def EmbeddingTiedRNNSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous):
cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
return seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell,
num_decoder_symbols,
embedding_size=2,
feed_previous=feed_previous)
def EmbeddingTiedRNNSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous):
cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=False)
return seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell,
num_decoder_symbols,
embedding_size=2,
feed_previous=feed_previous)
def testEmbeddingTiedRNNSeq2Seq(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)):
enc_inp = [
constant_op.constant(
1, dtypes.int32, shape=[2]) for i in range(2)
]
dec_inp = [
constant_op.constant(
i, dtypes.int32, shape=[2]) for i in range(3)
]
cell = core_rnn_cell_impl.BasicLSTMCell(2, state_is_tuple=True)
dec, mem = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2)
sess.run([variables.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
# Test when num_decoder_symbols is provided, the size of decoder output
# is num_decoder_symbols.
with variable_scope.variable_scope("decoder_symbols_seq2seq"):
dec, mem = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell,
num_symbols=5,
num_decoder_symbols=3,
embedding_size=2)
sess.run([variables.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 3), res[0].shape)
# Test externally provided output projection.
w = variable_scope.get_variable("proj_w", [2, 5])
b = variable_scope.get_variable("proj_b", [5])
with variable_scope.variable_scope("proj_seq2seq"):
dec, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell,
num_symbols=5,
embedding_size=2,
output_projection=(w, b))
sess.run([variables.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [constant_op.constant(0, dtypes.int32, shape=[2])] * 3
with variable_scope.variable_scope("other"):
d3, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp2,
cell,
num_symbols=5,
embedding_size=2,
feed_previous=constant_op.constant(True))
sess.run([variables.global_variables_initializer()])
variable_scope.get_variable_scope().reuse_variables()
d1, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell,
num_symbols=5,
embedding_size=2,
feed_previous=True)
d2, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp2,
cell,
num_symbols=5,
embedding_size=2,
feed_previous=True)
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
def testEmbeddingTiedRNNSeq2Seq(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)):
enc_inp = [
constant_op.constant(
1, dtypes.int32, shape=[2]) for i in range(2)
]
dec_inp = [
constant_op.constant(
i, dtypes.int32, shape=[2]) for i in range(3)
]
cell = functools.partial(
core_rnn_cell_impl.BasicLSTMCell,
2, state_is_tuple=True)
dec, mem = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell(), num_symbols=5, embedding_size=2)
sess.run([variables.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
# Test when num_decoder_symbols is provided, the size of decoder output
# is num_decoder_symbols.
with variable_scope.variable_scope("decoder_symbols_seq2seq"):
dec, mem = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell(),
num_symbols=5,
num_decoder_symbols=3,
embedding_size=2)
sess.run([variables.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 3), res[0].shape)
# Test externally provided output projection.
w = variable_scope.get_variable("proj_w", [2, 5])
b = variable_scope.get_variable("proj_b", [5])
with variable_scope.variable_scope("proj_seq2seq"):
dec, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell(),
num_symbols=5,
embedding_size=2,
output_projection=(w, b))
sess.run([variables.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [constant_op.constant(0, dtypes.int32, shape=[2])] * 3
with variable_scope.variable_scope("other"):
d3, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp2,
cell(),
num_symbols=5,
embedding_size=2,
feed_previous=constant_op.constant(True))
sess.run([variables.global_variables_initializer()])
variable_scope.get_variable_scope().reuse_variables()
d1, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp,
cell(),
num_symbols=5,
embedding_size=2,
feed_previous=True)
d2, _ = seq2seq_lib.embedding_tied_rnn_seq2seq(
enc_inp,
dec_inp2,
cell(),
num_symbols=5,
embedding_size=2,
feed_previous=True)
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
