Attention.py 文件源码

def __init__(self, attention_units, memory, sequence_length=None, time_major=True, mode=0):
        self.attention_units = attention_units
        self.enc_units = memory.get_shape()[-1].value

        if time_major:
            memory = tf.transpose(memory, perm=(1, 0, 2))

        self.enc_length = tf.shape(memory)[1]
        self.batch_size = tf.shape(memory)[0]
        self.mode = mode
        self.mask = array_ops.sequence_mask(sequence_length, self.enc_length, tf.float32) if sequence_length is not None else None

        self.memory = tf.reshape(memory, (tf.shape(memory)[0], self.enc_length, 1, self.enc_units))

        # pre-compute Uahj to minimize the computational cost
        with tf.variable_scope('attention'):
            Ua = tf.get_variable(name='Ua', shape=(1, 1, self.enc_units, self.attention_units))
        self.hidden_feats = tf.nn.conv2d(self.memory, Ua, [1, 1, 1, 1], "SAME")