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numpy实现RNN原理实现

这篇文章主要介绍了numpy实现RNN原理实现，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧

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]))

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