From 4f3d5146f8418daef5a2b32951801aef41ccc4e6 Mon Sep 17 00:00:00 2001 From: janhu Date: Sat, 9 Feb 2019 12:29:32 +0800 Subject: [PATCH] nn.functional 80% --- docs/1.0/nn_functional.md | 126 ++++++++++++++++++-------------------- 1 file changed, 59 insertions(+), 67 deletions(-) diff --git a/docs/1.0/nn_functional.md b/docs/1.0/nn_functional.md index c55cbd89..54928e48 100644 --- a/docs/1.0/nn_functional.md +++ b/docs/1.0/nn_functional.md @@ -1190,7 +1190,7 @@ torch.nn.functional.cosine_similarity(x1, x2, dim=1, eps=1e-8) → Tensor * Input: ![](http://latex.codecogs.com/gif.latex?(%5Cast_1%2C%20D%2C%20%5Cast_2)) where D is at position `dim`. * Output: ![](http://latex.codecogs.com/gif.latex?(%5Cast_1%2C%20%5Cast_2)) where 1 is at position `dim`. -Example: +例子: ```py >>> input1 = torch.randn(100, 128) @@ -1229,7 +1229,7 @@ torch.nn.functional.pdist(input, p=2) → Tensor torch.nn.functional.binary_cross_entropy(input, target, weight=None, size_average=None, reduce=None, reduction='mean') ``` -测量目标和输出之间二进制交叉熵的函数. +计算目标和输出之间二进制交叉熵的函数. 请参见 [`BCELoss`](#torch.nn.BCELoss "torch.nn.BCELoss"). @@ -1260,20 +1260,20 @@ torch.nn.functional.binary_cross_entropy(input, target, weight=None, size_averag torch.nn.functional.binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None) ``` -Function that measures Binary Cross Entropy between target and output logits. +计算目标和输出logits之间的二进制交叉熵的函数. 请参见 [`BCEWithLogitsLoss`](#torch.nn.BCEWithLogitsLoss "torch.nn.BCEWithLogitsLoss"). 参数: -* **input** – Tensor of arbitrary shape -* **target** – Tensor of the same shape as input -* **weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – a manual rescaling weight if provided it’s repeated to match input tensor shape -* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when reduce is `False`. 默认值: `True` -* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. 默认值: `True` -* **reduction** (_string__,_ _可选的_) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed. Note: `size_average` and `reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override `reduction`. 默认值: ‘mean’ -* **pos_weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – a weight of positive examples. Must be a vector with length equal to the number of classes. +* **input** – 任意形状的tensor +* **target** – 与输入形状相同的tensor +* **weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – 手动重新调整weight, 如果提供, 它重复来匹配输入张量的形状 +* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，批处理中的每个损失元素的平均损失。注意，对于某些损失，每个样本有多个元素。如果`size_average`设置为`False`，则对每个小批的损失进行汇总。reduce为False时忽略. 默认值: `True` +* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，根据size_average，对每个小批量的观察结果的损失进行平均或求和。 当reduce为False时，返回每批元素的损失并忽略`size_average`. 默认值: `True` +* **reduction** (_string__,_ _可选的_) – 指定要应用于输出的`reduction`：'none'| 'mean'| 'sum'。 'none'：没有reduction，'mean'：输出的总和将除以输出中的元素数量 'sum'：输出将被求和。 注意：`size_average`和`reduce`正在被弃用，同时，指定这两个args中的任何一个都将覆盖reduce。 默认值：'mean', 默认值: ‘mean’ +* **pos_weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – 正例样本的权重. 必须是长度等于类数的向量. @@ -1294,21 +1294,21 @@ Function that measures Binary Cross Entropy between target and output logits. torch.nn.functional.poisson_nll_loss(input, target, log_input=True, full=False, size_average=None, eps=1e-08, reduce=None, reduction='mean') ``` -Poisson negative log likelihood loss. +泊松负对数似然损失. 