cost function for neural network

nnCostFunc.py

def nnCostFunc(nn_params, input_layer_size, hidden_layer_size, num_labels, X, y, lmbda):
    theta1 = np.reshape(nn_params[:hidden_layer_size*(input_layer_size+1)], (hidden_layer_size, input_layer_size+1), 'F')
    theta2 = np.reshape(nn_params[hidden_layer_size*(input_layer_size+1):], (num_labels, hidden_layer_size+1), 'F')

    m = len(y)
    ones = np.ones((m,1))
    a1 = np.hstack((ones, X))
    a2 = sigmoid(a1 @ theta1.T)
    a2 = np.hstack((ones, a2))
    h = sigmoid(a2 @ theta2.T)
    
    y_d = pd.get_dummies(y.flatten())
    
    temp1 = np.multiply(y_d, np.log(h))
    temp2 = np.multiply(1-y_d, np.log(1-h))
    temp3 = np.sum(temp1 + temp2)
    
    sum1 = np.sum(np.sum(np.power(theta1[:,1:],2), axis = 1))
    sum2 = np.sum(np.sum(np.power(theta2[:,1:],2), axis = 1))
    
    return np.sum(temp3 / (-m)) + (sum1 + sum2) * lmbda / (2*m)