0.8194\n","Epoch 153/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.4029 - binary_accuracy: 0.8106\n","Epoch 154/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3675 - binary_accuracy: 0.8414\n","Epoch 155/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3702 - binary_accuracy: 0.8546\n","Epoch 156/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3840 - binary_accuracy: 0.8194\n","Epoch 157/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3824 - binary_accuracy: 0.8106\n","Epoch 158/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3715 - binary_accuracy: 0.8282\n","Epoch 159/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3559 - binary_accuracy: 0.8414\n","Epoch 160/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3681 - binary_accuracy: 0.8282\n","Epoch 161/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3614 - binary_accuracy: 0.8502\n","Epoch 162/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3587 - binary_accuracy: 0.8502\n","Epoch 163/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3604 - binary_accuracy: 0.8590\n","Epoch 164/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3581 - binary_accuracy: 0.8546\n","Epoch 165/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3568 - binary_accuracy: 0.8546\n","Epoch 166/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3565 - binary_accuracy: 0.8502\n","Epoch 167/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3620 - binary_accuracy: 0.8502\n","Epoch 168/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3607 - binary_accuracy: 0.8546\n","Epoch 169/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3616 - binary_accuracy: 0.8458\n","Epoch 170/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3551 - binary_accuracy: 0.8590\n","Epoch 171/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3557 - binary_accuracy: 0.8546\n","Epoch 172/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3647 - binary_accuracy: 0.8414\n","Epoch 173/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3541 - binary_accuracy: 0.8502\n","Epoch 174/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3617 - binary_accuracy: 0.8458\n","Epoch 175/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3601 - binary_accuracy: 0.8502\n","Epoch 176/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3640 - binary_accuracy: 0.8282\n","Epoch 177/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3604 - binary_accuracy: 0.8502\n","Epoch 178/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3548 - binary_accuracy: 0.8502\n","Epoch 179/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3600 - binary_accuracy: 0.8370\n","Epoch 180/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3549 - binary_accuracy: 0.8678\n","Epoch 181/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3540 - binary_accuracy: 0.8546\n","Epoch 182/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3611 - binary_accuracy: 0.8502\n","Epoch 183/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3686 - binary_accuracy: 0.8238\n","Epoch 184/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3580 - binary_accuracy: 0.8502\n","Epoch 185/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3513 - binary_accuracy: 0.8458\n","Epoch 186/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3722 - binary_accuracy: 0.8414\n","Epoch 187/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3650 - binary_accuracy: 0.8370\n","Epoch 188/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3568 - binary_accuracy: 0.8546\n","Epoch 189/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3551 - binary_accuracy: 0.8502\n","Epoch 190/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3572 - binary_accuracy: 0.8414\n","Epoch 191/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3551 - binary_accuracy: 0.8458\n","Epoch 192/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3562 - binary_accuracy: 0.8502\n","Epoch 193/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3559 - binary_accuracy: 0.8502\n","Epoch 194/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3583 - binary_accuracy: 0.8458\n","Epoch 195/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3607 - binary_accuracy: 0.8458\n","Epoch 196/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3564 - binary_accuracy: 0.8458\n","Epoch 197/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3558 - binary_accuracy: 0.8546\n","Epoch 198/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3563 - binary_accuracy: 0.8502\n","Epoch 199/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3575 - binary_accuracy: 0.8590\n","Epoch 200/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3609 - binary_accuracy: 0.8458\n","Epoch 201/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3610 - binary_accuracy: 0.8414\n","Epoch 202/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3534 - binary_accuracy: 0.8634\n","Epoch 203/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3598 - binary_accuracy: 0.8458\n","Epoch 204/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3575 - binary_accuracy: 0.8546\n","Epoch 205/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3563 - binary_accuracy: 