# Hyperparameters
training_epochs = 1000 # Total number of training epochs
learning_rate = 0.01 # The learning rate

# create a model
def create_model():
  model = Sequential()
  # Input layer
  model.add(Dense(14, input_dim=2, kernel_initializer='normal', activation='relu'))
  model.add(Dense(8,activation='relu'))
  # Output layer
  model.add(Dense(1, activation='sigmoid'))
 
  # Compile a model
  model.compile(loss='binary_crossentropy', optimizer=adam(learning_rate), metrics=['accuracy'])
  return model
model = create_model()
model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 14)                42        
_________________________________________________________________
dense_2 (Dense)              (None, 8)                 120       
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 9         
=================================================================
Total params: 171
Trainable params: 171
Non-trainable params: 0
_________________________________________________________________