softmax(action_logits, dim=-1) dist = torch.distributions.Categorical(action_probs) action = dist.sample() print(dist.log_prob(action), torch.log(action_probs[action])) 会发现输出的值相等。说明 dist.log_prob(action) 同torch.log(action_probs[action]) 等价。总的来说,会输出这个 action ...
log_prob(x)用来计算输入数据x在分布中的对于概率密度的对数 x = torch.tensor([1, 10, 10, 1], dtype=torch.float32).reshape(-1, 2) dist.log_prob(x)>>> tensor([[ -0.9189, -0.9189], [-41.4189, -41.4189]]) 其中,x = [[1, 10], [10, 1]],这一个x包括两个数组[1, 10]和[10,...
log_prob(x)用来计算输入数据x在分布中的对于概率密度的对数 x = torch.tensor([1, 10, 10, 1], dtype=torch.float32).reshape(-1, 2) dist.log_prob(x) >>> tensor([[ -0.9189, -0.9189], [-41.4189, -41.4189]]) 1. 2. 3. 4. 5. 6. 其中,x = [[1, 10], [10, 1]],这一个x...
log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad() loss.backward() optimizer.step() Mixup训练 beta_distribution = torch.distributions.beta.Beta(alpha, alpha) for images, labels in train_loader: images, labels...
# 1.将pi列表的权重转化为概率构建离散分布dist=torch.distributions.Categorical(logits=pi)dist.sample()# 按概率抽样下标dist.log_prob(action_batch)# 对action_batch中每个元素重构成仅1个索引值为1的独热编码序列,对每个序列计算交叉熵 # 2.构建(μ,σ)正态分布dist=torch.distributions.Normal# 构建3个标...
smoothed_labels = torch.full(size=(N, C), fill_value=0.1 / (C - 1)).cuda() smoothed_labels.scatter_(dim=1, index=torch.unsqueeze(labels, dim=1), value=0.9) score = model(images) log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed...
在PyTorch 中,可以使用torch.distributions.Categorical来基于给定的概率分布进行采样。 下面是一个示例: import torch import torch.distributions as dist # 创建一个大小为 (1, n) 的一行张量表示概率分布 probs = torch.tensor([0.1, 0.2, 0.3, 0.4]) ...
log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad() loss.backward() optimizer.step() Mixup训练 beta_distribution = torch.distributions.beta.Beta(alpha, alpha) ...
log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad loss.backward optimizer.step Mixup训练beta_distribution = torch.distributions.beta.Beta(alpha, alpha) forimages, labelsintrain_loader: ...
loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad() loss.backward() optimizer.step() Mixup beta_distribution = torch.distributions.beta.Beta(alpha, alpha) for images, labels in train_loader: images, labels = images.cuda(), labels.cuda() ...