tf_qrnn.py 文件源码

def forward(self, x):
        length = lambda mx: int(mx.get_shape()[0])

        with tf.variable_scope("QRNN/Forward"):
            if self.c is None:
                # init context cell
                self.c = tf.zeros([length(x), self.kernel.size], dtype=tf.float32)

            if self.conv_size <= 2:
                # x is batch_size x sentence_length x word_length
                # -> now, transpose it to sentence_length x batch_size x word_length
                _x = tf.transpose(x, [1, 0, 2])

                for i in range(length(_x)):
                    t = _x[i] # t is batch_size x word_length matrix
                    f, z, o = self.kernel.forward(t)
                    self._step(f, z, o)
            else:
                c_f, c_z, c_o = self.kernel.conv(x)
                for i in range(length(c_f)):
                    f, z, o = c_f[i], c_z[i], c_o[i]
                    self._step(f, z, o)

        return self.h