from __future__ import print_function
# Dependency imports
import numpy as np
import numpy.random as npr
import tensorflow as tf
from tensorflow.python.framework import dtypes
from kfac.python.ops import fisher_blocks as fb
from kfac.python.ops import fisher_factors as ff
# We need to set these constants since the numerical values used in the tests
# were chosen when these used to be the defaults.
ff.set_global_constants(init_covariances_at_zero=False,
zero_debias=False,
init_inverses_at_zero=False,
max_num_patches_per_dimension=1.0)
def make_damping_func(damping):
return fb._package_func(lambda: damping, damping)
class FisherFactorTestingDummy(ff.FisherFactor):
"""Dummy class to test the non-abstract methods on ff.FisherFactor."""
@property
def _var_scope(self):
return 'dummy/a_b_c'
@property
def _cov_shape(self):
from __future__ import print_function
# Dependency imports
import numpy as np
import tensorflow as tf
from kfac.python.ops import fisher_blocks as fb
from kfac.python.ops import fisher_factors as ff
from kfac.python.ops import layer_collection as lc
from kfac.python.ops import linear_operator as lo
from kfac.python.ops import utils
# We need to set these constants since the numerical values used in the tests
# were chosen when these used to be the defaults.
ff.set_global_constants(init_covariances_at_zero=False,
zero_debias=False,
init_inverses_at_zero=False)
def _make_psd(dim):
"""Constructs a PSD matrix of the given dimension."""
mat = np.ones((dim, dim), dtype=np.float32)
mat[np.arange(dim), np.arange(dim)] = 2. + np.arange(dim)
return tf.constant(mat)
class UtilsTest(tf.test.TestCase):
def testComputePiTracenorm(self):
with tf.Graph().as_default(), self.test_session() as sess:
tf.set_random_seed(200)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Dependency imports
import numpy as np
import tensorflow as tf
from kfac.python.ops import estimator
from kfac.python.ops import fisher_factors as ff
from kfac.python.ops import layer_collection as lc
from kfac.python.ops import utils
# We need to set these constants since the numerical values used in the tests
# were chosen when these used to be the defaults.
ff.set_global_constants(zero_debias=False)
_ALL_ESTIMATION_MODES = ["gradients", "empirical", "curvature_prop", "exact"]
class EstimatorTest(tf.test.TestCase):
def setUp(self):
self._graph = tf.Graph()
with self._graph.as_default():
self.layer_collection = lc.LayerCollection()
self.inputs = tf.random_normal((2, 2), dtype=tf.float32)
self.weights = tf.get_variable("w", shape=(2, 2), dtype=tf.float32)
self.bias = tf.get_variable("b",
initializer=tf.zeros_initializer(),
shape=(2, 1))