network.py 文件源码

def get_output_for(self, input, **kwargs):
        n_batches = input.shape[0]
        n_steps = input.shape[1]
        input = TT.reshape(input, (n_batches, n_steps, -1))
        h0s = TT.tile(TT.reshape(self.h0, (1, self.num_units)), (n_batches, 1))
        # flatten extra dimensions
        shuffled_input = input.dimshuffle(1, 0, 2)
        hs, _ = theano.scan(fn=self.step, sequences=[shuffled_input], outputs_info=h0s)
        shuffled_hs = hs.dimshuffle(1, 0, 2)
        return shuffled_hs