initialize_parameters.py

def initialize_parameters(layer_dims):
    
    """"
    INPUTS:
    layers_dim: represents the dimension of each hidden layer in the neural network, the index(i) represents the 
    layer while layers_dim[i] represents the number of nodes in that layer
    
    RETURN:
    this should return intialized paramenters from W1, b1 to Wl-1, bl-1 assuming l = length of layers_dim
    where Wi is of shape (L[i], L[i-1])
    
    """
    parameters = {}
    L = len(layer_dims)
    
    for l in range(1,L):
        parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) / np.sqrt(layer_dims[l-1]) #*0.01
        parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
        
        assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))
        assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))
    return parameters