def test(self):
np.random.seed(234354346)
# need many samples to get close to optimum and stable numbers
n = 1000
a_s = np.asarray([1, 0]).reshape((1, 2))
a_d = np.asarray([1, 1]).reshape((1, 2))
y = np.random.uniform(size=(n, 1))
eps = np.random.rand(n, 1)
X = y * a_s + eps * a_d
model = keras.models.Sequential([
keras.layers.Dense(1, input_shape=(2, ), use_bias=True),
])
model.compile(optimizer=keras.optimizers.Adam(lr=1), loss="mse")
history = model.fit(X, y, epochs=20, verbose=0).history
#print(history)
self.assertTrue(model.evaluate(X, y, verbose=0) < 0.05)
pc = PatternComputer(model, pattern_type="linear")
A = pc.compute(X)[0]
W = model.get_weights()[0]
#print(a_d, model.get_weights()[0])
#print(a_s, A)
def allclose(a, b):
return np.allclose(a, b, rtol=0.05, atol=0.05)
# perpendicular to a_d
self.assertTrue(allclose(a_d.ravel(), abs(W.ravel())))
# estimated pattern close to true pattern
self.assertTrue(allclose(a_s.ravel(), A.ravel()))
def method(model):
return PatternComputer(model, pattern_type="relu.positive")
def method(model):
return PatternComputer(model, pattern_type="linear")
def method(model):
return PatternComputer(model,
pattern_type="dummy",
compute_layers_in_parallel=False)