def test_nest_dense_chain():
p1 = pynn.Population(12, pynn.SpikeSourcePoisson(rate = 100))
p2 = pynn.Population(10, pynn.IF_cond_exp())
p3 = pynn.Population(2, pynn.IF_cond_exp())
p3.record('spikes')
d1 = v.Dense(p1, p2, v.ReLU())
d2 = v.Dense(p2, p3, v.ReLU())
pynn.run(1000)
assert len(p3.get_data().segments[-1].spiketrains) > 0
def test_nest_dense_normalisation():
p1 = pynn.Population(12, pynn.IF_cond_exp(**v.DEFAULT_NEURON_PARAMETERS))
p2 = pynn.Population(10, pynn.IF_cond_exp(**v.DEFAULT_NEURON_PARAMETERS))
l = v.Dense(p1, p2, v.ReLU(), weights=1)
m = v.Model(l)
out = m.predict(np.ones(12) * 12, 50)
assert np.allclose(np.ones(10), out, atol=0.1)
def test_nest_dense_create():
p1 = pynn.Population(12, pynn.IF_cond_exp())
p2 = pynn.Population(10, pynn.IF_cond_exp())
d = v.Dense(p1, p2, v.ReLU())
expected_weights = np.ones((12, 10))
actual_weights = d.projection.get('weight', format='array')
assert not np.allclose(actual_weights, expected_weights) # Should be normal distributed
assert abs(actual_weights.sum()) <= 24
def test_nest_dense_shape():
p1 = pynn.Population(12, pynn.SpikeSourcePoisson(rate = 10))
p2 = pynn.Population(10, pynn.IF_cond_exp())
d = v.Dense(p1, p2, v.ReLU(), weights = 1)
pynn.run(1000)
d.store_spikes()
assert d.input.shape == (12,)
assert d.output.shape[0] == 10
def test_nest_dense_increased_weight_fire():
p1 = pynn.Population(1, pynn.SpikeSourcePoisson(rate = 1))
p2 = pynn.Population(1, pynn.IF_cond_exp())
p2.record('spikes')
d = v.Dense(p1, p2, v.ReLU(), weights = 2)
pynn.run(1000)
spiketrains = p2.get_data().segments[-1].spiketrains
count1 = spiketrains[0].size
pynn.reset()
p1 = pynn.Population(1, pynn.SpikeSourcePoisson(rate = 1))
p2 = pynn.Population(1, pynn.IF_cond_exp())
p2.record('spikes')
d = v.Dense(p1, p2, v.ReLU(), weights = 2)
pynn.run(1000)
spiketrains = p2.get_data().segments[-1].spiketrains
count2 = spiketrains[0].size
assert count2 >= count1 * 2
def test_nest_model_predict_inactive():
p1 = pynn.Population(2, pynn.IF_cond_exp())
p2 = pynn.Population(2, pynn.IF_cond_exp())
l = v.Dense(p1, p2, v.ReLU(), decoder=v.spike_count)
m = v.Model(l)
out = m.predict(np.array([0, 0]), 10)
assert len(out) == 2
assert out.sum() < 10
def test_nest_model_linear_scaling():
p1 = pynn.Population(2, pynn.IF_cond_exp(**v.DEFAULT_NEURON_PARAMETERS))
p2 = pynn.Population(2, pynn.IF_cond_exp(**v.DEFAULT_NEURON_PARAMETERS))
l1 = v.Dense(p1, p2, v.ReLU(), decoder=v.spike_count, weights=1)
m = v.Model(l1)
xs = np.array([12, 12])
out = m.predict(xs, 50)
assert np.allclose([38, 38], out)
def test_nest_model_predict_active():
p1 = pynn.Population(2, pynn.IF_cond_exp())
p2 = pynn.Population(2, pynn.IF_cond_exp())
l = v.Dense(p1, p2, v.ReLU(), decoder=v.spike_count, weights=1)
m = v.Model(l)
out = m.predict(np.array([1, 1]), 1000)
assert len(out) == 2
assert abs(out[0] - out[1]) <= 10 # Must be approx same spikes
def test_replicate_can_replicate():
p1 = pynn.Population(6, pynn.IF_cond_exp(i_offset=10))
p2 = pynn.Population(6, pynn.IF_cond_exp())
p3 = pynn.Population(6, pynn.IF_cond_exp())
l = v.Replicate(p1, (p2, p3), v.ReLU(), weights=(1, 1))
pynn.run(1000)
l.store_spikes()
expected = np.ones((2, 6))
assert np.allclose(expected, l.get_output())
def test_replicate_create():
p1 = pynn.Population(6, pynn.IF_cond_exp())
p2 = pynn.Population(6, pynn.IF_cond_exp())
p3 = pynn.Population(6, pynn.IF_cond_exp())
l = v.Replicate(p1, (p2, p3), v.ReLU(), weights=(1, 1))
pynn.run(1000)
l.store_spikes()
assert l.layer1.input.shape == (6, 0)
assert l.layer2.input.shape == (6, 0)
