TensorFlow图形级seed操作

＃版权所有2015 TensorFlow作者.版权所有.
＃根据Apache许可证版本2.0(“许可证”)获得许可;
＃除了符合许可证外,您不得使用此文件.
＃您可以在获得许可证副本
＃http://www.apache.org/licenses/LICENSE-2.0
＃除非适用法律要求或书面同意,软件根据许可证分发,
＃没有任何形式的明示或暗示保证或条件.
＃请参阅许可证以了解特定语言的管理权限和权限
＃许可证下的限制.
＃==============================================================================
""根据图形级seed来seed个别的操作.""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.framework import ops


DEFAULT_GRAPH_SEED = 87654321
_MAXINT32 = 2**31 - 1

def _truncate_seed(seed):
  return seed % _MAXINT32  # Truncate to fit into 32-bit integer


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.
  """
  graph_seed = ops.get_default_graph().seed
  if graph_seed is not None:
    if op_seed is None:
      # pylint: disable=protected-access
      op_seed = ops.get_default_graph()._last_id
    seeds = _truncate_seed(graph_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


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.
  """
  ops.get_default_graph().seed = seed