PyTorch–Tensor几乎所有的深度学习框架背后的设计核心都是张量和计算图,PyTorch也不例外一.Tensor的简介Tensor,又名张量,可能对这个名词似曾相识,因它不仅在PyTorch中出现过,它也是Theano、TensorFlow、Torc...
# Concatenate the embeddings from each batch into a single tensorall_embeddings = torch.cat(output_embeddings, dim=0) return all_embeddings # `input_ids` is a list or tensor of the input IDs and `embedding_layer` is model's embedding la...
torch.utils.data.TensorDataset: 用于获取封装成 tensor 的数据集,每一个样本都通过索引张量来获得。class TensorDataset(Dataset): 代码语言:javascript 代码运行次数:0 运行 AI代码解释 def__init__(self,*tensor):assertall(tensors[0].size(0)==tensor.size(0)fortensorintensors)self.tensors=tensors def_...
torch.nonzero(tensor) # index of non-zero elementstorch.nonzero(tensor==0) # index of zero elementstorch.nonzero(tensor).size(0) # number of non-zero elementstorch.nonzero(tensor == 0).size(0) # number of zero elements 判断两个张量相等 torch.allclose(...
(batch_size) ] # Make random entry in the batch have max sequence length seq_len_list[random.randint(0, batch_size - 1)] = max_sequence_len return ( torch.nested.nested_tensor( [ torch.randn(seq_len, embed_dimension, dtype=dtype, device=device) for seq_len in seq_len_list ] ),...
inputs=tokenizer(batch,add_special_tokens=True,truncation=True,padding='max_length',max_length=seq_len,return_tensors='pt')batched_input_ids.append(inputs['input_ids'])returntorch.cat(batched_input_ids)# Assuming enwiki8_data is a list of sentencesinput_ids=batch_encode(tokenizer, enwiki8...
torch.layout是表现torch.Tensor内存分布的类,目前只支持torch.strided。 2.创建tensor 2.1 直接创建 AI检测代码解析 torch.tensor(data, dtype=None, device=None, requires_grad=False) data - 可以是list, tuple, numpy array, scalar或其他类型 dtype - 可以返回想要的tensor类型 ...
>>> torch.tensor([]) # Create an empty tensor (of size (0,)) tensor([]) 从numpy中获得数据 torch.from_numpy(ndarry) 注:生成返回的tensor会和ndarry共享数据,任何对tensor的操作都会影响到ndarry, 反之亦然 >>> a = numpy.array([1, 2, 3]) ...
view(batch_size, -1) # get the last batch of labels out = out[:, -1] return out def init_hidden(self, batch_size): return (Variable(torch.zeros(self.num_layers, batch_size, self.hidden_dim).to(device)), Variable(torch.zeros(self.num_layers, batch_size, self.hidden_dim).to(...
seq_len=100# Example sequence length batch_size=256# Example batch size last_batch_size=81# onlyforthe very last batchofthe dataset current_batch_size=batch_size different_batch_size=False h_new=None temp_buffer=None 这里的超参数,如模型维度(d_model)、状态大小、序列长度和批大小。