这段前向计算的代码的反向传播代码是什么def forwardprop(inputs, targets, a_prev, b_prev): # Since the RNN receives the sequence, the weights are not updated during one sequence. xs, ats, bts, ots = {}, {}, {}, {} # dictionary ats[-1] = np.copy(a_prev) # Copy previous hidden state vector to -1 key value. bts[-1] = np.copy(b_prev) # Copy previous hidden state vector to -1 key value. loss = 0 # loss initialization for t in range(len(inputs)-use_len+1): # t is a "time step" and is used as a key(dic). xs[t] = one_hot_encode_sequence(inputs[t:t+use_len], vocab_size).reshape(-1, 1) ats[t] = np.tanh(np.dot(U, xs[t]) + np.dot(W, ats[t-1]) + s1) bts[t] = np.tanh(np.dot(V, ats[t]) + np.dot(R, ats[t-1]) + np.dot(T, bts[t-1]) + s2) ots[t] = np.dot(Q, bts[t]) + s3 pts[t] = np.exp(ots[t]) / np.sum(np.exp(ots[t])) # 交叉熵计算loss函数 y_class = one_hot_encode_sequence(targets[t:t+use_len], vocab_size).reshape(-1, 1) loss += np.sum(y_class*(-np.log(ps[t]))) # softmax (cross-entropy loss) return loss, pts, ats, bts, xs

以下是反向传播代码的实现：

def backwardprop(inputs, targets, a_prev, b_prev, xs, ats, bts, ots, pts, parameters):
    dU, dV, dR, dT, dW, dQ = np.zeros_like(parameters['U']), np.zeros_like(parameters['V']), \
                             np.zeros_like(parameters['R']), np.zeros_like(parameters['T']), \
                             np.zeros_like(parameters['W']), np.zeros_like(parameters['Q'])
    da_next, db_next = np.zeros_like(a_prev), np.zeros_like(b_prev)
    loss = 0

    for t in reversed(range(len(inputs)-use_len+1)):
        y_class = one_hot_encode_sequence(targets[t:t+use_len], vocab_size).reshape(-1, 1)
        dy = np.copy(pts[t])
        dy[np.argmax(y_class)] -= 1

        dQ += np.dot(dy, bts[t].T)
        ds3 = np.sum(dy, axis=1, keepdims=True)

        db = np.dot(parameters['Q'].T, dy) * (1 - np.square(bts[t]))
        db += db_next
        dB += np.dot(db, ats[t-1].T)
        dR += np.dot(db, ats[t-1-1].T)
        dT += np.dot(db, bts[t-1-1].T)
        db_next = np.dot(parameters['R'].T, db)

        da = np.dot(parameters['W'].T, db) * (1 - np.square(ats[t]))
        da += da_next
        dW += np.dot(da, xs[t].T)
        dU += np.dot(da, xs[t].T)
        dV += np.dot(db, ats[t].T)
        dR += np.dot(db, ats[t-1].T)
        da_next = np.dot(parameters['V'].T, db)

        ds1 = np.sum(da, axis=1, keepdims=True)
        ds2 = np.sum(db, axis=1, keepdims=True)

        loss += np.sum(y_class*(-np.log(pts[t])))

    gradients = {'dU': dU, 'dV': dV, 'dR': dR, 'dT': dT, 'dW': dW, 'dQ': dQ, 'ds1': ds1, 'ds2': ds2, 'ds3': ds3}

    return loss, gradients

其中，`parameters`是一个包含所有权重矩阵的字典，`loss`是反向传播的损失，`gradients`是包含所有梯度矩阵的字典。在反向传播中，我们需要按时间步从后往前遍历，计算每个时间步的梯度，并将它们累加到相应的梯度矩阵中。最后，我们将所有梯度矩阵打包成一个字典并返回。