def testSequenceAppendTokenExtend(self):
x = np.asarray(
[[1, 2, 3, 0], [1, 2, 3, 4], [0, 0, 0, 0], [1, 0, 0, 0]], np.int32)
x_paddings = np.asarray(
[[0, 0, 0, 1], [0, 0, 0, 0], [1, 1, 1, 1], [0, 1, 1, 1]], np.int32)
with self.session() as sess:
x_appended, x_appended_paddings = insertion.SequenceAppendToken(
tf.convert_to_tensor(x), tf.convert_to_tensor(x_paddings), 10,
True)
x_appended, x_appended_paddings = sess.run(
[x_appended, x_appended_paddings])
# `x_appended_gold` is the same as `x` w/ token `10` appended, we also
# test for the condition of extend=True which requires +1 dim in the
# time dimension.
# `x_appended_paddings_gold` is the corresponding paddings.
x_appended_gold = np.asarray([[1, 2, 3, 10, 0], [1, 2, 3, 4, 10],
[10, 0, 0, 0, 0], [1, 10, 0, 0, 0]],
np.int32)
x_appended_paddings_gold = np.asarray(
[[0, 0, 0, 0, 1], [0, 0, 0, 0, 0], [0, 1, 1, 1, 1],
[0, 0, 1, 1, 1]], np.int32)
self.assertAllEqual(x_appended, x_appended_gold)
self.assertAllEqual(x_appended_paddings, x_appended_paddings_gold)
def testSequenceAppendToken(self):
x = np.asarray(
[[1, 2, 3, 0], [1, 2, 3, 4], [0, 0, 0, 0], [1, 0, 0, 0]], np.int32)
x_paddings = np.asarray(
[[0, 0, 0, 1], [0, 0, 0, 1], [1, 1, 1, 1], [0, 1, 1, 1]],
np.float32)
with self.session() as sess:
x_appended, x_appended_paddings = insertion.SequenceAppendToken(
tf.convert_to_tensor(x), tf.convert_to_tensor(x_paddings), 10)
x_appended, x_appended_paddings = sess.run([
tf.convert_to_tensor(x_appended),
tf.convert_to_tensor(x_appended_paddings)
])
# `x_appended_gold` is the same as `x` w/ token `10` appended.
# `x_appended_paddings_gold` is the corresponding paddings.
x_appended_gold = np.asarray(
[[1, 2, 3, 10], [1, 2, 3, 10], [10, 0, 0, 0], [1, 10, 0, 0]],
np.int32)
x_appended_paddings_gold = np.asarray(
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 1, 1], [0, 0, 1, 1]],
np.float32)
self.assertAllEqual(x_appended, x_appended_gold)
self.assertAllEqual(x_appended_paddings, x_appended_paddings_gold)
