def test_concat_raises(self):
args = {'a': tdb.Scalar(),
'b': tdb.Vector(4),
'c': tdb.Tensor([3, 3])}
six.assertRaisesRegex(
self, TypeError, 'Shapes for concat don\'t match:',
tdb.Pipe, args, tdb.Concat())
args = {'a': tdb.Vector(2),
'b': tdb.Vector(4)}
six.assertRaisesRegex(
self, TypeError, 'Concat argument.*has rank less than 2.',
tdb.Pipe, args, tdb.Concat(concat_dim=1))
args = {'a': tdb.Vector(2, dtype='int32'),
'b': tdb.Vector(4)}
six.assertRaisesRegex(
self, TypeError,
'Cannot concat tensors of different dtypes: int32 vs. float32',
tdb.Pipe, args, tdb.Concat())
args = ((tdb.Scalar(),), (tdb.Scalar(),))
six.assertRaisesRegex(
self, TypeError, 'contains nested tuples', tdb.Pipe, args, tdb.Concat())
args = ()
self.assertRaisesWithLiteralMatch(
TypeError, 'Concat requires at least one tensor as input',
tdb.Pipe, args, tdb.Concat())
def test_concat_dims(self):
record = {'a': [[1.0, 2.0, 3.0]],
'b': [[4.0, 5.0, 6.0]]}
block = {'a': tdb.Tensor([1, 3]),
'b': tdb.Tensor([1, 3])} >> tdb.Concat(concat_dim=0)
self.assertBuildsConst([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]],
block, record)
block = {'a': tdb.Tensor([1, 3]),
'b': tdb.Tensor([1, 3])} >> tdb.Concat(concat_dim=1)
self.assertBuildsConst([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]],
block, record)
def test_metrics_raises(self):
sp0 = _pos_neg_block([])
spn = _pos_neg_block([2])
block = {'foo': sp0, 'bar:': spn} >> tdb.Concat()
six.assertRaisesRegex(
self, TypeError, 'Metric [a-z]+tive has incompatible types',
tdc.Compiler.create, block)
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'])
def test_concat_nested(self):
block = (tdb.AllOf(tdb.AllOf(tdb.Scalar(), tdb.Scalar()),
tdb.AllOf(tdb.Scalar(), tdb.Scalar())) >>
tdb.Concat(flatten=True))
self.assertBuildsConst([42.0] * 4, block, 42.0)
def test_concat_scalar(self):
block = {'a': tdb.Scalar(),
'b': tdb.Vector(4),
'c': tdb.Scalar()} >> tdb.Concat()
self.assertBuildsConst([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], block,
{'a': 1.0, 'b': [2.0, 3.0, 4.0, 5.0], 'c': 6.0})
def test_concat(self):
block = {'a': tdb.Vector(1),
'b': tdb.Vector(4),
'c': tdb.Vector(1)} >> tdb.Concat()
self.assertBuildsConst([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], block,
{'a': [1.0], 'b': [2.0, 3.0, 4.0, 5.0], 'c': [6.0]})
def test_record_slice_key(self):
b = tdb.Record([
(0, tdb.Scalar()),
(slice(1, 3), (tdb.Scalar(), tdb.Scalar()) >> tdb.Concat())])
self.assertBuilds((1., [2., 3.]), b, [1, 2, 3])
def test_composition_connect_raises(self): self.assertRaises(TypeError, tdb.Pipe, tdb.Scalar(), tdb.Concat())
def test_repr(self):
goldens = {
tdb.Tensor([]): '<td.Tensor dtype=\'float32\' shape=()>',
tdb.Tensor([1, 2], 'int32', name='foo'):
'<td.Tensor \'foo\' dtype=\'int32\' shape=(1, 2)>',
tdb.Scalar('int64'): '<td.Scalar dtype=\'int64\'>',
tdb.Vector(42): '<td.Vector dtype=\'float32\' size=42>',
tdb.FromTensor(tf.zeros(3)): '<td.FromTensor \'zeros:0\'>',
tdb.Function(tf.negative,
name='foo'): '<td.Function \'foo\' tf_fn=\'negative\'>',
tdb.Identity(): '<td.Identity>',
tdb.Identity('foo'): '<td.Identity \'foo\'>',
tdb.InputTransform(ord): '<td.InputTransform py_fn=\'ord\'>',
