mindspore.set_seed

mindspore.set_seed(seed)[source]

Set global seed.

Note

The global seed is used by numpy.random, mindspore.common.Initializer, mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution.

If global seed is not set, these packages will use their own default seed independently, numpy.random and mindspore.common.Initializer will choose a random seed, mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution will use zero.

Seed set by numpy.random.seed() only used by numpy.random, while seed set by this API will also used by numpy.random, so just set all seed by this API is recommended.

Parameters

seed (int) – The seed to be set.

Raises

Examples

>>> import numpy as np
>>> import mindspore.ops as ops
>>> from mindspore import Tensor, set_seed, Parameter
>>> from mindspore.common.initializer import initializer
>>>
>>> # Note: (1) Please make sure the code is running in PYNATIVE MODE;
>>> # (2) Because Composite-level ops need parameters to be Tensors, for below examples,
>>> # when using ops.uniform operator, minval and maxval are initialised as:
>>> minval = Tensor(1.0, ms.float32)
>>> maxval = Tensor(2.0, ms.float32)
>>>
>>> # 1. If global seed is not set, numpy.random and initializer will choose a random seed:
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A2
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W1
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W2
>>> # Rerun the program will get different results:
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A3
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A4
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W3
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W4
>>>
>>> # 2. If global seed is set, numpy.random and initializer will use it:
>>> set_seed(1234)
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A2
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W1
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W2
>>> # Rerun the program will get the same results:
>>> set_seed(1234)
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A2
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W1
>>> w1 = Parameter(initializer("uniform", [2, 2], ms.float32), name="w1") # W2
>>>
>>> # 3. If neither global seed nor op seed is set, mindspore.ops.composite.random_ops and
>>> # mindspore.nn.probability.distribution will choose a random seed:
>>> c1 = ops.uniform((1, 4), minval, maxval) # C1
>>> c2 = ops.uniform((1, 4), minval, maxval) # C2
>>> # Rerun the program will get different results:
>>> c1 = ops.uniform((1, 4), minval, maxval) # C3
>>> c2 = ops.uniform((1, 4), minval, maxval) # C4
>>>
>>> # 4. If global seed is set, but op seed is not set, mindspore.ops.composite.random_ops and
>>> # mindspore.nn.probability.distribution will calculate a seed according to global seed and
>>> # default op seed. Each call will change the default op seed, thus each call get different
>>> # results.
>>> set_seed(1234)
>>> c1 = ops.uniform((1, 4), minval, maxval) # C1
>>> c2 = ops.uniform((1, 4), minval, maxval) # C2
>>> # Rerun the program will get the same results:
>>> set_seed(1234)
>>> c1 = ops.uniform((1, 4), minval, maxval) # C1
>>> c2 = ops.uniform((1, 4), minval, maxval) # C2
>>>
>>> # 5. If both global seed and op seed are set, mindspore.ops.composite.random_ops and
>>> # mindspore.nn.probability.distribution will calculate a seed according to global seed and
>>> # op seed counter. Each call will change the op seed counter, thus each call get different
>>> # results.
>>> set_seed(1234)
>>> c1 = ops.uniform((1, 4), minval, maxval, seed=2) # C1
>>> c2 = ops.uniform((1, 4), minval, maxval, seed=2) # C2
>>> # Rerun the program will get the same results:
>>> set_seed(1234)
>>> c1 = ops.uniform((1, 4), minval, maxval, seed=2) # C1
>>> c2 = ops.uniform((1, 4), minval, maxval, seed=2) # C2
>>>
>>> # 6. If op seed is set but global seed is not set, 0 will be used as global seed. Then
>>> # mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution act as in
>>> # condition 5.
>>> c1 = ops.uniform((1, 4), minval, maxval, seed=2) # C1
>>> c2 = ops.uniform((1, 4), minval, maxval, seed=2) # C2
>>> # Rerun the program will get the same results:
>>> c1 = ops.uniform((1, 4), minval, maxval, seed=2) # C1
>>> c2 = ops.uniform((1, 4), minval, maxval, seed=2) # C2
>>>
>>> # 7. Recall set_seed() in the program will reset numpy seed and op seed counter of
>>> # mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution.
>>> set_seed(1234)
>>> np_1 = np.random.normal(0, 1, [1]).astype(np.float32) # A1
>>> c1 = ops.uniform((1, 4), minval, maxval, seed=2) # C1
>>> set_seed(1234)
>>> np_2 = np.random.normal(0, 1, [1]).astype(np.float32) # still get A1
>>> c2 = ops.uniform((1, 4), minval, maxval, seed=2) # still get C1