def testEagerSeed(self):
with context.eager_mode():
# Ensure a context has been created
random_ops.random_normal([])
# Set the same seed twice and check that the values match
context.set_global_seed(42)
rnd1 = random_ops.random_normal([])
context.set_global_seed(42)
rnd2 = random_ops.random_normal([])
self.assertAllEqual(rnd1, rnd2)
def testEagerSeed(self):
with context.eager_mode():
# Ensure a context has been created
random_ops.random_normal([])
# Set the same seed twice and check that the values match
context.set_global_seed(42)
rnd1 = random_ops.random_normal([])
context.set_global_seed(42)
rnd2 = random_ops.random_normal([])
self.assertAllEqual(rnd1, rnd2)
def reset_seed():
return
s = 1
random.seed(s)
np.random.seed(s)
tf.set_random_seed(s)
random_seed.set_random_seed(s)
graph._next_id_counter = s
graph.seed = s
context.set_global_seed(s)
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.random.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
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
def set_random_seed(seed):
"""Sets the graph-level random seed for the default graph.
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. Within the
same version of tensorflow and user code, this sequence is deterministic.
However across different versions, this sequence might change. If the
code depends on particular seeds to work, specify both graph-level
and operation-level seeds explicitly.
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.compat.v1.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.compat.v1.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.compat.v1.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.compat.v1.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.compat.v1.random.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.compat.v1.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.compat.v1.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'
```
@compatibility(TF2)
'tf.compat.v1.set_random_seed' is compatible with eager mode. However,
in eager mode this API will set the global seed instead of the
graph-level seed of the default graph. In TF2 this API is changed to
[tf.random.set_seed]
(https://www.tensorflow.org/api_docs/python/tf/random/set_seed).
@end_compatibility
Args:
seed: integer.
"""
if context.executing_eagerly():
context.set_global_seed(seed)
else:
ops.get_default_graph().seed = seed