tdb.SerializedMessageToTree('foo'):
'<td.SerializedMessageToTree \'foo\' '
'py_fn=\'serialized_message_to_tree\'>',
tdb.GetItem(3, 'mu'): '<td.GetItem \'mu\' key=3>',
tdb.Length(): '<td.Length dtype=\'float32\'>',
tdb.Slice(stop=2): '<td.Slice key=slice(None, 2, None)>',
tdb.Slice(stop=2, name='x'):
'<td.Slice \'x\' key=slice(None, 2, None)>',
tdb.ForwardDeclaration(name='foo')():
'<td.ForwardDeclaration() \'foo\'>',
tdb.Composition(name='x').input: '<td.Composition.input \'x\'>',
tdb.Composition(name='x').output: '<td.Composition.output \'x\'>',
tdb.Composition(name='x'): '<td.Composition \'x\'>',
tdb.Pipe(): '<td.Pipe>',
tdb.Pipe(tdb.Scalar(), tdb.Identity()): '<td.Pipe>',
tdb.Record({}, name='x'): '<td.Record \'x\' ordered=False>',
tdb.Record((), name='x'): '<td.Record \'x\' ordered=True>',
tdb.AllOf(): '<td.AllOf>',
tdb.AllOf(tdb.Identity()): '<td.AllOf>',
tdb.AllOf(tdb.Identity(), tdb.Identity()): '<td.AllOf>',
tdb.AllOf(name='x'): '<td.AllOf \'x\'>',
tdb.AllOf(tdb.Identity(), name='x'): '<td.AllOf \'x\'>',
tdb.AllOf(tdb.Identity(), tdb.Identity(), name='x'): '<td.AllOf \'x\'>',
tdb.Map(tdb.Scalar(), name='x'):
'<td.Map \'x\' element_block=<td.Scalar dtype=\'float32\'>>',
tdb.Fold(tdb.Function(tf.add), tf.ones([]), name='x'):
'<td.Fold \'x\' combine_block=<td.Function tf_fn=\'add\'> '
'start_block=<td.FromTensor \'ones:0\'>>',
tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8))):
'<td.RNN>',
tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8)), name='x'):
'<td.RNN \'x\'>',
tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8)),
initial_state=tf.ones(8)):
'<td.RNN>',
tdb.RNN(tdl.ScopedLayer(tf.contrib.rnn.GRUCell(num_units=8)),
initial_state=tf.ones(8), name='x'):
'<td.RNN \'x\'>',
tdb.Reduce(tdb.Function(tf.add), name='x'):
'<td.Reduce \'x\' combine_block=<td.Function tf_fn=\'add\'>>',
tdb.Sum(name='foo'):
'<td.Sum \'foo\' combine_block=<td.Function tf_fn=\'add\'>>',
tdb.Min(name='foo'):
'<td.Min \'foo\' combine_block=<td.Function tf_fn=\'minimum\'>>',
tdb.Max(name='foo'):
'<td.Max \'foo\' combine_block=<td.Function tf_fn=\'maximum\'>>',
tdb.Mean(name='foo'): '<td.Mean \'foo\'>',
tdb.OneOf(ord, (tdb.Scalar(), tdb.Scalar()), name='x'):
'<td.OneOf \'x\'>',
tdb.Optional(tdb.Scalar(), name='foo'):
'<td.Optional \'foo\' some_case_block=<td.Scalar dtype=\'float32\'>>',
tdb.Concat(1, True, 'x'):
'<td.Concat \'x\' concat_dim=1 flatten=True>',
tdb.Broadcast(name='x'): '<td.Broadcast \'x\'>',
tdb.Zip(name='x'): '<td.Zip \'x\'>',
tdb.NGrams(n=42, name='x'): '<td.NGrams \'x\' n=42>',
tdb.OneHot(2, 3, name='x'):
'<td.OneHot \'x\' dtype=\'float32\' start=2 stop=3>',
tdb.OneHot(3): '<td.OneHot dtype=\'float32\' start=0 stop=3>',
tdb.OneHotFromList(['a', 'b']): '<td.OneHotFromList>',
tdb.OneHotFromList(['a', 'b'], name='foo'):
'<td.OneHotFromList \'foo\'>',
tdb.Nth(name='x'): '<td.Nth \'x\'>',
tdb.Zeros([], 'x'): '<td.Zeros \'x\'>',
tdb.Void(): '<td.Void>',
tdb.Void('foo'): '<td.Void \'foo\'>',
tdm.Metric('foo'): '<td.Metric \'foo\'>'}
for block, expected_repr in sorted(six.iteritems(goldens),
key=lambda kv: kv[1]):
self.assertEqual(repr(block), expected_repr)