损失函数(Loss functions) Vision functions) Convolution 函数 torch.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) 对由几个输入平面组成的输入信号应用一维卷积。 详细信息和输出形状,查看Conv1d 参数: ...
torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1)→ Tensorsource在由几个输入平面组成的输入图像上应用2D转置卷积,有时也被称为去卷积。 有关详细信息和输出形状,参考ConvTranspose2d。
loss_b=nn.CrossEntropyLoss() output_b = loss_b(input, target) print(output==output_b) #输出为TRUE 由输出为TRUE可知,torch.nn.CrossEntropyLoss相当于softmax + log + nllloss。 参考链接: 6 PoissonNLLLoss 真实标签服从泊松分布的负对数似然损失,神经网络的输出作为泊松分布的参数lambda。 没用过,看...
torch.nn.functional.nll_lossworks without any error when using cuda, but throws an error when using cpu. To Reproduce importtorchdefcpu():arg_0=torch.rand(torch.Size([500,6]),dtype=torch.float32)arg_1=torch.randint(-32768,32768,torch.Size([500]),dtype=torch.int64)res=torch.nn.function...
This function doesn’t work directly with NLLLoss, which expects the Log to be computed between the Softmax and itself. Use log_softmax instead (it’s faster and has better numerical properties). softshrink torch.nn.functional.softshrink(input, lambd=0.5) → Tensor ...
torch.nn.functional.poisson_nll_loss Supported 76 torch.nn.functional.cosine_embedding_loss Supported 77 torch.nn.functional.cross_entropy Unsupported. 78 torch.nn.functional.ctc_loss Supported 79 torch.nn.functional.hinge_embedding_loss Supported ...
poisson_nll_loss¶ torch.nn.functional.poisson_nll_loss(input, target, log_input=True, full=False, size_average=None, eps=1e-08, reduce=None, reduction='mean')[source]¶ Poisson negative log likelihood loss. See PoissonNLLLoss for details. Parameters input –expectation of underlying Poiss...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/torch/nn/modules/loss.py at main · pytorch/pytorch
# 其实核心代码和上面几乎一样,这里加了一个Log函数,并且加入了一个mask # 如果你是做chat robot 的,你应该对这个loss函数很熟悉 def maskNLLLoss(inp, target, mask): nTotal = mask.sum() crossEntropy = -torch.log(torch.gather(inp, 1, target.view(-1, 1)).squeeze(1)) loss = crossEntropy....
tensor(2.0115, grad_fn=<NllLossBackward>) 1. L1损失函数 torch.nn.L1Loss(size_average=None, reduce=None, reduction='mean') 1. **功能:**计算输出y和真实标签target之间的差值的绝对值。 我们需要知道的是,reduction参数决定了计算模式。有三种计算模式可选:none:逐个元素计算。 sum:所有元素求和,返回标...