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_optional(self):
block = tdb.Optional(tdb.Vector(4))
self.assertBuildsConst([1.0, 2.0, 3.0, 4.0], block, [1, 2, 3, 4])
self.assertBuildsConst([0.0, 0.0, 0.0, 0.0], block, None)
block2 = tdb.Optional(tdb.Scalar(), np.array(42.0, dtype='float32'))
self.assertBuildsConst(6.0, block2, 6)
self.assertBuildsConst(42.0, block2, None)
def test_output_type_inference(self):
# Identity and composite compute their output types from input types.
block = tdb.Scalar() >> (tdb.Identity() >> tdb.Identity())
self.assertBuildsConst(42., block, 42)
block = ({'a': tdb.Scalar(), 'b': tdb.Vector(2) >> tdb.Identity()} >>
tdb.Identity() >> (tdb.Identity() >> tdb.Identity()) >>
tdb.Identity())
self.assertBuildsConst((42., [5., 1.]), block, {'a': 42, 'b': [5, 1]})
def test_one_of_raises(self):
six.assertRaisesRegex(
self, TypeError, 'key_fn is not callable: 42',
tdb.OneOf, 42, (tdb.Scalar(),))
self.assertRaisesWithLiteralMatch(
ValueError, 'case_blocks must be non-empty', tdb.OneOf, lambda x: x, {})
six.assertRaisesRegex(
self, TypeError, 'Type mismatch between output type',
tdb.OneOf, lambda x: x, {0: tdb.Scalar(), 1: tdb.Vector(2)})
def test_rnn(self):
# We have to expand_dims to broadcast x over the batch.
def f(x, st):
return (tf.multiply(x, x), tf.add(st, tf.expand_dims(x, 1)))
intup = (tdb.Map(tdb.Scalar()), tdb.Vector(2))
block = intup >> tdb.RNN(tdb.Function(f), initial_state_from_input=True)
self.assertBuilds(([], [0.0, 0.0]), block,
([], [0.0, 0.0]), max_depth=0)
self.assertBuilds(([1.0, 4.0, 9.0, 16.0], [10.0, 10.0]), block,
([1.0, 2.0, 3.0, 4.0], [0.0, 0.0]), max_depth=4)
self.assertBuilds(([1.0, 4.0, 9.0, 16.0], [10.0, 10.0]), block,
([1.0, 2.0, 3.0, 4.0], [0.0, 0.0]), max_depth=4)
def test_rshift(self):
block = (tdl.FC(1, None, tf.constant_initializer(2.0)) >> tdl.FC(
1, None, tf.constant_initializer(3.0)))
with self.test_session():
self.assertEqual([6.0], (tdb.Vector(1) >> block).eval([1.0],
tolist=True))
def test_vector(self): self.assertBuildsConst([1., 2., 3.], tdb.Vector(3), [1, 2, 3])
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_optional_default_none(self):
block = tdb.Optional({'a': tdb.Map({'b': tdb.Scalar(), 'c': tdb.Scalar()}),
'd': tdb.Vector(3)})
self.assertBuildsConst(([(0., 1.)], [2., 3., 4.]), block,
{'a': [{'b': 0, 'c': 1}], 'd': [2, 3, 4]})
self.assertBuildsConst(([], [0., 0., 0.]), block, None)
def test_mean_2vector(self):
mean_2vectors = tdb.Map(tdb.Vector(2)) >> tdb.Mean()
self.assertBuilds([1.0, 4.0], mean_2vectors,
[[1.0, 5.0], [2.0, 3.0], [0.0, 4.0]], max_depth=3)
def test_max_2vectors(self):
max_2vectors = tdb.Map(tdb.Vector(2)) >> tdb.Max()
self.assertBuilds([2.0, 5.0], max_2vectors,
[[1.0, 5.0], [2.0, 3.0], [0.0, 4.0]], max_depth=2)
def test_sum_2vectors(self):
sum_2vectors = tdb.Map(tdb.Vector(2)) >> tdb.Sum()
self.assertBuilds([3.0, 12.0], sum_2vectors,
[[1.0, 5.0], [2.0, 3.0], [0.0, 4.0]], max_depth=2)
def test_map_pyobject_type_inference(self): b = tdb.Map(tdb.Identity()) >> tdb.Vector(2) self.assertBuildsConst([1., 2.], b, [1, 2])
def test_function_tuple_in_out(self):
# f(a, (b, c)) := ((c, a), b)
b = ((tdb.Vector(1), (tdb.Vector(2), tdb.Vector(3))) >>
tdb.Function(lambda x, y: ((y[1], x), y[0])))
self.assertBuilds((([4., 5., 6.], [1.]), [2., 3.]), b,
([1], ([2, 3], [4, 5, 6])))
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)