请参见 [`PoissonNLLLoss`](#torch.nn.PoissonNLLLoss "torch.nn.PoissonNLLLoss"). 参数: -* **input** – expectation of underlying Poisson distribution. -* **target** – random sample ![](http://latex.codecogs.com/gif.latex?target%20%5Csim%20%5Ctext%7BPoisson%7D(input)). -* **log_input** – if `True` the loss is computed as ![](http://latex.codecogs.com/gif.latex?%5Cexp(%5Ctext%7Binput%7D)%20-%20%5Ctext%7Btarget%7D%20*%20%5Ctext%7Binput%7D), if `False` then loss is ![](http://latex.codecogs.com/gif.latex?%5Ctext%7Binput%7D%20-%20%5Ctext%7Btarget%7D%20*%20%5Clog(%5Ctext%7Binput%7D%2B%5Ctext%7Beps%7D)). 默认值: `True` -* **full** – whether to compute full loss, i. e. to add the Stirling approximation term. 默认值: `False` ![](http://latex.codecogs.com/gif.latex?%5Ctext%7Btarget%7D%20*%20%5Clog(%5Ctext%7Btarget%7D)%20-%20%5Ctext%7Btarget%7D%20%2B%200.5%20*%20%5Clog(2%20*%20%5Cpi%20*%20%5Ctext%7Btarget%7D)). -* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when reduce is `False`. 默认值: `True` -* **eps** ([_float_](https://docs.python.org/3/library/functions.html#float "(in Python v3.7)")_,_ _可选的_) – Small value to avoid evaluation of ![](http://latex.codecogs.com/gif.latex?%5Clog(0)) when `log_input`=``False``. 默认值: 1e-8 -* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. 默认值: `True` -* **reduction** (_string__,_ _可选的_) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed. Note: `size_average` and `reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override `reduction`. 默认值: ‘mean’ +* **input** – 潜在泊松分布的期望. +* **target** – 随机抽样 ![](http://latex.codecogs.com/gif.latex?target%20%5Csim%20%5Ctext%7BPoisson%7D(input)). +* **log_input** – 如果为`True`，则损失计算为 ![](http://latex.codecogs.com/gif.latex?%5Cexp(%5Ctext%7Binput%7D)%20-%20%5Ctext%7Btarget%7D%20*%20%5Ctext%7Binput%7D), 如果为`False`，则损失计算为 ![](http://latex.codecogs.com/gif.latex?%5Ctext%7Binput%7D%20-%20%5Ctext%7Btarget%7D%20*%20%5Clog(%5Ctext%7Binput%7D%2B%5Ctext%7Beps%7D)). 默认值: `True` +* **full** – 是否计算全部损失, 即. 加入Stirling近似项. 默认值: `False` ![](http://latex.codecogs.com/gif.latex?%5Ctext%7Btarget%7D%20*%20%5Clog(%5Ctext%7Btarget%7D)%20-%20%5Ctext%7Btarget%7D%20%2B%200.5%20*%20%5Clog(2%20*%20%5Cpi%20*%20%5Ctext%7Btarget%7D)). +* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，批处理中的每个损失元素的平均损失。注意，对于某些损失，每个样本有多个元素。如果`size_average`设置为`False`，则对每个小批的损失进行汇总。reduce为False时忽略. 默认值: `True` +* **eps** ([_float_](https://docs.python.org/3/library/functions.html#float "(in Python v3.7)")_,_ _可选的_) – 一个小值避免求值 ![](http://latex.codecogs.com/gif.latex?%5Clog(0)) when `log_input`=``False``. 默认值: 1e-8 +* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，根据size_average，对每个小批量的观察结果的损失进行平均或求和。 当reduce为False时，返回每批元素的损失并忽略`size_average`. 默认值: `True` +* **reduction** (_string__,_ _可选的_) – 指定要应用于输出的`reduction`：'none'| 'mean'| 'sum'。 'none'：没有reduction，'mean'：输出的总和将除以输出中的元素数量 'sum'：输出将被求和。 注意：`size_average`和`reduce`正在被弃用，同时，指定这两个args中的任何一个都将覆盖reduce。 默认值：'mean', 默认值: ‘mean’ @@ -1327,7 +1327,7 @@ torch.nn.functional.cosine_embedding_loss(input1, input2, target, margin=0, size torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') ``` -This criterion combines `log_softmax` and `nll_loss` in a single function. +此函数结合了 `log_softmax` 和 `nll_loss`. 