def restore_pad(x, ref_x, pad_remover, mode):
    x = tf.squeeze(x, axis=0)
    if mode != ModeKeys.PREDICT:
        x = pad_remover.restore(x)
    x = expert_utils.reshape_like(x, ref_x)
    return x
def restore_pad(x, ref_x, pad_remover, mode):
  x = tf.squeeze(x, axis=0)
  if mode != ModeKeys.PREDICT:
    x = pad_remover.restore(x)
  x = expert_utils.reshape_like(x, ref_x)
  return x
Example #3
0
def tpu_gather(params, indices):
  vocab_size = params.get_shape().as_list()[0]
  indices_flat = tf.reshape(indices, [-1])
  out = tf.matmul(tf.one_hot(indices_flat, vocab_size), params)
  out = eu.reshape_like(out, tf.expand_dims(indices, -1))
  return out
Example #4
0
def tpu_gather(params, indices):
  vocab_size = params.get_shape().as_list()[0]
  indices_flat = tf.reshape(indices, [-1])
  out = tf.matmul(tf.one_hot(indices_flat, vocab_size), params)
  out = eu.reshape_like(out, tf.expand_dims(indices, -1))
  return out