def get_embedding_graph(self):
data = tf.placeholder(tf.int32, shape=[None, None], name='data')
embeddings = tf.constant(
self.indexer.vectors, tf.float32, name='embeddings')
vectors = tf.map_fn(
lambda d: tf.nn.embedding_lookup(embeddings, d),
data,
tf.float32)
padded = tf.pad(
vectors,
[[0, 0], [0, self.max_length - tf.shape(vectors)[1]], [0, 0]])
return {
'padded': padded,
'data': data
}
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