assert l.get_output().shape == (2, 6)
def test_nest_dense_reduced_weight_fire():
p1 = pynn.Population(1, pynn.IF_cond_exp(i_offset=10))
p2 = pynn.Population(2, pynn.IF_cond_exp())
d = v.Dense(p1, p2, v.ReLU(), weights = np.array([[1, 0]]))
pynn.run(1000)
spiketrains1 = p1.get_data().segments[-1].spiketrains
spiketrains2 = p2.get_data().segments[-1].spiketrains
assert spiketrains1[0].size > 0
assert spiketrains2[0].size > 0
assert spiketrains2[1].size == 0
def test_nest_create_input_populations():
p1 = pynn.Population(2, pynn.IF_cond_exp())
p2 = pynn.Population(2, pynn.IF_cond_exp())
l = v.Dense(p1, p2, v.ReLU())
m = v.Model(l)
assert len(m.input_populations) == 2
inp = np.array([1, 0.2])
m.set_input(inp)
assert m.input_populations[0].get('i_offset') == 1
assert m.input_populations[1].get('i_offset') == 0.2
def test_nest_dense_projection():
p1 = pynn.Population(12, pynn.SpikeSourcePoisson(rate = 10))
p2 = pynn.Population(10, pynn.IF_cond_exp())
p2.record('spikes')
d = v.Dense(p1, p2, v.ReLU(), weights = 1)
pynn.run(1000)
spiketrains = p2.get_data().segments[-1].spiketrains
assert len(spiketrains) == 10
avg_len = np.array(list(map(len, spiketrains))).mean()
# Should have equal activation
for train in spiketrains:
assert abs(len(train) - avg_len) <= 1
def test_nest_input_projection():
p1 = pynn.Population(2, pynn.IF_cond_exp(**v.DEFAULT_NEURON_PARAMETERS))
p2 = pynn.Population(2, pynn.IF_cond_exp(**v.DEFAULT_NEURON_PARAMETERS))
l = v.Dense(p1, p2, v.ReLU(), weights=1)
m = v.Model(l)
t = v.LinearTranslation()
assert np.allclose(l.get_weights(), np.ones((2, 2)))
assert m.input_projection[0].weight == t.weights(1, 1)
assert m.input_projection[1].weight == t.weights(1, 1)
m.predict([1, 1], 50)
spiketrains = l.output
assert abs(len(spiketrains[0]) - len(spiketrains[1])) <= 20
def test_nest_dense_restore():
p1 = pynn.Population(12, pynn.IF_cond_exp())
p2 = pynn.Population(10, pynn.IF_cond_exp())
d = v.Dense(p1, p2, v.ReLU(), weights = 2)
d.set_weights(-1)
t = v.LinearTranslation()
assert np.array_equal(d.projection.get('weight', format='array'),
t.weights(np.ones((12, 10)) * -1, 12))
d.projection.set(weight = 1) # Simulate reset()
assert np.array_equal(d.projection.get('weight', format='array'),
np.ones((12, 10)))
d.restore_weights()
assert np.array_equal(d.projection.get('weight', format='array'),
t.weights(np.ones((12, 10)) * -1, 12))
def test_nest_model_backwards():
p1 = pynn.Population(2, pynn.IF_cond_exp())
p2 = pynn.Population(3, pynn.IF_cond_exp())
l1 = v.Dense(p1, p2, v.ReLU(), decoder=v.spike_count_linear, weights=1)
m = v.Model(l1)
xs = np.array([12, 12])
spikes = m.predict(xs, 50)
def l(w, g, b, bg):
return w - g, b - bg
m.backward([0, 1, 1], l) # no learning rate
expected_weights = np.array([[1, 0, 0], [1, 0, 0]])
assert np.allclose(l1.get_weights(), expected_weights, atol=0.2)
def test_nest_model_backwards_reset():
p1 = pynn.Population(2, pynn.IF_cond_exp())
p2 = pynn.Population(2, pynn.IF_cond_exp())
l1 = v.Dense(p1, p2, v.ReLU(), decoder=v.spike_count_normalised, weights=1)
m = v.Model(l1)
xs1 = np.array([10, 10])
ys1 = np.array([0, 10])
xs2 = np.array([10, 10])
ys2 = np.array([0, 10])
# First pass
target1 = m.predict(xs1, 50)
m.backward([0, 1], lambda w, g, b, bg: (w - g, b - bg))
expected_weights = np.array([[1, 0], [1, 0]])
assert np.allclose(l1.get_weights(), expected_weights)
# Second pass
target2 = m.predict(xs2, 50)
m.backward([1, 0], lambda w, g, b, bg: (w - g, b - bg))
expected_weights = np.array([[-1, 0], [-1, 0]])
assert np.allclose(l1.get_weights(), expected_weights)