def test_forwardFork(self):
g = gt.Graph()
g.add_vertex(3)
g.add_edge(g.vertex(0), g.vertex(1))
g.add_edge(g.vertex(0), g.vertex(2))
n = Net(1, 2, g)
# Init activation functions
for v in n.g.vertices():
n.activation[v] = Identity()
# All weights and bias == 0
assert_allclose(n.forward([1.0]), [0.0, 0.0])
# Init weights
for e in n.g.edges():
n.weightProp[e] = 1.0
assert_allclose(n.forward([0.0]), [0.0, 0.0])
assert_allclose(n.forward([1.0]), [1.0, 1.0])
assert_allclose(n.forward([12.0]), [12.0, 12.0])
assert_allclose(n.forward([-12.0]), [-12.0, -12.0])
# Init bias
for v in n.g.vertices():
n.biasProp[v] = 1.0
assert_allclose(n.forward([0.0]), [1.0, 1.0])
assert_allclose(n.forward([1.0]), [2.0, 2.0])
def test_construc(self):
g = gt.Graph()
g.add_vertex(4)
g.add_edge(g.vertex(0), g.vertex(1))
g.add_edge(g.vertex(0), g.vertex(2))
g.add_edge(g.vertex(1), g.vertex(3))
g.add_edge(g.vertex(2), g.vertex(3))
g.add_vertex(1)
g.add_edge(g.vertex(4), g.vertex(0))
n = Net(1, 1, g)
for e in g.edges():
n.weightProp[e] = 1
for v in g.vertices():
n.activation[v] = LogSigmoid(-1, 1)
n.prepare()
n.backward([1])
n.forward([1])
n.backward([2])