o #j@snddlZddlZddlmZmZmZddlmZddlmZddl m Z ddl m Z m Z mZmZddlmZdd lmZdd lmZgZd Zdad dZdbddZ    dcddZddddZddZ    deddZ dfdd Z dgd!d"Z     dhd#d$Z did&d'Z! djd)d*Z"dkd+d,Z# dld-d.Z$   /  dmd0d1Z%dkd2d3Z&dkd4d5Z'  dnd6d7Z( 8  dod9d:Z) % ; ( <   dpd=d>Z*ed?d@dAdBdC    dcdDdEZ+      ( dqdFdGZ,  H I J drdKdLZ- dfdMdNZ.dsdOdPZ/ dtdQdRZ0  %   dudSdTZ1 % U V   dvdWdXZ2 A %   dwdYe3dZe4fd[d\Z5dkd]d^Z6  V  dxd_d`Z7dS)yN)_C_opsbasein_dynamic_mode)Assert) deprecated)check_variable_and_dtype)_current_expected_placecorein_dynamic_or_pir_mode in_pir_mode) LayerHelper)Variable)reshapeh㈵>cCsZ|jtjtjfvs J|jtjtjfvsJt|jdks!Jdt|jt|jks9Jdt|jt|jf|jddksIJd|jd|jdd|jddks[Jd|d krg|d kskJd t |dg}tj j ||jd}t tdt|j}tj|||d }tj||d tj||d }d|d||}t|S) a Dice loss for comparing the similarity between the input predictions and the label. This implementation is for binary classification, where the input is sigmoid predictions of each pixel, usually used for segmentation task. The dice loss can be defined as the following equation: .. math:: dice\_loss &= 1 - \frac{2 * intersection\_area}{total\_area} \\ &= \frac{(total\_area - intersection\_area) - intersection\_area}{total\_area} \\ &= \frac{(union\_area - intersection\_area)}{total\_area} Parameters: input (Tensor): Tensor, rank>=2, shape is :math:`[N_1, N_2, ..., N_k, D]`, where :math:`N_1` is the batch_size, :math:`D` is the number of categories. It is usually the output predictions of sigmoid activation. The data type can be float32 or float64. label (Tensor): Tensor, the groud truth with the same rank as input, shape is :math:`[N_1, N_2, ..., N_k, 1]`. where :math:`N_1` is the batch_size. The data type can be int32 or int64. epsilon (float): The epsilon will be added to the numerator and denominator. If both input and label are empty, it makes sure dice is 1. Default: 0.00001 name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: 0-D Tensor, which shape is [], data type is the same as `input` . Example: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> x = paddle.randn((3,224,224,2)) >>> label = paddle.randint(high=2, shape=(3,224,224,1)) >>> predictions = F.softmax(x) >>> loss = F.dice_loss(input=predictions, label=label) z7The rank of input should be greater than or equal to 2.zOThe rank of input and label should be equal, but received input: %d, label: %d.z9The last dimension of label should be 1, but received %d.Nz3All dimensions should be equal except the last one.rz6Any dimension of input and label cannot be equal to 0.axis)dtypepaddlefloat32float64int32int64lenshapeZnumelsqueezenn functionalone_hotlistrangesummean)inputlabelepsilonnameZ reduce_dimZinseZdice_denominatorZ dice_scorer+Z/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/nn/functional/loss.py dice_loss's<* r--C6?cCs|tr t|||Std it}t|ddgdt|ddgd|j|jd}|jd|g|gdd|gid|id |S) a **Negative Log Loss Layer** This layer accepts input predictions and target label and returns the negative log loss. .. math:: Out = -label * \log{(input + \epsilon)} - (1 - label) * \log{(1 - input + \epsilon)} Args: input (Tensor|list): A 2-D tensor with shape [N x 1], where N is the batch size. This input is a probability computed by the previous operator. Data type float32. label (Tensor|list): The ground truth which is a 2-D tensor with shape [N x 1], where N is the batch size. Data type float32. epsilon (float, optional): A small number for numerical stability. Default 1e-4. name(str|None): For detailed information, please refer to :ref:`api_guide_Name` . Usually name is no need to set and None by default. Returns: Tensor, which shape is [N x 1], data type is float32. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> label = paddle.randn((10,1)) >>> prob = paddle.randn((10,1)) >>> cost = F.log_loss(input=prob, label=label) log_lossr'rr(r)Z PredictedZLabelsLossr)typeinputsoutputsattrsN)r/) r rr/r localsr"create_variable_for_type_inferencer append_op)r'r(r)r*helperlossr+r+r,r/qs% r/FTrc Cs tt|j}|dkrtdtt|j}|d|kr+||kr+td|d|d|d|kr8tj||d}trPt|||d|||\} } |sL| S| | fS||||d } t dit } | j |j d } | j |j d } | | d } | j d ||d | | d|r| | fS| S)a This operator implements the cross entropy loss function with softmax. This function combines the calculation of the softmax operation and the cross entropy loss function to provide a more numerically stable gradient. Because this operator performs a softmax on logits internally, it expects unscaled logits. This operator should not be used with the output of softmax operator since that would produce incorrect results. When the attribute :attr:`soft_label` is set :attr:`False`, this operators expects mutually exclusive hard labels, each sample in a batch is in exactly one class with a probability of 1.0. Each sample in the batch will have a single label. The equation is as follows: 1) Hard label (one-hot label, so every sample has exactly one class) .. math:: \\loss_j=-\text{logits}_{label_j} +\log\left(\sum_{i=0}^{K}\exp(\text{logits}_i)\right), j = 1,..., K 2) Soft label (each sample can have a distribution over all classes) .. math:: \\loss_j= -\sum_{i=0}^{K}\text{label}_i\left(\text{logits}_i - \log\left(\sum_{i=0}^{K}\exp(\text{logits}_i)\right)\right), j = 1,...,K 3) If :attr:`numeric_stable_mode` is :attr:`True`, softmax is calculated first by: .. math:: \\max_j&=\max_{i=0}^{K}{\text{logits}_i} \\ log\_max\_sum_j &= \log\sum_{i=0}^{K}\exp(logits_i - max_j)\\ softmax_j &= \exp(logits_j - max_j - {log\_max\_sum}_j) and then cross entropy loss is calculated by softmax and label. Args: logits (Tensor): A multi-dimension ``Tensor`` , and the data type is float32 or float64. The input tensor of unscaled log probabilities. label (Tensor): The ground truth ``Tensor`` , data type is the same as the ``logits`` . If :attr:`soft_label` is set to :attr:`True`, Label is a ``Tensor`` in the same shape with :attr:`logits`. If :attr:`soft_label` is set to :attr:`True`, Label is a ``Tensor`` in the same shape with :attr:`logits` expect shape in dimension :attr:`axis` as 1. soft_label (bool, optional): A flag to indicate whether to interpretant the given labels as soft labels. Default False. ignore_index (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. Only valid if :attr:`soft_label` is set to :attr:`False`. Default: kIgnoreIndex(-100). numeric_stable_mode (bool, optional): A flag to indicate whether to use a more numerically stable algorithm. Only valid when :attr:`soft_label` is :attr:`False` and GPU is used. When :attr:`soft_label` is :attr:`True` or CPU is used, the algorithm is always numerically stable. Note that the speed may be slower when use stable algorithm. Default: True. return_softmax (bool, optional): A flag indicating whether to return the softmax along with the cross entropy loss. Default: False. axis (int, optional): The index of dimension to perform softmax calculations. It should be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of input :attr:`logits`. Default: -1. Returns: - If `return_softmax` is False, return the cross entropy loss as a ``Tensor``. The dtype is the same as the input ``logits``. The shape is consistent with ``logits`` except in dimension :attr:`axis` as 1. - If `return_softmax` is True, return a tuple of two ``Tensor``: the cross entropy loss and the softmax result. The dtype of the cross entropy loss is the same as the input ``logits``, and the shape is consistent with ``logits`` except in dimension :attr:`axis` as 1. The dtype and shape of the softmax result are the same as the input ``logits``. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> logits = paddle.to_tensor([0.4, 0.6, 0.9]) >>> label = paddle.randint(high=2, shape=[1], dtype="int64") >>> out = paddle.nn.functional.softmax_with_cross_entropy(logits=logits, label=label) >>> print(out) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.15328646]) rz2The dimension of input should be larger than zero!r\Expected input_dims - 1 = label_dims or input_dims == label_dims (got input_dims , label_dims)rT) soft_label ignore_indexnumeric_stable_modersoftmax_with_cross_entropyr0ZSoftmaxr1ZLogitsLabelr2NrB)rr#r ValueErrorr unsqueezer rcross_entropy_with_softmaxr r7r8rr9)logitsr(r?r@rAreturn_softmaxr input_dims label_dimssoftmaxr;r6r:r5r+r+r,base_softmax_with_cross_entropysZ^   rOMb`?c Cs>|jdkr td|jdkrtdt|dddgdt|dddgdt|d gd d d }|jd}tj||d gd }tj|d |gd}t|tj|d dgd d}|tj |d dd}t t t |d t t t |d }|||}tj ||ddd}t||dd}t ||d} t | } || S)a Npair loss requires paired data. Npair loss has two parts: the first part is L2 regularizer on the embedding vector; the second part is cross entropy loss which takes the similarity matrix of anchor and positive as logits. For more information, please refer to: `Improved Deep Metric Learning with Multi class N pair Loss Objective `_ Args: anchor(Tensor): embedding vector for the anchor image. shape=[batch_size, embedding_dims], the data type is float32 or float64. positive(Tensor): embedding vector for the positive image. shape=[batch_size, embedding_dims], the data type is float32 or float64. labels(Tensor): 1-D tensor. shape=[batch_size], the data type is float32 or float64 or int64. l2_reg(float32): L2 regularization term on embedding vector, default: 0.002. Returns: A 0-D Tensor representing the npair loss, the data type is the same as anchor, the shape is []. Examples: .. code-block:: python >>> import paddle >>> DATATYPE = "float32" >>> paddle.seed(2023) >>> anchor = paddle.rand(shape=(18, 6), dtype=DATATYPE) >>> positive = paddle.rand(shape=(18, 6), dtype=DATATYPE) >>> labels = paddle.rand(shape=(18,), dtype=DATATYPE) >>> npair_loss = paddle.nn.functional.npair_loss(anchor, positive, labels, l2_reg = 0.002) >>> print(npair_loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 2.94269347) rz,The dims of anchor should be greater than 0.z.The dims of positive should be greater than 0.anchorrr npair_losspositivelabels)rrr?rr)Z repeat_times)permT)rZkeepdimF) transpose_x transpose_y)rJr(r?)sizerGrrrrZtileequal transposeastyper%r&squarematmulrO) rQrSrTZl2_regBeta batch_sizeZl2lossZsimilarity_matrixZ softmax_ce cross_entropyZcelossr+r+r,rR9sD (       rRcCstrt||}t|}|St|dddgdt|dddgdtdit}|j|jd}|j d|g|gdd |gid |j|jd}|j d d |gid |gid |S)a This op accepts input predictions and target label and returns the squared error cost. For predictions label, and target label, the equation is: .. math:: Out = (input - label)^2 Parameters: input (Tensor): Input tensor, the data type should be float32. label (Tensor): Label tensor, the data type should be float32. Returns: Tensor, The tensor storing the element-wise squared error difference between input and label. Examples: .. code-block:: python >>> import paddle >>> input = paddle.to_tensor([1.1, 1.9]) >>> label = paddle.to_tensor([1.0, 2.0]) >>> output = paddle.nn.functional.square_error_cost(input, label) >>> print(output) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [0.01000000, 0.01000000]) r'rrsquare_error_costr(r0Zelementwise_subXYOutr3r4r5r^reN)rc) r rsubtractr^rr r7r8rr9)r'r(Z minus_outZ square_outr:r+r+r,rcs4!     