Created
May 12, 2018 13:57
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def additive_attention(ref, query, ref_dim, qdim, | |
normalize=False, blend=False): | |
# infer timesteps | |
timesteps = tf.shape(ref)[1] | |
U = tf.get_variable('U', | |
shape=[ref_dim, qdim], | |
dtype=tf.float32, | |
initializer=tf.random_uniform_initializer(-0.01, 0.01)) | |
V = tf.get_variable('V', | |
shape=[qdim, qdim], | |
dtype=tf.float32, | |
initializer=tf.random_uniform_initializer(-0.01, 0.01)) | |
Av = tf.get_variable('Av', | |
shape=[qdim, 1], | |
dtype=tf.float32, | |
initializer=tf.random_uniform_initializer(-0.01, 0.01)) | |
# NOTE : reference should be in batch_major format | |
ref_proj = tf.reshape( | |
tf.matmul(tf.reshape(ref, [-1, ref_dim]), U), # collapse dims to matmul | |
[-1, timesteps, qdim]) # expand again | |
hi = tf.expand_dims(tf.matmul(query, V), | |
axis=1) # expand time dim to add to reference | |
# sum up ref, query | |
blended = (ref_proj + hi) | |
scores = tf.reshape(tf.matmul( | |
tf.reshape(blended, [-1, qdim]), # collapse dims | |
Av), # matmul with attention vector | |
[-1, timesteps]) # attention weights across timesteps | |
# normalize scores | |
probs = tf.nn.softmax(scores) | |
if normalize: | |
return probs | |
if blend: # reduce reference based on attention weights | |
return tf.reduce_sum(ref * tf.expand_dims(probs, axis=-1), | |
axis=1) # reduce across time dimension | |
return scores # return score |
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