class torch.distributions.categorical(probs) 其作用是创建以参数probs为标准的类别分布,样本是来自“0,...,K-1”的整数,K是probs参数的长度。也就是说,按照probs的概率,在相应的位置进行采样,采样返回的是该位置的整数索引。 如果probs是长度为K的一维列表,则每个元素是对该索引处的类进行采样的相对概率。
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...
# 需要导入模块: from torch.distributions.categorical import Categorical [as 别名]# 或者: from torch.distributions.categorical.Categorical importsample[as 别名]defsample_gmm(batch_size, mixture_weights):cat = Categorical(probs=mixture_weights) cluster = cat.sample([batch_size])# [B]mean = (cluste...
sample() print("final sampling took", time.time() - start) nSamples after expansion has shape [20_000, 3, 2], probs is shape [20_000, 3, 2] Takes 3 minutes. I would expect generating 120,000 values to take on the order of .1s 👍 1 rickstaa mentioned this issue Oct 7, ...
dist.sample(sample_shape=(), seed=None, name='sample', **kwargs):按probs的分布采样种类 举例 >>>importtensorflowastf>>>importtensorflow_probabilityastfp >>>dist = tfp.distributions.Categorical(probs=[0.1,0.2,0.7], dtype='float32')>>>print(dist.probs)tf.Tensor([0.1 0.2 0.7], shape=...
示例1: sample ▲点赞 6▼ # 需要导入模块: from tensorflow.python.ops.distributions import categorical [as 别名]# 或者: from tensorflow.python.ops.distributions.categorical importCategorical[as 别名]defsample(self, time, outputs, state, name=None):"""Gets a sample for one step."""deltime, st...
I think the issue with the current code is the expand step which in this case expands probs to have shape more than 2**16 x 2**16! I think we should be able to use your solution to work for the batched case too as follows: def sample(self, sample_shape=torch.Size()): probs...
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Draws a random sample from a categorical distribution specified by a list of probablities. Q# operationDrawCategorical (probs :Double[]) :Int Description The probability of selecting a specific index is proportional to the value of the array element at that index. Array elements that are equal ...
// red 2, blue 1, green 1 >> var probs = {}; >> d.each(function (category, probability, rank) { probs[category] = probability; }); >> probs { blue: 0.25, green: 0.25, red: 0.5 } d.map(iterator, [context]) Call an iterator function once for each category in their probab...