rcc Cs2tdit}|durDt|dkrD|jdd}|jdd}|jdd|gid|gid |id |}|jdd|gid|gid |id |}trPt|||||St|d dgdt|d dgd|g|gd } |dury|dury|g| d<|g| d<|jdd} |jdd} |jd| | g| gdd|id | | fS)a This op computes the edit distances, also called Levenshtein distance, between a batch of hypothesis strings and their references. It measures how dissimilar two strings are by counting the minimum number of operations to transform one string into another. The operations include insertion, deletion, and substitution. For example, given hypothesis string A = "kitten" and reference B = "sitting", A will be transformed into B at least after two substitutions and one insertion: "kitten" -> "sitten" -> "sittin" -> "sitting" So the edit distance between A and B is 3. The input is a Tensor, the input_length and label_length should be supported. The `batch_size` of labels should be same as `input`. The output include the edit distance value between every pair of input and related label, and the number of sequence. If Attr(normalized) is true, the edit distance value will be divided by the length of label. Parameters: input(Tensor): The input tensor, its rank should be equal to 2 and its data type should be int64. label(Tensor): The label tensor, its rank should be equal to 2 and its data type should be int64. normalized(bool, default True): Indicated whether to normalize the edit distance. ignored_tokens(list, default None): Tokens that will be removed before calculating edit distance. input_length(Tensor): The length for each sequence in `input` if it's of Tensor type, it should have shape `(batch_size, )` and its data type should be int64. label_length(Tensor): The length for each sequence in `label` if it's of Tensor type, it should have shape `(batch_size, )` and its data type should be int64. NOTE: To be avoid unexpected result, the value of every elements in input_length and label_length should be equal to the value of the second dimension of input and label. For example, The input: [[1,2,3,4],[5,6,7,8],[9,10,11,12]], the shape of input is [3,4] and the input_length should be [4,4,4] Returns: Tuple: distance(Tensor): edit distance result, its data type is float32, and its shape is (batch_size, 1). sequence_num(Tensor): sequence number, its data type is float32, and its shape is (1,). Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> input = paddle.to_tensor([[1,2,3],[4,5,6],[4,4,4],[1,1,1]], dtype='int64') >>> label = paddle.to_tensor([[1,3,4,1],[4,5,8,1],[7,7,7,1],[1,1,1,1]], dtype='int64') >>> input_len = paddle.to_tensor([3,3,3,3], dtype='int64') >>> label_len = paddle.to_tensor([4,4,4,4], dtype='int64') >>> distance, sequence_num = F.loss.edit_distance(input=input, label=label, input_length=input_len, label_length=label_len, normalized=False) >>> print(distance) Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, [4]) >>> print(sequence_num) Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[3.], [2.], [4.], [1.]]) >>> distance, sequence_num = F.loss.edit_distance(input=input, label=label, input_length=input_len, label_length=label_len, normalized=True) >>> print(distance) Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, [4]) >>> print(sequence_num) Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.75000000], [0.50000000], [1. ], [0.25000000]]) edit_distanceNrrr0Zsequence_eraserergtokensr2r'r()ZHypsZRefsZ HypsLengthZ RefsLength)rgZ SequenceNum normalized)rj) r r7rr8r9rrrjr) r'r(rlZignored_tokens input_length label_lengthr:Z erased_inputZ erased_label this_inputsZedit_distance_outZ sequence_numr+r+r,rjsJP        rjr&c CsR|dvr td|tr6t||}|durt||dd}|dkr+t|gddS|dkr4t|S|St|d gd d t|d gd d |durR|d krR|nd}td |d}|j |j d}|j d|g|gdd|gid|durt |t jjr|d kr|nd}t j|||d}ntd|dkrt j||dS|dkrt j||dS|S)a Measure the binary_cross_entropy loss between input predictions ``input`` and target labels ``label`` . The binary_cross_entropy loss can be described as: If :attr:`weight` is set, the loss is: .. math:: Out = -1 * weight * (label * log(input) + (1 - label) * log(1 - input)) If :attr:`weight` is None, the loss is: .. math:: Out = -1 * (label * log(input) + (1 - label) * log(1 - input)) If :attr:`reduction` set to ``'none'``, the interface will return the original loss `Out`. If :attr:`reduction` set to ``'mean'``, the reduced mean loss is: .. math:: Out = MEAN(Out) If :attr:`reduction` set to ``'sum'``, the reduced sum loss is: .. math:: Out = SUM(Out) Note that the input predictions ``input`` always be the output of sigmoid, and the target labels ``label`` should be numbers between 0 and 1. Parameters: input (Tensor): The input predications tensor. 2-D tensor with shape: [N, *], N is batch_size, `*` means number of additional dimensions. The ``input`` should always be the output of sigmod. Available dtype is float16, float32, float64. label (Tensor): The target labels tensor. 2-D tensor with the same shape as ``input``. The target labels which values should be numbers between 0 and 1. Available dtype is float16, float32, float64. weight (Tensor, optional): A manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size nbatch and the data type is float32, float64. Default is ``'None'``. reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. If ``reduction`` is ``'none'``, the shape of output is same as ``input`` , else the shape of output is scalar. Examples: .. code-block:: python >>> import paddle >>> input = paddle.to_tensor([0.5, 0.6, 0.7], 'float32') >>> label = paddle.to_tensor([1.0, 0.0, 1.0], 'float32') >>> output = paddle.nn.functional.binary_cross_entropy(input, label) >>> print(output) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.65537095) r%r&nonezzThe value of 'reduction' in binary_cross_entropy should be 'sum', 'mean' or 'none', but received %s, which is not allowed.Nrrr%Fr&r'float16rrbinary_cross_entropyr(rqr*r0bce_lossrerErgrhz5The weight is not a Tensor, please convert to Tensor.)rGrrrvmultiplyr%mean_allrr r8rr9 isinstancerZstaticrr&) r'r(weight reductionr*outZsub_namer: weight_namer+r+r,rtIsbE    rtc Cs|dvr td|trTtdgd|jt}|dur*tt|t|||}t |||dd}|dur=t||}|dkrIt |gddS|d krRt |S|St |d d d gd t |dd d gd d}|dkrv|durv|durv|}t dit} | j|jd}tjdgd|jd}|durt |dd d gd tt|t|||}| jd|||dtddd|id|durt |dd d gd |dkr|nd} tj||| d}|dkrtj ||dS|d krtj||dS|S)a Combine the sigmoid layer and the :ref:`api_paddle_nn_BCELoss` layer. This measures the element-wise probability error in classification tasks in which each class is independent. This can be thought of as predicting labels for a data-point, where labels are not mutually exclusive. For example, a news article can be about politics, technology or sports at the same time or none of these. Firstly, calculate loss function as follows: .. math:: Out = -Labels * \log(\sigma(Logit)) - (1 - Labels) * \log(1 - \sigma(Logit)) We know that :math:`\sigma(Logit) = \frac{1}{1 + e^{-Logit}}`. By substituting this we get: .. math:: Out = Logit - Logit * Labels + \log(1 + e^{-Logit}) For stability and to prevent overflow of :math:`e^{-Logit}` when Logit < 0, we reformulate the loss as follows: .. math:: Out = \max(Logit, 0) - Logit * Labels + \log(1 + e^{-\|Logit\|}) Then, if ``weight`` or ``pos_weight`` is not None, then multiply the weight tensor on the loss `Out`. The ``weight`` tensor will attach different weight on every items in the batch. The ``pos_weight`` will attach different weight on the positive label of each class. Finally, apply reduce operation on the loss. If :attr:`reduction` set to ``'none'``, will return the original loss `Out`. If :attr:`reduction` set to ``'mean'``, the reduced mean loss is :math:`Out = MEAN(Out)`. If :attr:`reduction` set to ``'sum'``, the reduced sum loss is :math:`Out = SUM(Out)`. Note that the target labels ``label`` should be numbers between 0 and 1. Args: logit (Tensor): The input predications tensor. 2-D tensor with shape: [N, *], N is batch_size, `*` means number of additional dimensions. The ``logit`` is usually the output of Linear layer. Available dtype is float32, float64. label (Tensor): The target labels tensor. 2-D tensor with the same shape as ``logit``. The target labels which values should be numbers between 0 and 1. Available dtype is float32, float64. weight (Tensor, optional): A manual rescaling weight given to the loss of each batch element. If given, it has to be a 1D Tensor whose size is `[N, ]`, The data type is float32, float64. Default is ``'None'``. reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default is ``'mean'``. pos_weight (Tensor, optional): A weight of positive examples. Must be a vector with length equal to the number of classes. The data type is float32, float64. Default is ``'None'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor. If ``reduction`` is ``'none'``, the shape of output is same as ``logit`` , else the shape of output is scalar. Examples: .. code-block:: python >>> import paddle >>> logit = paddle.to_tensor([5.0, 1.0, 3.0]) >>> label = paddle.to_tensor([1.0, 0.0, 1.0]) >>> output = paddle.nn.functional.binary_cross_entropy_with_logits(logit, label) >>> print(output) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.45618808) rpzThe value of 'reduction' in binary_cross_entropy_with_logits should be 'sum', 'mean' or 'none', but received %s, which is not allowed.r?NFrr%r&logitrr binary_cross_entropy_with_logitsr(rq!sigmoid_cross_entropy_with_logitsr0rZ fill_valuer pos_weight)rerEr)r@ normalizerg)r3r4r6r5r{ru)r)rGr rfullrr addrxrirr%ryrr r7r8rr9 kIgnoreIndexr&) rr(r{r|rr*oner}Z sigmoid_namer:r~r+r+r,rsP    rc  Cs&|dkr td|dtr!t||||||||| \} } } | St|dddgdt|dd gdt|d ddgd|d urHt|d ddgd|d urTt|d d gd|d ur`t|dd gd||d} ||||||d} tdit} | |j} | |j}| ||d}| j d| || d| S)al The hierarchical sigmoid organizes the classes into a complete binary tree to reduce the computational complexity and speed up the model training, especially the training of language model. Each leaf node of the complete binary tree represents a class(word) and each non-leaf node acts as a binary classifier. For each class(word), there's a unique path from root to itself, hsigmoid calculate the cost for each non-leaf node on the path, and sum them to get a total cost. Comparing to softmax, hsigmoid can reduce the computational complexity from :math:`O(N)` to :math:`O(logN)`, where :math:`N` represents the number of classes or the size of word dict. The API supports default tree and custom tree. For the default tree, you can refer to `Hierarchical Probabilistic Neural Network Language Model `_. For the custom tree, you need to set :attr:`is_custom` to True, and do the following steps (take the language model as an example): 1. Using a custom word dict to build a binary tree, each leaf node should be an word in the word dict. 2. Creating a dict map word_id -> path that from the word to the root node, we call it path_table. 3. Creating a dict map word_id -> code of path that from the word to the root node, we call it path_code. Code means the label of each binary classifier, 1 indicate true, 0 indicate false. 4. Now, each word should has its path and code along the path, you can pass a batch of path and code related to the same batch of inputs. Parameters: input (Tensor): A tensor with the shape [N, D], where N is the size of mini-batch, and D is the feature size. Its data type supports float32 or float64. label (Tensor): A tensor contains the labels of training data. Its shape is [N, 1] and data type is int64. num_classes (int): The number of classes or the size of word dict, must be greater than 2. If the default tree is used (path_code and path_table is None are None), `num_classes` should not be None. If the custom tree is used (path_code and path_table is None are not None), `num_classes` should be the number of non-leaf nodes, which indicates the num of classes using by the binary classifier. weight (Tensor): A tensor with shape (num_classes - 1, D), with the same data type as `input`. bias (Tensor, optional): A tensor with shape (num_classes - 1, 1), with the same data type as `input`. If `bias` is None, no bias will be add. Default is None. path_table (Tensor, optional): A tensor that stores each batch of samples' path from leaf to root node, its shape is [N, L] and data type is int64, where L is the length of path. For each sample i, path_table[i] is a np.array like structure and each element in this array is the indexes in parent nodes' weight matrix. If `path_table` and `path_code` are None, the default tree will be used. Default is None. path_code (Tensor, optional): A tensor that stores each batch of samples' code of path from leaf to root node, its shape is [N, L] and data type is int64, which is the same as :attr:`path_table`. Each code of path is consisted with the code of nodes from leaf to root node. If `path_table` and `path_code` are None, the default tree will be used. Default is None. is_sparse (bool, optional): Whether use sparse updating instead of dense updating. If `is_sparse` is True, the gradient of `weight` and `input` will be sparse. Default is False. