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mindspore.ops.RandomCategorical

class mindspore.ops.RandomCategorical(dtype=mstype.int64)

Generates random samples from a given categorical distribution tensor.

Parameters

dtype (mindspore.dtype) – The type of output. Its value must be one of mstype.int16, mstype.int32 and mstype.int64. Default: mstype.int64.

Inputs:

• logits (Tensor) - The input tensor. 2-D Tensor with shape $(batch\mathrm{\_}size,num\mathrm{\_}classes)$.

• num_sample (int) - Number of sample to be drawn. Only constant values is allowed.

• seed (int) - Random seed. Default: 0. Only constant values is allowed.

Outputs:

• output (Tensor) - The output Tensor with shape $(batc{h}_{s}ize,nu{m}_{s}amples)$.

Raises

• TypeError – If dtype is not one of the following: mstype.int16, mstype.int32, mstype.int64.

• TypeError – If logits is not a Tensor.

• TypeError – If neither num_sample nor seed is an int.

Supported Platforms:

Ascend GPU CPU

Examples

>>> class Net(nn.Cell):
...   def __init__(self, num_sample):
...     super(Net, self).__init__()
...     self.random_categorical = ops.RandomCategorical(mindspore.int64)
...     self.num_sample = num_sample
...   def construct(self, logits, seed=0):
...     return self.random_categorical(logits, self.num_sample, seed)
...
>>> x = np.random.random((10, 5)).astype(np.float32)
>>> net = Net(8)
>>> output = net(Tensor(x))
>>> result = output.shape
>>> print(result)
(10, 8)