基于Numpy的RNN前向传播原理实现

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发布时间：2026-10-14| 4人浏览过

原创

本文通过纯Numpy库从零实现循环神经网络（RNN）的前向传播过程，旨在帮助开发者深入理解RNN内部数据流动与状态更新机制。代码未包含梯度下降与反向传播，仅聚焦于前向计算逻辑，适用于学习RNN基础原理及手动推导网络结构，不支持GPU加速。

RNN前向传播原理与代码说明

首先需要明确，本段代码仅用于辅助理解RNN的工作机制，并未包含梯度下降等反向传播部分的实现，且默认参数已固定，这不会影响对核心原理的理解。该代码主要展示RNN的前向传播原理，全程仅使用Numpy库，因此无法利用GPU进行加速计算。

RNN类核心实现代码


import numpy as np


class Rnn():

  def __init__(self, input_size, hidden_size, num_layers, bidirectional=False):
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.num_layers = num_layers
    self.bidirectional = bidirectional

  def feed(self, x):
    '''

    :param x: [seq, batch_size, embedding]
    :return: out, hidden
    '''

    # x.shape [sep, batch, feature]
    # hidden.shape [hidden_size, batch]
    # Whh0.shape [hidden_size, hidden_size] Wih0.shape [hidden_size, feature]
    # Whh1.shape [hidden_size, hidden_size] Wih1.size [hidden_size, hidden_size]

    out = []
    x, hidden = np.array(x), [np.zeros((self.hidden_size, x.shape[1])) for i in range(self.num_layers)]
    Wih = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(1, self.num_layers)]
    Wih.insert(0, np.random.random((self.hidden_size, x.shape[2])))
    Whh = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(self.num_layers)]

    time = x.shape[0]
    for i in range(time):
      hidden[0] = np.tanh((np.dot(Wih[0], np.transpose(x[i, ...], (1, 0))) +
               np.dot(Whh[0], hidden[0])
               ))

      for i in range(1, self.num_layers):
        hidden[i] = np.tanh((np.dot(Wih[i], hidden[i-1]) +
                   np.dot(Whh[i], hidden[i])
                   ))

      out.append(hidden[self.num_layers-1])

    return np.array(out), np.array(hidden)


def sigmoid(x):
  return 1.0/(1.0 + 1.0/np.exp(x))


if __name__ == '__main__':
  rnn = Rnn(1, 5, 4)
  input = np.random.random((6, 2, 1))
  out, h = rnn.feed(input)
  print(f'seq is {input.shape[0]}, batch_size is {input.shape[1]} ', 'out.shape ', out.shape, ' h.shape ', h.shape)
  # print(sigmoid(np.random.random((2, 3))))
  #
  # element-wise multiplication
  # print(np.array([1, 2])*np.array([2, 1]))