def test_disconnecting():
node1, node2 = _create_graph()
Connector.disconnect_input(node2.inputs.input1)
assert node2.inputs.input1.connection is None
assert 0 == len(node1.outputs.output1.connections)
def __init__(self,
input_dims,
rf_output_dims,
stride: Tuple[int, int] = None,
expert_params: ExpertParams = None,
num_flocks: int = 1,
name="",
seed: int = None,
sub_field_size=6):
super().__init__(input_dims, rf_output_dims, stride, expert_params,
num_flocks, name, seed)
n_sub_fields = len(self.expert_flock_nodes)
subfield_node = RandomSubfieldForkNode(n_outputs=n_sub_fields,
n_samples=sub_field_size,
first_non_expanded_dim=-3)
self.add_node(subfield_node)
Connector.connect(self.lrf_node.outputs.output,
subfield_node.inputs.input)
for i, expert_flock in enumerate(self.expert_flock_nodes):
Connector.disconnect_input(expert_flock.inputs.sp.data_input)
Connector.connect(subfield_node.outputs[i],
expert_flock.inputs.sp.data_input)
def set_testing_model(self):
# noinspection PyProtectedMember
self._node_spatial_pooler._unit.copy_to(
self._node_spatial_pooler_backup._unit)
self._node_spatial_pooler.switch_learning(False)
self._node_mnist.skip_execution = True
self._node_mnist_test.skip_execution = False
Connector.disconnect_input(self._noise_node.inputs[0])
Connector.connect(self._node_mnist_test.outputs.data,
self._noise_node.inputs[0])
