简单介绍numpy实现RNN原理实现

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首先说明代码只是帮助理解，并未写出梯度下降部分，默认参数已经被固定，不影响理解。代码主要实现 RNN 原理，只使用 numpy 库，不可用于 GPU 加速。

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]))

到此这篇关于 numpy 实现 RNN 原理实现的文章就介绍到这了。

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