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A tensor with the cost of hierarchical sigmoid, its shape is [N, 1] and data type is the same as `input`. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> paddle.set_device('cpu') >>> paddle.seed(2023) >>> input = paddle.uniform([4, 3]) >>> print(input) Tensor(shape=[4, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.73167229, 0.04029441, -0.48078126], [ 0.81050646, -0.15199822, -0.18717426], [ 0.94041789, 0.48874724, 0.03570259], [ 0.46585739, 0.95573163, -0.91368192]]) >>> label = paddle.to_tensor([0, 1, 4, 5]) >>> num_classes = 5 >>> weight = paddle.uniform([num_classes - 1, 3]) >>> print(weight) Tensor(shape=[4, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.14721161, 0.43916738, -0.58377075], [-0.60536981, -0.23151302, -0.70793629], [-0.54572451, -0.10784978, -0.56684279], [ 0.35370791, -0.07079649, 0.84765708]]) >>> out = F.hsigmoid_loss(input, label, num_classes, weight) >>> print(out) Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=True, [[2.23681736], [1.97140026], [1.66425037], [2.54727197]]) rzExpected num_classes >= 2 (got r>r'rr hsigmoid_lossr(rr{Nbias path_table path_code) num_classes is_sparse)reWZBiasZ PathTableZPathCoderE)rgZPreOutZW_OutZhierarchical_sigmoidr2)r) rGrrrrr r7r8rr9)r'r(rr{rrrrr*r}_r6r4r:Zpre_outr5r+r+r,rusnb          rrcCstr t|||}n;t|dgddt|dgddtdit}|j|d}|j|d}|jd||d||dd |id |d vrPt d ||d krV|S|dkr_t |S|dkrht |SdS)as Calculate smooth_l1_loss. Creates a criterion that uses a squared term if the absolute element-wise error falls below 1 and an L1 term otherwise. In some cases it can prevent exploding gradients and it is more robust and less sensitivity to outliers. Also known as the Huber loss: .. math:: loss(x,y) = \frac{1}{n}\sum_{i}z_i where :math:`z_i` is given by: .. math:: \mathop{z_i} = \left\{\begin{array}{rcl} 0.5(x_i - y_i)^2 & & {if |x_i - y_i| < \delta} \\ \delta * |x_i - y_i| - 0.5 * \delta^2 & & {otherwise} \end{array} \right. Parameters: input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes, and if shape is more than 2D, this is (N, C, D1, D2,..., Dk), k >= 1. label (Tensor): Label tensor, the data type is float32 or float64. The shape of label is the same as the shape of input. reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. If :attr:`reduction` is ``'none'``, the unreduced loss is returned. Default is ``'mean'``. delta (float, optional): Specifies the hyperparameter :math:`\delta` to be used. The value determines how large the errors need to be to use L1. Errors smaller than delta are minimized with L2. Parameter is ignored for negative/zero values. Default = 1.0 name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor, The tensor variable storing the smooth_l1_loss of input and label. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> input = paddle.rand([3, 3]).astype('float32') >>> label = paddle.rand([3, 3]).astype('float32') >>> output = paddle.nn.functional.smooth_l1_loss(input, label) >>> print(output) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.08307374) r')rsrruint16smooth_l1_lossr( huber_lossr0rd)rgZResidualdeltar2rpztThe value of 'reduction' in smooth_l1_loss should be 'sum', 'mean' or 'none', but received %s, which is not allowed.rqr&r%N)r) rrrrr r7r8Z input_dtyper9rGrr&r%)r'r(r|rr*r}r:Zresidualr+r+r,rsP9  rc Cs|dvr td|trHt||}t||}|dkr,tj|g|jd}t||}t |}|dkr=t |gddS|dkrFt |S|St dit }t|d d d gd t|dd d gd t|dd d gd t||}t|}t||}|dkr|jj|jd} tjdg||jd} t|| }||j} |dkr|jdd|id| id| S|dkrtjj |}dgddd} |jdd|id| i| d| S|dkrtjj |}|jdd|id| iid| SdS)ab Calcluate the margin rank loss between the input, other and label, use the math function as follows. .. math:: margin\_rank\_loss = max(0, -label * (input - other) + margin) If :attr:`reduction` set to ``'mean'``, the reduced mean loss is: .. math:: Out = MEAN(margin\_rank\_loss) If :attr:`reduction` set to ``'sum'``, the reduced sum loss is: .. math:: Out = SUM(margin\_rank\_loss) If :attr:`reduction` set to ``'none'``, just return the origin ``margin_rank_loss``. Parameters: input(Tensor): the first input tensor, it's data type should be float32, float64. other(Tensor): the second input tensor, it's data type should be float32, float64. label(Tensor): the label value corresponding to input, it's data type should be float32, float64. margin (float, optional): The margin value to add, default value is 0; reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'``, ``'mean'``, ``'sum'``.If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[]`, otherwise the shape is the same as `input` .The same dtype as input tensor. Examples: .. code-block:: python >>> import paddle >>> input = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32') >>> other = paddle.to_tensor([[2, 1], [2, 4]], dtype='float32') >>> label = paddle.to_tensor([[1, -1], [-1, -1]], dtype='float32') >>> loss = paddle.nn.functional.margin_ranking_loss(input, other, label) >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.75000000) rpzwThe value of 'reduction' in MarginRankingLoss should be 'sum', 'mean' or 'none', but received %s, which is not allowed.rr0r%NFr&margin_ranking_lossr'rrZmargin_rank_lossotherr(rrrqrelurergrhrT)dimZkeep_dimZ reduce_allZ reduce_sumr2)r)rGr rrirxrZ to_tensorrrrr%ryr r7rnegblockZ create_varrr8r9r r!) r'rr(marginr|r*r}r:Z neg_labelZ margin_varZ result_outr6r+r+r,r{s~0              rcCs|dvr td|tr-tt||}|dkrt|S|dkr+t|gddS|St|dgdd t|d gdd |dkrTttj||d }tj||d S|dkrittj||d }tj ||d Sttj|||d S)a Computes the L1 Loss of Tensor ``input`` and ``label`` as follows. If `reduction` set to ``'none'``, the loss is: .. math:: Out = \lvert input - label \rvert If `reduction` set to ``'mean'``, the loss is: .. math:: Out = MEAN(\lvert input - label \rvert) If `reduction` set to ``'sum'``, the loss is: .. math:: Out = SUM(\lvert input - label \rvert) Parameters: input (Tensor): The input tensor. The shapes is [N, `*`], where N is batch size and `*` means any number of additional dimensions. It's data type should be float32, float64, int32, int64. label (Tensor): label. The shapes is [N, `*`], same shape as ``input`` . It's data type should be float32, float64, int32, int64. reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If `reduction` is ``'none'``, the unreduced loss is returned; If `reduction` is ``'mean'``, the reduced mean loss is returned. If `reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the L1 Loss of Tensor ``input`` and ``label``. If `reduction` is ``'none'``, the shape of output loss is :math:`[N, *]`, the same as ``input`` . If `reduction` is ``'mean'`` or ``'sum'``, the shape of output loss is []. Examples: .. code-block:: python >>> import paddle >>> input = paddle.to_tensor([[1.5, 0.8], [0.2, 1.3]]) >>> label = paddle.to_tensor([[1.7, 1], [0.4, 0.5]]) >>> l1_loss = paddle.nn.functional.l1_loss(input, label) >>> print(l1_loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.34999999) >>> l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='none') >>> print(l1_loss) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.20000005, 0.19999999], [0.20000000, 0.79999995]]) >>> l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='sum') >>> print(l1_loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.39999998) rpzlThe value of 'reduction' in L1Loss should be 'sum', 'mean' or 'none', but received %s, which is not allowed.r&r%NFr')rrrrrsl1_lossr()xyru)rrr*) rGrrabsriryr%rrr&)r'r(r|r*Z unreducedr+r+r,rs@> rcCsD|dvr td|t|j}t|}t|j}t|} |d| kr1|| kr1td|d| d|dkr=td|d|ddkrMtd |dd|d } |d} tr|dkr||d kr|t|| | dd g}t|| dd g}| g|dd } t|||||\} }|dkr|d kr|dkrt| | } | Stdit }|dkr|d krt|| | dd gd}t|| dd gd}| g|dd } t |dddgdt |ddgd||d}||d}|d urt |t r||d<|j |jd} |j |jd}| |d}|jd|||d|dkr |d kr |dkr t| | d} | S)ax This api returns negative log likelihood. See more detail in :ref:`NLLLoss ` . Parameters: input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes. But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`. The data type is float32, float64. label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`. The data type is int64. weight (Tensor, optional): Weight tensor, a manual rescaling weight given to each class. If given, it has to be a 1D Tensor whose size is `[C, ]`. Otherwise, it treated as if having all ones. the data type is float32, float64, Default is ``'None'``. ignore_index (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. Default is -100. reduction (str, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If `reduction` is ``'mean'``, the reduced mean loss is returned; if `reduction` is ``'sum'``, the reduced sum loss is returned; if `reduction` is ``'none'``, no reduction will be apllied. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: `Tensor`, the value of negative log likelihood loss. Examples: .. code-block:: python >>> import paddle >>> from paddle.nn.functional import nll_loss >>> log_softmax = paddle.nn.LogSoftmax(axis=1) >>> input = paddle.to_tensor([[0.88103855, 0.9908683 , 0.6226845 ], ... [0.53331435, 0.07999352, 0.8549948 ], ... [0.25879037, 0.39530203, 0.698465 ], ... [0.73427284, 0.63575995, 0.18827209], ... [0.05689114, 0.0862954 , 0.6325046 ]], "float32") >>> log_out = log_softmax(input) >>> label = paddle.to_tensor([0, 2, 1, 1, 0], "int64") >>> result = nll_loss(log_out, label) >>> print(result) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.07202101) rpznThe value of 'reduction' in nll_loss should be 'sum', 'mean' or 'none', but received %s, which is not allowed.rr<r=r>rz#Expected 2 or more dimensions (got z,Expected 1 or more classess (got num classesrrNrqnll_lossrVr'rrr(rrw)r|r@ZWeightr0)rgZ Total_weightr2)r)rGr#rrr rrrr r7rrzrr8rr9)r'r(r{r@r|r* input_shaperLZ label_shaperMnc out_shaper} total_weightr:r4r6r5r+r+r,rTs|4           r:0yE>c Cs|dkr td||dvrtd|t|dgddt|dgdd|j|jks0td t||j}d}|rEt|||}n ||t||}|ru|t||d td tj |}|t |d k|t |7}|d krt |}|S|dkrt |}|S)a Poisson negative log likelihood loss. See more detail in :ref:`PoissonNLLLoss ` . Parameters: input (Tensor): Input tensor, expectation of underlying Poisson distribution. The shape of input tensor should be `(N, *)` or `(*)` where `(*)` denotes any number of extra dimensions. It's data type should be float16, bfloat16, float32, float64. label (Tensor): Label tensor, random sampled from Poisson distribution :math:`label \sim \text{Poisson}(input)`. The shape of input tensor should be `(N, *)` or `(*)`, same shape as the input tensor. It's data type should be float16, bfloat16, float32, float64. log_input (bool, optional): Whether to the treat input tensor as log input. If ``True`` the loss is computed as, :math:`\exp(\text{input}) - \text{label} * \text{input}` . If ``False`` then loss is :math:`\text{input} - \text{label} * \log(\text{input}+\text{epsilon})` . Default: ``True``. full (bool, optional): Whether to compute full loss. If ``True``, the Stirling approximation term is added. If ``False``, the Stirling approximation is dropped. Default: ``False``. epsilon (float, optional): A small value to avoid evaluation of :math:`\log(0)` when `log_input`\ =\ ``False``. ``epsilon > 0``. Default: 1e-8. reduction (str, optional): Indicate how to reduce the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If `reduction` is ``'mean'``, the reduced mean loss is returned; if `reduction` is ``'sum'``, the reduced sum loss is returned; if `reduction` is ``'none'``, no reduction will be apllied. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> paddle.seed(2023) >>> input = paddle.randn([5, 2], dtype=paddle.float32) >>> label = paddle.randn([5, 2], dtype=paddle.float32) >>> loss = F.poisson_nll_loss(input, label, log_input=True, reduction='none') >>> print(loss) Tensor(shape=[5, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 1.09998012, 3.68829036], [ 1.95291090, 0.69603068], [-0.39289063, -2.03713036], [ 4.52518702, 1.28625548], [ 3.94454789, 0.53521496]]) >>> loss = F.poisson_nll_loss(input, label, reduction='mean') >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.52983975) rzcThe value of `epsilon` in poisson_nll_loss should be positve, but received %f, which is not allowedrpzvThe value of 'reduction' in poisson_nll_loss should be 'sum', 'mean' or 'none', but received %s, which is not allowed.r')rsrrrpoisson_nll_lossr()input's shape must equal to label's shape?rrr&r%)rGrrrcastrexplogmathpiwhere zeros_liker&r%) r'r(Z log_inputrr)r|r*loss_outZstirling_approxr+r+r,rsbC    rcCstj|jdkrtj|jdkrt|d}ntj|jdkr1tj|jdkr1t|d}trjt||d}|dkrFt |}|S|dkrQt |}|S|dkrht |j dkrh|j d}t ||}|St dit}t|d ddgdt|d ddgdtj|d td|j|jd }|jd ||dd|id did|dkrt |}|S|dkrt |}|S|dkrt |d}t ||}|S)az Calculate the Kullback-Leibler divergence loss between Input(X) and Input(Target). Notes that Input(X) is the log-probability and Input(Target) is the probability. KL divergence loss is calculated as follows: $$l(x, y) = y * (\log(y) - x)$$ Here :math:`x` is input and :math:`y` is label. If `reduction` is ``'none'``, the output loss is the same shape as the input, and the loss at each point is calculated separately. There is no reduction to the result. If `reduction` is ``'mean'``, the output loss is the shape of [], and the output is the average of all losses. If `reduction` is ``'sum'``, the output loss is the shape of [], and the output is the sum of all losses. If `reduction` is ``'batchmean'``, the output loss is the shape of [N], N is the batch size, and the output is the sum of all losses divided by the batch size. Args: input (Tensor): The input tensor. The shapes is [N, *], where N is batch size and `*` means any number of additional dimensions. It's data type should be float32, float64. label (Tensor): label. The shapes is [N, *], same shape as ``input`` . It's data type should be float32, float64. reduction (str, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'batchmean'`` | ``'mean'`` | ``'sum'``. If `reduction` is ``'mean'``, the reduced mean loss is returned; If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned; if `reduction` is ``'sum'``, the reduced sum loss is returned; if `reduction` is ``'none'``, no reduction will be apllied. Default is ``'mean'``. name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The KL divergence loss. The data type is same as input tensor Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> paddle.seed(2023) >>> shape = (5, 20) >>> # input(x) should be a distribution in the log space >>> x = F.log_softmax(paddle.randn(shape), axis=1).astype('float32') >>> target = paddle.uniform(shape, min=-10, max=10).astype('float32') >>> # 'batchmean' reduction, loss shape will be [], who is 0-D Tensor >>> pred_loss = F.kl_div(x, target, reduction='batchmean') >>> print(pred_loss.shape) [] >>> # 'mean' reduction, loss shape will be [], who is 0-D Tensor >>> pred_loss = F.kl_div(x, target, reduction='mean') >>> print(pred_loss.shape) [] >>> # 'sum' reduction, loss shape will be [], who is 0-D Tensor >>> pred_loss = F.kl_div(x, target, reduction='sum') >>> print(pred_loss.shape) [] >>> # 'none' reduction, loss shape is same with input shape >>> pred_loss = F.kl_div(x, target, reduction='none') >>> print(pred_loss.shape) [5, 20] rrrqr&r%Z batchmeanrkl_divr'r(r|r0 kldiv_loss)reZTargetr1r2N)r)r data_feederZ convert_dtyperrrrrrr&r%rrr r7rZ check_typestrr8r9)r'r(r|r*r}rar:r;r+r+r,rFsXJ        rcCs|dvr td|dts!t|dddgdt|dddgd|d kr0tjt|||d S|d krBtjtt|||d Stjtt|||d S) a Accept input predications and label and returns the mean square error. If :attr:`reduction` is set to ``'none'``, loss is calculated as: .. math:: Out = (input - label)^2 If :attr:`reduction` is set to ``'mean'``, loss is calculated as: .. math:: Out = \operatorname{mean}((input - label)^2) If :attr:`reduction` is set to ``'sum'``, loss is calculated as: .. math:: Out = \operatorname{sum}((input - label)^2) Parameters: input (Tensor): Input tensor, the data type should be float32 or float64. label (Tensor): Label tensor, the data type should be float32 or float64. reduction (string, optional): The reduction method for the output, could be 'none' | 'mean' | 'sum'. If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. If :attr:`reduction` is ``'none'``, the unreduced loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, The tensor tensor storing the mean square error difference of input and label. Examples: .. code-block:: python >>> import paddle >>> mse_loss = paddle.nn.loss.MSELoss() >>> input = paddle.to_tensor(1.5) >>> label = paddle.to_tensor(1.7) >>> output = mse_loss(input, label) >>> print(output) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.04000002) rpzJ'reduction' in 'mse_loss' should be 'sum', 'mean' or 'none', but received .r'rrmse_lossr(rqrur&)rGrrrr^rir&r%)r'r(r|r*r+r+r,rs,1  rc Csn    d dd}|||||||}t|dg}|dvsJ|dkr,t||}|S|d kr5t|}|S) aw An operator integrating the open source Warp-CTC library (https://github.com/baidu-research/warp-ctc) to compute Connectionist Temporal Classification (CTC) loss. It can be aliased as softmax with CTC, since a native softmax activation is interated to the Warp-CTC library to normalize values for each row of the input tensor. Parameters: log_probs (Tensor): The unscaled probability sequence with padding, which is a 3-D Tensor. The tensor shape is [max_logit_length, batch_size, num_classes + 1], where max_logit_length is the longest length of input logit sequence. The data type should be float32 or float64. labels (Tensor): The ground truth sequence with padding, which must be a 3-D Tensor. The tensor shape is [batch_size, max_label_length], where max_label_length is the longest length of label sequence. The data type must be int32. input_lengths (Tensor): The length for each input sequence, it should have shape [batch_size] and dtype int64. label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64. blank (int, optional): The blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). The data type must be int32. Default: 0. reduction (str, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the output loss will be divided by the label_lengths, and then return the mean of quotient; If :attr:`reduction` is ``'sum'``, return the sum of loss; If :attr:`reduction` is ``'none'``, no reduction will be applied. Default: ``'mean'``. norm_by_times (bool, optional): Whether to normalize the gradients by the number of time-step, which is also the sequence's length. There is no need to normalize the gradients if reduction mode is 'mean'. Default: False. Returns: Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``log_probs``. Examples: .. code-block:: python >>> # declarative mode >>> import paddle.nn.functional as F >>> import paddle >>> import numpy as np >>> # length of the longest logit sequence >>> max_seq_length = 4 >>> #length of the longest label sequence >>> max_label_length = 3 >>> # number of logit sequences >>> batch_size = 2 >>> # class num >>> class_num = 3 >>> log_probs = paddle.to_tensor(np.array([ ... [[4.17021990e-01, 7.20324516e-01, 1.14374816e-04], ... [3.02332580e-01, 1.46755889e-01, 9.23385918e-02]], ... [[1.86260208e-01, 3.45560730e-01, 3.96767467e-01], ... [5.38816750e-01, 4.19194520e-01, 6.85219526e-01]], ... [[2.04452246e-01, 8.78117442e-01, 2.73875929e-02], ... [6.70467496e-01, 4.17304814e-01, 5.58689833e-01]], ... [[1.40386939e-01, 1.98101491e-01, 8.00744593e-01], ... [9.68261600e-01, 3.13424170e-01, 6.92322612e-01]], ... [[8.76389146e-01, 8.94606650e-01, 8.50442126e-02], ... [3.90547849e-02, 1.69830427e-01, 8.78142476e-01]] ... ]), dtype="float32") >>> labels = paddle.to_tensor([[1, 2, 2], ... [1, 2, 2]], dtype="int32") >>> input_lengths = paddle.to_tensor([5, 5], dtype="int64") >>> label_lengths = paddle.to_tensor([3, 3], dtype="int64") >>> loss = F.ctc_loss(log_probs, labels, ... input_lengths, ... label_lengths, ... blank=0, ... reduction='none') >>> print(loss) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [3.91798496, 2.90765190]) >>> loss = F.ctc_loss(log_probs, labels, ... input_lengths, ... label_lengths, ... blank=0, ... reduction='mean') >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.13760614) rFNc Sstr|dus |durtdt||||||}|Stdit}t|dddgdt|ddgd|g|gd}|dur]|dur]t|d d gdt|d d gd|g|d <|g|d <|j|jd }|j|jd } |j d|| g|gd ||dd|S)Nz?input_length and label_length must not be None in dygraph mode!warpctcr'rrr(rrDZ LogitsLengthrZ LabelLengthr0)Z WarpCTCGradr1)blank norm_by_timesr2)r) r rGrrr r7rr8rr9) r'r(rrrmrnrr:rograd_outr+r+r,r`sN        zctc_loss..warpctcrr&r%rqr&r%)rFNN)rrr&r%) Z log_probsrT input_lengths label_lengthsrr|rrrr+r+r,ctc_loss s V 4   rMbP?c Csl d dd}|jd} |||||||} |dvsJ|dkr)tj| |d| } | S|dkr4tj| |d} | S) a An operator integrating the open source Warp-Transducer library (https://github.com/b-flo/warp-transducer.git) to compute Sequence Transduction with Recurrent Neural Networks (RNN-T) loss. Parameters: input (Tensor): The logprobs sequence with padding, which is a 4-D Tensor. The tensor shape is [B, Tmax, Umax, D], where Tmax is the longest length of input logit sequence. The data type should be float32 or float64. label (Tensor): The ground truth sequence with padding, which must be a 2-D Tensor. The tensor shape is [B, Umax], where Umax is the longest length of label sequence. The data type must be int32. input_lengths (Tensor): The length for each input sequence, it should have shape [batch_size] and dtype int64. label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64. blank (int, optional): The blank label index of RNN-T loss, which is in the half-opened interval [0, B). The data type must be int32. Default is 0. fastemit_lambda (float, default 0.001): Regularization parameter for FastEmit (https://arxiv.org/pdf/2010.11148.pdf) reduction (string, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the output will be sum of loss and be divided by the batch_size; If :attr:`reduction` is ``'sum'``, return the sum of loss; If :attr:`reduction` is ``'none'``, no reduction will be applied. