def _testFullyConnectedKroneckerFactorInit(self, has_bias, final_shape):
with tf_ops.Graph().as_default():
random_seed.set_random_seed(200)
tensor = array_ops.ones((2, 3), name='a/b/c')
factor = ff.FullyConnectedKroneckerFactor((tensor, ),
has_bias=has_bias)
self.assertEqual(final_shape,
factor.get_cov().get_shape().as_list())
def testMakeCovarianceUpdateOpNoBias(self):
with tf_ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
tensor = array_ops.constant([[1., 2.], [3., 4.]], name='a/b/c')
factor = ff.FullyConnectedKroneckerFactor((tensor,))
sess.run(tf_variables.global_variables_initializer())
new_cov = sess.run(factor.make_covariance_update_op(.5))
self.assertAllClose([[3, 3.5], [3.5, 5.5]], new_cov)
def _testFullyConnectedKroneckerFactorInit(self,
has_bias,
final_shape,
dtype=dtypes.float32_ref):
with tf_ops.Graph().as_default():
random_seed.set_random_seed(200)
tensor = array_ops.ones((2, 3), dtype=dtype, name='a/b/c')
factor = ff.FullyConnectedKroneckerFactor(((tensor,),), has_bias=has_bias)
factor.instantiate_cov_variables()
cov = factor.get_cov()
self.assertEqual(cov.dtype, dtype)
self.assertEqual(final_shape, cov.get_shape().as_list())