0.8546\n","Epoch 206/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3573 - binary_accuracy: 0.8502\n","Epoch 207/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3517 - binary_accuracy: 0.8634\n","Epoch 208/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3532 - binary_accuracy: 0.8502\n","Epoch 209/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3538 - binary_accuracy: 0.8502\n","Epoch 210/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3530 - binary_accuracy: 0.8590\n","Epoch 211/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3605 - binary_accuracy: 0.8634\n","Epoch 212/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3542 - binary_accuracy: 0.8634\n","Epoch 213/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3565 - binary_accuracy: 0.8414\n","Epoch 214/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3747 - binary_accuracy: 0.8282\n","Epoch 215/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3764 - binary_accuracy: 0.8194\n","Epoch 216/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3866 - binary_accuracy: 0.8370\n","Epoch 217/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3643 - binary_accuracy: 0.8414\n","Epoch 218/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3712 - binary_accuracy: 0.8238\n","Epoch 219/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3703 - binary_accuracy: 0.8326\n","Epoch 220/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3648 - binary_accuracy: 0.8326\n","Epoch 221/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3503 - binary_accuracy: 0.8502\n","Epoch 222/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3587 - binary_accuracy: 0.8414\n","Epoch 223/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3600 - binary_accuracy: 0.8634\n","Epoch 224/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3640 - binary_accuracy: 0.8326\n","Epoch 225/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3506 - binary_accuracy: 0.8414\n","Epoch 226/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3607 - binary_accuracy: 0.8546\n","Epoch 227/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3544 - binary_accuracy: 0.8590\n","Epoch 228/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3506 - binary_accuracy: 0.8634\n","Epoch 229/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3537 - binary_accuracy: 0.8414\n","Epoch 230/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3556 - binary_accuracy: 0.8502\n","Epoch 231/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3498 - binary_accuracy: 0.8634\n","Epoch 232/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3566 - binary_accuracy: 0.8590\n","Epoch 233/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3533 - binary_accuracy: 0.8590\n","Epoch 234/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3540 - binary_accuracy: 0.8546\n","Epoch 235/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3626 - binary_accuracy: 0.8546\n","Epoch 236/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3549 - binary_accuracy: 0.8414\n","Epoch 237/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3613 - binary_accuracy: 0.8634\n","Epoch 238/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3500 - binary_accuracy: 0.8634\n","Epoch 239/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3532 - binary_accuracy: 0.8546\n","Epoch 240/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3507 - binary_accuracy: 0.8502\n","Epoch 241/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3558 - binary_accuracy: 0.8590\n","Epoch 242/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3505 - binary_accuracy: 0.8590\n","Epoch 243/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3537 - binary_accuracy: 0.8634\n","Epoch 244/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3535 - binary_accuracy: 0.8590\n","Epoch 245/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3569 - binary_accuracy: 0.8546\n","Epoch 246/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3476 - binary_accuracy: 0.8634\n","Epoch 247/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3530 - binary_accuracy: 0.8546\n","Epoch 248/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3585 - binary_accuracy: 0.8502\n","Epoch 249/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3506 - binary_accuracy: 0.8634\n","Epoch 250/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3494 - binary_accuracy: 0.8634\n","Epoch 251/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3587 - binary_accuracy: 0.8546\n","Epoch 252/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3537 - binary_accuracy: 0.8590\n","Epoch 253/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3654 - binary_accuracy: 0.8238\n","Epoch 254/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3523 - binary_accuracy: 0.8502\n","Epoch 255/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3561 - binary_accuracy: 0.8370\n","Epoch 256/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3637 - binary_accuracy: 0.8414\n","Epoch 257/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3466 - binary_accuracy: 0.8722\n","Epoch 258/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3611 - binary_accuracy: 