new_zeros() pytorch版本的转换方式 如下所示: logprobs.new_zeros(logprobs.size()) pytorch 0.4版本中用到的 新建一个与logprobs类型相同的Variable 转换为pytorch0.2等版本 logprobs.new(logprobs.size()).zero_() 以上这篇new_zeros() pytorch版本的转换方式就是小编分享给大家的全部内容了,希望能给大...
图二中的new_zeros函数: Returns a Tensor of sizesizefilled with0. By default, the returned Tensor has the sametorch.dtypeandtorch.deviceas this tensor. 也就是说new_zeros创建的tensor的数据类型和device类型与weight是一样的,这样不需要再指定数据类型及device类型,更方便。 图一中的zeros函数:Returns a...
new_zeros（）pytorch版本的转换方式 new_zeros（）pytorch版本的转换⽅式 如下所⽰：logprobs.new_zeros(logprobs.size())pytorch 0.4版本中⽤到的 新建⼀个与logprobs类型相同的Variable 转换为pytorch0.2等版本 logprobs.new(logprobs.size()).zero_()以上这篇new_zeros() pytorch版本的转换⽅式...
# y: [batch, n_output, EMBEDDING_LENGTH] prev_s = x.new_zeros(batch, 1, self.decoder_dim) prev_y = x.new_zeros(batch, 1, EMBEDDING_LENGTH) y = x.new_empty(batch, n_output, EMBEDDING_LENGTH) tmp_states = None for i_output in range(n_output): # repeat_s: [batch, n_squen...
new_zeros(self, size, dtype=None, device=None, requires_grad=False) nextafter(self, other) nextafter_(self, other) ne_(self, other) nonzero(self) norm(self, p=2, dim=None, keepdim=False) normal_(self, mean=0, std=1, *args, **kwargs) ...
The example given in the docs for new_zeros() is actually an example for new_ones(). Should be a trivial fix. (Sorry, not quite sure what the etiquette/process is for fixing small errors like these). Thanks. pytorch/torch/_tensor_docs.py...
其中,第一个方法是new_tensor方法,具体用法和torch.tensor方法类似。我们可以看到,在这里新的张量类型不再是torch.int64,而是和前面创建的张量的类型一样,即torch.float32。和前面一样,可以用new_zeros方法生成和原始张量类型相同且元素全为0的张量,用new_ones方法生成和原始张量类型相同且元素全为1的张量。另外需要...
>>> torch.zeros_like(x, dtype=torch.int) tensor([0,0,0], dtype=torch.int32) 当然如果是单纯想要得到属性与前者相同的Tensor, 但是shape不想要一致: >>>x = torch.randn(3, dtype=torch.float64) >>> x.new_ones(2)# 属性一致tensor([1.,1.], dtype=torch.float64) ...
class LSTMLM(torch.nn.Module): def __init__(self, vocab_size, dim=17): super().__init__() self.cell = LSTMCell(dim, dim) self.embeddings = torch.nn.Parameter(torch.rand(vocab_size, dim)) self.c_0 = torch.nn.Parameter(torch.zeros(dim)) @property def hc_0(self): return (...
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, 1) def forward(self, x): # Set initial states h0 = torch.zeros(se...