def layering(self, activator=tfe.Activator.ReLU.value):
self.activator = activator
self.affine0 = tfl.Affine(self.params['W0'],
self.input_node,
self.params['b0'],
name="A0",
graph=self)
self.activation0 = activator(self.affine0, name="O0", graph=self)
self.affine1 = tfl.Affine(self.params['W1'],
self.activation0,
self.params['b1'],
name="A1",
graph=self)
self.output = activator(self.affine1, name="O1", graph=self)
self.error = tfl.SquaredError(self.output,
self.target_node,
name="SE",
graph=self)
self.affine0 = tfl.Affine(self.params['W0'],
self.input_node,
self.params['b0'],
name="A0",
graph=self)
self.activation0 = activator(self.affine0, name="O0", graph=self)
self.affine1 = tfl.Affine(self.params['W1'],
self.activation0,
self.params['b1'],
name="A1",
graph=self)
self.output = activator(self.affine1, name="O1", graph=self)
self.error = tfl.SquaredError(self.output,
self.target_node,
name="SE",
graph=self)
def layering(self, activator=tfe.Activator.ReLU.value):
self.activator = activator
self.affine = tfl.Affine(self.params['W0'],
self.input_node,
self.params['b0'],
name="A",
graph=self)
self.output = activator(self.affine, name="O", graph=self)
self.error = tfl.SquaredError(self.output,
self.target_node,
name="SE",
graph=self)
def layering(self, activator=tfe.Activator.ReLU.value):
self.activator = activator
u = tfl.Affine(self.params['W0'],
self.input_node,
self.params['b0'],
name="A")
self.output = activator(u, name="O")
self.error = tfl.SquaredError(self.output, self.target_node, name="SE")
if isinstance(self, nx.Graph):
self.add_edge(self.params['W0'], u)
self.add_edge(self.input_node, u)
self.add_edge(self.params['b0'], u)
self.add_edge(u, self.output)
self.add_edge(self.output, self.error)
self.add_edge(self.error, self.target_node)
def layering(self, activator=tfe.Activator.ReLU.value):
self.activator = activator
for idx in range(self.hidden_layer_num):
self.layers['affine' + str(idx)] = tfl.Affine(
self.params['W' + str(idx)], self.input_node, self.params['b' + str(idx)], name='affine' + str(idx), graph=self
)
self.layers['activation' + str(idx)] = activator(self.layers['affine' + str(idx)], name='activation' + str(idx), graph=self)
idx = self.hidden_layer_num
self.layers['affine' + str(idx)] = tfl.Affine(
self.params['W' + str(idx)], self.input_node, self.params['b' + str(idx)], name='affine' + str(idx), graph=self
)
self.output = activator(self.layers['affine' + str(idx)], name='output', graph=self)
#self.last_layer = SoftmaxWithCrossEntropyLoss()
self.error = tfl.SquaredError(self.output, self.target_node, name="SE", graph=self)
def layering(self, activator=tfe.Activator.ReLU.value):
self.activator = activator
u0 = tfl.Affine(self.params['W0'],
self.input_node,
self.params['b0'],
name="A0")
o0 = activator(u0, name="O0")
u1 = tfl.Affine(self.params['W1'], o0, self.params['b1'], name="A1")
self.output = activator(u1, name="O1")
self.error = tfl.SquaredError(self.output, self.target_node, name="SE")
if isinstance(self, nx.Graph):
self.add_edge(self.params['W0'], u0)
self.add_edge(self.input_node, u0)
self.add_edge(self.params['b0'], u0)
self.add_edge(u0, o0)
self.add_edge(self.params['W1'], u1)
self.add_edge(o0, u1)
self.add_edge(self.params['b1'], u1)
self.add_edge(u1, self.output)
self.add_edge(self.output, self.error)
self.add_edge(self.error, self.target_node)