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"""For seeding individual ops based on a graph-level seed.
"""
from tensorflow.python.framework import ops
_DEFAULT_GRAPH_SEED = 87654321
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
[`set_random_seed`](constant_op.md#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 not None:
return graph_seed, op_seed
else:
return graph_seed, ops.get_default_graph()._last_id
else:
if op_seed is not None:
return _DEFAULT_GRAPH_SEED, op_seed
else:
return None, None
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 different
# 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
|
