probs = policy_network(state) # Note that this is equivalent to what used to be called multinomial m = Categorical(probs) action = m.sample() next_state, reward = env.step(action) loss = -m.log_prob(action) * reward loss.backward() Pathwise derivative 实现这些随机/策略梯度的另一种方...
🐛 Bug When torch.distributions.Categorical is initialized with probs, the implementation normalizes it even if it is already normalized. However, if we give normalized values to probs, this normalization leads to incorrect gradients. Thi...
实现的时候,会先从网络输出构造一个分布,然后从分布中采样一个action,将action作用于环境,然后使用log_prob()函数来构建一个损失函数,代码如下(PyTorch官方提供): probs = policy_network(state) # Note that this is equivalent to what used to be called multinomial m = Categorical(probs) action = m.sam...
实现的时候,会先从网络输出构造一个分布,然后从分布中采样一个action,将action作用于环境,然后使用log_prob()函数来构建一个损失函数,代码如下(PyTorch官方提供): probs = policy_network(state) # Note that this is equivalent to what used to be called multinomial m = Categorical(probs) action = m.sam...
categorical_dist = distributions.Categorical(torch.tensor([0.1, 0.3, 0.6])) ``` 2.计算概率密度函数: ```python #计算正态分布在某个点的概率密度函数值 x = torch.tensor([0.0, 1.0, 2.0]) pdf = normal_dist.log_prob(x) #计算伯努利分布的概率密度函数值 x = torch.tensor([0, 1, 0]) pd...
pytorch混合分布的工作方式是通过在原始的Normal分布上使用3个额外的分布Independent、Categorical和MixtureSameFamily来实现的。从本质上讲,它创建了一个混合,基于给定Categorical分布的概率权重。因为我们的新均值和标准集有一个额外的轴,这个轴被用作独立的轴,需要决定从中得出哪个均值/标准集的值。
There is some unfortunate confusion in that many libraries implement random categorical samplers but name them multinomial samplers: PyTorch, Numpy, TensorFlow. On the one hand it would be nice to have an actual multinomial; on the other hand it would be nice to follow the naming convention of...
probs = policy_network(state) # Note that this is equivalent to what used to be called multinomial m = Categorical(probs) action = m.sample() next_state, reward = env.step(action) loss = -m.log_prob(action) * reward loss.backward() Pathwise derivative 实现这些随机/策略梯度的另一种方...
log probabilitiesand can therefore be any real number. It will likewise be normalized so thatthe resulting probabilities sum to 1 along the last dimension. attr:`logits`will return this normalized value.See also: :func:`torch.distributions.Categorical` for specifications of:attr:`probs` and :...
经全套训练的融合基础等级的模型 (个体分类器)。 然后,基于测试集,在预测期间获得的融合输出上训练元模型。 在这种情况下,融合的基本分类器的输出成为新训练的分类器的输入数据,该分类器本身是合并器。 这种方法称为 "复杂合并" 或 "通过学习泛化",更常见的是 "堆叠"。