def testEmbeddingWrapperWithDynamicRnn(self):
with self.test_session() as sess:
with variable_scope.variable_scope("root"):
inputs = ops.convert_to_tensor([[[0], [0]]],
dtype=dtypes.int64)
input_lengths = ops.convert_to_tensor([2], dtype=dtypes.int64)
embedding_cell = core_rnn_cell_impl.EmbeddingWrapper(
core_rnn_cell_impl.BasicLSTMCell(1, state_is_tuple=True),
embedding_classes=1,
embedding_size=2)
outputs, _ = rnn.dynamic_rnn(cell=embedding_cell,
inputs=inputs,
sequence_length=input_lengths,
dtype=dtypes.float32)
sess.run([variables_lib.global_variables_initializer()])
# This will fail if output's dtype is inferred from input's.
sess.run(outputs)
def testEmbeddingWrapper(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)):
x = array_ops.zeros([1, 1], dtype=dtypes.int32)
m = array_ops.zeros([1, 2])
embedding_cell = core_rnn_cell_impl.EmbeddingWrapper(
core_rnn_cell_impl.GRUCell(2),
embedding_classes=3,
embedding_size=2)
self.assertEqual(embedding_cell.output_size, 2)
g, new_m = embedding_cell(x, m)
sess.run([variables_lib.global_variables_initializer()])
res = sess.run([g, new_m], {
x.name: np.array([[1]]),
m.name: np.array([[0.1, 0.1]])
})
self.assertEqual(res[1].shape, (1, 2))
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.17139, 0.17139]])