basic_knowledge

rnn.py

#生成文本
class RNNNumpy:

    def __init__(self, word_dim=128, hidden_dim=100):

        self.word_dim = word_dim
        self.hidden_dim = hidden_dim
        self.U = np.random.uniform(-np.sqrt(1./word_dim), np.sqrt(1./word_dim), (hidden_dim, word_dim))
        self.V = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (word_dim, hidden_dim))
        self.W = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (hidden_dim, hidden_dim))


    def forward_propagation(self, x):
        T = len(x)
        s = np.zeros((T + 1, self.hidden_dim))
        s[-1] = np.zeros(self.hidden_dim)
        o = np.zeros((T, self.word_dim))
        for t in np.arange(T):
            s[t] = np.tanh(self.U[:,x[t]] + self.W.dot(s[t-1]))
            o[t] = softmax(self.V.dot(s[t]))
        return [o, s]