def test_fail_on_inconsistent_shape(self):
size, shape = 1, [2, 3]
my_buffer = tensor_buffer.TensorBuffer(size, shape, name='my_buffer')
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
'Appending value of inconsistent shape.'):
my_buffer.append(tf.ones(shape=[3, 4], dtype=tf.int32))
def __init__(self, population_size, selection_probability):
"""Initialize the PrivacyLedger.
Args:
population_size: An integer (may be variable) specifying the size of the
population, i.e. size of the training data used in each epoch.
selection_probability: A float (may be variable) specifying the
probability each record is included in a sample.
Raises:
ValueError: If selection_probability is 0.
"""
self._population_size = population_size
self._selection_probability = selection_probability
if tf.executing_eagerly():
if tf.equal(selection_probability, 0):
raise ValueError('Selection probability cannot be 0.')
init_capacity = tf.cast(tf.math.ceil(1 / selection_probability),
tf.int32)
else:
if selection_probability == 0:
raise ValueError('Selection probability cannot be 0.')
init_capacity = np.int(np.ceil(1 / selection_probability))
# The query buffer stores rows corresponding to P_exponentialSumQueryEntries.
#这里的query buffer需要存储的是p_exponentialsumquery=[exponents,samples_counts, clipping bound, dimension, beta],故每一个query所需的size=4
self._query_buffer = tensor_buffer.TensorBuffer(
init_capacity, [5], tf.float32, 'query')
#弄清楚_sample_var，_sample_buffer，_sample_count的不同，后两者很好理解，主要是_sample_var是什么意思._sample_var=[exponents,population_size,selection_probability,query_count]
self._sample_var = tf.Variable(initial_value=tf.zeros([3]),
trainable=False,
name='sample')
# The sample buffer stores rows corresponding to SampleEntries.
self._sample_buffer = tensor_buffer.TensorBuffer(
init_capacity, [3], tf.float32, 'sample')
self._sample_count = tf.Variable(initial_value=0.0,
trainable=False,
name='sample_count')
self._query_count = tf.Variable(initial_value=0.0,
trainable=False,
name='query_count')
self._cs = tf.CriticalSection()
def __init__(self, population_size, selection_probability):
"""Initializes the PrivacyLedger.
Args:
population_size: An integer (may be variable) specifying the size of the
population, i.e. size of the training data used in each epoch.
selection_probability: A floating point value (may be variable) specifying
the probability each record is included in a sample.
Raises:
ValueError: If `selection_probability` is 0.
"""
self._population_size = population_size
self._selection_probability = selection_probability
if tf.executing_eagerly():
if tf.equal(selection_probability, 0):
raise ValueError('Selection probability cannot be 0.')
init_capacity = tf.cast(tf.math.ceil(1 / selection_probability),
tf.int32)
else:
if selection_probability == 0:
raise ValueError('Selection probability cannot be 0.')
init_capacity = np.int(np.ceil(1 / selection_probability))
# The query buffer stores rows corresponding to GaussianSumQueryEntries.
self._query_buffer = tensor_buffer.TensorBuffer(
init_capacity, [3], tf.float32, 'query')
self._sample_var = tf.Variable(initial_value=tf.zeros([3]),
trainable=False,
name='sample')
# The sample buffer stores rows corresponding to SampleEntries.
self._sample_buffer = tensor_buffer.TensorBuffer(
init_capacity, [3], tf.float32, 'sample')
self._sample_count = tf.Variable(initial_value=0.0,
trainable=False,
name='sample_count')
self._query_count = tf.Variable(initial_value=0.0,
trainable=False,
name='query_count')
self._cs = tf.CriticalSection()
def test_basic(self):
size, shape = 2, [2, 3]
my_buffer = tensor_buffer.TensorBuffer(size, shape, name='my_buffer')
value1 = [[1, 2, 3], [4, 5, 6]]
my_buffer.append(value1)
self.assertAllEqual(my_buffer.values.numpy(), [value1])
value2 = [[4, 5, 6], [7, 8, 9]]
my_buffer.append(value2)
self.assertAllEqual(my_buffer.values.numpy(), [value1, value2])
def test_noresize(self):
"""Test buffer does not resize if capacity is not exceeded."""
with self.cached_session() as sess:
size, shape = 2, [2, 3]
my_buffer = tensor_buffer.TensorBuffer(size, shape, name='my_buffer')
value1 = [[1, 2, 3], [4, 5, 6]]
with tf.control_dependencies([my_buffer.append(value1)]):
value2 = [[7, 8, 9], [10, 11, 12]]
with tf.control_dependencies([my_buffer.append(value2)]):
values = my_buffer.values
current_size = my_buffer.current_size
capacity = my_buffer.capacity
self.evaluate(tf.global_variables_initializer())
v, cs, cap = sess.run([values, current_size, capacity])
self.assertAllEqual(v, [value1, value2])
self.assertEqual(cs, 2)
self.assertEqual(cap, 2)
def test_resize(self):
size, shape = 2, [2, 3]
my_buffer = tensor_buffer.TensorBuffer(size, shape, name='my_buffer')
# Append three buffers. Third one should succeed after resizing.
value1 = [[1, 2, 3], [4, 5, 6]]
my_buffer.append(value1)
self.assertAllEqual(my_buffer.values.numpy(), [value1])
self.assertAllEqual(my_buffer.current_size.numpy(), 1)
self.assertAllEqual(my_buffer.capacity.numpy(), 2)
value2 = [[4, 5, 6], [7, 8, 9]]
my_buffer.append(value2)
self.assertAllEqual(my_buffer.values.numpy(), [value1, value2])
self.assertAllEqual(my_buffer.current_size.numpy(), 2)
self.assertAllEqual(my_buffer.capacity.numpy(), 2)
value3 = [[7, 8, 9], [10, 11, 12]]
my_buffer.append(value3)
self.assertAllEqual(my_buffer.values.numpy(), [value1, value2, value3])
self.assertAllEqual(my_buffer.current_size.numpy(), 3)
# Capacity should have doubled.
self.assertAllEqual(my_buffer.capacity.numpy(), 4)
def test_fail_on_scalar(self):
with self.assertRaisesRegexp(ValueError, 'Shape cannot be scalar.'):
tensor_buffer.TensorBuffer(1, ())
