359 lines
12 KiB
Python
359 lines
12 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""For seeding individual ops based on a graph-level seed.
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"""
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import weakref
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from tensorflow.python.eager import context
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from tensorflow.python.framework import config
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from tensorflow.python.framework import ops
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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import tf_export
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DEFAULT_GRAPH_SEED = 87654321
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_MAXINT32 = 2**31 - 1
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_graph_to_seed_dict = weakref.WeakKeyDictionary()
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def _truncate_seed(seed):
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return seed % _MAXINT32 # Truncate to fit into 32-bit integer
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@tf_export(v1=['random.get_seed', 'get_seed'])
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@deprecation.deprecated_endpoints('get_seed')
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def get_seed(op_seed):
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"""Returns the local seeds an operation should use given an op-specific seed.
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Given operation-specific seed, `op_seed`, this helper function returns two
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seeds derived from graph-level and op-level seeds. Many random operations
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internally use the two seeds to allow user to change the seed globally for a
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graph, or for only specific operations.
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For details on how the graph-level seed interacts with op seeds, see
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`tf.compat.v1.random.set_random_seed`.
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Args:
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op_seed: integer.
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Returns:
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A tuple of two integers that should be used for the local seed of this
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operation.
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"""
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eager = context.executing_eagerly()
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if eager:
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global_seed = context.global_seed()
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else:
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global_seed = ops.get_default_graph().seed
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if global_seed is not None:
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if op_seed is None:
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# pylint: disable=protected-access
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if hasattr(ops.get_default_graph(), '_seed_used'):
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ops.get_default_graph()._seed_used = True
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if eager:
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op_seed = context.internal_operation_seed()
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else:
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op_seed = _graph_to_seed_dict.setdefault(ops.get_default_graph(), 0)
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_graph_to_seed_dict[ops.get_default_graph()] += 1
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seeds = _truncate_seed(global_seed), _truncate_seed(op_seed)
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else:
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if op_seed is not None:
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seeds = DEFAULT_GRAPH_SEED, _truncate_seed(op_seed)
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else:
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seeds = None, None
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if seeds == (None, None) and config.is_op_determinism_enabled():
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raise RuntimeError( # pylint: disable=g-doc-exception
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'Random ops require a seed to be set when determinism is enabled. '
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'Please set a seed before running the op, e.g. by calling '
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'tf.random.set_seed(1).')
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# Avoid (0, 0) as the C++ ops interpret it as nondeterminism, which would
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# be unexpected since Python docs say nondeterminism is (None, None).
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if seeds == (0, 0):
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return (0, _MAXINT32)
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return seeds
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@tf_export(v1=['random.set_random_seed', 'set_random_seed'])
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def set_random_seed(seed):
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"""Sets the graph-level random seed for the default graph.
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Operations that rely on a random seed actually derive it from two seeds:
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the graph-level and operation-level seeds. This sets the graph-level seed.
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Its interactions with operation-level seeds is as follows:
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1. If neither the graph-level nor the operation seed is set:
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A random seed is used for this op.
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2. If the graph-level seed is set, but the operation seed is not:
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The system deterministically picks an operation seed in conjunction with
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the graph-level seed so that it gets a unique random sequence. Within the
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same version of tensorflow and user code, this sequence is deterministic.
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However across different versions, this sequence might change. If the
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code depends on particular seeds to work, specify both graph-level
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and operation-level seeds explicitly.
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3. If the graph-level seed is not set, but the operation seed is set:
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A default graph-level seed and the specified operation seed are used to
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determine the random sequence.
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4. If both the graph-level and the operation seed are set:
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Both seeds are used in conjunction to determine the random sequence.
