def test_leabra_init_input_output(self):
L = LeabraMechanism(input_size=3, output_size=3, name='L1')
val = L.execute([[0, 0, 0], [0, 0, 0]])
assert L.input_size == 3
assert L.output_size == 3
assert L.name == 'L1'
assert np.sum(np.abs(val)) <= 0.001
def test_leabra_init_empty(self):
L = LeabraMechanism()
val = L.execute([[10], [0]])
assert len(val) == 1 and len(val[0]) == 1
assert "LeabraMechanism" in L.name
assert L.input_size == 1
assert L.output_size == 1
assert val[0][0] > 0.9
def test_leabra_init_no_hidden_sizes(self):
L = LeabraMechanism(input_size=4,
output_size=4,
hidden_layers=2,
training_flag=False)
val = L.execute([[1, 2, 3, 4], [0, 0, 0, 0]])
assert L.hidden_layers == 2
assert L.hidden_sizes == 4
assert len(val[0]) == 4
def test_leabra_runtime_alone(self):
n_input = 4
n_output = 3
n_hidden = 2
hidden_sizes = None
inputs = [[.1, .2, .3, .4], [.4, .5, .6, .7], [-.6, -.7, -.8, -.9]]
train_input = [1, 0, -1]
random.seed(10)
L1 = LeabraMechanism(input_size=n_input,
output_size=n_output,
hidden_layers=n_hidden,
hidden_sizes=None,
training_flag=False) # training flag is false
random.seed(10)
L2 = LeabraMechanism(input_size=n_input,
output_size=n_output,
hidden_layers=n_hidden,
hidden_sizes=None,
training_flag=True) # training flag is true
random.seed(10)
net = build_leabra_network(n_input, n_output, n_hidden, hidden_sizes,
False)
pnl_output1_1 = L1.execute(input=[inputs[0], train_input],
runtime_params={"training_flag": False})
pnl_output2_1 = L2.execute(input=[inputs[0], train_input],
runtime_params={"training_flag": False})
net_output_1 = run_leabra_network(net, input_pattern=inputs[0])
np.testing.assert_allclose(pnl_output1_1[0], net_output_1, atol=1e-08)
np.testing.assert_allclose(pnl_output2_1[0], net_output_1, atol=1e-08)
pnl_output1_2 = L1.execute(input=[inputs[1], train_input],
runtime_params={"training_flag": True})
pnl_output2_2 = L2.execute(input=[inputs[1], train_input],
runtime_params={"training_flag": True})
net_output_2 = train_leabra_network(net,
input_pattern=inputs[1],
output_pattern=train_input)
np.testing.assert_allclose(pnl_output1_2[0], net_output_2, atol=1e-08)
np.testing.assert_allclose(pnl_output2_2[0], net_output_2, atol=1e-08)
pnl_output1_3 = L1.execute(input=[inputs[2], train_input],
runtime_params={"training_flag": False})
pnl_output2_3 = L2.execute(input=[inputs[2], train_input],
runtime_params={"training_flag": False})
net_output_3 = run_leabra_network(net, input_pattern=inputs[2])
np.testing.assert_allclose(pnl_output1_3[0], net_output_3, atol=1e-08)
np.testing.assert_allclose(pnl_output2_3[0], net_output_3, atol=1e-08)