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Class TestNetworkMonitor

test/network/test_monitors.py:48–84  ·  view source on GitHub ↗

Testing NetworkMonitor object.

Source from the content-addressed store, hash-verified

46
47
48class TestNetworkMonitor:
49 """
50 Testing NetworkMonitor object.
51 """
52
53 network = Network()
54
55 inpt = Input(25)
56 network.add_layer(inpt, name="X")
57 _if = IFNodes(75)
58 network.add_layer(_if, name="Y")
59 conn = Connection(inpt, _if, w=torch.rand(inpt.n, _if.n))
60 network.add_connection(conn, source="X", target="Y")
61
62 mon = NetworkMonitor(network, state_vars=["s", "v", "w"])
63 network.add_monitor(mon, name="monitor")
64
65 network.run(inputs={"X": torch.bernoulli(torch.rand(50, inpt.n))}, time=50)
66
67 recording = mon.get()
68
69 assert recording["X"]["s"].size() == torch.Size([50, 1, inpt.n])
70 assert recording["Y"]["s"].size() == torch.Size([50, 1, _if.n])
71 assert recording["Y"]["s"].size() == torch.Size([50, 1, _if.n])
72
73 del network.monitors["monitor"]
74
75 mon = NetworkMonitor(network, state_vars=["s", "v", "w"], time=50)
76 network.add_monitor(mon, name="monitor")
77
78 network.run(inputs={"X": torch.bernoulli(torch.rand(50, inpt.n))}, time=50)
79
80 recording = mon.get()
81
82 assert recording["X"]["s"].size() == torch.Size([50, 1, inpt.n])
83 assert recording["Y"]["s"].size() == torch.Size([50, 1, _if.n])
84 assert recording["Y"]["s"].size() == torch.Size([50, 1, _if.n])
85
86
87if __name__ == "__main__":

Callers 1

test_monitors.pyFile · 0.85

Calls 10

NetworkClass · 0.90
InputClass · 0.90
IFNodesClass · 0.90
ConnectionClass · 0.90
NetworkMonitorClass · 0.90
add_layerMethod · 0.80
add_connectionMethod · 0.80
add_monitorMethod · 0.80
runMethod · 0.80
getMethod · 0.45

Tested by

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