请参见 [`CrossEntropyLoss`](#torch.nn.CrossEntropyLoss "torch.nn.CrossEntropyLoss"). @@ -1336,11 +1336,11 @@ This criterion combines `log_softmax` and `nll_loss` in a single function. * **input** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")) – ![](http://latex.codecogs.com/gif.latex?(N%2C%20C)) where `C = number of classes` or ![](http://latex.codecogs.com/gif.latex?(N%2C%20C%2C%20H%2C%20W)) in case of 2D Loss, or ![](http://latex.codecogs.com/gif.latex?(N%2C%20C%2C%20d_1%2C%20d_2%2C%20...%2C%20d_K)) where ![](http://latex.codecogs.com/gif.latex?K%20%3E%201) in the case of K-dimensional loss. * **target** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")) – ![](http://latex.codecogs.com/gif.latex?(N)) where each value is ![](http://latex.codecogs.com/gif.latex?0%20%5Cleq%20%5Ctext%7Btargets%7D%5Bi%5D%20%5Cleq%20C-1), or ![](http://latex.codecogs.com/gif.latex?(N%2C%20d_1%2C%20d_2%2C%20...%2C%20d_K)) where ![](http://latex.codecogs.com/gif.latex?K%20%5Cgeq%201) for K-dimensional loss. -* **weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – a manual rescaling weight given to each class. If given, has to be a Tensor of size `C` -* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when reduce is `False`. 默认值: `True` +* **weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – 给每个类别的手动重定权重. 如果给定，必须是大小为`C`的tensor +* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，批处理中的每个损失元素的平均损失。注意，对于某些损失，每个样本有多个元素。如果`size_average`设置为`False`，则对每个小批的损失进行汇总。reduce为False时忽略. 默认值: `True` * **ignore_index** ([_int_](https://docs.python.org/3/library/functions.html#int "(in Python v3.7)")_,_ _可选的_) – Specifies a target value that is ignored and does not contribute to the input gradient. When `size_average` is `True`, the loss is averaged over non-ignored targets. 默认值: -100 -* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. 默认值: `True` -* **reduction** (_string__,_ _可选的_) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed. Note: `size_average` and `reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override `reduction`. 默认值: ‘mean’ +* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，根据size_average，对每个小批量的观察结果的损失进行平均或求和。 当reduce为False时，返回每批元素的损失并忽略`size_average`. 默认值: `True` +* **reduction** (_string__,_ _可选的_) – 指定要应用于输出的`reduction`：'none'| 'mean'| 'sum'。 'none'：没有reduction，'mean'：输出的总和将除以输出中的元素数量 'sum'：输出将被求和。 注意：`size_average`和`reduce`正在被弃用，同时，指定这两个args中的任何一个都将覆盖reduce。 默认值：'mean', 默认值: ‘mean’ @@ -1361,7 +1361,7 @@ This criterion combines `log_softmax` and `nll_loss` in a single function. torch.nn.functional.ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean') ``` -The Connectionist Temporal Classification loss. +Connectionist Temporal Classification损失. 请参见 [`CTCLoss`](#torch.nn.CTCLoss "torch.nn.CTCLoss"). @@ -1371,9 +1371,8 @@ The Connectionist Temporal Classification loss. 注意 -When using the CUDA backend, this operation may induce nondeterministic behaviour in be backward that is not easily switched off. Please see the notes on [Reproducibility](notes/randomness.html) for background. +当使用CUDA后端时，此操作可能会导致不确定的向后行为，并且不容易关闭。请参阅关于[Reproducibility](notes/randomness.html)的注释。 - 参数: * **log_probs** – ![](http://latex.codecogs.com/gif.latex?(T%2C%20N%2C%20C)) where `C = number of characters in alphabet including blank`, `T = input length`, and `N = batch size`. The logarithmized probabilities of the outputs (e.g. obtained with [`torch.nn.functional.log_softmax()`](#torch.nn.functional.log_softmax "torch.nn.functional.log_softmax")). @@ -1381,12 +1380,10 @@ When using the CUDA backend, this operation may induce nondeterministic behaviou * **input_lengths** – ![](http://latex.codecogs.com/gif.latex?