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, The RNN-T loss between ``logprobs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is []. Data type is the same as ``logprobs``. Examples: .. code-block:: python >>> # declarative mode >>> import paddle.nn.functional as F >>> import numpy as np >>> import paddle >>> import functools >>> fn = functools.partial(F.rnnt_loss, reduction='sum', fastemit_lambda=0.0, blank=0) >>> acts = np.array([[ ... [[0.1, 0.6, 0.1, 0.1, 0.1], ... [0.1, 0.1, 0.6, 0.1, 0.1], ... [0.1, 0.1, 0.2, 0.8, 0.1]], ... [[0.1, 0.6, 0.1, 0.1, 0.1], ... [0.1, 0.1, 0.2, 0.1, 0.1], ... [0.7, 0.1, 0.2, 0.1, 0.1]] ... ]]) >>> labels = [[1, 2]] >>> acts = paddle.to_tensor(acts, stop_gradient=False) >>> lengths = [acts.shape[1]] * acts.shape[0] >>> label_lengths = [len(l) for l in labels] >>> labels = paddle.to_tensor(labels, paddle.int32) >>> lengths = paddle.to_tensor(lengths, paddle.int32) >>> label_lengths = paddle.to_tensor(label_lengths, paddle.int32) >>> costs = fn(acts, labels, lengths, label_lengths) >>> print(costs) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False, -2.85042444) rrc Sstrt||||||}|Stdit}t|dddgdt|ddgdt|ddgdt|ddgd|g|g|g|gd }|j|jd }|j|jd } |jd|| g|gd ||d d |S)Nwarprnntr'rrr(rrr)r'r(rrr0)Z warprnntgradr;)rfastemit_lambdar2)r) r rrr r7rr8rr9) r'r(rmrnrrrr:rorr+r+r,rsJ     zrnnt_loss..warprnntrr&rur%N)rr)rrr%) r'r(rrrrr|r*rBrr+r+r, rnnt_losssA -  rrP@c  Cs0|dvsJ|dus|dust|dstd|t|dr%|s%dSd} d} d} |durY|dur5dn|j} trYtj } | j } |durJ| n| | } |durV| j n|j } tt|j}tt|j}|d|kr|||kr|td|d |d |d|krtj|d d }trt|||| | | |||| \}}|d krt|}n |dkrt|}|s|S||fSd}t|fit}|j|jd}|j|jd}t|dgddt|dddgd|j|||d||d|| | | ||||dd|d krt|}n |dkrt|}|s|S||fS)aO& .. math:: L=-\frac{1}{N}\sum^N_{i=1}\log\frac{e^{s(cos(m_{1}\theta_{y_i}+m_{2})-m_{3})}}{e^{s(cos(m_{1}\theta_{y_i}+m_{2})-m_{3})}+\sum^n_{j=1,j\neq y_i} e^{scos\theta_{y_i}}} where the :math:`\theta_{y_i}` is the angle between the feature :math:`x` and the representation of class :math:`i`. The details of ArcFace loss could be referred to https://arxiv.org/abs/1801.07698. .. hint:: The API supports single GPU and multi GPU, and don't supports CPU. For data parallel mode, set ``group=False``. For model parallel mode, set ``group=None`` or the group instance return by paddle.distributed.new_group. And logits.shape[-1] can be different at each rank. Args: logits (Tensor): shape[N, local_num_classes], the output of the normalized X multiply the normalized W. The logits is shard_logits when using model parallel. label (Tensor): shape[N] or shape[N, 1], the groud truth label. margin1 (float, optional): m1 of margin loss, default value is `1.0`. margin2 (float, optional): m2 of margin loss, default value is `0.5`. margin3 (float, optional): m3 of margin loss, default value is `0.0`. scale (float, optional): s of margin loss, default value is `64.0`. group (Group, optional): The group instance return by paddle.distributed.new_group or ``None`` for global default group or ``False`` for data parallel (do not communication cross ranks). Default is ``None``. return_softmax (bool, optional): Whether return softmax probability. Default value is `False`. reduction (str, optional): The candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, return the average of loss; If :attr:`reduction` is ``'sum'``, return the sum of loss; If :attr:`reduction` is ``'none'``, no reduction will be applied. Default value is `'mean'`. Returns: Tensor|tuple[Tensor, Tensor], return the cross entropy loss if `return_softmax` is False, otherwise the tuple (loss, softmax), softmax is shard_softmax when using model parallel, otherwise softmax is in the same shape with input logits. If ``reduction == None``, the shape of loss is ``[N, 1]``, otherwise the shape is ``[]``. Examples: .. code-block:: python :name: code-example1 >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.seed(2023) >>> paddle.device.set_device('gpu') >>> m1 = 1.0 >>> m2 = 0.5 >>> m3 = 0.0 >>> s = 64.0 >>> batch_size = 2 >>> feature_length = 4 >>> num_classes = 4 >>> label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64') >>> X = paddle.randn( ... shape=[batch_size, feature_length], ... dtype='float64') >>> X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True)) >>> X = paddle.divide(X, X_l2) >>> W = paddle.randn( ... shape=[feature_length, num_classes], ... dtype='float64') >>> W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True)) >>> W = paddle.divide(W, W_l2) >>> logits = paddle.matmul(X, W) >>> loss, softmax = paddle.nn.functional.margin_cross_entropy( ... logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None) >>> print(logits) Tensor(shape=[2, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True, [[-0.59561850, 0.32797505, 0.80279214, 0.00144975], [-0.16265212, 0.84155098, 0.62008629, 0.79126072]]) >>> print(label) Tensor(shape=[2], dtype=int64, place=Place(gpu:0), stop_gradient=True, [1, 0]) >>> print(loss) Tensor(shape=[2, 1], dtype=float64, place=Place(gpu:0), stop_gradient=True, [[61.94391901], [93.30853839]]) >>> print(softmax) Tensor(shape=[2, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True, [[0.00000000, 0.00000000, 1. , 0.00000000], [0.00000000, 0.96152676, 0.00000067, 0.03847257]]) .. code-block:: python :name: code-example2 >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> # Multi GPU, test_margin_cross_entropy.py >>> import paddle >>> import paddle.distributed as dist >>> paddle.seed(2023) >>> strategy = dist.fleet.DistributedStrategy() >>> dist.fleet.init(is_collective=True, strategy=strategy) >>> rank_id = dist.get_rank() >>> m1 = 1.0 >>> m2 = 0.5 >>> m3 = 0.0 >>> s = 64.0 >>> batch_size = 2 >>> feature_length = 4 >>> num_class_per_card = [4, 8] >>> num_classes = paddle.sum(paddle.to_tensor(num_class_per_card)) >>> label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64') >>> label_list = [] >>> dist.all_gather(label_list, label) >>> label = paddle.concat(label_list, axis=0) >>> X = paddle.randn( ... shape=[batch_size, feature_length], ... dtype='float64') >>> X_list = [] >>> dist.all_gather(X_list, X) >>> X = paddle.concat(X_list, axis=0) >>> X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True)) >>> X = paddle.divide(X, X_l2) >>> W = paddle.randn( ... shape=[feature_length, num_class_per_card[rank_id]], ... dtype='float64') >>> W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True)) >>> W = paddle.divide(W, W_l2) >>> logits = paddle.matmul(X, W) >>> loss, softmax = paddle.nn.functional.margin_cross_entropy( ... logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None) >>> print(logits) >>> print(label) >>> print(loss) >>> print(softmax) >>> # python -m paddle.distributed.launch --gpus=0,1 --log_dir log test_margin_cross_entropy.py >>> # cat log/workerlog.0 >>> # Tensor(shape=[4, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True, >>> # [[-0.59561850, 0.32797505, 0.80279214, 0.00144975], >>> # [-0.16265212, 0.84155098, 0.62008629, 0.79126072], >>> # [-0.59561850, 0.32797505, 0.80279214, 0.00144975], >>> # [-0.16265212, 0.84155098, 0.62008629, 0.79126072]]) >>> # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, >>> # [5, 4, 5, 4]) >>> # Tensor(shape=[4, 1], dtype=float64, place=Place(gpu:0), stop_gradient=True, >>> # [[104.27437027], >>> # [113.40243782], >>> # [104.27437027], >>> # [113.40243782]]) >>> # Tensor(shape=[4, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True, >>> # [[0.00000000, 0.00000000, 0.01210039, 0.00000000], >>> # [0.00000000, 0.96152674, 0.00000067, 0.03847257], >>> # [0.00000000, 0.00000000, 0.01210039, 0.00000000], >>> # [0.00000000, 0.96152674, 0.00000067, 0.03847257]]) >>> # cat log/workerlog.1 >>> # Tensor(shape=[4, 8], dtype=float64, place=Place(gpu:1), stop_gradient=True, >>> # [[-0.34913275, -0.35180883, -0.53976657, -0.75234331, 0.70534995, >>> # 0.87157838, 0.31064437, 0.19537700], >>> # [-0.63941012, -0.05631600, -0.02561853, 0.09363013, 0.56571130, >>> # 0.13611246, 0.08849565, 0.39219619], >>> # [-0.34913275, -0.35180883, -0.53976657, -0.75234331, 0.70534995, >>> # 0.87157838, 0.31064437, 0.19537700], >>> # [-0.63941012, -0.05631600, -0.02561853, 0.09363013, 0.56571130, >>> # 0.13611246, 0.08849565, 0.39219619]]) >>> # Tensor(shape=[4], dtype=int64, place=Place(gpu:1), stop_gradient=True, >>> # [5, 4, 5, 4]) >>> # Tensor(shape=[4, 1], dtype=float64, place=Place(gpu:1), stop_gradient=True, >>> # [[104.27437027], >>> # [113.40243782], >>> # [104.27437027], >>> # [113.40243782]]) >>> # Tensor(shape=[4, 8], dtype=float64, place=Place(gpu:1), stop_gradient=True, >>> # [[0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00002368, 0.98787593, >>> # 0.00000000, 0.00000000], >>> # [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000002, 0.00000000, >>> # 0.00000000, 0.00000000], >>> # [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00002368, 0.98787593, >>> # 0.00000000, 0.00000000], >>> # [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000002, 0.00000000, >>> # 0.00000000, 0.00000000]]) )r&r%rqNFN is_memberzmExpected group is False, None or instance of paddle.distributed.collective.Group (got group: {})rrr<r=r>rrr&r%margin_cross_entropyr0rJrrr(rrrDrC)rKring_idranknranksmargin1margin2margin3scaler2)hasattrrGformatridr Zis_compiled_with_distr distributedZ ParallelEnvrZget_group_rankZ world_sizerrr#rrHrrrr&r%r r7r8rrr9)rJr(rrrrgrouprKr|rrrZ parallel_envZ global_rankrLrMrNr;Zop_typer:r+r+r,r s G         rz2.0.0z"paddle.nn.functional.cross_entropyrzPlease notice that behavior of "paddle.nn.functional.softmax_with_cross_entropy" and "paddle.nn.functional.cross_entropy" is different.)ZsinceZ update_tolevelreasoncCst|||||||S)a This operator implements the cross entropy loss function with softmax. This function combines the calculation of the softmax operation and the cross entropy loss function to provide a more numerically stable gradient. Because this operator performs a softmax on logits internally, it expects unscaled logits. This operator should not be used with the output of softmax operator since that would produce incorrect results. When the attribute :attr:`soft_label` is set :attr:`False`, this operators expects mutually exclusive hard labels, each sample in a batch is in exactly one class with a probability of 1.0. Each sample in the batch will have a single label. The equation is as follows: 1) Hard label (one-hot label, so every sample has exactly one class) .. math:: \\loss_j=-\text{logits}_{label_j} +\log\left(\sum_{i=0}^{K}\exp(\text{logits}_i)\right), j = 1,..., K 2) Soft label (each sample can have a distribution over all classes) .. math:: \\loss_j= -\sum_{i=0}^{K}\text{label}_i\left(\text{logits}_i - \log\left(\sum_{i=0}^{K}\exp(\text{logits}_i)\right)\right), j = 1,...,K 3) If :attr:`numeric_stable_mode` is :attr:`True`, softmax is calculated first by: .. math:: \\max_j&=\max_{i=0}^{K}{\text{logits}_i} \\ log\_max\_sum_j &= \log\sum_{i=0}^{K}\exp(logits_i - max_j)\\ softmax_j &= \exp(logits_j - max_j - {log\_max\_sum}_j) and then cross entropy loss is calculated by softmax and label. Args: logits (Tensor): A multi-dimension ``Tensor`` , and the data type is float32 or float64. The input tensor of unscaled log probabilities. label (Tensor): The ground truth ``Tensor`` , data type is the same as the ``logits`` . If :attr:`soft_label` is set to :attr:`True`, Label is a ``Tensor`` in the same shape with :attr:`logits`. If :attr:`soft_label` is set to :attr:`True`, Label is a ``Tensor`` in the same shape with :attr:`logits` expect shape in dimension :attr:`axis` as 1. soft_label (bool, optional): A flag to indicate whether to interpretant the given labels as soft labels. Default False. ignore_index (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. Only valid if :attr:`soft_label` is set to :attr:`False`. Default: kIgnoreIndex(-100). numeric_stable_mode (bool, optional): A flag to indicate whether to use a more numerically stable algorithm. Only valid when :attr:`soft_label` is :attr:`False` and GPU is used. When :attr:`soft_label` is :attr:`True` or CPU is used, the algorithm is always numerically stable. Note that the speed may be slower when use stable algorithm. Default: True. return_softmax (bool, optional): A flag indicating whether to return the softmax along with the cross entropy loss. Default: False. axis (int, optional): The index of dimension to perform softmax calculations. It should be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of input :attr:`logits`. Default: -1. Returns: - If `return_softmax` is False, return the cross entropy loss as a ``Tensor``. The dtype is the same as the input ``logits``. The shape is consistent with ``logits`` except in dimension :attr:`axis` as 1. - If `return_softmax` is True, return a tuple of two ``Tensor``: the cross entropy loss and the softmax result. The dtype of the cross entropy loss is the same as the input ``logits``, and the shape is consistent with ``logits`` except in dimension :attr:`axis` as 1. The dtype and shape of the softmax result are the same as the input ``logits``. Examples: .. code-block:: python >>> import paddle >>> logits = paddle.to_tensor([0.4, 0.6, 0.9], dtype="float32") >>> label = paddle.to_tensor([1], dtype="int64") >>> out = paddle.nn.functional.softmax_with_cross_entropy(logits=logits, label=label) >>> print(out) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [1.15328646]) )rO)rJr(r?r@rArKrr+r+r,rBL sfrBc " Csd|dvr td||dkr|rtd|tt|j} | dkr%tdtt|j} | d| kr9tj||d}| d| krN| | krNtd| d | d |d krd }| d| krltj||d}tjj ||jd }tjjj ||d}| |j }tt|j} t r|stj||k|j d|} t||||d ||\} }|dur^|rtjt||j |dd d}t|j}t||d}t||j }t||}n|j||jd krtd|j|d|jd dt||k|j }|jdkr|j|dkrt||}|d kr:|| jdkr:tt|| jtt|| jd| j|| jg}t|| |}nt|| }t||}t|j}t||d}t||j }t||}|dkrkt|gddS|dkr||k}|}t|dkrt|gdd}|durtj||j d}t|gdd}|||d k}|St||j }t||}t|gdd}|||d k}|S|durt|gdd}t|gdd}|||d kSt|S| d| krtj||d}|St|dgddt|dgddtrt||||d ||\}}n/||d ||d}td(it}|j|j d}|j|j d}||d } |j d||d!