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To illustrate the user-visible effects, consider these examples:
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To generate different sequences across sessions, set neither
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graph-level nor op-level seeds:
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```python
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a = tf.random.uniform([1])
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b = tf.random.normal([1])
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print("Session 1")
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with tf.compat.v1.Session() as sess1:
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print(sess1.run(a)) # generates 'A1'
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print(sess1.run(a)) # generates 'A2'
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print(sess1.run(b)) # generates 'B1'
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print(sess1.run(b)) # generates 'B2'
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print("Session 2")
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with tf.compat.v1.Session() as sess2:
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print(sess2.run(a)) # generates 'A3'
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print(sess2.run(a)) # generates 'A4'
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print(sess2.run(b)) # generates 'B3'
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print(sess2.run(b)) # generates 'B4'
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```
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To generate the same repeatable sequence for an op across sessions, set the
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seed for the op:
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```python
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a = tf.random.uniform([1], seed=1)
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b = tf.random.normal([1])
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# Repeatedly running this block with the same graph will generate the same
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# sequence of values for 'a', but different sequences of values for 'b'.
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print("Session 1")
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with tf.compat.v1.Session() as sess1:
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print(sess1.run(a)) # generates 'A1'
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print(sess1.run(a)) # generates 'A2'
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print(sess1.run(b)) # generates 'B1'
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print(sess1.run(b)) # generates 'B2'
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print("Session 2")
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with tf.compat.v1.Session() as sess2:
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print(sess2.run(a)) # generates 'A1'
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print(sess2.run(a)) # generates 'A2'
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print(sess2.run(b)) # generates 'B3'
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print(sess2.run(b)) # generates 'B4'
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```
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To make the random sequences generated by all ops be repeatable across
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sessions, set a graph-level seed:
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```python
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tf.compat.v1.random.set_random_seed(1234)
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a = tf.random.uniform([1])
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b = tf.random.normal([1])
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# Repeatedly running this block with the same graph will generate the same
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# sequences of 'a' and 'b'.
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print("Session 1")
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with tf.compat.v1.Session() as sess1:
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print(sess1.run(a)) # generates 'A1'
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print(sess1.run(a)) # generates 'A2'
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print(sess1.run(b)) # generates 'B1'
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print(sess1.run(b)) # generates 'B2'
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print("Session 2")
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with tf.compat.v1.Session() as sess2:
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print(sess2.run(a)) # generates 'A1'
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print(sess2.run(a)) # generates 'A2'
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print(sess2.run(b)) # generates 'B1'
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print(sess2.run(b)) # generates 'B2'
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```
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@compatibility(TF2)
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'tf.compat.v1.set_random_seed' is compatible with eager mode. However,
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in eager mode this API will set the global seed instead of the
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graph-level seed of the default graph. In TF2 this API is changed to
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[tf.random.set_seed]
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(https://www.tensorflow.org/api_docs/python/tf/random/set_seed).
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@end_compatibility
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Args:
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seed: integer.
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"""
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if context.executing_eagerly():
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context.set_global_seed(seed)
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else:
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ops.get_default_graph().seed = seed
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@tf_export('random.set_seed', v1=[])
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def set_seed(seed):
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"""Sets the global random seed.
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Operations that rely on a random seed actually derive it from two seeds:
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the global and operation-level seeds. This sets the global seed.
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Its interactions with operation-level seeds is as follows:
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1. If neither the global seed nor the operation seed is set: A randomly
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picked seed is used for this op.
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2. If the global seed is set, but the operation seed is not:
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The system deterministically picks an operation seed in conjunction with
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the global seed so that it gets a unique random sequence. Within the
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same version of tensorflow and user code, this sequence is deterministic.
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However across different versions, this sequence might change. If the
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code depends on particular seeds to work, specify both global
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and operation-level seeds explicitly.
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3. If the operation seed is set, but the global seed is not set:
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A default global seed and the specified operation seed are used to
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determine the random sequence.
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4. If both the global and the operation seed are set:
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Both seeds are used in conjunction to determine the random sequence.
