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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""For seeding individual ops based on a graph-level seed.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
DEFAULT_GRAPH_SEED = 87654321
_MAXINT32 = 2**31 - 1
def _truncate_seed(seed):
return seed % _MAXINT32 # Truncate to fit into 32-bit integer
@tf_export('random.get_seed', 'get_seed')
@deprecation.deprecated_endpoints('get_seed')
def get_seed(op_seed):
"""Returns the local seeds an operation should use given an op-specific seed.
Given operation-specific seed, `op_seed`, this helper function returns two
seeds derived from graph-level and op-level seeds. Many random operations
internally use the two seeds to allow user to change the seed globally for a
graph, or for only specific operations.
For details on how the graph-level seed interacts with op seeds, see
`tf.set_random_seed`.
Args:
op_seed: integer.
Returns:
A tuple of two integers that should be used for the local seed of this
operation.
"""
eager = context.executing_eagerly()
if eager:
global_seed = context.global_seed()
else:
global_seed = ops.get_default_graph().seed
if global_seed is not None:
if op_seed is None:
# pylint: disable=protected-access
if eager:
op_seed = context.internal_operation_seed()
else:
op_seed = ops.get_default_graph()._last_id
seeds = _truncate_seed(global_seed), _truncate_seed(op_seed)
else:
if op_seed is not None:
seeds = DEFAULT_GRAPH_SEED, _truncate_seed(op_seed)
else:
seeds = None, None
# Avoid (0, 0) as the C++ ops interpret it as nondeterminism, which would
# be unexpected since Python docs say nondeterminism is (None, None).
if seeds == (0, 0):
return (0, _MAXINT32)
return seeds
@tf_export('random.set_random_seed', 'set_random_seed')
def set_random_seed(seed):
"""Sets the graph-level random seed.
Operations that rely on a random seed actually derive it from two seeds:
the graph-level and operation-level seeds. This sets the graph-level seed.
Its interactions with operation-level seeds is as follows:
1. If neither the graph-level nor the operation seed is set:
A random seed is used for this op.
2. If the graph-level seed is set, but the operation seed is not:
The system deterministically picks an operation seed in conjunction
with the graph-level seed so that it gets a unique random sequence.
3. If the graph-level seed is not set, but the operation seed is set:
A default graph-level seed and the specified operation seed are used to
determine the random sequence.
4. If both the graph-level and the operation seed are set:
Both seeds are used in conjunction to determine the random sequence.
To illustrate the user-visible effects, consider these examples:
To generate different sequences across sessions, set neither
graph-level nor op-level seeds:
```python
a = tf.random_uniform([1])
b = tf.random_normal([1])
print("Session 1")
with tf.Session() as sess1:
print(sess1.run(a)) # generates 'A1'
print(sess1.run(a)) # generates 'A2'
print(sess1.run(b)) # generates 'B1'
print(sess1.run(b)) # generates 'B2'
print("Session 2")
with tf.Session() as sess2:
print(sess2.run(a)) # generates 'A3'
print(sess2.run(a)) # generates 'A4'
print(sess2.run(b)) # generates 'B3'
print(sess2.run(b)) # generates 'B4'
```
To generate the same repeatable sequence for an op across sessions, set the
seed for the op:
```python
a = tf.random_uniform([1], seed=1)
b = tf.random_normal([1])
# Repeatedly running this block with the same graph will generate the same
# sequence of values for 'a', but different sequences of values for 'b'.
print("Session 1")
with tf.Session() as sess1:
print(sess1.run(a)) # generates 'A1'
print(sess1.run(a)) # generates 'A2'
print(sess1.run(b)) # generates 'B1'
print(sess1.run(b)) # generates 'B2'
print("Session 2")
with tf.Session() as sess2:
print(sess2.run(a)) # generates 'A1'
print(sess2.run(a)) # generates 'A2'
print(sess2.run(b)) # generates 'B3'
print(sess2.run(b)) # generates 'B4'
```
To make the random sequences generated by all ops be repeatable across
sessions, set a graph-level seed:
```python
tf.set_random_seed(1234)
a = tf.random_uniform([1])
b = tf.random_normal([1])
# Repeatedly running this block with the same graph will generate the same
# sequences of 'a' and 'b'.
print("Session 1")
with tf.Session() as sess1:
print(sess1.run(a)) # generates 'A1'
print(sess1.run(a)) # generates 'A2'
print(sess1.run(b)) # generates 'B1'
print(sess1.run(b)) # generates 'B2'
print("Session 2")
with tf.Session() as sess2:
print(sess2.run(a)) # generates 'A1'
print(sess2.run(a)) # generates 'A2'
print(sess2.run(b)) # generates 'B1'
print(sess2.run(b)) # generates 'B2'
```
Args:
seed: integer.
"""
if context.executing_eagerly():
context.set_global_seed(seed)
else:
ops.get_default_graph().seed = seed
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