| |d"|durt|d#d$d%gd|d&kr_| nd}!|rtjt||j |dd d}t|j}t||d}t||j }n|j||jd krtd|j|d|jd dttj||k|j d|} t||k|j }|jdkr|j|dkrt||}|d kr|| jdkrtt|| jtt|| jd| j|| jg}t|t| |}nt|| }t||}t|j}t||d}tj|||!d'}|dkr+tj|| d'S|dkr|dkrtj|| d'}||k}|dur`tj||j d}tj|| d'}||t!|d }|St||j }t||}tj|| d'}||t!|d }|S|durtj|| d'}t|}||t!|d Stj"|| d'S| d| krtj||d}|S))a. By default, the cross entropy loss function is implemented using softmax. This function combines the calculation of the softmax operation and the cross entropy loss function to provide a more numerically stable computing. Calculate the cross entropy loss function without softmax when use_softmax=False. By default, calculate the mean of the result, and you can also affect the default behavior by using the reduction parameter. Please refer to the part of parameters for details. Can be used to calculate the softmax cross entropy loss with soft and hard labels. Where, the hard labels mean the actual label value, 0, 1, 2, etc. And the soft labels mean the probability of the actual label, 0.6, 0.8, 0.2, etc. The calculation includes the following two steps. - **1.softmax cross entropy** 1. Hard label (each sample can only be assigned into one category) 1.1. when use_softmax=True .. math:: \\loss_j=-\text{logits}_{label_j}+\log\left(\sum_{i=0}^{C}\exp(\text{logits}_i)\right) , j = 1,...,N where, N is the number of samples and C is the number of categories. 1.2. when use_softmax=False .. math:: \\loss_j=-\log\left({P}_{label_j}\right) , j = 1,...,N where, N is the number of samples and C is the number of categories, P is input(the output of softmax). 2. Soft label (each sample is assigned to multiple categories with a certain probability, and the probability sum is 1). 2.1. when use_softmax=True .. math:: \\loss_j=-\sum_{i=0}^{C}\text{label}_i\left(\text{logits}_i-\log\left(\sum_{i=0}^{C}\exp(\text{logits}_i)\right)\right) , j = 1,...,N where, N is the number of samples and C is the number of categories. 2.2. when use_softmax=False .. math:: \\loss_j=-\sum_{j=0}^{C}\left({label}_j*\log\left({P}_{label_j}\right)\right) , j = 1,...,N where, N is the number of samples and C is the number of categories, P is input(the output of softmax). - **2. Weight and reduction processing** 1. Weight If the ``weight`` parameter is ``None`` , go to the next step directly. If the ``weight`` parameter is not ``None`` , the cross entropy of each sample is weighted by weight according to soft_label = False or True as follows. 1.1. Hard labels (soft_label = False) .. math:: \\loss_j=loss_j*weight[label_j] 1.2. Soft labels (soft_label = True) .. math:: \\loss_j=loss_j*\sum_{i}\left(weight[label_i]*logits_i\right) 2. reduction 2.1 if the ``reduction`` parameter is ``none`` Return the previous result directly 2.2 if the ``reduction`` parameter is ``sum`` Return the sum of the previous results .. math:: \\loss=\sum_{j}loss_j 2.3 if the ``reduction`` parameter is ``mean`` , it will be processed according to the ``weight`` parameter as follows. 2.3.1. If the ``weight`` parameter is ``None`` Return the average value of the previous results .. math:: \\loss=\sum_{j}loss_j/N where, N is the number of samples and C is the number of categories. 2.3.2. If the 'weight' parameter is not 'None', the weighted average value of the previous result will be returned 1. Hard labels (soft_label = False) .. math:: \\loss=\sum_{j}loss_j/\sum_{j}weight[label_j] 2. Soft labels (soft_label = True) .. math:: \\loss=\sum_{j}loss_j/\sum_{j}\left(\sum_{i}weight[label_i]\right) Parameters: input (Tensor): the data type is float32, float64. Shape is :math:`[N_1, N_2, ..., N_k, C]`, where C is number of classes, ``k >= 1`` . Note: 1. when use_softmax=True, it expects unscaled logits. This operator should not be used with the output of softmax operator, which will produce incorrect results. 2. when use_softmax=False, it expects the output of softmax operator. label (Tensor): 1. If soft_label=False, the shape is :math:`[N_1, N_2, ..., N_k]` or :math:`[N_1, N_2, ..., N_k, 1]`, k >= 1. the data type is int32, int64, float32, float64, where each value is [0, C-1]. 2. If soft_label=True and no label_smoothing, the shape and data type should be same with ``input`` , and the sum of the labels for each sample should be 1. 3. If has label_smoothing, (i.e. label_smoothing > 0.0), no matter what ``soft_label`` is, the shape and data type of ``label`` could be either the situation 1 or situation 2. In other words, if label_smoothing > 0.0, the format of label could be one-hot label or integer label. weight (Tensor, optional): a manual rescaling weight given to each class. If given, has to be a Tensor of size C and the data type is float32, float64. Default is ``'None'`` . ignore_index (int64, optional): Specifies a target value that is ignored and does not contribute to the loss. A negative value means that no label value needs to be ignored. Only valid when soft_label = False. Default is ``-100`` . reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned. If :attr:`reduction` is ``'none'``, the unreduced loss is returned. Default is ``'mean'``. soft_label (bool, optional): Indicate whether label is soft. Default is ``False``. label_smoothing (float, optional): A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets become a mixture of the original ground truth and a uniform distribution as described in paper 'Rethinking the Inception Architecture for Computer Vision'. Default is ``0.0``. axis (int, optional):The index of dimension to perform softmax calculations. It should be in range :math:`[-1, rank - 1]`, where :math:`rank` is the number of dimensions of input :attr:`input`. Default is ``-1`` . use_softmax (bool, optional): Indicate whether compute softmax before cross_entropy. Default is ``True``. name (str, optional): The name of the operator. Default is ``None`` . For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor. Return the softmax cross_entropy loss of ``input`` and ``label``. The data type is the same as input. If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the dimension of return value is ``1``. If :attr:`reduction` is ``'none'``: 1. If soft_label = False, the dimension of return value is the same with ``label`` . 2. if soft_label = True, the dimension of return value is :math:`[N_1, N_2, ..., N_k, 1]` . Examples: .. code-block:: python :name: code-example1 >>> # hard labels >>> import paddle >>> paddle.seed(99999) >>> N=100 >>> C=200 >>> reduction='mean' >>> input = paddle.rand([N, C], dtype='float64') >>> label = paddle.randint(0, C, shape=[N], dtype='int64') >>> weight = paddle.rand([C], dtype='float64') >>> cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( ... weight=weight, reduction=reduction) >>> dy_ret = cross_entropy_loss( ... input, ... label) >>> print(dy_ret) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, 5.35419278) .. code-block:: python :name: code-example2 >>> # soft labels >>> # case1: soft labels without label_smoothing >>> import paddle >>> paddle.seed(99999) >>> axis = -1 >>> N = 4 >>> C = 3 >>> shape = [N, C] >>> reduction='mean' >>> weight = None >>> logits = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0) >>> labels = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0) >>> labels /= paddle.sum(labels, axis=axis, keepdim=True) >>> paddle_loss_mean = paddle.nn.functional.cross_entropy( ... logits, ... labels, ... soft_label=True, ... axis=axis, ... weight=weight, ... reduction=reduction) >>> print(paddle_loss_mean) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, 1.12801195) >>> # case2: soft labels with label_smoothing >>> import paddle >>> paddle.seed(99999) >>> axis = -1 >>> N = 4 >>> C = 3 >>> shape = [N, C] >>> label_smoothing = 0.4 >>> reduction='mean' >>> weight = None >>> logits = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0) >>> interger_labels = paddle.randint(low=0, high=C, shape=[N], dtype='int64') >>> one_hot_labels = paddle.nn.functional.one_hot(interger_labels, C).astype('float32') >>> # integer labels >>> paddle_interger_loss_mean = paddle.nn.functional.cross_entropy( ... logits, ... interger_labels, ... axis=axis, ... weight=weight, ... label_smoothing=label_smoothing, ... reduction=reduction) >>> print(paddle_interger_loss_mean) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, 1.08317309) >>> # one_hot labels >>> paddle_one_hot_loss_mean = paddle.nn.functional.cross_entropy( ... logits, ... one_hot_labels, ... axis=axis, ... weight=weight, ... label_smoothing=label_smoothing, ... reduction=reduction) >>> print(paddle_one_hot_loss_mean) Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, 1.08317309) rpzzThe value of 'reduction' in softmax_cross_entropyshould be 'sum', 'mean' or 'none', but received %s, which is not allowed.rzWhen soft_label == True, the value of 'ignore_index' in softmax_cross_entropyshould be '-100', but received %s, which is not allowed.z2The dimention of input should be larger than zero!rrzZExpected nput_dims - 1 = label_dims or input_dims == label_dims (got nput_dimsr=r>rTrr)r0NF)rrrXrYrVzinput's class_dimension(z)) must equal to weight's class_dimension(z) when weight is providedr%r&r'rrZsoftmax_cross_entropyr()Zuint8Zint8Zint16rrrr)r?r@rAr use_softmaxrBrCrDr2r{rrrqrurF)#rGrr#rrrHrr r!r"Z label_smoothr]rrrrrIr_rrxndimr$Z gather_ndr\r%Z count_nonzeroryrr r r7r8r9r[r&)"r'r(r{r@r|r?rrZlabel_smoothingr*rLrMZ valid_labelrr}Z weight_gatherrZweight_gather_reshapeZignore_weight_maskZ temp_permrZ is_ignoremaskZout_sumcountretZweight_ignoredZ weight_sumrrNr6r:r5r~r+r+r,rb s                                      rbrU@r%c Cs|dvr td||dur+t|dddgdt|j}t|}|dkr+td |trt} t |jd |j | } t ||dd d } t |} t t| |tt| | t| |} tjjj|g| j d }t t||tt| |t| |}t|| } tjjj|g| j d }tt| | |}t|| } |durt| |} |dkrt| gdd S|dkrt| S| St|dddgdt|dddgdd}|dkr|dur|}tjjj||dd|d} tjj |} | |d| d|} ||d|d|}t|| } td| |}t|| } |dur5|dkr+|nd}tj| ||d} |dkrCtj| |d} | S|dkrOtj| |d} | S)aK `Focal Loss `_ is proposed to address the foreground-background class imbalance for classification tasks. It down-weights easily-classified examples and thus focuses training on hard examples. For example, it is used in one-stage object detection where the foreground-background class imbalance is extremely high. This operator measures focal loss function as follows: .. math:: Out = -Labels * alpha * {(1 - \sigma(Logit))}^{gamma}\log(\sigma(Logit)) - (1 - Labels) * (1 - alpha) * {\sigma(Logit)}^{gamma}\log(1 - \sigma(Logit)) We know that :math:`\sigma(Logit) = \frac{1}{1 + \exp(-Logit)}`. Then, if :attr:`normalizer` is