def test_rop_lop():
mx = tensor.matrix('mx')
mv = tensor.matrix('mv')
v = tensor.vector('v')
y = matrix_inverse(mx).sum(axis=0)
yv = tensor.Rop(y, mx, mv)
yv2 = tensor.Rop_via_Lop(y, mx, mv)
rop_f = function([mx, mv], [yv, yv2])
sy, _ = theano.scan(lambda i, y, x, v: (tensor.grad(y[i], x) * v).sum(),
sequences=tensor.arange(y.shape[0]),
non_sequences=[y, mx, mv])
scan_f = function([mx, mv], sy)
rng = np.random.RandomState(utt.fetch_seed())
vx = np.asarray(rng.randn(4, 4), theano.config.floatX)
vv = np.asarray(rng.randn(4, 4), theano.config.floatX)
v1 = scan_f(vx, vv)
v2, v3 = rop_f(vx, vv)
assert _allclose(v2, v1), ('Rop mismatch: %s %s' % (v2, v1))
assert _allclose(v3, v1), ('Rop_via_Lop mismatch: %s %s' % (v3, v1))
raised = False
try:
tensor.Rop(theano.clone(y, replace={mx: break_op(mx)}), mx, mv)
except ValueError:
raised = True
if not raised:
raise Exception(('Op did not raised an error even though the function'
' is not differentiable'))
try:
tensor.Rop_via_Lop(theano.clone(y, replace={mx: break_op(mx)}), mx, mv)
except theano.gradient.NullTypeGradError:
raised = True
except theano.gradient.DisconnectedInputError:
raised = True
if not raised:
raise Exception((
'Rop_via_Lop for Op did not raise an error even though the function'
' is not differentiable'))
vv = np.asarray(rng.uniform(size=(4, )), theano.config.floatX)
yv = tensor.Lop(y, mx, v)
lop_f = function([mx, v], yv)
sy = tensor.grad((v * y).sum(), mx)
scan_f = function([mx, v], sy)
v1 = lop_f(vx, vv)
v2 = scan_f(vx, vv)
assert _allclose(v1, v2), ('LOP mismatch: %s %s' % (v1, v2))
def test_rop_lop():
mx = tensor.matrix('mx')
mv = tensor.matrix('mv')
v = tensor.vector('v')
y = matrix_inverse(mx).sum(axis=0)
yv = tensor.Rop(y, mx, mv)
rop_f = function([mx, mv], yv)
sy, _ = theano.scan(lambda i, y, x, v: (tensor.grad(y[i], x) * v).sum(),
sequences=tensor.arange(y.shape[0]),
non_sequences=[y, mx, mv])
scan_f = function([mx, mv], sy)
rng = numpy.random.RandomState(utt.fetch_seed())
vx = numpy.asarray(rng.randn(4, 4), theano.config.floatX)
vv = numpy.asarray(rng.randn(4, 4), theano.config.floatX)
v1 = rop_f(vx, vv)
v2 = scan_f(vx, vv)
assert _allclose(v1, v2), ('ROP mismatch: %s %s' % (v1, v2))
raised = False
try:
tensor.Rop(
theano.clone(y, replace={mx: break_op(mx)}),
mx,
mv)
except ValueError:
raised = True
if not raised:
raise Exception((
'Op did not raised an error even though the function'
' is not differentiable'))
vv = numpy.asarray(rng.uniform(size=(4,)), theano.config.floatX)
yv = tensor.Lop(y, mx, v)
lop_f = function([mx, v], yv)
sy = tensor.grad((v * y).sum(), mx)
scan_f = function([mx, v], sy)
v1 = lop_f(vx, vv)
v2 = scan_f(vx, vv)
assert _allclose(v1, v2), ('LOP mismatch: %s %s' % (v1, v2))
def test_rop_lop():
mx = tensor.matrix("mx")
mv = tensor.matrix("mv")
v = tensor.vector("v")
y = matrix_inverse(mx).sum(axis=0)
yv = tensor.Rop(y, mx, mv)
rop_f = function([mx, mv], yv)
sy, _ = theano.scan(
lambda i, y, x, v: (tensor.grad(y[i], x) * v).sum(),
sequences=tensor.arange(y.shape[0]),
non_sequences=[y, mx, mv],
)
scan_f = function([mx, mv], sy)
rng = np.random.RandomState(utt.fetch_seed())
vx = np.asarray(rng.randn(4, 4), theano.config.floatX)
vv = np.asarray(rng.randn(4, 4), theano.config.floatX)
v1 = rop_f(vx, vv)
v2 = scan_f(vx, vv)
assert _allclose(v1, v2), "ROP mismatch: %s %s" % (v1, v2)
raised = False
try:
tensor.Rop(theano.clone(y, replace={mx: break_op(mx)}), mx, mv)
except ValueError:
raised = True
if not raised:
raise Exception(("Op did not raised an error even though the function"
" is not differentiable"))
vv = np.asarray(rng.uniform(size=(4, )), theano.config.floatX)
yv = tensor.Lop(y, mx, v)
lop_f = function([mx, v], yv)
sy = tensor.grad((v * y).sum(), mx)
scan_f = function([mx, v], sy)
v1 = lop_f(vx, vv)
v2 = scan_f(vx, vv)
assert _allclose(v1, v2), "LOP mismatch: %s %s" % (v1, v2)