title: Sigmoid function output
body: <p>I have the following neural network model.</p>
  <pre><code>nn_classifier = Sequential()
  nn_classifier.add(Dense(output_dim = 16 ,activation='relu',input_dim = 13))
  nn_classifier.add(Dense(output_dim = 16,activation='relu'))
  nn_classifier.add(Dense(output_dim = 1, activation = 'sigmoid'))
  
  nn_classifier.compile(optimizer = 'sgd', loss = 'binary_crossentropy', metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.5)])
  model=nn_classifier.fit(X_train, Y_train ,validation_split=0.33, batch_size = 10, nb_epoch = 100)
  Y_pred = nn_classifier.predict(X_test)
  </code></pre>
  <p>As I have used the sigmoid function in my output layer, I was expecting the predicted values (Y_pred) are either 0 or 1. But I get some decimal values. Is my understanding wrong?</p>