自学教程：numpy实现RNN原理实现

import numpy as npclass 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]))