def initialize_hyper_parameters(layer_acts, learning_rate):
    '\n    Initialize parameters for different levels of the network\n\n    Arguments:\n    layer_acts -- python array (list) containing the activation functions of each layer in the network\n    learning_rate -- float value used as constant for gradient descent\n\n    Returns:\n    hyper_parameters -- python dictionary containing hyper_parameters (can be further extended)\n\n    '
    hyper_parameters = {}
    activations = {}
    L = len(layer_acts)
    for l in range(0, L):
        activations[(l + 1)] = layer_acts[l]
    hyper_parameters['activations'] = activations
    hyper_parameters['learning_rate'] = learning_rate
    return hyper_parameters
Initialize parameters for different levels of the network

Arguments:
layer_acts -- python array (list) containing the activation functions of each layer in the network
learning_rate -- float value used as constant for gradient descent

Returns:
hyper_parameters -- python dictionary containing hyper_parameters (can be further extended)
def initialize_hyper_parameters(layer_acts, learning_rate):
    '\n    Initialize parameters for different levels of the network\n\n    Arguments:\n    layer_acts -- python array (list) containing the activation functions of each layer in the network\n    learning_rate -- float value used as constant for gradient descent\n\n    Returns:\n    hyper_parameters -- python dictionary containing hyper_parameters (can be further extended)\n\n    '
    hyper_parameters = {}
    activations = {}
    L = len(layer_acts)
    for l in range(0, L):
        activations[(l + 1)] = layer_acts[l]
    hyper_parameters['activations'] = activations
    hyper_parameters['learning_rate'] = learning_rate
    return hyper_parameters