def testRandomSeed(self):
zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero')
one_t = constant_op.constant(1, dtype=dtypes.int64, name='one')
intmax_t = constant_op.constant(2**31 - 1,
dtype=dtypes.int64,
name='intmax')
test_cases = [
# Each test case is a tuple with input to get_seed:
# (input_graph_seed, input_op_seed)
# and output from get_seed:
# (output_graph_seed, output_op_seed)
((None, None), (0, 0)),
((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)),
((1, 1), (1, 1)),
((0, 0), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output
((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either
((0, 2**31 - 1), (0,
2**31 - 1)), # Wrapping for the other argument
# Once more, with tensor-valued arguments
((None, one_t), (random_seed.DEFAULT_GRAPH_SEED, 1)),
((1, one_t), (1, 1)),
((0, zero_t), (0,
2**31 - 1)), # Avoid nondeterministic (0, 0) output
((2**31 - 1, zero_t), (0,
2**31 - 1)), # Don't wrap to (0, 0) either
((0, intmax_t), (0, 2**31 - 1)), # Wrapping for the other argument
]
for tc in test_cases:
tinput, toutput = tc[0], tc[1]
random_seed.set_random_seed(tinput[0])
g_seed, op_seed = data_random_seed.get_seed(tinput[1])
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
msg = 'test_case = {0}, got {1}, want {2}'.format(
tinput, (g_seed, op_seed), toutput)
self.assertEqual((g_seed, op_seed), toutput, msg=msg)
random_seed.set_random_seed(None)
if context.in_graph_mode():
random_seed.set_random_seed(1)
tinput = (1, None)
toutput = (1, ops.get_default_graph()._last_id) # pylint: disable=protected-access
random_seed.set_random_seed(tinput[0])
g_seed, op_seed = data_random_seed.get_seed(tinput[1])
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
msg = 'test_case = {0}, got {1}, want {2}'.format(
1, (g_seed, op_seed), toutput)
self.assertEqual((g_seed, op_seed), toutput, msg=msg)
random_seed.set_random_seed(None)
def __init__(self, seed=None):
"""A `Dataset` of pseudorandom values."""
self._seed, self._seed2 = random_seed.get_seed(seed)
variant_tensor = gen_experimental_dataset_ops.experimental_random_dataset(
seed=self._seed,
seed2=self._seed2,
**dataset_ops.flat_structure(self))
super(RandomDatasetV2, self).__init__(variant_tensor)
def testRandomSeed(self):
zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero')
one_t = constant_op.constant(1, dtype=dtypes.int64, name='one')
intmax_t = constant_op.constant(
2**31 - 1, dtype=dtypes.int64, name='intmax')
test_cases = [
# Each test case is a tuple with input to get_seed:
# (input_graph_seed, input_op_seed)
# and output from get_seed:
# (output_graph_seed, output_op_seed)
((None, None), (0, 0)),
((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)),
((1, 1), (1, 1)),
((0, 0), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output
((2**31 - 1, 0), (0, 2**31 - 1)), # Don't wrap to (0, 0) either
((0, 2**31 - 1), (0, 2**31 - 1)), # Wrapping for the other argument
# Once more, with tensor-valued arguments
((None, one_t), (random_seed.DEFAULT_GRAPH_SEED, 1)),
((1, one_t), (1, 1)),
((0, zero_t), (0, 2**31 - 1)), # Avoid nondeterministic (0, 0) output
((2**31 - 1, zero_t), (0, 2**31 - 1)), # Don't wrap to (0, 0) either
((0, intmax_t), (0, 2**31 - 1)), # Wrapping for the other argument
]
for tc in test_cases:
tinput, toutput = tc[0], tc[1]
random_seed.set_random_seed(tinput[0])
g_seed, op_seed = data_random_seed.get_seed(tinput[1])
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
msg = 'test_case = {0}, got {1}, want {2}'.format(
tinput, (g_seed, op_seed), toutput)
self.assertEqual((g_seed, op_seed), toutput, msg=msg)
random_seed.set_random_seed(None)
if not context.executing_eagerly():
random_seed.set_random_seed(1)
tinput = (1, None)
toutput = (1, ops.get_default_graph()._last_id) # pylint: disable=protected-access
random_seed.set_random_seed(tinput[0])
g_seed, op_seed = data_random_seed.get_seed(tinput[1])
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
msg = 'test_case = {0}, got {1}, want {2}'.format(1, (g_seed, op_seed),
toutput)
self.assertEqual((g_seed, op_seed), toutput, msg=msg)
random_seed.set_random_seed(None)
def _checkEqual(self, tinput, toutput):
random_seed.set_random_seed(tinput[0])
g_seed, op_seed = data_random_seed.get_seed(tinput[1])
g_seed = self.evaluate(g_seed)
op_seed = self.evaluate(op_seed)
msg = "test_case = {0}, got {1}, want {2}".format(
tinput, (g_seed, op_seed), toutput)
self.assertEqual((g_seed, op_seed), toutput, msg=msg)
def __init__(self, seed=None):
"""A `Dataset` of pseudorandom values."""