(N)). Lengths of the inputs (must each be ![](http://latex.codecogs.com/gif.latex?%5Cleq%20T)) * **target_lengths** – ![](http://latex.codecogs.com/gif.latex?(N)). Lengths of the targets * **blank** ([_int_](https://docs.python.org/3/library/functions.html#int "(in Python v3.7)")_,_ _可选的_) – Blank label. 默认 ![](http://latex.codecogs.com/gif.latex?0). -* **reduction** (_string__,_ _可选的_) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘mean’: the output losses will be divided by the target lengths and then the mean over the batch is taken. 默认值: ‘mean’ +* **reduction** (_string__,_ _可选的_) - 指定要应用于输出的`reduction`：'none'| 'mean'| 'sum'。 'none'：不会应用`reduce`，'mean'：输出损失将除以目标长度，然后得到批次的平均值。 默认值：'mean' - - -Example: +例子: ```py >>> log_probs = torch.randn(50, 16, 20).log_softmax(2).detach().requires_grad_() @@ -1412,7 +1409,7 @@ torch.nn.functional.hinge_embedding_loss(input, target, margin=1.0, size_average torch.nn.functional.kl_div(input, target, size_average=None, reduce=None, reduction='mean') ``` -The [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback-Leibler_divergence) Loss. +[Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback-Leibler_divergence) 损失. 请参见 [`KLDivLoss`](#torch.nn.KLDivLoss "torch.nn.KLDivLoss"). @@ -1421,16 +1418,14 @@ The [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback-Leibler * **input** – Tensor of arbitrary shape * **target** – Tensor of the same shape as input -* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when reduce is `False`. 默认值: `True` -* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. 默认值: `True` -* **reduction** (_string__,_ _可选的_) – Specifies the reduction to apply to the output: ‘none’ | ‘batchmean’ | ‘sum’ | ‘mean’. ‘none’: no reduction will be applied ‘batchmean’: the sum of the output will be divided by the batchsize ‘sum’: the output will be summed ‘mean’: the output will be divided by the number of elements in the output 默认值: ‘mean’ - - +* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，批处理中的每个损失元素的平均损失。注意，对于某些损失，每个样本有多个元素。如果`size_average`设置为`False`，则对每个小批的损失进行汇总。reduce为False时忽略. 默认值: `True` +* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，根据size_average，对每个小批量的观察结果的损失进行平均或求和。 当reduce为False时，返回每批元素的损失并忽略`size_average`. 默认值: `True` +* **reduction** (_string__,_ _可选的_) – 指定要应用于输出的缩减：'none'| 'batchmean'| 'sum'| 'mean'。 'none'：不会应用`reduction` 'batchmean'：输出的总和将除以batchsize 'sum'：输出将被加总 'mean'：输出将除以输出中的元素数 默认值：'mean' -:param .. note:: `size_average` and `reduce` are in the process of being deprecated,: and in the meantime, specifying either of those two args will override `reduction`. :param .. note:: `reduction=’mean’` doesn’t return the true kl divergence value, please use: `reduction=’batchmean’` which aligns with KL math definition. +:param . .注::`size average`和`reduce`正在被弃用，:同时，指定这两个arg中的一个将覆盖reduce。:param . .注意::`reduce = mean`不返回真实的kl散度值，请使用:`reduce = batchmean`，它符合kl的数学定义。 -> In the next major release, ‘mean’ will be changed to be the same as ‘batchmean’. +> 在下一个主要版本中，“mean”将被修改为与“batchmean”相同. ### l1_loss @@ -1438,7 +1433,7 @@ The [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback-Leibler torch.nn.functional.l1_loss(input, target, size_average=None, reduce=None, reduction='mean') → Tensor ``` -Function that takes the mean element-wise absolute value difference. +函数的作用是求元素的绝对值的平均值. 