Defining acivation function, Should I write lambda x: numpy.tanh(x) OR only numpy.tanh?

Question

While defining activation function (tanh), do I need to write lambda x: numpy.tanh(x)? Or Should I write only activation function = numpy.tanh?

This is my code class neuralNetwork:

# initialise the neural network
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
    # set number of nodes in each input, hidden, output layer
    self.inodes = inputnodes
    self.hnodes = hiddennodes
    self.onodes = outputnodes
    
    # link weight matrices, wih and who
    # weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
    # w11 w21
    # w12 w22 etc 
    self.wih = numpy.random.normal(0.0, pow(self.inodes, -0.5), (self.hnodes, self.inodes))
    self.who = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.onodes, self.hnodes))

    # learning rate
    self.lr = learningrate
    
    # activation function is the sigmoid function
    self.activation_function = numpy.tanh
    
    pass

Answer 1

The difference is that

g = lambda x: f(x)

vs

g = f

creates an additional, anonymous function, that acts by calling f. Consequently this introduced an extra computational cost for absolutely no benefit.