def test_input_elementwise_sum_node():
for s in [(),
(3, 4, 5)]:
network = tn.ContainerNode(
"all",
[tn.InputElementwiseSumNode("ies"),
tn.SequentialNode(
"seq1",
[tn.InputNode("i1", shape=s),
tn.SendToNode("st1", reference="ies", to_key="in1")]),
tn.SequentialNode(
"seq2",
[tn.InputNode("i2", shape=s),
tn.SendToNode("st2", reference="ies", to_key="in2")]),
tn.SequentialNode(
"seq3",
[tn.InputNode("i3", shape=s),
tn.SendToNode("st3", reference="ies", to_key="in3")])]
).network()
fn = network.function(["i1", "i2", "i3"], ["ies"])
i1 = np.array(np.random.rand(*s), dtype=fX)
i2 = np.array(np.random.rand(*s), dtype=fX)
i3 = np.array(np.random.rand(*s), dtype=fX)
np.testing.assert_allclose(i1 + i2 + i3,
fn(i1, i2, i3)[0],
rtol=1e-5)
def fcn_network(combine_fn):
network = tn.ContainerNode("c", [
tn.SequentialNode(
"s1",
[tn.InputNode("in1", shape=(3, 4, 5)),
tn.SendToNode("stn1", reference="fcn", to_key="b")]),
tn.SequentialNode(
"s2",
[tn.InputNode("in2", shape=(3, 4, 5)),
tn.SendToNode("stn2", reference="fcn", to_key="a")]),
tn.InputFunctionCombineNode("fcn", combine_fn=combine_fn)
]).network()
return network.function(["in1", "in2"], ["fcn"])
def test_node_with_generated_children_can_serialize():
root_node = tn.ContainerNode("c", [
tn.SequentialNode("s1", [
tn.InputNode("in", shape=(3, 4, 5)),
tn.SendToNode("stn1", reference="s2")
]),
tn.SequentialNode("s2", [tn.SendToNode("stn2", reference="stn3")]),
tn.SequentialNode("s3", [tn.SendToNode("stn3", reference="i")]),
tn.IdentityNode("i"),
])
root_node.network().build()
root2 = treeano.core.node_from_data(treeano.core.node_to_data(root_node))
nt.assert_equal(root_node, root2)
def test_network_doesnt_mutate():
root_node = tn.ContainerNode("c", [
tn.SequentialNode("s1", [
tn.InputNode("in", shape=(3, 4, 5)),
tn.SendToNode("stn1", reference="s2")
]),
tn.SequentialNode("s2", [tn.SendToNode("stn2", reference="stn3")]),
tn.SequentialNode("s3", [tn.SendToNode("stn3", reference="i")]),
tn.IdentityNode("i"),
])
original_dict = copy.deepcopy(root_node.__dict__)
root_node.network().build()
nt.assert_equal(original_dict, root_node.__dict__)
def test_send_to_node():
network = tn.ContainerNode("c", [
tn.SequentialNode("s1", [
tn.InputNode("in", shape=(3, 4, 5)),
tn.SendToNode("stn1", reference="s2")
]),
tn.SequentialNode("s2", [tn.SendToNode("stn2", reference="stn3")]),
tn.SequentialNode("s3", [tn.SendToNode("stn3", reference="i")]),
tn.IdentityNode("i"),
]).network()
fn = network.function(["in"], ["i"])
x = np.random.randn(3, 4, 5).astype(fX)
np.testing.assert_allclose(fn(x)[0], x)
def architecture_children(self):
return [
tn.AuxiliaryNode(
self.name + "_auxiliary",
tn.SequentialNode(
self.name + "_sequential",
[ElementwiseKLSparsityPenaltyNode(
self.name + "_sparsitypenalty"),
tn.AggregatorNode(self.name + "_aggregator"),
tn.MultiplyConstantNode(self.name + "_multiplyweight"),
tn.SendToNode(self.name + "_sendto", to_key=self.name)]))]
def architecture_children(self):
inner = self.raw_children()
input_node = tn.IdentityNode(self.name + "_input")
return [
tn.SequentialNode(self.name + "_sequential", [
input_node, inner,
tn.AuxiliaryNode(
self.name + "_auxiliary",
tn.SequentialNode(self.name + "_innerseq", [
ElementwiseContractionPenaltyNode(
self.name + "_contractionpenalty",
input_reference=input_node.name),
tn.AggregatorNode(self.name + "_aggregator"),
tn.MultiplyConstantNode(self.name + "_multiplyweight"),
tn.SendToNode(self.name + "_sendto", to_key=self.name)
]))
])
]
def test_send_to_node_serialization():
tn.check_serialization(tn.SendToNode("a"))
tn.check_serialization(tn.SendToNode("a", reference="bar"))