self._seed, self._seed2 = random_seed.get_seed(seed)
if compat.forward_compatible(2019, 8, 3):
variant_tensor = gen_experimental_dataset_ops.random_dataset(
seed=self._seed, seed2=self._seed2, **self._flat_structure)
else:
variant_tensor = gen_experimental_dataset_ops.experimental_random_dataset(
seed=self._seed, seed2=self._seed2, **self._flat_structure)
super(RandomDatasetV2, self).__init__(variant_tensor)
def __init__(self, input_dataset, buffer_size, count=None, seed=None):
super(_ShuffleAndRepeatDataset, self).__init__(input_dataset)
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=dtypes.int64, name="buffer_size")
if count is None:
self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
else:
self._count = ops.convert_to_tensor(
count, dtype=dtypes.int64, name="count")
self._seed, self._seed2 = random_seed.get_seed(seed)
def __init__(self, input_dataset, buffer_size, count=None, seed=None):
super(_ShuffleAndRepeatDataset, self).__init__(input_dataset)
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=dtypes.int64, name="buffer_size")
if count is None:
self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
else:
self._count = ops.convert_to_tensor(
count, dtype=dtypes.int64, name="count")
self._seed, self._seed2 = random_seed.get_seed(seed)
def __init__(self, input_dataset, buffer_size, count=None, seed=None):
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=dtypes.int64, name="buffer_size")
if count is None:
self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
else:
self._count = ops.convert_to_tensor(
count, dtype=dtypes.int64, name="count")
self._seed, self._seed2 = random_seed.get_seed(seed)
variant_tensor = gen_dataset_ops.shuffle_and_repeat_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
**dataset_ops.flat_structure(self))
super(_ShuffleAndRepeatDataset, self).__init__(input_dataset,
variant_tensor)
def __init__(self, input_dataset, buffer_size, count=None, seed=None):
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=dtypes.int64, name="buffer_size")
if count is None:
self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
else:
self._count = ops.convert_to_tensor(
count, dtype=dtypes.int64, name="count")
self._seed, self._seed2 = random_seed.get_seed(seed)
variant_tensor = gen_dataset_ops.shuffle_and_repeat_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
buffer_size=self._buffer_size,
count=self._count,
seed=self._seed,
seed2=self._seed2,
**dataset_ops.flat_structure(self))
super(_ShuffleAndRepeatDataset, self).__init__(input_dataset,
variant_tensor)
def __init__(self,
input_dataset,
buffer_size,
seed=None,
reshuffle_each_iteration=None):
"""Randomly shuffles the elements of this dataset."""
self._input_dataset = input_dataset
self._buffer_size = ops.convert_to_tensor(
buffer_size, dtype=int64, name="buffer_size")
self._seed, self._seed2 = random_seed.get_seed(seed)
if reshuffle_each_iteration is None:
reshuffle_each_iteration = True
self._reshuffle_each_iteration = reshuffle_each_iteration
variant_tensor = gen_dataset_ops.shuffle_dataset(
input_dataset._variant_tensor, # pylint: disable=protected-access
buffer_size=self._buffer_size,
seed=self._seed,
seed2=self._seed2,
reshuffle_each_iteration=self._reshuffle_each_iteration,
**self._flat_structure)
super().__init__(input_dataset, variant_tensor)
def __init__(self, seed=None):
"""A `Dataset` of pseudorandom values."""
self._seed, self._seed2 = random_seed.get_seed(seed)
variant_tensor = gen_experimental_dataset_ops.experimental_random_dataset(
seed=self._seed, seed2=self._seed2, **dataset_ops.flat_structure(self))
super(RandomDatasetV2, self).__init__(variant_tensor)
def __init__(self, seed=None):
"""A `Dataset` of pseudorandom values."""
super(RandomDataset, self).__init__()
self._seed, self._seed2 = random_seed.get_seed(seed)
def __init__(self, seed=None): """A `Dataset` of pseudorandom values.""" super(RandomDatasetV2, self).__init__() self._seed, self._seed2 = random_seed.get_seed(seed)