0.8458\n","Epoch 259/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3562 - binary_accuracy: 0.8634\n","Epoch 260/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3537 - binary_accuracy: 0.8678\n","Epoch 261/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3515 - binary_accuracy: 0.8546\n","Epoch 262/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3538 - binary_accuracy: 0.8546\n","Epoch 263/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3621 - binary_accuracy: 0.8326\n","Epoch 264/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3781 - binary_accuracy: 0.8326\n","Epoch 265/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3762 - binary_accuracy: 0.8282\n","Epoch 266/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3618 - binary_accuracy: 0.8502\n","Epoch 267/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3487 - binary_accuracy: 0.8546\n","Epoch 268/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3657 - binary_accuracy: 0.8414\n","Epoch 269/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3537 - binary_accuracy: 0.8634\n","Epoch 270/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3517 - binary_accuracy: 0.8634\n","Epoch 271/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3500 - binary_accuracy: 0.8678\n","Epoch 272/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3548 - binary_accuracy: 0.8502\n","Epoch 273/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3725 - binary_accuracy: 0.8326\n","Epoch 274/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3627 - binary_accuracy: 0.8370\n","Epoch 275/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3692 - binary_accuracy: 0.8370\n","Epoch 276/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3512 - binary_accuracy: 0.8722\n","Epoch 277/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3510 - binary_accuracy: 0.8678\n","Epoch 278/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3550 - binary_accuracy: 0.8546\n","Epoch 279/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3484 - binary_accuracy: 0.8634\n","Epoch 280/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3506 - binary_accuracy: 0.8634\n","Epoch 281/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3533 - binary_accuracy: 0.8590\n","Epoch 282/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3485 - binary_accuracy: 0.8634\n","Epoch 283/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3517 - binary_accuracy: 0.8546\n","Epoch 284/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3508 - binary_accuracy: 0.8590\n","Epoch 285/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3546 - binary_accuracy: 0.8370\n","Epoch 286/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3553 - binary_accuracy: 0.8502\n","Epoch 287/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3597 - binary_accuracy: 0.8546\n","Epoch 288/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3555 - binary_accuracy: 0.8590\n","Epoch 289/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3566 - binary_accuracy: 0.8458\n","Epoch 290/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3556 - binary_accuracy: 0.8546\n","Epoch 291/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3562 - binary_accuracy: 0.8458\n","Epoch 292/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3649 - binary_accuracy: 0.8326\n","Epoch 293/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3561 - binary_accuracy: 0.8414\n","Epoch 294/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3547 - binary_accuracy: 0.8414\n","Epoch 295/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3533 - binary_accuracy: 0.8546\n","Epoch 296/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3564 - binary_accuracy: 0.8502\n","Epoch 297/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3499 - binary_accuracy: 0.8678\n","Epoch 298/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3518 - binary_accuracy: 0.8590\n","Epoch 299/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3484 - binary_accuracy: 0.8634\n","Epoch 300/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3573 - binary_accuracy: 0.8458\n","Epoch 301/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3531 - binary_accuracy: 0.8546\n","Epoch 302/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3545 - binary_accuracy: 0.8502\n","Epoch 303/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3498 - binary_accuracy: 0.8678\n","Epoch 304/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3516 - binary_accuracy: 0.8634\n","Epoch 305/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3593 - binary_accuracy: 0.8370\n","Epoch 306/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3527 - binary_accuracy: 0.8502\n","Epoch 307/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3485 - binary_accuracy: 0.8634\n","Epoch 308/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3517 - binary_accuracy: 0.8590\n","Epoch 309/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3485 - binary_accuracy: 0.8590\n","Epoch 310/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3497 - binary_accuracy: 0.8634\n","Epoch 311/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3525 - binary_accuracy: 