Method test_mstdpet

test/network/test_learning.py:207–249

(self)

        )

    def test_mstdpet(self):
        # Connection test
        network = Network(dt=1.0)
        network.add_layer(Input(n=100), name="input")
        network.add_layer(LIFNodes(n=100), name="output")
        network.add_connection(
            Connection(
                source=network.layers["input"],
                target=network.layers["output"],
                nu=1e-2,
                update_rule=MSTDPET,
            ),
            source="input",
            target="output",
        )
        network.run(
            inputs={"input": torch.bernoulli(torch.rand(250, 100)).byte()},
            time=250,
            reward=1.0,
        )

        # Conv2dConnection test
        network = Network(dt=1.0)
        network.add_layer(Input(shape=[1, 10, 10]), name="input")
        network.add_layer(LIFNodes(shape=[32, 8, 8]), name="output")
        network.add_connection(
            Conv2dConnection(
                source=network.layers["input"],
                target=network.layers["output"],
                kernel_size=3,
                stride=1,
                nu=1e-2,
                update_rule=MSTDPET,
            ),
            source="input",
            target="output",
        )

        network.run(
            inputs={"input": torch.bernoulli(torch.rand(250, 1, 1, 10, 10)).byte()},
            time=250,
            reward=1.0,
        )

    def test_rmax(self):
        # Connection test