if use_sample: # sampling temperature forces a peakier distribution p_y_t_index = F.softmax(score_for_y_t_index * self._sampling_temperature, dim=1) # method 1: choose most likely word # _, y_t_index = torch.max(p_y_t_index, 1) # method 2: sample from the distribution y_t...
sample_size (int): the max length of the samples temperature (float): accentuates or flattens the distribution. 0.0 < temperature < 1.0 will make it peakier. temperature > 1.0 will make it more uniform Returns: indices (torch.Tensor): the matrix of indices; shape = (num_samples, sample...
labels=['Class A','Class B','Class C']# 类别标签sizes=[300,400,300]# 样本分布大小plt.figure(figsize=(6,6))# 创建 6x6 的图形plt.pie(sizes,labels=labels,autopct='%1.1f%%',startangle=90)# 绘制饼图plt.title('Sample Class Distribution')# 图表标题plt.axis('equal')# 保证饼图是圆形plt...
fromtorch.distributionsimportNormal mu= torch.tensor([1, 10], dtype=torch.float32) sigma= torch.tensor([1], dtype=torch.float32) dist= Normal(mu, sigma)#设置高斯分布的均值和方差dist.sample()#采样>>> tensor([-0.9153, 8.3727]) 设置的高斯分布中sigma虽然只传入了1,这里应该是广播机制,会生成...
%matplotlib inlineimporttorchfromtorch.distributionsimportmultinomialfromd2limporttorchasd2l 先说几个统计学中的名词: 抽样(sampling):从概率分布中抽取样本的过程。 分布(distribution):类似于对事件概率分配。 多项分布(multionmial distribution):将概率分配给一些离散选择的分布。
# sample z_t from the distribution z_dist with pyro.poutine.scale(None, annealing_factor): z_t = pyro.sample("z_%d" % t, z_dist.mask(mini_batch_mask[:, t - 1:t]) .to_event(1)) # the latent sampled at this time step will be conditioned ...
sample(batch_size) loss = loss_module(tensordict) Advantage computation from torchrl.objectives.value.functional import vec_td_lambda_return_estimate advantage = vec_td_lambda_return_estimate(gamma, lmbda, next_state_value, reward, done, terminated) a generic trainer class(1) that executes the ...
张量命名是一个非常有用的方法,这样可以方便地使用维度的名字来做索引或其他操作,大大提高了可读性、易用性,防止出错。 #在PyTorch 1.3之前,需要使用注释# Tensor[N, C, H, W]images = torch.randn(32, 3, 56, 56)images.sum(dim=1)images.select(dim=...
Follow along with a code sample that performs language identification from audio samples using the Hugging Face SpeechBrain* toolkit. Learn how to optimize the model for inference on CPU or GPU using Intel Extension for PyTorch. Read Predict Forest Fires Using Transfer Learning on a CPU This appli...
This repo aims to cover Pytorch details, Pytorch example implementations, Pytorch sample codes, running Pytorch codes with Google Colab (with K80 GPU/CPU) in a nutshell. Running in Colab Two way: Clone or download all repo, then upload your drive root file ('/drive/'), open .ipynb file...