0.8546\n","Epoch 312/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3481 - binary_accuracy: 0.8546\n","Epoch 313/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3505 - binary_accuracy: 0.8634\n","Epoch 314/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3505 - binary_accuracy: 0.8502\n","Epoch 315/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3542 - binary_accuracy: 0.8546\n","Epoch 316/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3536 - binary_accuracy: 0.8590\n","Epoch 317/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3477 - binary_accuracy: 0.8546\n","Epoch 318/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3553 - binary_accuracy: 0.8634\n","Epoch 319/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3490 - binary_accuracy: 0.8590\n","Epoch 320/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3512 - binary_accuracy: 0.8590\n","Epoch 321/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3558 - binary_accuracy: 0.8502\n","Epoch 322/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3655 - binary_accuracy: 0.8502\n","Epoch 323/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3434 - binary_accuracy: 0.8634\n","Epoch 324/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3661 - binary_accuracy: 0.8370\n","Epoch 325/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3642 - binary_accuracy: 0.8414\n","Epoch 326/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3625 - binary_accuracy: 0.8502\n","Epoch 327/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3663 - binary_accuracy: 0.8370\n","Epoch 328/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3467 - binary_accuracy: 0.8634\n","Epoch 329/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3643 - binary_accuracy: 0.8502\n","Epoch 330/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3568 - binary_accuracy: 0.8326\n","Epoch 331/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3438 - binary_accuracy: 0.8590\n","Epoch 332/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3566 - binary_accuracy: 0.8414\n","Epoch 333/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3588 - binary_accuracy: 0.8414\n","Epoch 334/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3569 - binary_accuracy: 0.8458\n","Epoch 335/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3489 - binary_accuracy: 0.8502\n","Epoch 336/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3548 - binary_accuracy: 0.8502\n","Epoch 337/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3477 - binary_accuracy: 0.8634\n","Epoch 338/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3482 - binary_accuracy: 0.8634\n","Epoch 339/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3583 - binary_accuracy: 0.8502\n","Epoch 340/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3597 - binary_accuracy: 0.8458\n","Epoch 341/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3452 - binary_accuracy: 0.8502\n","Epoch 342/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3506 - binary_accuracy: 0.8502\n","Epoch 343/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3943 - binary_accuracy: 0.8370\n","Epoch 344/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3511 - binary_accuracy: 0.8502\n","Epoch 345/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3533 - binary_accuracy: 0.8502\n","Epoch 346/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3531 - binary_accuracy: 0.8546\n","Epoch 347/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3483 - binary_accuracy: 0.8546\n","Epoch 348/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3494 - binary_accuracy: 0.8590\n","Epoch 349/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3511 - binary_accuracy: 0.8546\n","Epoch 350/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3500 - binary_accuracy: 0.8590\n","Epoch 351/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3445 - binary_accuracy: 0.8634\n","Epoch 352/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3611 - binary_accuracy: 0.8414\n","Epoch 353/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3767 - binary_accuracy: 0.8414\n","Epoch 354/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3649 - binary_accuracy: 0.8326\n","Epoch 355/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3482 - binary_accuracy: 0.8590\n","Epoch 356/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3693 - binary_accuracy: 0.8370\n","Epoch 357/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3589 - binary_accuracy: 0.8458\n","Epoch 358/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3518 - binary_accuracy: 0.8546\n","Epoch 359/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3611 - binary_accuracy: 0.8326\n","Epoch 360/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3540 - binary_accuracy: 0.8546\n","Epoch 361/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3501 - binary_accuracy: 0.8326\n","Epoch 362/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3597 - binary_accuracy: 0.8326\n","Epoch 363/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3664 - binary_accuracy: 0.8502\n","Epoch 364/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3422 - binary_accuracy: 