本文整理汇总了Python中torch.Tensor.new_empty方法的典型用法代码示例。如果您正苦于以下问题：Python Tensor.new_empty方法的具体用法？Python Tensor.new_empty怎么用？Python Tensor.new_empty使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.Tensor的用法示例。
在下文中一共展示了Tensor.new_empty方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: forward
# 需要导入模块: from torch import Tensor [as 别名]
# 或者: from torch.Tensor import new_empty [as 别名]
def forward(self, # pylint: disable=arguments-differ
inputs: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
state_accumulator: torch.Tensor,
memory_accumulator: torch.Tensor,
dropout_mask: torch.Tensor,
lengths: torch.Tensor,
gates: torch.Tensor) -> Tuple[torch.Tensor, None]:
sequence_length, batch_size, input_size = inputs.size()
tmp_i = inputs.new_empty(batch_size, 6 * self.hidden_size)
tmp_h = inputs.new_empty(batch_size, 5 * self.hidden_size)
is_training = 1 if self.train else 0
highway_lstm_layer.highway_lstm_forward_cuda(input_size, # type: ignore # pylint: disable=no-member
self.hidden_size,
batch_size,
self.num_layers,
sequence_length,
inputs,
lengths,
state_accumulator,
memory_accumulator,
tmp_i,
tmp_h,
weight,
bias,
dropout_mask,
gates,
is_training)
self.save_for_backward(inputs, lengths, weight, bias, state_accumulator,
memory_accumulator, dropout_mask, gates)
# The state_accumulator has shape: (num_layers, sequence_length + 1, batch_size, hidden_size)
# so for the output, we want the last layer and all but the first timestep, which was the
# initial state.
output = state_accumulator[-1, 1:, :, :]
return output, state_accumulator[:, 1:, :, :]