def test_elemexp_values_1():
npr.seed(1)
for ii in xrange(NUM_TRIALS):
np_A = npr.randn(5, 6)
A = kayak.Parameter(np_A)
C = kayak.ElemExp(A)
assert C.shape == np_A.shape
assert np.all(close_float(C.value, np.exp(np_A)))
def test_elemexp_values_2():
npr.seed(2)
for ii in xrange(NUM_TRIALS):
np_A = npr.randn(1)
A = kayak.Parameter(np_A)
D = kayak.ElemExp(A)
assert D.shape == np_A.shape
assert np.all(close_float(D.value, np.exp(np_A)))
def test_elemexp_grad_2():
npr.seed(9)
for ii in xrange(NUM_TRIALS):
np_A = npr.randn(1)
A = kayak.Parameter(np_A)
D = kayak.ElemExp(A)
E = kayak.MatSum(D)
E.value
assert kayak.util.checkgrad(A, E) < MAX_GRAD_DIFF
def test_elemexp_grad_1():
npr.seed(8)
for ii in xrange(NUM_TRIALS):
np_A = npr.randn(5, 6)
A = kayak.Parameter(np_A)
C = kayak.ElemExp(A)
D = kayak.MatSum(C)
D.value
assert kayak.util.checkgrad(A, D) < MAX_GRAD_DIFF
# Build network.
kyk_inputs = kayak.Inputs(X, kyk_batcher)
# Labels.
kyk_targets = kayak.Targets(Y, kyk_batcher)
# Weights.
W = 0.01 * npr.randn(D, P)
kyk_W = kayak.Parameter(W)
# Linear layer.
kyk_activation = kayak.MatMult(kyk_inputs, kyk_W)
# Exponential inverse-link function.
kyk_lam = kayak.ElemExp(kyk_activation)
# Poisson negative log likelihood.
kyk_nll = kyk_lam - kayak.ElemLog(kyk_lam) * kyk_targets
# Sum the losses.
kyk_loss = kayak.MatSum(kyk_nll)
for ii in xrange(100):
for batch in kyk_batcher:
loss = kyk_loss.value
print loss, np.sum((kyk_W.value - true_W)**2)
grad = kyk_loss.grad(kyk_W)
kyk_W.value -= learn * grad