def test_adjust_gradient(self):
X = dot(randn(5, 5), randn(5, 1000)) + randn(5, 1)
Y = dot(randn(2, 2), randn(2, 1000)) + dot(randn(2, 5), X)
meanIn = randn(5, 1)
meanOut = randn(2, 1)
preIn = randn(5, 5)
preOut = randn(2, 2)
predictor = randn(2, 5)
pre = AffinePreconditioner(meanIn, meanOut, preIn, preOut, predictor)
f = lambda X, Y: sum(hstack([p.ravel() for p in pre(X, Y)]))
# compute analytic gradient
dfdX, dfdY = pre.adjust_gradient(ones_like(X), ones_like(Y))
# compute numerical gradient
h = 0.1
dfdXn = zeros_like(X)
X_copy = X.copy()
for i in range(X.shape[0]):
for j in range(X.shape[1]):
X_copy[i, j] = X[i, j] + h
fp = f(X_copy, Y)
X_copy[i, j] = X[i, j] - h
fn = f(X_copy, Y)
dfdXn[i, j] = (fp - fn) / (2. * h)
X_copy[i, j] = X[i, j]
self.assertLess(max(abs(dfdXn - dfdX)), 1e-7)
def test_adjust_gradient(self): X = dot(randn(5, 5), randn(5, 1000)) + randn(5, 1) Y = dot(randn(2, 2), randn(2, 1000)) + dot(randn(2, 5), X) meanIn = randn(5, 1) meanOut = randn(2, 1) preIn = randn(5, 5) preOut = randn(2, 2) predictor = randn(2, 5) pre = AffinePreconditioner( meanIn, meanOut, preIn, preOut, predictor) f = lambda X, Y: sum(hstack([p.ravel() for p in pre(X, Y)])) # compute analytic gradient dfdX, dfdY = pre.adjust_gradient(ones_like(X), ones_like(Y)) # compute numerical gradient h = 0.1 dfdXn = zeros_like(X) X_copy = X.copy() for i in range(X.shape[0]): for j in range(X.shape[1]): X_copy[i, j] = X[i, j] + h fp = f(X_copy, Y) X_copy[i, j] = X[i, j] - h fn = f(X_copy, Y) dfdXn[i, j] = (fp - fn) / (2. * h) X_copy[i, j] = X[i, j] self.assertLess(max(abs(dfdXn - dfdX)), 1e-7)
