tf.random.set_seed

Sets the global random seed.

tf.random.set_seed(
    seed
)

Used in the notebooks

Used in the guide Used in the tutorials

Operations that rely on a random seed actually derive it from two seeds: the global and operation-level seeds. This sets the global seed.

Its interactions with operation-level seeds is as follows:

  1. If neither the global seed nor the operation seed is set: A randomly picked seed is used for this op.
  2. If the global seed is set, but the operation seed is not: The system deterministically picks an operation seed in conjunction with the global 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 global and operation-level seeds explicitly.
  3. If the operation seed is set, but the global seed is not set: A default global seed and the specified operation seed are used to determine the random sequence.
  4. If both the global 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:

If neither the global seed nor the operation seed is set, we get different results for every call to the random op and every re-run of the program:

print(tf.random.uniform([1]))  # generates 'A1'
print(tf.random.uniform([1]))  # generates 'A2'

(now close the program and run it again)

print(tf.random.uniform([1]))  # generates 'A3'
print(tf.random.uniform([1]))  # generates 'A4'

If the global seed is set but the operation seed is not set, we get different results for every call to the random op, but the same sequence for every re-run of the program:

tf.random.set_seed(1234)
print(tf.random.uniform([1]))  # generates 'A1'
print(tf.random.uniform([1]))  # generates 'A2'

(now close the program and run it again)

tf.random.set_seed(1234)
print(tf.random.uniform([1]))  # generates 'A1'
print(tf.random.uniform([1]))  # generates 'A2'

The reason we get 'A2' instead 'A1' on the second call of tf.random.uniform above is because the second call uses a different operation seed.

Note that tf.function acts like a re-run of a program in this case. When the global seed is set but operation seeds are not set, the sequence of random numbers are the same for each tf.function. For example:

tf.random.set_seed(1234)

@tf.function
def f():
  a = tf.random.uniform([1])
  b = tf.random.uniform([1])
  return a, b

@tf.function
def g():
  a = tf.random.uniform([1])
  b = tf.random.uniform([1])
  return a, b

print(f())  # prints '(A1, A2)'
print(g())  # prints '(A1, A2)'

If the operation seed is set, we get different results for every call to the random op, but the same sequence for every re-run of the program:

print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'

(now close the program and run it again)

print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'

The reason we get 'A2' instead 'A1' on the second call of tf.random.uniform above is because the same tf.random.uniform kernel (i.e. internal representation) is used by TensorFlow for all calls of it with the same arguments, and the kernel maintains an internal counter which is incremented every time it is executed, generating different results.

Calling tf.random.set_seed will reset any such counters:

tf.random.set_seed(1234)
print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'
tf.random.set_seed(1234)
print(tf.random.uniform([1], seed=1))  # generates 'A1'
print(tf.random.uniform([1], seed=1))  # generates 'A2'

When multiple identical random ops are wrapped in a tf.function, their behaviors change because the ops no long share the same counter. For example:

@tf.function
def foo():
  a = tf.random.uniform([1], seed=1)
  b = tf.random.uniform([1], seed=1)
  return a, b
print(foo())  # prints '(A1, A1)'
print(foo())  # prints '(A2, A2)'

@tf.function
def bar():
  a = tf.random.uniform([1])
  b = tf.random.uniform([1])
  return a, b
print(bar())  # prints '(A1, A2)'
print(bar())  # prints '(A3, A4)'

The second call of foo returns '(A2, A2)' instead of '(A1, A1)' because tf.random.uniform maintains an internal counter. If you want foo to return '(A1, A1)' every time, use the stateless random ops such as tf.random.stateless_uniform. Also see tf.random.experimental.Generator for a new set of stateful random ops that use external variables to manage their states.

seed integer.