def _distance_logits(self, x, keys):
init = get_keras_initialization(self.init)
key_w = tf.get_variable("key_w", shape=(keys.shape.as_list()[-1], self.projected_size), initializer=init, dtype=tf.float32)
key_logits = tf.tensordot(keys, key_w, axes=[[2], [0]]) # (batch, key_len, projected_size)
if self.shared_project:
x_w = key_w
else:
x_w = tf.get_variable("x_w", shape=(x.shape.as_list()[-1], self.projected_size), initializer=init, dtype=tf.float32)
x_logits = tf.tensordot(x, x_w, axes=[[2], [0]]) # (batch, x_len, projected_size)
summed = tf.expand_dims(x_logits, axis=2) + tf.expand_dims(key_logits, axis=1) # (batch, key_len, x_len, poject_size)
summed = get_keras_activation(self.activation)(summed)
combine_w = tf.get_variable("combine_w", shape=self.projected_size, initializer=init, dtype=tf.float32)
return tf.tensordot(summed, combine_w, axes=[[3], [0]]) # (batch, key_len, x_len)
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