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To illustrate the user-visible effects, consider these examples:
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If neither the global seed nor the operation seed is set, we get different
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results for every call to the random op and every re-run of the program:
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```python
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print(tf.random.uniform([1])) # generates 'A1'
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print(tf.random.uniform([1])) # generates 'A2'
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```
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(now close the program and run it again)
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```python
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print(tf.random.uniform([1])) # generates 'A3'
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print(tf.random.uniform([1])) # generates 'A4'
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```
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If the global seed is set but the operation seed is not set, we get different
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results for every call to the random op, but the same sequence for every
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re-run of the program:
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```python
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tf.random.set_seed(1234)
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print(tf.random.uniform([1])) # generates 'A1'
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print(tf.random.uniform([1])) # generates 'A2'
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```
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(now close the program and run it again)
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```python
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tf.random.set_seed(1234)
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print(tf.random.uniform([1])) # generates 'A1'
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print(tf.random.uniform([1])) # generates 'A2'
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```
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The reason we get 'A2' instead 'A1' on the second call of `tf.random.uniform`
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above is because the second call uses a different operation seed.
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Note that `tf.function` acts like a re-run of a program in this case. When
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the global seed is set but operation seeds are not set, the sequence of random
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numbers are the same for each `tf.function`. For example:
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```python
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tf.random.set_seed(1234)
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@tf.function
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def f():
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a = tf.random.uniform([1])
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b = tf.random.uniform([1])
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return a, b
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@tf.function
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def g():
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a = tf.random.uniform([1])
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b = tf.random.uniform([1])
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return a, b
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print(f()) # prints '(A1, A2)'
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print(g()) # prints '(A1, A2)'
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```
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If the operation seed is set, we get different results for every call to the
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random op, but the same sequence for every re-run of the program:
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```python
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print(tf.random.uniform([1], seed=1)) # generates 'A1'
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print(tf.random.uniform([1], seed=1)) # generates 'A2'
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```
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(now close the program and run it again)
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```python
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print(tf.random.uniform([1], seed=1)) # generates 'A1'
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print(tf.random.uniform([1], seed=1)) # generates 'A2'
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```
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The reason we get 'A2' instead 'A1' on the second call of `tf.random.uniform`
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above is because the same `tf.random.uniform` kernel (i.e. internal
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representation) is used by TensorFlow for all calls of it with the same
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arguments, and the kernel maintains an internal counter which is incremented
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every time it is executed, generating different results.
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Calling `tf.random.set_seed` will reset any such counters:
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```python
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tf.random.set_seed(1234)
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print(tf.random.uniform([1], seed=1)) # generates 'A1'
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print(tf.random.uniform([1], seed=1)) # generates 'A2'
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tf.random.set_seed(1234)
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print(tf.random.uniform([1], seed=1)) # generates 'A1'
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print(tf.random.uniform([1], seed=1)) # generates 'A2'
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```
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When multiple identical random ops are wrapped in a `tf.function`, their
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behaviors change because the ops no long share the same counter. For example:
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```python
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@tf.function
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def foo():
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a = tf.random.uniform([1], seed=1)
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b = tf.random.uniform([1], seed=1)
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return a, b
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print(foo()) # prints '(A1, A1)'
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print(foo()) # prints '(A2, A2)'
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@tf.function
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def bar():
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a = tf.random.uniform([1])
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b = tf.random.uniform([1])
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return a, b
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print(bar()) # prints '(A1, A2)'
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print(bar()) # prints '(A3, A4)'
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```
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The second call of `foo` returns '(A2, A2)' instead of '(A1, A1)' because
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`tf.random.uniform` maintains an internal counter. If you want `foo` to return
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'(A1, A1)' every time, use the stateless random ops such as
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`tf.random.stateless_uniform`. Also see `tf.random.experimental.Generator` for
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a new set of stateful random ops that use external variables to manage their
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states.
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Args:
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seed: integer.
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"""
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set_random_seed(seed)
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