请参见 [`L1Loss`](#torch.nn.L1Loss "torch.nn.L1Loss"). @@ -1448,7 +1443,7 @@ Function that takes the mean element-wise absolute value difference. torch.nn.functional.mse_loss(input, target, size_average=None, reduce=None, reduction='mean') → Tensor ``` -Measures the element-wise mean squared error. + 测量元素的均方误差. 请参见 [`MSELoss`](#torch.nn.MSELoss "torch.nn.MSELoss"). @@ -1495,26 +1490,24 @@ reduce=None, reduction=’mean’) -> Tensor ```py torch.nn.functional.nll_loss(input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean') ``` - -The negative log likelihood loss. +负的对数似然函数. 请参见 [`NLLLoss`](#torch.nn.NLLLoss "torch.nn.NLLLoss"). 参数: -* **input** – ![](http://latex.codecogs.com/gif.latex?(N%2C%20C)) where `C = number of classes` or ![](http://latex.codecogs.com/gif.latex?(N%2C%20C%2C%20H%2C%20W)) in case of 2D Loss, or ![](http://latex.codecogs.com/gif.latex?(N%2C%20C%2C%20d_1%2C%20d_2%2C%20...%2C%20d_K)) where ![](http://latex.codecogs.com/gif.latex?K%20%3E%201) in the case of K-dimensional loss. -* **target** – ![](http://latex.codecogs.com/gif.latex?(N)) where each value is ![](http://latex.codecogs.com/gif.latex?0%20%5Cleq%20%5Ctext%7Btargets%7D%5Bi%5D%20%5Cleq%20C-1), or ![](http://latex.codecogs.com/gif.latex?(N%2C%20d_1%2C%20d_2%2C%20...%2C%20d_K)) where ![](http://latex.codecogs.com/gif.latex?K%20%5Cgeq%201) for K-dimensional loss. -* **weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – a manual rescaling weight given to each class. If given, has to be a Tensor of size `C` -* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when reduce is `False`. 默认值: `True` -* **ignore_index** ([_int_](https://docs.python.org/3/library/functions.html#int "(in Python v3.7)")_,_ _可选的_) – Specifies a target value that is ignored and does not contribute to the input gradient. When `size_average` is `True`, the loss is averaged over non-ignored targets. 默认值: -100 -* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – Deprecated (see `reduction`). By 默认, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. 默认值: `True` -* **reduction** (_string__,_ _可选的_) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed. Note: `size_average` and `reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override `reduction`. 默认值: ‘mean’ +* **input** – ![](http://latex.codecogs.com/gif.latex?(N%2C%20C)) `C = 类别的数量` 或者 ![](http://latex.codecogs.com/gif.latex?(N%2C%20C%2C%20H%2C%20W)) 在二维损失的情况下, 或者 ![](http://latex.codecogs.com/gif.latex?(N%2C%20C%2C%20d_1%2C%20d_2%2C%20...%2C%20d_K)) ![](http://latex.codecogs.com/gif.latex?K%20%3E%201) 在K维损失的情况下. +* **target** – ![](http://latex.codecogs.com/gif.latex?(N)) 每个值是 ![](http://latex.codecogs.com/gif.latex?0%20%5Cleq%20%5Ctext%7Btargets%7D%5Bi%5D%20%5Cleq%20C-1), 或者 ![](http://latex.codecogs.com/gif.latex?(N%2C%20d_1%2C%20d_2%2C%20...%2C%20d_K)) ![](http://latex.codecogs.com/gif.latex?K%20%5Cgeq%201) K维损失. +* **weight** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")_,_ _可选的_) – 给每个类别的手动重定权重。如果给定，必须是大小为`C`的tensor +* **size_average** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，批处理中的每个损失元素的平均损失。注意，对于某些损失，每个样本有多个元素。如果`size_average`设置为`False`，则对每个小批的损失进行汇总。