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])
def mlp(sizes, weightGenerator=np.random.random, biasGenerator=np.random.random, activationFunction=LogSigmoid(-1, 1)):
r"""Generate Multilayer Perceptron (MLP) and return as a Net
:param sizes: Size of each layer
:type sizes: list of integers
:param functor weightGenerator: Functor for generating weights
:param functor biasGenerator: Functor for generating bias values
:param activationFunction: Activation function - the same for all neurons
:type activationFunction: Activation function
:returns: Net object with the requested architecture
:rtype: :class:`~gtnn.network.Net`
"""
n = Net(sizes[0], sizes[-1])
layerId = n.addVertexProperty("layerId", "short")
lastLayer = []
presentLayer = []
# Create all layers
for layerIdx, size in enumerate(sizes):
layerProp = n.addLayer() # TODO: test layer
for i in range(size):
v = n.g.add_vertex()
layerProp[v] = True
layerId[v] = layerIdx
n.activation[v] = activationFunction
n.biasProp[v] = biasGenerator()
presentLayer.append(v)
for l in lastLayer:
e = n.g.add_edge(l, v)
n.weightProp[e] = weightGenerator()
lastLayer = list(presentLayer)
presentLayer = list()
# Create a subgraph
n.prepare()
return n
