def test_flow_det(flow_spec):
z0 = tt.arange(0, 20).astype('float32')
flow = flow_spec(dim=20, z0=z0.dimshuffle('x', 0))
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
def test_flow_det(flow_spec):
z0 = tt.arange(0, 20).astype('float32')
flow = flow_spec(dim=20, z0=z0.dimshuffle('x', 0))
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
def test_flow_det_local(flow_spec):
z0 = tt.arange(0, 12).astype('float32')
spec = flow_spec.cls.get_param_spec_for(d=12)
params = dict()
for k, shp in spec.items():
params[k] = np.random.randn(1, *shp).astype('float32')
flow = flow_spec(dim=12, z0=z0.reshape((1, 1, 12)), **params)
assert flow.batched
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
def test_flow_det_local(flow_spec):
z0 = tt.arange(0, 12).astype('float32')
spec = flow_spec.cls.get_param_spec_for(d=12)
params = dict()
for k, shp in spec.items():
params[k] = np.random.randn(1, *shp).astype('float32')
flow = flow_spec(dim=12, z0=z0.reshape((1, 1, 12)), **params)
assert flow.batched
with change_flags(compute_test_value='off'):
z1 = flow.forward.flatten()
J = tt.jacobian(z1, z0)
logJdet = tt.log(tt.abs_(tt.nlinalg.det(J)))
det = flow.logdet[0]
np.testing.assert_allclose(logJdet.eval(), det.eval(), atol=0.0001)