reduce为False时忽略. 默认值: `True` +* **ignore_index** ([_int_](https://docs.python.org/3/library/functions.html#int "(in Python v3.7)")_,_ _可选的_) – 指定一个被忽略的目标值，该值不会影响输入梯度。当`size_average`为`True`时，损耗在未忽略的目标上平均. 默认值: -100 +* **reduce** ([_bool_](https://docs.python.org/3/library/functions.html#bool "(in Python v3.7)")_,_ _可选的_) – 废弃的 (见 `reduction`). 默认情况下，根据size_average，对每个小批量的观察结果的损失进行平均或求和。 当reduce为False时，返回每批元素的损失并忽略`size_average`. 默认值: `True` +* **reduction** (_string__,_ _可选的_) – 指定要应用于输出的`reduction`：'none'| 'mean'| 'sum'。 'none'：没有reduction，'mean'：输出的总和将除以输出中的元素数量 'sum'：输出将被求和。 注意：`size_average`和`reduce`正在被弃用，同时，指定这两个args中的任何一个都将覆盖reduce。 默认值：'mean', 默认值: ‘mean’ - -Example: +例子: ```py >>> # input is of size N x C = 3 x 5 @@ -1532,7 +1525,7 @@ Example: torch.nn.functional.smooth_l1_loss(input, target, size_average=None, reduce=None, reduction='mean') ``` -Function that uses a squared term if the absolute element-wise error falls below 1 and an L1 term otherwise. + 如果绝对元素误差低于1，则使用平方项，否则使用L1项的函数. 请参见 [`SmoothL1Loss`](#torch.nn.SmoothL1Loss "torch.nn.SmoothL1Loss"). @@ -1552,7 +1545,7 @@ torch.nn.functional.triplet_margin_loss(anchor, positive, negative, margin=1.0, 请参见 [`TripletMarginLoss`](#torch.nn.TripletMarginLoss "torch.nn.TripletMarginLoss") -## Vision functions +## 视觉函数 ### pixel_shuffle @@ -1560,16 +1553,15 @@ torch.nn.functional.triplet_margin_loss(anchor, positive, negative, margin=1.0, torch.nn.functional.pixel_shuffle() ``` -Rearranges elements in a tensor of shape ![](http://latex.codecogs.com/gif.latex?(*%2C%20C%20%5Ctimes%20r%5E2%2C%20H%2C%20W)) to a tensor of shape ![](http://latex.codecogs.com/gif.latex?(C%2C%20H%20%5Ctimes%20r%2C%20W%20%5Ctimes%20r)). + 重新排列tensor中的元素, 从形状 ![](http://latex.codecogs.com/gif.latex?(*%2C%20C%20%5Ctimes%20r%5E2%2C%20H%2C%20W)) 到 ![](http://latex.codecogs.com/gif.latex?(C%2C%20H%20%5Ctimes%20r%2C%20W%20%5Ctimes%20r)). 请参见 [`PixelShuffle`](#torch.nn.PixelShuffle "torch.nn.PixelShuffle"). 参数: -* **input** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")) – the input tensor -* **upscale_factor** ([_int_](https://docs.python.org/3/library/functions.html#int "(in Python v3.7)")) – factor to increase spatial resolution by - +* **input** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")) –输入 tensor +* **upscale_factor** ([_int_](https://docs.python.org/3/library/functions.html#int "(in Python v3.7)")) – 提高空间解析度的参数 @@ -1589,13 +1581,13 @@ torch.Size([1, 1, 12, 12]) torch.nn.functional.pad(input, pad, mode='constant', value=0) ``` -Pads tensor. +填充 tensor. ```py Pading size: ``` -The number of dimensions to pad is ![](http://latex.codecogs.com/gif.latex?%5Cleft%5Clfloor%5Cfrac%7B%5Ctext%7Blen(pad)%7D%7D%7B2%7D%5Cright%5Crfloor) and the dimensions that get padded begins with the last dimension and moves forward. For example, to pad the last dimension of the input tensor, then pad has form (padLeft, padRight); to pad the last 2 dimensions of the input tensor, then use (padLeft, padRight, padTop, padBottom); to pad the last 3 dimensions, use (padLeft, padRight, padTop, padBottom, padFront, padBack). +要填充的维度数为 ![](http://latex.codecogs.com/gif.latex?%5Cleft%5Clfloor%5Cfrac%7B%5Ctext%7Blen(pad)%7D%7D%7B2%7D%5Cright%5Crfloor)填充的维度从最后一个维度开始向前移动. 