# Using Sequential to create a small model.model = nn.Sequential(nn.Conv2d(1,20,5),nn.ReLU(),nn.Conv2d(20,64,5),nn.ReLU())# Using Sequential with OrderedDict. This is functionally the# same as the above codemodel = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(1,20,5)),('...
parameters_to_vector vector_to_parameters weight_norm remove_weight_norm spectral_norm remove_spectral_norm PackedSequence pack_padded_sequence pad_packed_sequence pad_sequence pack_sequence Recurrent layers RNN class torch.nn.RNN(*args, **kwargs)[source] Applies a multi-layer Elman RNN with tanh...
torch.nn.utils.clip_grad_norm_ Unsupported. 188 torch.nn.utils.clip_grad_value_ Unsupported. 189 torch.nn.utils.parameters_to_vector Supported 190 torch.nn.utils.vector_to_parameters Supported 197 torch.nn.utils.prune.PruningContainer Supported 198 torch.nn.utils.prune.PruningContai...
如果torch.nn.utils.clip_grad_norm_ 传入的 parameters 不一致,其 total_norm 值会不一致,在其它参数一致的情况下裁剪后的梯度会对不齐。 torch.nn.utils.clip_grad_value_ torch.nn.utils.clip_grad_value_将参数的 grad 裁剪到[−clip_value,clip_value]之间。 def clip_grad_value_(parameters: _tenso...
norm of the gradients from :attr:``parameters`` is ``nan``, ``inf``, or ``-inf``. Default: False (will switch to True in the future) Returns: Total norm of the parameters (viewed as a single vector). """ifisinstance(parameters,torch.Tensor):parameters=[parameters]parameters=[pfor...
clip_grad_norm_ (@rohan-varma [c++ api] Add clip_grad_norm_ to c++ api #26140) clip_grad_value_ (@jokerkeny Add C++ API clip_grad_value_ for nn:utils #28736) parameters_to_vector (@lsrock1 C++ parity, convert_parameters #29267) vector_to_parameters (@lsrock1 C++ parity, convert...
nn.utils.vector_to_parameters Currently not support on GRAPH mode nn.quantized.FloatFunctional Supported nn.quantized.FXFloatFunctional Supported nn.Module.add_module Supported nn.Module.apply Supported nn.Module.bfloat16 Partly supported Currently only support on CPU nn.Module.buffers Supported...
4)直接使用nn.DataParallel的时候,训练采用多卡训练,会出现一个warning? UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector. 说明: 每张卡上的loss都是要汇总到第0张卡上求梯度,更新好以后把权重分发到其余卡。但是为什么会出...
torch.utils.hooks.RemovableHandle register_parameter(name,param)[source] Adds a parameter to the module. The parameter can be accessed as an attribute using given name. Parameters name(string) – name of the parameter. The parameter can be accessed from this module using the given name ...
import torch.nn.utils.prune as prune prune.random_unstructured( # 传入要剪枝的层。不知道层的名称可以使用 model.named_parameters() 获得 model.conv1, # 这里只对 weight 作剪枝,也可以传入 bias name="weight", amount=0.25 ) 上面是对特定的一层进行剪枝。如果想按照module 或者 layer 进行剪枝,可以使...