def testMultiplyInverseTuple(self):
with ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
inputs = array_ops.constant([[1., 2., 3.], [3., 4., 5.],
[5., 6., 7.]])
outputs = array_ops.constant([[3., 4.], [5., 6.]])
block = fb.FullyConnectedKFACBasicFB(lc.LayerCollection(),
has_bias=False)
block.register_additional_minibatch(inputs, outputs)
grads = outputs**2
block.instantiate_factors(([grads], ), 0.5)
# Make sure our inverse is something other than the identity.
sess.run(tf_variables.global_variables_initializer())
sess.run(block._input_factor.make_inverse_update_ops())
sess.run(block._output_factor.make_inverse_update_ops())
vector = (
np.arange(2, 6).reshape(2, 2).astype(np.float32), #
np.arange(1, 3).reshape(2, 1).astype(np.float32))
output = block.multiply_inverse(
(array_ops.constant(vector[0]), array_ops.constant(vector[1])))
output = sess.run(output)
self.assertAllClose([[0.686291, 1.029437], [1.372583, 1.715729]],
output[0])
self.assertAllClose([0.343146, 0.686291], output[1])
def testMultiplyInverseAgainstExplicit(self):
with ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
input_dim, output_dim = 3, 2
inputs = array_ops.zeros([32, input_dim])
outputs = array_ops.zeros([32, output_dim])
params = array_ops.zeros([input_dim, output_dim])
block = fb.FullyConnectedKFACBasicFB(lc.LayerCollection(),
has_bias=False)
block.register_additional_minibatch(inputs, outputs)
grads = outputs**2
damping = 0. # This test is only valid without damping.
block.instantiate_factors(([grads], ), damping)
sess.run(state_ops.assign(block._input_factor._cov, _make_psd(3)))
sess.run(state_ops.assign(block._output_factor._cov, _make_psd(2)))
sess.run(block._input_factor.make_inverse_update_ops())
sess.run(block._output_factor.make_inverse_update_ops())
v_flat = np.arange(6, dtype=np.float32)
vector = utils.column_to_tensors(params,
array_ops.constant(v_flat))
output = block.multiply_inverse(vector)
output_flat = sess.run(utils.tensors_to_column(output)).ravel()
full = sess.run(block.full_fisher_block())
explicit = np.dot(np.linalg.inv(full + damping * np.eye(6)),
v_flat)
self.assertAllClose(output_flat, explicit)
def testMultiplyInverseNotTuple(self):
with ops.Graph().as_default(), self.cached_session() as sess:
random_seed.set_random_seed(200)
inputs = array_ops.constant([[1., 2.], [3., 4.]])
outputs = array_ops.constant([[3., 4.], [5., 6.]])
block = fb.FullyConnectedKFACBasicFB(lc.LayerCollection(),
has_bias=False)
block.register_additional_tower(inputs, outputs)
grads = outputs**2
block.instantiate_factors(((grads, ), ), 0.5)
block._input_factor.instantiate_cov_variables()
block._output_factor.instantiate_cov_variables()
block.register_inverse()
block._input_factor.instantiate_inv_variables()
block._output_factor.instantiate_inv_variables()
# Make sure our inverse is something other than the identity.
sess.run(tf_variables.global_variables_initializer())
sess.run(block._input_factor.make_inverse_update_ops())
sess.run(block._output_factor.make_inverse_update_ops())
vector = np.arange(2, 6).reshape(2, 2).astype(np.float32)
output = block.multiply_inverse(array_ops.constant(vector))
self.assertAllClose([[0.686291, 1.029437], [1.372583, 1.715729]],
sess.run(output))
def testFullyConnectedKFACBasicFBInit(self):
with ops.Graph().as_default():
random_seed.set_random_seed(200)
inputs = array_ops.constant([1., 2.])
outputs = array_ops.constant([3., 4.])
block = fb.FullyConnectedKFACBasicFB(lc.LayerCollection())
block.register_additional_minibatch(inputs, outputs)
self.assertAllEqual([outputs], block.tensors_to_compute_grads())
def testInstantiateFactorsNoBias(self):
with ops.Graph().as_default():
random_seed.set_random_seed(200)
inputs = array_ops.constant([[1., 2.], [3., 4.]])
outputs = array_ops.constant([[3., 4.], [5., 6.]])
block = fb.FullyConnectedKFACBasicFB(lc.LayerCollection(), has_bias=False)
block.register_additional_minibatch(inputs, outputs)
grads = outputs**2
block.instantiate_factors(((grads,),), 0.5)
def testInstantiateFactorsHasBias(self):
with ops.Graph().as_default():
random_seed.set_random_seed(200)
inputs = array_ops.constant([[1., 2.], [3., 4.]])
outputs = array_ops.constant([[3., 4.], [5., 6.]])
block = fb.FullyConnectedKFACBasicFB(
lc.LayerCollection(), inputs, outputs, has_bias=True)
grads = outputs**2
block.instantiate_factors((grads,), 0.5)
def register_fully_connected(self,
params,
inputs,
outputs,
approx=APPROX_KRONECKER_NAME):
has_bias = isinstance(params, (tuple, list))
if approx == APPROX_KRONECKER_NAME:
self.register_block(params,
fb.FullyConnectedKFACBasicFB(self, inputs, outputs,
has_bias))
elif approx == APPROX_DIAGONAL_NAME:
self.register_block(params,
fb.FullyConnectedDiagonalFB(self, inputs, outputs,
has_bias))
else:
raise ValueError("Bad value {} for approx.".format(approx))