not None, this operator divides the normalizer tensor on the loss `Out`: .. math:: Out = \frac{Out}{normalizer} Finally, this operator applies reduce operation on the loss. If :attr:`reduction` set to ``'none'``, the operator will return the original loss `Out`. If :attr:`reduction` set to ``'mean'``, the reduced mean loss is :math:`Out = MEAN(Out)`. If :attr:`reduction` set to ``'sum'``, the reduced sum loss is :math:`Out = SUM(Out)`. Note that the target ``label`` is 0 for the negative class and is 1 for the positive class. Args: logit (Tensor): The input logit tensor. The shape is [N, *], where N is batch_size, `*` means any number of additional dimensions. The ``logit`` is usually the output of a convolution layer. Available dtype is float32, float64. label (Tensor): The target label tensor with the same shape as ``logit``. The target label whose value should be numbers between 0 and 1. Available dtype is float32, float64. normalizer (Tensor, optional): The number normalizes the focal loss. It has to be a 1-D Tensor with shape `[1, ]` or 0-D Tensor with shape `[]`. The data type is float32, float64. For object detection task, it is the number of positive samples. If set to None, the focal loss will not be normalized. Default is None. alpha(int|float, optional): Hyper-parameter to balance the positive and negative example, it should be between 0 and 1. Default value is set to 0.25. gamma(int|float, optional): Hyper-parameter to modulate the easy and hard examples. Default value is set to 2.0. reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default is ``'sum'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, if :attr:`reduction` is ``'mean'`` or ``'sum'``, the out shape is :math:`[]`, otherwise the shape is the same as ``logit``. The same dtype as ``logit`` tensor. Examples: .. code-block:: python >>> import paddle >>> logit = paddle.to_tensor([[0.97, 0.91, 0.03], [0.55, 0.43, 0.71]], dtype='float32') >>> label = paddle.to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype='float32') >>> one = paddle.to_tensor([1.], dtype='float32') >>> fg_label = paddle.greater_equal(label, one) >>> fg_num = paddle.sum(paddle.cast(fg_label, dtype='float32')) >>> output = paddle.nn.functional.sigmoid_focal_loss(logit, label, normalizer=fg_num) >>> print(output) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.65782464) rpzxThe value of 'reduction' in sigmoid_focal_loss should be 'sum', 'mean' or 'none', but received %s, which is not allowed.N normalizerrrsigmoid_focal_lossrzNExpected zero or one dimension of normalizer in sigmoid_focal_loss but got {}.rFrr0r%r&rr(rq)r|r*ru)rGrr#rrrrr rrrrZsigmoidrrxrirZdygraphZ to_variablepowdivider%ryrr r!rr&)rr(ralphagammar|r*Znormalizer_shapeZnormalizer_dimsZplacerr;predZp_tZalpha_tZgamma_tZbce_nameZnormalizer_namer+r+r,r sP                 rcCs|dvr td|d|j|jkstd|jd|jts3t|dddgd t|d ddgd |tjj|d |tjj|  }|d ur]tsYt|d ddgd ||}|jdd}|dkri|S|dkrrt|S|dkr{t |Sd S)at Calculate a multi-class multi-classification hinge loss (margin-based loss) between input :math:`x` (a 2D mini-batch `Tensor`) and output :math:`y` (which is a 2D `Tensor` of target class indices). For each sample in the mini-batch: .. math:: \text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\text{x.size}(0)} where :math:`x \in \left\{0, \; \cdots , \; \text{x.size}(0) - 1\right\}`, \ :math:`y \in \left\{0, \; \cdots , \; \text{y.size}(0) - 1\right\}`, \ :math:`0 \leq y[j] \leq \text{x.size}(0)-1`, \ and :math:`i \neq y[j]` for all :math:`i` and :math:`j`. :math:`y` and :math:`x` must have the same size. Parameters: input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes, and if shape is more than 2D, this is (N, C, D1, D2,..., Dk), k >= 1. label (Tensor): Label tensor, the data type is float32 or float64. The shape of label is the same as the shape of input. weight (Tensor,optional): a manual rescaling weight given to each class. If given, has to be a Tensor of size C and the data type is float32, float64. Default is ``'None'`` . reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default: ``'mean'`` name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means number of classes, available dtype is float32, float64. The sum operationoperates over all the elements. label: N-D Tensor, same shape as the input. weight:N-D Tensor, the shape is [N,1] output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input. Returns: Tensor, The tensor variable storing the multi_label_soft_margin_loss of input and label. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32) >>> # label elements in {1., -1.} >>> label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32) >>> loss = F.multi_label_soft_margin_loss(input, label, reduction='none') >>> print(loss) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [3.49625897, 0.71111226, 0.43989015]) >>> loss = F.multi_label_soft_margin_loss(input, label, reduction='mean') >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.54908717) rpz^'reduction' in 'multi_label_soft_margin_loss' should be 'sum', 'mean' or 'none', but received rz>> import paddle >>> import paddle.nn.functional as F >>> input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32) >>> # label elements in {1., -1.} >>> label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32) >>> loss = F.hinge_embedding_loss(input, label, margin=1.0, reduction='none') >>> print(loss) Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0., -2., 0.], [ 0., -1., 2.], [ 1., 1., 1.]]) >>> loss = F.hinge_embedding_loss(input, label, margin=1.0, reduction='mean') >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.22222222) rpzV'reduction' in 'hinge_embedding_loss' should be 'sum', 'mean' or 'none', but received rr'rrhinge_embedding_lossr(rr0rgr&rur%rqN) rGrrrzerosrrr r!rr&r%)r'r(rr|r*Zzero_r;r+r+r,r% s0L  rcCsZt|jdkr td|j|jkrtdt|jdkr td|jtjtjfvr-td|jtjtjtjtjfvr>td||j dd }t |j dd d }t |j dd d }t ||} || } t | } d| } tj | |d d } t|dk| | }t|dk| | }||}|d kr|S|dkrtj||dS|dkrtj ||dSdS)a, Compute the cosine embedding loss of Tensor ``input1``, ``input2`` and ``label`` as follows. If label = 1, then the loss value can be calculated as follow: .. math:: Out = 1 - cos(input1, input2) If label = -1, then the loss value can be calculated as follow: .. math:: Out = max(0, cos(input1, input2)) - margin The operator cos can be described as follow: .. math:: cos(x1, x2) = \frac{x1 \cdot{} x2}{\Vert x1 \Vert_2 * \Vert x2 \Vert_2} Parameters: input1 (Tensor): tensor with shape: [N, M] or [M], 'N' means batch size, which can be 0, 'M' means the length of input array. Available dtypes are float32, float64. input2 (Tensor): tensor with shape: [N, M] or [M], 'N' means batch size, which can be 0, 'M' means the length of input array. Available dtypes are float32, float64. label (Tensor): tensor with shape: [N] or [1], 'N' means the length of input array. The target labels values should be -1 or 1. Available dtypes are int32, int64, float32, float64. margin (float, optional): Should be a number from :math:`-1` to :math:`1`, :math:`0` to :math:`0.5` is suggested. If :attr:`margin` is missing, the default value is :math:`0`. reduction (string, optional): 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. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor, the cosine embedding Loss of Tensor ``input1`` ``input2`` and ``label``. If `reduction` is ``'none'``, the shape of output loss is [N], the same as ``input`` . If `reduction` is ``'mean'`` or ``'sum'``, the shape of output loss is []. Examples: .. code-block:: python >>> import paddle >>> input1 = paddle.to_tensor([[1.6, 1.2, -0.5], [3.2, 2.6, -5.8]], 'float32') >>> input2 = paddle.to_tensor([[0.5, 0.5, -1.8], [2.3, -1.4, 1.1]], 'float32') >>> label = paddle.to_tensor([1, -1], 'int64') >>> output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='mean') >>> print(output) # 0.21155193 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.21155193) >>> output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='sum') >>> print(output) # 0.42310387 Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.42310387) >>> output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='none') >>> print(output) # [0.42310387, 0. ] Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [0.42310387, 0. ]) rz51D target tensor expected, multi-target not supportedzdthe shape of input tensor 1 should be equal to input tensor 2, but found inputs with different sizesrzV1D target tensor expects 1D or 2D input tensors, but found inputs with different sizes>The data type of input Variable must be 'float32' or 'float64'zNThe data type of label Variable must be 'int32', 'int64', 'float32', 'float64'rrgdy=rminrqr&rur%N)rrrGrrrrrrr%r^sqrtrcliprr&)Zinput1Zinput2r(rr|r*Zprod_sumZ mag_square1Z mag_square2denomcosrposrZout_posZout_negr}r+r+r,cosine_embedding_loss sVA  rc CsJ|dvr td|d|dkrtdts2t|dddgd t|d ddgd t|d ddgd |j|jkrB|jksGtd td |d urM|ntjd}|||}|||} |rj|||} t| | } t|dkrxt| dks|tdtj || |dd} |dkrtj | |dS|dkrtj | |dS|dkr| Sd S)ay Measures the triplet loss given an input tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater than :math:`0`. This is used for measuring a relative similarity between samples. A triplet is composed by `input`, `positive` and `negative` (i.e., `input`, `positive examples` and `negative examples` respectively). The shapes of all input tensors should be :math:`(N, D)`. The loss function for each sample in the mini-batch is: .. math:: L(input, pos, neg) = \max \{d(input_i, pos_i) - d(input_i, neg_i) + {\rm margin}, 0\} where the default distance function .. math:: d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p or user can defined their own distance functions. `margin` is a nonnegative margin representing the minimum difference between the positive and negative distances that is required for the loss to be 0. If `swap` is true, it will compare distance of (input, negative) with distance of (negative, positive) and change it to the smaller one. For more details see http://www.bmva.org/bmvc/2016/papers/paper119/paper119.pdf. Parameters: input (Tensor):Input tensor, the data type is float32 or float64. the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64. positive (Tensor):Positive tensor, the data type is float32 or float64. The shape of label is the same as the shape of input. negative (Tensor):Negative tensor, the data type is float32 or float64. The shape of label is the same as the shape of input. distance_function (callable, optional): Quantifies the distance between two tensors. if not specified, 2 norm functions will be used. margin (float, optional): A nonnegative margin representing the minimum difference between the positive and negative distances required for the loss to be 0. Default value is :math:`1`. swap (bool, optional):The distance swap changes the negative distance to the swap distance (distance between positive samples and negative samples) if swap distance smaller than negative distance. Default: ``False``. reduction (str, optional):Indicate how to average the loss by batch_size. the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default: ``'mean'`` name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Output: Tensor. The tensor variable storing the triplet_margin_with_distance_loss of input and positive and negative. