Grid Search the number of hidden layers with keras

Question

I am trying to optimize the hyperparameters of my NN using Keras and sklearn. I am wrapping up with KerasClassifier (it´s a classification problem). I am trying to optimize the number of hidden layers. I can´t figure it out how to do it with keras (actually I am wondering how to set up the function create_model in order to maximize the number of hidden layers) Could anyone please help me?

My code (just the important part):

## Import `Sequential` from `keras.models`
from keras.models import Sequential

# Import `Dense` from `keras.layers`
from keras.layers import Dense

def create_model(optimizer='adam', activation = 'sigmoid'):
  # Initialize the constructor
  model = Sequential()
  # Add an input layer
  model.add(Dense(5, activation=activation, input_shape=(5,)))
  # Add one hidden layer
  model.add(Dense(8, activation=activation))
  # Add an output layer 
  model.add(Dense(1, activation=activation))
  #compile model
  model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=
  ['accuracy'])
  return model
my_classifier = KerasClassifier(build_fn=create_model, verbose=0)# Create 
hyperparameter space
epochs = [5, 10]
batches = [5, 10, 100]
optimizers = ['rmsprop', 'adam']
activation1 = ['relu','sigmoid']
# Create grid search
grid = RandomizedSearchCV(estimator=my_classifier, 
param_distributions=hyperparameters) #inserir param_distributions

# Fit grid search
grid_result = grid.fit(X_train, y_train)
# Create hyperparameter options
hyperparameters = dict(optimizer=optimizers, epochs=epochs, 
batch_size=batches, activation=activation1)
# View hyperparameters of best neural network
grid_result.best_params_

Answer 1

If you want to make the number of hidden layers a hyperparameter you have to add it as parameter to your KerasClassifier build_fn like:

def create_model(optimizer='adam', activation = 'sigmoid', hidden_layers=1):
  # Initialize the constructor
  model = Sequential()
  # Add an input layer
  model.add(Dense(5, activation=activation, input_shape=(5,)))

  for i in range(hidden_layers):
      # Add one hidden layer
      model.add(Dense(8, activation=activation))

  # Add an output layer 
  model.add(Dense(1, activation=activation))
  #compile model
  model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=
  ['accuracy'])
  return model

Then you will be able to optimize the number of hidden layers by adding it to the dictionary, which is passed to RandomizedSearchCV's param_distributions.

One more thing, you probably should separate the activation you use for the output layer from the other layers. Different classes of activation functions are suitable for hidden layers and for output layers used in binary classification.