def test_rnn_with_cells(self):
gru_cell1 = tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=16), 'gru1')
gru_cell2 = tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=16), 'gru2')
with tf.variable_scope('gru3') as vscope:
gru_cell3 = tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=16), vscope)
lstm_cell = tdl.ScopedLayer(
tf.contrib.rnn.BasicLSTMCell(num_units=16), 'lstm')
gru1 = (tdb.InputTransform(lambda s: [ord(c) for c in s]) >>
tdb.Map(tdb.Scalar('int32') >>
tdb.Function(tdl.Embedding(128, 8))) >>
tdb.RNN(gru_cell1))
gru2 = (tdb.InputTransform(lambda s: [ord(c) for c in s]) >>
tdb.Map(tdb.Scalar('int32') >>
tdb.Function(tdl.Embedding(128, 8))) >>
tdb.RNN(gru_cell2, initial_state=tf.ones(16)))
gru3 = (tdb.InputTransform(lambda s: [ord(c) for c in s]) >>
tdb.Map(tdb.Scalar('int32') >>
tdb.Function(tdl.Embedding(128, 8))) >>
tdb.RNN(gru_cell3, initial_state=tdb.FromTensor(tf.ones(16))))
lstm = (tdb.InputTransform(lambda s: [ord(c) for c in s]) >>
tdb.Map(tdb.Scalar('int32') >>
tdb.Function(tdl.Embedding(128, 8))) >>
tdb.RNN(lstm_cell))
with self.test_session():
gru1.eval('abcde')
gru2.eval('ABCDE')
gru3.eval('vghj')
lstm.eval('123abc')
def test_embedding_raises(self):
self.assertRaises(ValueError, tdl.Embedding, 2, 2, np.zeros([3, 3]))
six.assertRaisesRegex(self, TypeError,
'Embeddings take scalar inputs.',
tdl.Embedding(2, 2),
tf.constant([[0, 0]], 'int32'))
six.assertRaisesRegex(self, TypeError,
'Embeddings take integer inputs.',
tdl.Embedding(2, 2), tf.constant([0], 'float32'))
def test_lstm_cell(self):
# Test to make sure examples from the documentation compile.
td = tdb
num_hidden = 32
# Create an LSTM cell, the hard way.
lstm_cell = td.Composition()
with lstm_cell.scope():
in_state = td.Identity().reads(lstm_cell.input[1])
bx = td.Concat().reads(lstm_cell.input[0], in_state[1])
bi = td.Function(tdl.FC(num_hidden, tf.nn.sigmoid)).reads(bx)
bf = td.Function(tdl.FC(num_hidden, tf.nn.sigmoid)).reads(bx)
bo = td.Function(tdl.FC(num_hidden, tf.nn.sigmoid)).reads(bx)
bg = td.Function(tdl.FC(num_hidden, tf.nn.tanh)).reads(bx)
bc = td.Function(lambda c, i, f, g: c*f + i*g).reads(
in_state[0], bi, bf, bg)
by = td.Function(lambda c, o: tf.tanh(c) * o).reads(bc, bo)
out_state = td.Identity().reads(bc, by)
lstm_cell.output.reads(by, out_state)
str_lstm = (td.InputTransform(lambda s: [ord(c) for c in s]) >>
td.Map(td.Scalar('int32') >>
td.Function(tdl.Embedding(128, 16))) >>
td.RNN(lstm_cell,
initial_state=td.AllOf(tf.zeros(32), tf.zeros(32))))
with self.test_session():
str_lstm.eval('The quick brown fox.')
def test_embedding(self):
weights = np.array([[1, 2], [3, 4]], dtype='float32')
embedding = tdl.Embedding(2, 2, initializer=weights)
with self.test_session() as sess:
embeddings = [embedding(tf.constant([x])) for x in [0, 1, 7, -5]]
sess.run(tf.global_variables_initializer())
self.assertAllEqual([[[1, 2]], [[3, 4]], [[3, 4]], [[3, 4]]],
sess.run(embeddings))
def test_embedding_initializer(self):
embedding = tdl.Embedding(2,
2,
initializer=tf.constant_initializer(1.0))
with self.test_session() as sess:
embeddings = [embedding(tf.constant([x])) for x in [0, 1, 7]]
sess.run(tf.global_variables_initializer())
self.assertAllEqual([[[1, 1]], [[1, 1]], [[1, 1]]],
sess.run(embeddings))
def test_embedding_nomod(self):
weights = np.array([[1, 2], [3, 4]], dtype='float32')
embedding = tdl.Embedding(2, 2, initializer=weights, mod_inputs=False)
with self.test_session() as sess:
embeddings = [embedding(tf.constant([x])) for x in [0, 1]]
sess.run(tf.global_variables_initializer())
self.assertAllEqual([[[1, 2]], [[3, 4]]], sess.run(embeddings))
self.assertRaises(
Exception, # API doesn't specify what tf.gather() throws
sess.run,
embedding(tf.constant([2])))
def test_record_doc_example(self):
# Test to make sure examples from the documentation compile.
example_datum = {'id': 8,
'name': 'Joe Smith',
'location': (2.5, 7.0)}
num_ids = 16
embed_len = 16
td = tdb
char_rnn = (td.InputTransform(lambda s: [ord(c) for c in s]) >>
td.Map(td.Scalar('int32') >>
td.Function(tdl.Embedding(128, 16))) >>
td.Fold(td.Concat() >> td.Function(tdl.FC(32)),
td.FromTensor(tf.zeros(32))))
r = (td.Record([('id', (td.Scalar('int32') >>
td.Function(tdl.Embedding(num_ids, embed_len)))),
('name', char_rnn),
('location', td.Vector(2))])
>> td.Concat() >> td.Function(tdl.FC(256)))
with self.test_session():
r.eval(example_datum)
def test_hierarchical_rnn(self):
char_cell = tdl.ScopedLayer(
tf.contrib.rnn.BasicLSTMCell(num_units=16), 'char_cell')
word_cell = tdl.ScopedLayer(
tf.contrib.rnn.BasicLSTMCell(num_units=32), 'word_cell')
char_lstm = (tdb.InputTransform(lambda s: [ord(c) for c in s]) >>
tdb.Map(tdb.Scalar('int32') >>
tdb.Function(tdl.Embedding(128, 8))) >>
tdb.RNN(char_cell))
word_lstm = (tdb.Map(char_lstm >> tdb.GetItem(1) >> tdb.Concat()) >>
tdb.RNN(word_cell))
with self.test_session():
word_lstm.eval(['the', 'cat', 'sat', 'on', 'a', 'mat'])