0.8722\n","Epoch 365/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3731 - binary_accuracy: 0.8326\n","Epoch 366/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3613 - binary_accuracy: 0.8414\n","Epoch 367/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3533 - binary_accuracy: 0.8458\n","Epoch 368/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3625 - binary_accuracy: 0.8546\n","Epoch 369/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3487 - binary_accuracy: 0.8590\n","Epoch 370/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3503 - binary_accuracy: 0.8590\n","Epoch 371/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3496 - binary_accuracy: 0.8502\n","Epoch 372/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3553 - binary_accuracy: 0.8502\n","Epoch 373/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3731 - binary_accuracy: 0.8370\n","Epoch 374/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3414 - binary_accuracy: 0.8678\n","Epoch 375/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3579 - binary_accuracy: 0.8546\n","Epoch 376/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3558 - binary_accuracy: 0.8502\n","Epoch 377/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3556 - binary_accuracy: 0.8458\n","Epoch 378/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3575 - binary_accuracy: 0.8634\n","Epoch 379/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3520 - binary_accuracy: 0.8414\n","Epoch 380/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3604 - binary_accuracy: 0.8370\n","Epoch 381/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3519 - binary_accuracy: 0.8502\n","Epoch 382/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3510 - binary_accuracy: 0.8634\n","Epoch 383/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3539 - binary_accuracy: 0.8546\n","Epoch 384/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3493 - binary_accuracy: 0.8590\n","Epoch 385/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3526 - binary_accuracy: 0.8414\n","Epoch 386/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3488 - binary_accuracy: 0.8678\n","Epoch 387/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3448 - binary_accuracy: 0.8634\n","Epoch 388/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3497 - binary_accuracy: 0.8722\n","Epoch 389/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3564 - binary_accuracy: 0.8546\n","Epoch 390/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3454 - binary_accuracy: 0.8634\n","Epoch 391/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3490 - binary_accuracy: 0.8502\n","Epoch 392/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3529 - binary_accuracy: 0.8458\n","Epoch 393/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3644 - binary_accuracy: 0.8502\n","Epoch 394/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3444 - binary_accuracy: 0.8590\n","Epoch 395/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3550 - binary_accuracy: 0.8414\n","Epoch 396/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3645 - binary_accuracy: 0.8414\n","Epoch 397/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3738 - binary_accuracy: 0.8590\n","Epoch 398/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3566 - binary_accuracy: 0.8458\n","Epoch 399/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3654 - binary_accuracy: 0.8458\n","Epoch 400/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3546 - binary_accuracy: 0.8458\n","Epoch 401/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3492 - binary_accuracy: 0.8458\n","Epoch 402/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3519 - binary_accuracy: 0.8590\n","Epoch 403/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3459 - binary_accuracy: 0.8634\n","Epoch 404/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3470 - binary_accuracy: 0.8634\n","Epoch 405/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3527 - binary_accuracy: 0.8590\n","Epoch 406/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3641 - binary_accuracy: 0.8502\n","Epoch 407/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3613 - binary_accuracy: 0.8414\n","Epoch 408/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3511 - binary_accuracy: 0.8590\n","Epoch 409/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3506 - binary_accuracy: 0.8458\n","Epoch 410/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3651 - binary_accuracy: 0.8370\n","Epoch 411/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3454 - binary_accuracy: 0.8590\n","Epoch 412/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3437 - binary_accuracy: 0.8678\n","Epoch 413/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3514 - binary_accuracy: 0.8634\n","Epoch 414/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3451 - binary_accuracy: 0.8634\n","Epoch 415/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3470 - binary_accuracy: 0.8678\n","Epoch 416/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3443 - binary_accuracy: 0.8678\n","Epoch 417/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3502 - binary_accuracy: 