Categorical¶
- class paddle.distribution. Categorical ( logits, name=None ) [source]
-
Categorical distribution is a discrete probability distribution that describes the possible results of a random variable that can take on one of K possible categories, with the probability of each category separately specified.
The probability mass function (pmf) is:
\[pmf(k; p_i) = \prod_{i=1}^{k} p_i^{[x=i]}\]In the above equation:
\([x=i]\) : it evaluates to 1 if \(x==i\) , 0 otherwise.
- Parameters
-
logits (list|tuple|numpy.ndarray|Tensor) – The logits input of categorical distribution. The data type is float32 or float64.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
Examples
import paddle from paddle.distribution import Categorical paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] paddle.seed(200) # on CPU device y = paddle.rand([6]) print(y) # [0.77663314 0.90824795 0.15685187 # 0.04279523 0.34468332 0.7955718 ] cat = Categorical(x) cat2 = Categorical(y) paddle.seed(1000) # on CPU device cat.sample([2,3]) # [[0, 0, 5], # [3, 4, 5]] cat.entropy() # [1.77528] cat.kl_divergence(cat2) # [0.071952] value = paddle.to_tensor([2,1,3]) cat.probs(value) # [0.00608027 0.108298 0.269656] cat.log_prob(value) # [-5.10271 -2.22287 -1.31061]
-
sample
(
shape
)
sample¶
-
Generate samples of the specified shape.
- Parameters
-
shape (list) – Shape of the generated samples.
- Returns
-
A tensor with prepended dimensions shape.
- Return type
-
Tensor
Examples
import paddle from paddle.distribution import Categorical paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] cat = Categorical(x) paddle.seed(1000) # on CPU device cat.sample([2,3]) # [[0, 0, 5], # [3, 4, 5]]
- property batch_shape
-
Returns batch shape of distribution
- Returns
-
batch shape
- Return type
-
Sequence[int]
- property event_shape
-
Returns event shape of distribution
- Returns
-
event shape
- Return type
-
Sequence[int]
-
kl_divergence
(
other
)
[source]
kl_divergence¶
-
The KL-divergence between two Categorical distributions.
- Parameters
-
other (Categorical) – instance of Categorical. The data type is float32.
- Returns
-
kl-divergence between two Categorical distributions.
- Return type
-
Tensor
Examples
import paddle from paddle.distribution import Categorical paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] paddle.seed(200) # on CPU device y = paddle.rand([6]) print(y) # [0.77663314 0.90824795 0.15685187 # 0.04279523 0.34468332 0.7955718 ] cat = Categorical(x) cat2 = Categorical(y) cat.kl_divergence(cat2) # [0.071952]
- property mean
-
Mean of distribution
-
prob
(
value
)
prob¶
-
Probability density/mass function evaluated at value.
- Parameters
-
value (Tensor) – value which will be evaluated
-
rsample
(
shape=()
)
rsample¶
-
reparameterized sample
- property variance
-
Variance of distribution
-
entropy
(
)
entropy¶
-
Shannon entropy in nats.
- Returns
-
Shannon entropy of Categorical distribution. The data type is float32.
- Return type
-
Tensor
Examples
import paddle from paddle.distribution import Categorical paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] cat = Categorical(x) cat.entropy() # [1.77528]
-
probs
(
value
)
probs¶
-
Probabilities of the given category (
value).If
logitsis 2-D or higher dimension, the last dimension will be regarded as category, and the others represents the different distributions. At the same time, ifvlaueis 1-D Tensor,valuewill be broadcast to the same number of distributions aslogits. Ifvalueis not 1-D Tensor,valueshould have the same number distributions withlogits. That is, ``value[:-1] = logits[:-1].- Parameters
-
value (Tensor) – The input tensor represents the selected category index.
- Returns
-
probability according to the category index.
- Return type
-
Tensor
Examples
import paddle from paddle.distribution import Categorical paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] cat = Categorical(x) value = paddle.to_tensor([2,1,3]) cat.probs(value) # [0.00608027 0.108298 0.269656]
-
log_prob
(
value
)
log_prob¶
-
Log probabilities of the given category. Refer to
probsmethod.- Parameters
-
value (Tensor) – The input tensor represents the selected category index.
- Returns
-
Log probability.
- Return type
-
Tensor
Examples
import paddle from paddle.distribution import Categorical paddle.seed(100) # on CPU device x = paddle.rand([6]) print(x) # [0.5535528 0.20714243 0.01162981 # 0.51577556 0.36369765 0.2609165 ] cat = Categorical(x) value = paddle.to_tensor([2,1,3]) cat.log_prob(value) # [-5.10271 -2.22287 -1.31061]