blackout.py 文件源码

def build(self):
        # blackout version output probability
        # correct word probability (b,1)
        c_o_t = T.exp(T.sum(self.W[self.y] * self.x, axis=-1) + self.b[self.y])

        # negative word probability (b,k)
        n_o_t = T.exp(T.sum(self.W[self.y_neg] * self.x.dimshuffle(0, 'x', 1), axis=-1) + self.b[self.y_neg])

        # sample set probability
        t_o = (self.q_w[self.y] * c_o_t) + T.sum(self.q_w[self.y_neg] * n_o_t,axis=-1)

        # positive probability (b,1)
        c_o_p = self.q_w[self.y] * c_o_t / t_o

        # negative probability (b,k)
        n_o_p = self.q_w[self.y_neg] * n_o_t / t_o.dimshuffle(0,'x')
        self.sumed=t_o
        self.other=T.log(c_o_p) + T.sum(T.log(1. - n_o_p),axis=-1)

        # cost for each y in blackout
        self.activation = -T.sum((T.log(c_o_p) + T.sum(T.log(1. - n_o_p),axis=-1))*self.y_mask)/(T.sum(self.y_mask))#*(self.k+1))
        att = T.nnet.softmax(T.dot(self.x, self.W) + self.b)
        self.predict = T.argmax(att, axis=-1)