0.8590\n","Epoch 418/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3495 - binary_accuracy: 0.8502\n","Epoch 419/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3440 - binary_accuracy: 0.8634\n","Epoch 420/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3486 - binary_accuracy: 0.8634\n","Epoch 421/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3447 - binary_accuracy: 0.8722\n","Epoch 422/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3455 - binary_accuracy: 0.8546\n","Epoch 423/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3496 - binary_accuracy: 0.8546\n","Epoch 424/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3573 - binary_accuracy: 0.8458\n","Epoch 425/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3503 - binary_accuracy: 0.8546\n","Epoch 426/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3448 - binary_accuracy: 0.8811\n","Epoch 427/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3507 - binary_accuracy: 0.8502\n","Epoch 428/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3789 - binary_accuracy: 0.8458\n","Epoch 429/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3800 - binary_accuracy: 0.8326\n","Epoch 430/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3613 - binary_accuracy: 0.8282\n","Epoch 431/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3499 - binary_accuracy: 0.8502\n","Epoch 432/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3561 - binary_accuracy: 0.8414\n","Epoch 433/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3524 - binary_accuracy: 0.8590\n","Epoch 434/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3477 - binary_accuracy: 0.8634\n","Epoch 435/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3421 - binary_accuracy: 0.8634\n","Epoch 436/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3515 - binary_accuracy: 0.8458\n","Epoch 437/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3446 - binary_accuracy: 0.8678\n","Epoch 438/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3462 - binary_accuracy: 0.8634\n","Epoch 439/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3419 - binary_accuracy: 0.8590\n","Epoch 440/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3445 - binary_accuracy: 0.8590\n","Epoch 441/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3449 - binary_accuracy: 0.8590\n","Epoch 442/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3413 - binary_accuracy: 0.8722\n","Epoch 443/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3431 - binary_accuracy: 0.8634\n","Epoch 444/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3423 - binary_accuracy: 0.8722\n","Epoch 445/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3415 - binary_accuracy: 0.8678\n","Epoch 446/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3436 - binary_accuracy: 0.8590\n","Epoch 447/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3446 - binary_accuracy: 0.8722\n","Epoch 448/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3432 - binary_accuracy: 0.8634\n","Epoch 449/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3556 - binary_accuracy: 0.8414\n","Epoch 450/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3416 - binary_accuracy: 0.8590\n","Epoch 451/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3450 - binary_accuracy: 0.8634\n","Epoch 452/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3621 - binary_accuracy: 0.8414\n","Epoch 453/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3479 - binary_accuracy: 0.8678\n","Epoch 454/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3541 - binary_accuracy: 0.8502\n","Epoch 455/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3487 - binary_accuracy: 0.8634\n","Epoch 456/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3491 - binary_accuracy: 0.8546\n","Epoch 457/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3492 - binary_accuracy: 0.8590\n","Epoch 458/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3383 - binary_accuracy: 0.8634\n","Epoch 459/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3487 - binary_accuracy: 0.8634\n","Epoch 460/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3412 - binary_accuracy: 0.8678\n","Epoch 461/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3443 - binary_accuracy: 0.8590\n","Epoch 462/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3418 - binary_accuracy: 0.8678\n","Epoch 463/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3412 - binary_accuracy: 0.8678\n","Epoch 464/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3452 - binary_accuracy: 0.8546\n","Epoch 465/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3448 - binary_accuracy: 0.8590\n","Epoch 466/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3400 - binary_accuracy: 0.8678\n","Epoch 467/500\n","5/5 [==============================] - 0s 3ms/step - loss: 0.3444 - binary_accuracy: 0.8722\n","Epoch 468/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3452 - binary_accuracy: 0.8590\n","Epoch 469/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3447 - binary_accuracy: 0.8590\n","Epoch 470/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3422 - binary_accuracy: 0.8634\n","Epoch 471/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3435 - binary_accuracy: 0.8590\n","Epoch 472/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3460 - binary_accuracy: 0.8678\n","Epoch 473/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3775 - binary_accuracy: 0.8370\n","Epoch 474/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3629 - binary_accuracy: 0.8502\n","Epoch 475/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3709 - binary_accuracy: 0.8502\n","Epoch 476/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3509 - binary_accuracy: 0.8502\n","Epoch 477/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3509 - binary_accuracy: 0.8546\n","Epoch 478/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3433 - binary_accuracy: 0.8590\n","Epoch 479/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3444 - binary_accuracy: 0.8722\n","Epoch 480/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3411 - binary_accuracy: 0.8590\n","Epoch 481/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3425 - binary_accuracy: 0.8634\n","Epoch 482/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3531 - binary_accuracy: 0.8502\n","Epoch 483/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3561 - binary_accuracy: 0.8546\n","Epoch 484/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3576 - binary_accuracy: 0.8458\n","Epoch 485/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3469 - binary_accuracy: 0.8634\n","Epoch 486/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3445 - binary_accuracy: 0.8634\n","Epoch 487/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3440 - binary_accuracy: 0.8634\n","Epoch 488/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3438 - binary_accuracy: 0.8590\n","Epoch 489/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3428 - binary_accuracy: 0.8590\n","Epoch 490/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3390 - binary_accuracy: 0.8546\n","Epoch 491/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3437 - binary_accuracy: 0.8722\n","Epoch 492/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3417 - binary_accuracy: 0.8546\n","Epoch 493/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3371 - binary_accuracy: 0.8634\n","Epoch 494/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3466 - binary_accuracy: 0.8590\n","Epoch 495/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3433 - binary_accuracy: 0.8678\n","Epoch 496/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3459 - binary_accuracy: 0.8634\n","Epoch 497/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3502 - binary_accuracy: 0.8634\n","Epoch 498/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3398 - binary_accuracy: 0.8678\n","Epoch 499/500\n","5/5 [==============================] - 0s 2ms/step - loss: 0.3409 - binary_accuracy: 0.8590\n","Epoch 500/500\n","5/5 [==============================] - 0s 1ms/step - loss: 0.3433 - binary_accuracy: 0.8546\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7fce832b9dd8>"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"Un-zsQltKCP2","executionInfo":{"status":"ok","timestamp":1601592525751,"user_tz":180,"elapsed":766,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"d5dd51ed-f485-485d-b210-9e6192c0309e","colab":{"base_uri":"https://localhost:8080/","height":493}},"source":["# Visualizando os pesos entre as camadas\n","pesos = model.layers[0].get_weights()\n","pesos"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[array([[-0.02157836,  0.0024056 ,  0.00604095, -0.00215505, -0.00821324,\n","          0.06115719, -0.01775025,  0.0692767 ],\n","        [ 0.01842202, -0.8910829 , -0.76725006,  0.03330428, -0.77869904,\n","         -0.81949   ,  0.02745223,  0.69699347],\n","        [-0.00943412,  0.40637022,  0.54053414, -0.00628068,  0.6433346 ,\n","          0.56288445, -0.01061447, -0.5148528 ],\n","        [-0.02660693,  0.01966362, -0.0384967 ,  0.0288269 ,  0.05008361,\n","         -0.0361105 ,  0.03982718,  0.08403765],\n","        [-0.04850925,  0.00901129,  0.03607363, -0.02970959, -0.0189352 ,\n","          0.01349736, -0.04193456,  0.07766937],\n","        [ 0.02393718,  0.06813786,  0.21192406, -0.03225064,  0.22574387,\n","          0.253409  ,  0.04874843, -0.1536949 ],\n","        [ 0.03476876,  0.07988603,  0.42540962, -0.04511298,  0.4030594 ,\n","          0.39129776, -0.04547603, -0.3473112 ],\n","        [-0.00457907, -0.01020389,  0.02167551, -0.00730823,  0.03293279,\n","          0.02301517, -0.00183226,  0.02353563],\n","        [-0.03389584, -0.32654777, -0.48013055,  0.04017964, -0.49489033,\n","         -0.5237347 ,  0.03633691,  0.57388693],\n","        [-0.0167076 , -0.32612044, -0.38870826,  0.02813107, -0.41813785,\n","         -0.39507893,  0.0238561 ,  0.4384243 ],\n","        [-0.03331486,  0.34946775,  0.3877942 ,  0.02447257,  0.35332075,\n","          0.37113535, -0.02861511, -0.30453822],\n","        [ 0.0074747 , -1.0844456 , -0.38116276, -0.00445417, -0.3783234 ,\n","         -0.42194134,  0.05017313,  0.36755472],\n","        [-0.00866854, -0.5243134 , -0.41689312, -0.03684217, -0.45341066,\n","         -0.430218  ,  0.0202733 ,  0.5027155 ]], dtype=float32),\n"," array([ 0.        ,  0.013686  ,  0.0435634 ,  0.        ,  0.04965337,\n","         0.03678064, -0.01152272,  0.00924835], dtype=float32)]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"EShimRkRKSnu","executionInfo":{"status":"ok","timestamp":1601592571607,"user_tz":180,"elapsed":782,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"668b9750-19a9-4f28-9f1e-a1334f5d3a45","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Realizando a predição do modelo\n","previsoes = model.predict(previsores_teste)\n","previsoes = (previsoes > 0.5)\n","previsoes"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ True],\n","       [False],\n","       [False],\n","       [False],\n","       [False],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [False],\n","       [False],\n","       [ True],\n","       [False],\n","       [ True],\n","       [False],\n","       [False],\n","       [False],\n","       [False],\n","       [False],\n","       [False],\n","       [False],\n","       [False],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [False],\n","       [False],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [False],\n","       [ True],\n","       [ True],\n","       [False],\n","       [False],\n","       [ True],\n","       [ True],\n","       [ True],\n","       [False]])"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"UDXcHckxduf7","executionInfo":{"status":"ok","timestamp":1601592577559,"user_tz":180,"elapsed":761,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"444b2476-3be4-4d28-8f5a-f75e039bd743","colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["classe_t = (classe_teste > 0.5) \n","classe_t"],"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ True, False, False, False, False,  True,  True,  True,  True,\n","       False, False, False, False,  True, False, False, False, False,\n","       False, False, False,  True,  True,  True,  True, False,  True,\n","        True,  True,  True,  True,  True,  True,  True,  True,  True,\n","       False,  True, False,  True, False,  True,  True, False, False,\n","        True,  True,  True,  True,  True, False, False,  True,  True,\n","       False,  True,  True, False, False, False, False, False,  True,\n","        True, False, False,  True, False, False,  True, False, False,\n","        True,  True,  True, False])"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"5YToxR--scT0"},"source":["Importando as funções `confusion_matrix` e `accuracy_score` para visualizarmos os resultados provenientes do modelo"]},{"cell_type":"code","metadata":{"id":"qY3j181kKA9T","executionInfo":{"status":"ok","timestamp":1601592668483,"user_tz":180,"elapsed":818,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["from sklearn.metrics import confusion_matrix, plot_confusion_matrix, accuracy_score"],"execution_count":33,"outputs":[]},{"cell_type":"code","metadata":{"id":"XCnwVZATKagV","executionInfo":{"status":"ok","timestamp":1601592697069,"user_tz":180,"elapsed":710,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"21e4b646-e99c-4d80-9b7e-732b6685d25c","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["precisao = accuracy_score(classe_teste, previsoes)\n","matriz = confusion_matrix(classe_teste, previsoes)\n","precisao"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.868421052631579"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"markdown","metadata":{"id":"UbXOksudxBCu"},"source":["Importando as bibliotecas `seaborn` e `matplotlib` para uma visualização melhorada da matriz de confusão"]},{"cell_type":"code","metadata":{"id":"y4lKZyuruscQ","executionInfo":{"status":"ok","timestamp":1601592734278,"user_tz":180,"elapsed":770,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}}},"source":["import seaborn as sn\n","import matplotlib.pyplot as plt"],"execution_count":36,"outputs":[]},{"cell_type":"code","metadata":{"id":"IjQ3zIeqKULx","executionInfo":{"status":"ok","timestamp":1601592780913,"user_tz":180,"elapsed":970,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"d6294cf7-b4fa-41d5-d3d9-8a22c7f72f86","colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["df = pd.DataFrame(matriz, range(2), range(2))\n","sn.heatmap(df, annot=True, annot_kws={\"size\": 15})"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fce828e5390>"]},"metadata":{"tags":[]},"execution_count":37},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"pYKJLjhVxUOY"},"source":["Obtendo a acurácia do nosso modelo na base de teste\n"]},{"cell_type":"code","metadata":{"id":"Fc9CTTYBKnEF","executionInfo":{"status":"ok","timestamp":1601592982839,"user_tz":180,"elapsed":827,"user":{"displayName":"Franklin Anthony","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7IabJYagnfiPmtpfZ4YYZ1_DdlUKY41y_xKiP=s64","userId":"05372586869166040201"}},"outputId":"e88a5084-c60b-4e2a-db13-c35ac83f560f","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["resultado = model.evaluate(previsores_teste, classe_teste)"],"execution_count":38,"outputs":[{"output_type":"stream","text":["3/3 [==============================] - 0s 2ms/step - loss: 0.3870 - binary_accuracy: 0.8684\n"],"name":"stdout"}]}]}