def run_feedforward_nn_for_samples(theta_matrix_1, theta_matrix_2, samples, true_output): assert(mathutil.is_np_2d_array(samples)) num_samples, num_features = samples.shape predicted_y = np.empty((num_samples,)) for row_idx, sample in enumerate(samples): predicted_y[row_idx] = run_feedforward_nn_for_sample(theta_matrix_1, theta_matrix_2, sample) num_correctly_classified = np.count_nonzero(true_output[:,0] == predicted_y) print(num_correctly_classified/num_samples)
def reshape_to_np_1darray(x): assert(mathutil.is_np_2d_array(x)) return x.reshape((-1))