multilayer_perceptron.py 文件源码

def _compute_cost_grad(self, layer, n_samples, activations, deltas,
                           coef_grads, intercept_grads):
        """Compute the cost gradient for the layer."""
        coef_grads[layer] = safe_sparse_dot(activations[layer].T,
                                            deltas[layer]) / n_samples
        coef_grads[layer] += (self.alpha * self.layers_coef_[layer])

        intercept_grads[layer] = np.mean(deltas[layer], 0)

        return coef_grads, intercept_grads