an example model for Keras hyperparameter optimization with Talos

breast_cancer_model.py

# first we have to make sure to input data and params into the function
def breast_cancer_model(x_train, y_train, x_val, y_val, params):

    # next we can build the model exactly like we would normally do it
    model = Sequential()
    model.add(Dense(10, input_dim=x_train.shape[1],
                    activation=params['activation'],
                    kernel_initializer='normal'))
    
    model.add(Dropout(params['dropout']))
    
    # if we want to also test for number of layers and shapes, that's possible
    hidden_layers(model, params, 1)
   
    # then we finish again with completely standard Keras way
    model.add(Dense(1, activation=params['last_activation'],
                    kernel_initializer='normal'))
    
    model.compile(loss=params['losses'],
                  # here we add a regulizer normalization function from Talos
                  optimizer=params['optimizer'](lr=lr_normalizer(params['lr'],params['optimizer'])),
                  metrics=['acc', fmeasure])
    
    history = model.fit(x_train, y_train, 
                        validation_data=[x_val, y_val],
                        batch_size=params['batch_size'],
                        epochs=params['epochs'],
                        verbose=0)
    
    # finally we have to make sure that history object and model are returned
    return history, model