diff --git a/docs/api/paddle/distribution/Bernoulli_cn.rst b/docs/api/paddle/distribution/Bernoulli_cn.rst index cf87130de6e..baa827c8c7a 100644 --- a/docs/api/paddle/distribution/Bernoulli_cn.rst +++ b/docs/api/paddle/distribution/Bernoulli_cn.rst @@ -27,25 +27,7 @@ Bernoulli 代码示例 :::::::::::: -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - # init `probs` with a float - rv = Bernoulli(probs=0.3) - - print(rv.mean) - # Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, - # 0.30000001) - - print(rv.variance) - # Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, - # 0.21000001) - - print(rv.entropy()) - # Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, - # 0.61086434) +COPY-FROM: paddle.distribution.Bernoulli 属性 ::::::::: @@ -86,26 +68,7 @@ Tensor，样本，其维度为 :math:`\text{sample shape} + \text{batch shape} + **代码示例** -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - rv = Bernoulli(paddle.full((), 0.3)) - print(rv.sample([100]).shape) - # [100] - - rv = Bernoulli(paddle.to_tensor(0.3)) - print(rv.sample([100]).shape) - # [100] - - rv = Bernoulli(paddle.to_tensor([0.3, 0.5])) - print(rv.sample([100]).shape) - # [100, 2] - - rv = Bernoulli(paddle.to_tensor([0.3, 0.5])) - print(rv.sample([100, 2]).shape) - # [100, 2, 2] +COPY-FROM: paddle.distribution.Bernoulli.sample rsample(shape, temperature=1.0) ''''''''' @@ -132,46 +95,7 @@ Tensor，样本，其维度为 :math:`\text{sample shape} + \text{batch shape} + **代码示例** -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - paddle.seed(2023) - - rv = Bernoulli(paddle.full((), 0.3)) - print(rv.sample([100]).shape) - # [100] - - rv = Bernoulli(0.3) - print(rv.rsample([100]).shape) - # [100] - - rv = Bernoulli(paddle.to_tensor([0.3, 0.5])) - print(rv.rsample([100]).shape) - # [100, 2] - - rv = Bernoulli(paddle.to_tensor([0.3, 0.5])) - print(rv.rsample([100, 2]).shape) - # [100, 2, 2] - - # `rsample` has to be followed by a `sigmoid` - rv = Bernoulli(0.3) - rsample = rv.rsample([3]) - rsample_sigmoid = paddle.nn.functional.sigmoid(rsample) - print(rsample, rsample_sigmoid) - # Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True, - # [-2.37732768, -0.61203325, -3.18344760]) Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True, - # [0.08491799, 0.35159552, 0.03979339]) - - # The smaller the `temperature`, the distribution of `rsample` closer to `sample`, with `probs` of 0.3. - print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=1.0)).sum()) - # Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, - # 361.06829834) - - print(paddle.nn.functional.sigmoid(rv.rsample([1000, ], temperature=0.1)).sum()) - # Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, - # 288.66418457) +COPY-FROM: paddle.distribution.Bernoulli.rsample cdf(value) ''''''''' @@ -197,15 +121,7 @@ Tensor， ``value`` 的累积分布函数。 **代码示例** -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - rv = Bernoulli(0.3) - print(rv.cdf(paddle.to_tensor([1.0]))) - # Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, - # [1.]) +COPY-FROM: paddle.distribution.Bernoulli.cdf log_prob(value) ''''''''' @@ -222,15 +138,7 @@ Tensor， ``value`` 的对数概率密度函数。 **代码示例** -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - rv = Bernoulli(0.3) - print(rv.log_prob(paddle.to_tensor([1.0]))) - # Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, - # [-1.20397282]) +COPY-FROM: paddle.distribution.Bernoulli.log_prob prob(value) ''''''''' @@ -255,15 +163,7 @@ Tensor， ``value`` 的概率密度函数。 **代码示例** -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - rv = Bernoulli(0.3) - print(rv.prob(paddle.to_tensor([1.0]))) - # Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, - # [0.29999998]) +COPY-FROM: paddle.distribution.Bernoulli.prob entropy() ''''''''' @@ -282,15 +182,7 @@ Tensor，伯努利分布的信息熵。 **代码示例** -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - rv = Bernoulli(0.3) - print(rv.entropy()) - # Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, - # 0.61086434) +COPY-FROM: paddle.distribution.Bernoulli.entropy kl_divergence(other) ''''''''' @@ -313,14 +205,4 @@ Tensor，两个伯努利分布之间的 KL 散度。 **代码示例** -.. code-block:: python - - import paddle - from paddle.distribution import Bernoulli - - rv = Bernoulli(0.3) - rv_other = Bernoulli(0.7) - - print(rv.kl_divergence(rv_other)) - # Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, - # 0.33891910) +COPY-FROM: paddle.distribution.Bernoulli.kl_divergence diff --git a/docs/api/paddle/distribution/Categorical_cn.rst b/docs/api/paddle/distribution/Categorical_cn.rst index 20b831eb2f6..97df2ee6b5f 100644 --- a/docs/api/paddle/distribution/Categorical_cn.rst +++ b/docs/api/paddle/distribution/Categorical_cn.rst @@ -30,43 +30,7 @@ Categorical 代码示例 :::::::::::: -.. code-block:: python - - 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] +COPY-FROM: paddle.distribution.Categorical 方法 @@ -87,23 +51,7 @@ sample(shape) **代码示例** -.. code-block:: python - - 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]] +COPY-FROM: paddle.distribution.Categorical.sample kl_divergence(other) ''''''''' @@ -120,28 +68,7 @@ kl_divergence(other) **代码示例** -.. code-block:: python - - 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] +COPY-FROM: paddle.distribution.Categorical.kl_divergence entropy() ''''''''' @@ -154,21 +81,7 @@ entropy() **代码示例** -.. code-block:: python - - 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 +COPY-FROM: paddle.distribution.Categorical.entropy probs(value) ''''''''' @@ -186,22 +99,7 @@ probs(value) 给定类别下标的概率。 -.. code-block:: python - - 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] +COPY-FROM: paddle.distribution.Categorical.probs log_prob(value) ''''''''' @@ -216,19 +114,4 @@ log_prob(value) 对数概率。 -.. code-block:: python - - 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] +COPY-FROM: paddle.distribution.Categorical.log_prob