例如, to pad the last dimension of the input tensor, then pad has form (padLeft, padRight); to pad the last 2 dimensions of the input tensor, then use (padLeft, padRight, padTop, padBottom); to pad the last 3 dimensions, use (padLeft, padRight, padTop, padBottom, padFront, padBack). ```py Padding mode: @@ -1605,7 +1597,7 @@ Padding mode: 注意 -When using the CUDA backend, this operation may induce nondeterministic behaviour in be backward that is not easily switched off. Please see the notes on [Reproducibility](notes/randomness.html) for background. +当使用CUDA后端时，此操作可能会导致不确定的向后行为，并且不容易关闭。请参阅关于[Reproducibility](notes/randomness.html)的注释。 参数: @@ -1672,7 +1664,7 @@ With `align_corners = True`, the linearly interpolating modes (`linear`, `biline 注意 -When using the CUDA backend, this operation may induce nondeterministic behaviour in be backward that is not easily switched off. Please see the notes on [Reproducibility](notes/randomness.html) for background. +当使用CUDA后端时，此操作可能会导致不确定的向后行为，并且不容易关闭。请参阅关于[Reproducibility](notes/randomness.html)的注释。 ### upsample @@ -1688,7 +1680,7 @@ This function is deprecated in favor of [`torch.nn.functional.interpolate()`](#t 注意 -When using the CUDA backend, this operation may induce nondeterministic behaviour in be backward that is not easily switched off. Please see the notes on [Reproducibility](notes/randomness.html) for background. +当使用CUDA后端时，此操作可能会导致不确定的向后行为，并且不容易关闭。请参阅关于[Reproducibility](notes/randomness.html)的注释。 The algorithm used for upsampling is determined by `mode`. @@ -1740,7 +1732,7 @@ Currently spatial and volumetric upsampling are supported (i.e. expected inputs 注意 -When using the CUDA backend, this operation may induce nondeterministic behaviour in be backward that is not easily switched off. Please see the notes on [Reproducibility](notes/randomness.html) for background. +当使用CUDA后端时，此操作可能会导致不确定的向后行为，并且不容易关闭。请参阅关于[Reproducibility](notes/randomness.html)的注释。 ### upsample_bilinear @@ -1768,7 +1760,7 @@ Expected inputs are spatial (4 dimensional). Use `upsample_trilinear` fo volumet 注意 -When using the CUDA backend, this operation may induce nondeterministic behaviour in be backward that is not easily switched off. Please see the notes on [Reproducibility](notes/randomness.html) for background. +当使用CUDA后端时，此操作可能会导致不确定的向后行为，并且不容易关闭。请参阅关于[Reproducibility](notes/randomness.html)的注释。 ### grid_sample @@ -1798,7 +1790,7 @@ This function is often used in building Spatial Transformer Networks. 注意 -When using the CUDA backend, this operation may induce nondeterministic behaviour in be backward that is not easily switched off. Please see the notes on [Reproducibility](notes/randomness.html) for background. +当使用CUDA后端时，此操作可能会导致不确定的向后行为，并且不容易关闭。请参阅关于[Reproducibility](notes/randomness.html)的注释。 参数: @@ -1827,7 +1819,7 @@ Generates a 2d flow field, given a batch of affine matrices `theta` Generally us 参数: * **theta** ([_Tensor_](tensors.html#torch.Tensor "torch.Tensor")) – input batch of affine matrices (![](http://latex.codecogs.com/gif.latex?N%20%5Ctimes%202%20%5Ctimes%203)) -* **size** (_torch.Size_) – the target output image size (![](http://latex.codecogs.com/gif.latex?N%20%5Ctimes%20C%20%5Ctimes%20H%20%5Ctimes%20W)) Example: torch.Size((32, 3, 24, 24)) +* **size** (_torch.Size_) – the target output image size (![](http://latex.codecogs.com/gif.latex?N%20%5Ctimes%20C%20%5Ctimes%20H%20%5Ctimes%20W)) 例子: torch.Size((32, 3, 24, 24)) -- GitLab