题目
题目链接
循环神经网络（RNN）是一种能够处理序列数据的神经网络，其特点是能够处理时间序列数据。
RNN的具体步骤如下：
- 计算隐藏状态更新\[h_t = \tanh(W_x \cdot x_t + W_h \cdot h_{t-1} + b) \]
- 计算输出\[y_t = W_y \cdot h_t + b_y \]
- 计算损失\[loss = \sum_{t=1}^{T} (y_t - \hat{y}_t)^2 \]
- 反向传播\[\frac{\partial loss}{\partial W_h} = \sum_{t=1}^{T} \frac{\partial loss}{\partial y_t} \cdot \frac{\partial y_t}{\partial h_t} \cdot \frac{\partial h_t}{\partial W_h} \]
本题只要求实现前向传播，反向传播不要求实现。
标准代码如下
def rnn_forward(input_sequence, initial_hidden_state, Wx, Wh, b):h = np.array(initial_hidden_state)Wx = np.array(Wx)Wh = np.array(Wh)b = np.array(b)for x in input_sequence:x = np.array(x)h = np.tanh(np.dot(Wx, x) + np.dot(Wh, h) + b)final_hidden_state = np.round(h, 4)return final_hidden_state.tolist()
