How A Neural Network Function within a Baysian Optimization Function

Question

I made a neural network model that uses 4 inputs and 1 output to replace an original function that takes too long to compute. I want to use the trained neural network in place of the original function in a Baysian Optimization function. The original code I had just called the original function and gave it some inputs. I am trying to figure out how to use the neural network as a function within this function.

Any help would be greatly appreciated! I'm thinking about just defining a new function that has the neural network's parameters and weights within it, but I'm not sure how to do that.

I tried calling the model.predict() function, but then I get an error from the neural network library complaining that there are no inputs to the model.

`# Optimizing the surrogate function
# Create the optimizer. The black box function to optimize is not
# specified here, as we will call that function directly later 
optimizer = BayesianOptimization(f = model.predict(), 
                             pbounds = {'gammap': [1,10],
                                        'k': [6,10],
                                        'lambdap': [1, 20],
                                        'Nrp': [1, 10]}, 
                             verbose = 2, random_state = 1234)
# let the init_points = 5
# let the iteration = 100

a = optimizer.maximize(init_points = 5, n_iter = 100)
print("Best result: {}; f(x) = 
{}.".format(optimizer.max["params"], -1 * 
optimizer.max["target"]))`

Answer 1

You could try using a surrogate function.

The surrogate_function function takes the four input parameters and passes them through a trained neural network model to get the output, which is then returned as a scalar value. This function is then passed as the black box function to the BayesianOptimization function, which optimizes the function over the specified input bounds

def surrogate_function(gammap, k, lambdap, Nrp):
    # Reshape the inputs into the format expected by the neural network
    inputs = np.array([[gammap, k, lambdap, Nrp]])

    # Pass the inputs through the neural network to get the output
    output = model.predict(inputs)

    # Return the output as a scalar value
    return -output[0][0]

# Optimizing the surrogate function
# Create the optimizer. The black box function to optimize is not
# specified here, as we will call that function directly later 
optimizer = BayesianOptimization(f = surrogate_function, 
                                 pbounds = {'gammap': [1,10],
                                            'k': [6,10],
                                            'lambdap': [1, 20],
                                            'Nrp': [1, 10]}, 
                                 verbose = 2, random_state = 1234)

# let the init_points = 5
# let the iteration = 100

a = optimizer.maximize(init_points = 5, n_iter = 100)
print("Best result: {}; f(x) = {}.".format(optimizer.max["params"], -1 * optimizer.max["target"]))