def testSparsityDictErdosRenyiSparsitiesScale(
self, shape1, shape2, default_sparsity):
_ = self._setup_session()
all_masks = [tf.get_variable(shape=shape1, name='var1/mask'),
tf.get_variable(shape=shape2, name='var2/mask')]
custom_sparsity = {}
sparsities = sparse_utils.get_sparsities(
all_masks, 'erdos_renyi', default_sparsity, custom_sparsity)
sparsity1 = sparsities[all_masks[0].name]
size1 = np.prod(shape1)
sparsity2 = sparsities[all_masks[1].name]
size2 = np.prod(shape2)
# Ensure that total number of connections are similar.
expected_zeros_uniform = (
sparse_utils.get_n_zeros(size1, default_sparsity) +
sparse_utils.get_n_zeros(size2, default_sparsity))
# Ensure that total number of connections are similar.
expected_zeros_current = (
sparse_utils.get_n_zeros(size1, sparsity1) +
sparse_utils.get_n_zeros(size2, sparsity2))
# Due to rounding we can have some difference. This is expected but should
# be less than number of rounding operations we make.
diff = abs(expected_zeros_uniform - expected_zeros_current)
tolerance = 2
self.assertLessEqual(diff, tolerance)
# Ensure that ErdosRenyi proportions are preserved.
factor1 = (shape1[-1] + shape1[-2]) / float(shape1[-1] * shape1[-2])
factor2 = (shape2[-1] + shape2[-2]) / float(shape2[-1] * shape2[-2])
self.assertAlmostEqual((1 - sparsity1) / factor1,
(1 - sparsity2) / factor2)
def testSparsityDictErdosRenyiError(self, default_sparsity):
_ = self._setup_session()
all_masks = [tf.get_variable(shape=(2, 4), name='var1/mask'),
tf.get_variable(shape=(2, 3), name='var2/mask'),
tf.get_variable(shape=(1, 1, 3), name='var3/mask')]
custom_sparsity = {'var3': 0.8}
sparsities = sparse_utils.get_sparsities(
all_masks, 'erdos_renyi', default_sparsity, custom_sparsity)
self.assertEqual(sparsities[all_masks[2].name], 0.8)
def testSparsityDictRandom(self, default_sparsity):
_ = self._setup_session()
all_masks = [tf.get_variable(shape=(2, 3), name='var1/mask'),
tf.get_variable(shape=(2, 3), name='var2/mask'),
tf.get_variable(shape=(1, 1, 3), name='var3/mask')]
custom_sparsity = {'var1': 0.8}
sparsities = sparse_utils.get_sparsities(
all_masks, 'random', default_sparsity, custom_sparsity)
self.assertEqual(sparsities[all_masks[0].name], 0.8)
self.assertEqual(sparsities[all_masks[1].name], default_sparsity)
self.assertEqual(sparsities[all_masks[2].name], default_sparsity)