def testUnknownSize(self):
value = array_ops.placeholder(dtype=dtypes.float32)
sparsity = nn_impl.zero_fraction(value)
with self.cached_session() as sess:
self.assertAllClose(
0.25,
sess.run(sparsity, {value: [[0., 1.], [0.3, 2.]]}))
def testUnknownSize(self):
value = array_ops.placeholder(dtype=dtypes.float32)
sparsity = nn_impl.zero_fraction(value)
with self.cached_session() as sess:
self.assertAllClose(
0.25,
sess.run(sparsity, {value: [[0., 1.], [0.3, 2.]]}))
def add_pruning_summaries(self):
"""Adds summaries of weight sparsities and thresholds."""
with ops.name_scope(self._spec.name + '_summaries'):
summary.scalar('sparsity', self._sparsity)
summary.scalar('last_mask_update_step', self._last_update_step)
masks = get_masks()
thresholds = get_thresholds()
for mask, threshold in zip(masks, thresholds):
summary.scalar(mask.op.name + '/sparsity', nn_impl.zero_fraction(mask))
summary.scalar(threshold.op.name + '/threshold', threshold)
def add_pruning_summaries(self):
"""Adds summaries of weight sparsities and thresholds."""
with ops.name_scope(self._spec.name + '_summaries'):
summary.scalar('sparsity', self._sparsity)
summary.scalar('last_mask_update_step', self._last_update_step)
masks = get_masks()
thresholds = get_thresholds()
for mask, threshold in zip(masks, thresholds):
summary.scalar(mask.op.name + '/sparsity', nn_impl.zero_fraction(mask))
summary.scalar(threshold.op.name + '/threshold', threshold)
def get_weight_sparsity():
"""Get sparsity of the weights.
Args:
None
Returns:
A list containing the sparsity of each of the weight tensors
"""
masks = get_masks()
return [nn_impl.zero_fraction(mask) for mask in masks]
def testZeroFraction(self):
x_shape = [5, 17]
x_np = np.random.randint(0, 2, size=x_shape).astype(np.float32)
y_np = self._ZeroFraction(x_np)
with self.test_session():
x_tf = constant_op.constant(x_np)
x_tf.set_shape(x_shape)
y_tf = nn_impl.zero_fraction(x_tf)
y_tf_np = y_tf.eval()
eps = 1e-8
self.assertAllClose(y_tf_np, y_np, eps)
def testZeroFraction(self):
x_shape = [5, 17]
x_np = np.random.randint(0, 2, size=x_shape).astype(np.float32)
y_np = self._ZeroFraction(x_np)
with self.test_session():
x_tf = constant_op.constant(x_np)
x_tf.set_shape(x_shape)
y_tf = nn_impl.zero_fraction(x_tf)
y_tf_np = y_tf.eval()
eps = 1e-8
self.assertAllClose(y_tf_np, y_np, eps)
def get_weight_sparsity():
"""Get sparsity of the weights.
Args:
None
Returns:
A list containing the sparsity of each of the weight tensors
"""
masks = get_masks()
return [nn_impl.zero_fraction(mask) for mask in masks]
def add_pruning_summaries(self):
"""Adds summaries for this pruning spec.
Args: none
Returns: none
"""
with ops.name_scope(self._spec.name + '_summaries'):
summary.scalar('sparsity', self._sparsity)
summary.scalar('last_mask_update_step', self._last_update_step)
masks = get_masks()
thresholds = get_thresholds()
for mask, threshold in zip(masks, thresholds):
if not self._exists_in_do_not_prune_list(mask.name):
summary.scalar(mask.op.name + '/sparsity',
nn_impl.zero_fraction(mask))
summary.scalar(threshold.op.name + '/threshold', threshold)
def add_pruning_summaries(self):
"""Adds summaries for this pruning spec.
Args: none
Returns: none
"""
with ops.name_scope(self._spec.name + '_summaries'):
summary.scalar('sparsity', self._sparsity)
summary.scalar('last_mask_update_step', self._last_update_step)
masks = get_masks()
thresholds = get_thresholds()
for index, mask in enumerate(masks):
if not self._exists_in_do_not_prune_list(mask.name):
summary.scalar(mask.name + '/sparsity', nn_impl.zero_fraction(mask))
summary.scalar(thresholds[index].op.name + '/threshold',
thresholds[index])
def add_zero_fraction_summary(tensor, name=None, prefix=None,
print_summary=False):
"""Adds a summary for the percentage of zero values in the given tensor.
Args:
tensor: a variable or op tensor.
name: the optional name for the summary.
prefix: An optional prefix for the summary names.
print_summary: If `True`, the summary is printed to stdout when the summary
is computed.
Returns:
A scalar `Tensor` of type `string` whose contents are the serialized
`Summary` protocol buffer.
"""
name = _get_summary_name(tensor, name, prefix, 'Fraction of Zero Values')
tensor = nn.zero_fraction(tensor)
return add_scalar_summary(tensor, name, print_summary=print_summary)
def add_zero_fraction_summary(tensor, name=None, prefix=None,
print_summary=False):
"""Adds a summary for the percentage of zero values in the given tensor.
Args:
tensor: a variable or op tensor.
name: the optional name for the summary.
prefix: An optional prefix for the summary names.
print_summary: If `True`, the summary is printed to stdout when the summary
is computed.
Returns:
A scalar `Tensor` of type `string` whose contents are the serialized
`Summary` protocol buffer.
"""
name = _get_summary_name(tensor, name, prefix, 'Fraction_of_Zero_Values')
tensor = nn.zero_fraction(tensor)
return add_scalar_summary(tensor, name, print_summary=print_summary)
def testZeroFractionEmpty(self): x = np.zeros(0) y = self.evaluate(nn_impl.zero_fraction(x)) self.assertTrue(np.isnan(y))
def testZeroFractionEmpty(self):
with self.test_session():
x = np.zeros(0)
y = nn_impl.zero_fraction(x).eval()
self.assertTrue(np.isnan(y))
def testZeroFraction2_27Ones(self):
sparsity = nn_impl.zero_fraction(
array_ops.ones([int(2**27 * 1.01)], dtype=dtypes.int8))
with self.cached_session():
self.assertAllClose(0.0, sparsity.eval())
def testZeroFraction2_27Ones(self):
sparsity = nn_impl.zero_fraction(
array_ops.ones([int(2**27 * 1.01)], dtype=dtypes.int8))
self.assertAllClose(0.0, self.evaluate(sparsity))
def testZeroFraction2_27Ones(self):
sparsity = nn_impl.zero_fraction(
array_ops.ones([int(2**27 * 1.01)], dtype=dtypes.int8))
self.assertAllClose(0.0, self.evaluate(sparsity))
def testZeroFractionEmpty(self): x = np.zeros(0) y = self.evaluate(nn_impl.zero_fraction(x)) self.assertTrue(np.isnan(y))
def testZeroFractionEmpty(self):
with self.test_session():
x = np.zeros(0)
y = nn_impl.zero_fraction(x).eval()
self.assertTrue(np.isnan(y))
def testZeroFraction2_27Ones(self):
sparsity = nn_impl.zero_fraction(
array_ops.ones([int(2**27 * 1.01)], dtype=dtypes.int8))
with self.cached_session():
self.assertAllClose(0.0, sparsity.eval())