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32) >>> positive = paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32) >>> negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32) >>> loss = F.triplet_margin_with_distance_loss(input, positive, negative, margin=1.0, reduction='none') >>> print(loss) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.57496595, 0. ]) >>> loss = F.triplet_margin_with_distance_loss(input, positive, negative, margin=1.0, reduction='mean') >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.19165532) rpzc'reduction' in 'triplet_margin_with_distance_loss' should be 'sum', 'mean' or 'none', but received rrRThe margin between positive samples and negative samples should be greater than 0.r'rr!triplet_margin_with_distance_lossrSnegativeBinput's shape must equal to positive's shape and negative's shapeNrznThe positive distance or negative distance should be greater than 0, The distance functions should be checked.rrr&rur%rq) rGrrrrr PairwiseDistanceminimumallrr&r%) r'rSrdistance_functionrswapr|r* positive_dist negative_dist swap_distr;r+r+r,r stT     rrư>c Cs|dvr td|d|dkrtdts2t|dddgd t|d ddgd t|d ddgd |j|jkrB|jksGtd td tjj||d } | ||} | ||} |rf| ||} t| | } tj| | |dd} |dkr|tj | |dS|dkrtj | |dS|dkr| SdS)a{ Measures the triplet loss given an input tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater than :math:`0`. This is used for measuring a relative similarity between samples. A triplet is composed by `input`, `positive` and `negative` (i.e., `input`, `positive examples` and `negative examples` respectively). The shapes of all input tensors should be :math:`(N, *)`. The loss function for each sample in the mini-batch is: .. math:: L(input, pos, neg) = \max \{d(input_i, pos_i) - d(input_i, neg_i) + {\rm margin}, 0\} where .. math:: d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p Parameters: input (Tensor): Input tensor, the data type is float32 or float64. the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64. positive (Tensor): Positive tensor, the data type is float32 or float64. The shape of label is the same as the shape of input. negative (Tensor): Negative tensor, the data type is float32 or float64. The shape of label is the same as the shape of input. margin (float, Optional): Default: :math:`1`. p (int, Optional): The norm degree for pairwise distance. Default: :math:`2`. epsilon (float, Optional): Add small value to avoid division by zero, default value is 1e-6. swap (bool,Optional): The distance swap change the negative distance to the distance between positive sample and negative sample. For more details, see `Learning shallow convolutional feature descriptors with triplet losses`. Default: ``False``. reduction (str, Optional):Indicate how to average the loss by batch_size. the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default: ``'mean'`` name (str, Optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Output: Tensor. The tensor variable storing the triplet_margin_loss of input and positive and negative. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32) >>> positive = paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32) >>> negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32) >>> loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='none') >>> print(loss) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.57496595, 0. ]) >>> loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='mean') >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.19165532) rpzU'reduction' in 'triplet_margin_loss' should be 'sum', 'mean' or 'none', but received rrrr'rrtriplet_margin_lossrSrrrrrr&rur%rqN) rGrrrrr rrrr&r%)r'rSrrpr)rr|r*rrrrr;r+r+r,rsTU       rrrc Cs|dvr td|dts!t|dddgdt|dd d gd|jd |jd ks9td |jd |jd |d }t||}|durtsTt|dddgd|jd|jd ksltd|jd |jdtj||d dd }tj t tj ||||dd|dd|||t|d}ntj t tj |||dd|dd||t|d}|dkrtj ||dS|dkrtj ||dS|dkr|SdS)a{ Measures a multi-class classification hinge loss between input :math:`input` and label :math:`label`: For i-th mini-batch sample, the loss in terms of the 1D input :math:`input_i` and scalar output :math:`label_i` is: .. math:: \text{loss}(input_i, label_i) = \frac{\sum_{j} \max(0, \text{margin} - input_i[label_i] + input_i[j])^p}{\text{C}} where :math:`0 \leq j \leq \text{C}-1`, :math:`0 \leq i \leq \text{N}-1` and :math:`j \neq label_i`. Optionally, you can give non-equal weighting on the classes by passing a 1D :attr:`weight` tensor into the constructor. The loss function for i-th sample then becomes: .. math:: \text{loss}(input_i, label_i) = \frac{\sum_{j} \max(0, weight[label_i] * (\text{margin} - input_i[label_i] + input_i[j]))^p}{\text{C}} Parameters: input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes. label (Tensor): Label tensor, the data type is int32 or int64. The shape of label is (N,) p (int, Optional): The power num. Default: :math:`1`. margin (float, Optional): Default: :math:`1`. weight (Tensor,optional): a manual rescaling weight given to each class. If given, has to be a Tensor of shape (C,) and the data type is float32, float64. Default is ``'None'`` . reduction (str, Optional):Indicate how to calculate the loss by batch_size. the candidates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default: ``'mean'`` name (str, Optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Output: Tensor. The tensor variable storing the multi_margin_loss of input and label. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32) >>> label = paddle.to_tensor([1, 2, 1], dtype=paddle.int32) >>> loss = F.multi_margin_loss(input, label, margin=1.0, reduction='none') >>> print(loss) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.66666663, 0. ]) rpzS'reduction' in 'multi_margin_loss' should be 'sum', 'mean' or 'none', but received rr'rrmulti_margin_lossr(rrrztThe label's shape[0] should be equal to input's shape[0], but received input's shape[0] {} and label's shape[0]:{}. )rrNr{rztThe weight's shape[0] should be equal to input's shape[1]but received weight's shape[0]: {} and input's shape[1]: {}rrrr&rur%rq) rGrrrrrr index_sampleZgatherr&rrr%) r'r(rrr{r|r*rr;r+r+r,rsxF       rcCs|dvr td|ts#tj|dddgdtj|dgdd|j|jks-td t||j}t d t | |}|d krLtj ||d S|d krWtj ||d S|S)a The API measures the soft margin loss between input predictions ``input`` and target labels ``label`` . It can be described as: .. math:: Out = log(1 + exp((-label * input))) Parameters: input (Tensor): The input predications tensor with shape: ``[N, *]``, N is batch_size, `*` means any number of additional dimensions. The ``input`` ranges from -inf to inf. Available dtype is float32, float64. label (Tensor): The target labels tensor with the same shape as ``input``. The target labels which values should be numbers -1 or 1. Available dtype is int32, int64, float32, float64. reduction (str, optional): Indicate how to average the loss by batch_size, the candidates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Output (Tensor): If ``reduction`` is ``'none'``, the shape of output is same as ``input`` , else the shape of output is []. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> input = paddle.to_tensor([[0.5, 0.6, 0.7],[0.3, 0.5, 0.2]], 'float32') >>> label = paddle.to_tensor([[1.0, -1.0, 1.0],[-1.0, 1.0, 1.0]], 'float32') >>> output = paddle.nn.functional.soft_margin_loss(input, label) >>> print(output) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.64022040) >>> input = paddle.uniform(shape=(5, 5), dtype="float32", min=0.1, max=0.8) >>> label = paddle.randint(0, 2, shape=(5, 5), dtype="int64") >>> label[label==0] = -1 >>> output = paddle.nn.functional.soft_margin_loss(input, label, reduction='none') >>> print(output) Tensor(shape=[5, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.10725629, 0.48778144, 0.56217247, 1.12581408, 0.51430041], [0.90375793, 0.37761253, 0.43007556, 0.95089805, 0.43288314], [1.16043591, 0.63015938, 0.51362717, 0.43617544, 0.57783306], [0.81927848, 0.52558368, 0.59713912, 0.83100700, 0.50811619], [0.82684207, 1.02064908, 0.50296998, 1.13461733, 0.93222517]]) rpzvThe value of 'reduction' in soft_margin_loss should be 'sum', 'mean' or 'none', but received %s, which is not allowed.r'rrsoft_margin_lossr()rrrrrrr%rur&) rGrrrrrrrrrrr%r&)r'r(r|r*r}r+r+r,rs2<  rc Cs|j|jkr1|jdd|jkrt|d}n|jdd|jddkr-|jddkr-ntd|dkrC|dkrC|dkrCt|dt|d d d gd t|d d d gd t|dd d gd tspt|dk}t||gdn2|jtj tj fvr}td|jtj tj fvrtd|jtj tj fvrtdt |dkrtd| }t tj||d}Wdn1swYdt|t|||}|r|dtdtj7}|dkrtj||dS|dkrtj||dS|dkr|SdS)aGaussian negative log likelihood loss. Gaussian negative log likelihood loss among ``input``, ``variance`` and ``label``. Note that the ``label`` is treated as samples from Gaussian distributions. This function is used to train a neural network predicts the ``input`` and ``variance`` of a gaussian distribution that ``label`` are supposed to be coming from. This means ``input`` and ``variance`` should be functions(the neural network) of some inputs. For a ``label`` having Gaussian distribution with ``input`` and ``variance`` predicted by neural network the loss is calculated as follows: .. math:: \text{loss} = \frac{1}{2}\left(\log\left(\text{max}\left(\text{var}, \ \text{epsilon}\right)\right) + \frac{\left(\text{input} - \text{label}\right)^2} {\text{max}\left(\text{var}, \ \text{epsilon}\right)}\right) + \text{const.} where :attr:`epsilon` is used for stability. By default, the constant term of the loss function is omitted unless :attr:`full` is ``True``. If ``variance`` is not the same size as ``input`` (due to a homoscedastic assumption), it must either have a final dimension of 1 or have one fewer dimension (with all other sizes being the same) for correct broadcasting. Args: input (Tensor): input tensor, :math:`(N, *)` or :math:`(*)` where :math:`*` means any number of additional dimensions. Expectation of the Gaussian distribution, available dtype is float32, float64. label (Tensor): target label tensor, :math:`(N, *)` or :math:`(*)`, same shape as the input, or same shape as the input but with one dimension equal to 1 (to allow for broadcasting). Sample from the Gaussian distribution, available dtype is float32, float64. variance (Tensor): tensor of positive variance(s), :math:`(N, *)` or :math:`(*)`, same shape as the input, or same shape as the input but with one dimension equal to 1, or same shape as the input but with one fewer dimension (to allow for broadcasting). One for each of the expectations in the input (heteroscedastic), or a single one (homoscedastic), available dtype is float32, float64. full (bool, optional): include the constant term in the loss calculation. Default: ``False``. epsilon (float, optional): value used to clamp ``variance`` (see note below), for stability. Default: 1e-6. reduction (str, optional): specifies the reduction to apply to the output:``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the output is the average of all batch member losses, ``'sum'``: the output is the sum of all batch member losses. Default: ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: output (Tensor): If ``reduction`` is ``'none'``, the shape of output is same as ``input`` , else the shape of output is []. Examples:: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> paddle.seed(2023) >>> input = paddle.randn([5, 2], dtype=paddle.float32) >>> label = paddle.randn([5, 2], dtype=paddle.float32) >>> variance = paddle.ones([5, 2], dtype=paddle.float32) >>> loss = F.gaussian_nll_loss(input, label, variance, reduction='none') >>> print(loss) Tensor(shape=[5, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.21808575, 1.43013096], [1.05245590, 0.00394560], [1.20861185, 0.00000062], [0.56946373, 0.73300570], [0.37142906, 0.12038800]]) >>> loss = F.gaussian_nll_loss(input, label, variance, reduction='mean') >>> print(loss) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 0.57075173) Note: The clamping of ``variance`` is ignored with respect to autograd, and so the gradients are unaffected by it. Nrrzvariance is of incorrect shaperqr&r%z is not validZInputrrgaussian_nll_lossrEZVariancerrzs$        J:  QB   /  $d tf ~ w |P   .  k M 5 oh w    `