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。
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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])) |
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原文链接：https://blog.csdn.net/qq_43056256/article/details/114272542
