def test_fold(self): const_ten = np.array(10.0, dtype='float32') ten_plus_sum = (tdb.Map(tdb.Scalar()) >> tdb.Fold(tdb.Function(tf.add), const_ten)) self.assertBuilds(16.0, ten_plus_sum, [1.0, 2.0, 3.0], max_depth=3) self.assertBuilds(16.0, ten_plus_sum, [3.0, 2.0, 1.0], max_depth=3) self.assertBuilds(20.0, ten_plus_sum, [1.0, 2.0, 3.0, 4.0], max_depth=4) self.assertBuilds(20.0, ten_plus_sum, [4.0, 3.0, 2.0, 1.0], max_depth=4)
def test_forward_declaration_orphaned_nested(self): fwd1 = tdb.ForwardDeclaration(tdt.VoidType(), tdt.TensorType([])) fwd2 = tdb.ForwardDeclaration(tdt.SequenceType(tdt.TensorType([])), tdt.TensorType([])) b = tdb.Map(tdb.Scalar()) >> fwd2() >> tdb.Function(tf.negative) fwd2.resolve_to(tdb.Fold(tdb.Function(tf.add), fwd1())) fwd1.resolve_to(tdb.FromTensor(tf.ones([]))) self.assertBuilds(-8., b, [3, 4], max_depth=3)
def test_max_depth(self): self.assertEqual(0, tdb.Scalar().max_depth(42)) block = (tdb.Map(tdb.Scalar()) >> tdb.Fold(tdb.Function(tf.add), tf.zeros([]))) for i in xrange(5): self.assertEqual(i, block.max_depth(range(i)))
def test_fold_pyobject(self): block = tdb.Fold((tdb.Identity(), tdb.Scalar()) >> tdb.Sum(), tdb.Zeros([])) self.assertBuilds(5., block, (2, 3), max_depth=None)
def test_fold_tuple(self): block = ((tdb.Scalar(), tdb.Scalar()) >> tdb.Fold(tdb.Function(tf.add), tf.ones([]))) self.assertBuilds(6., block, (2, 3), max_depth=2)
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)