loss.py 74.5 KB
Newer Older
1
# -*- coding: utf-8 -*
2 3 4 5 6 7 8 9 10 11 12 13 14 15
#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

16
# TODO: define loss functions of neural network
17
import numpy as np
L
Leo Chen 已提交
18
import paddle.fluid as fluid
19
import paddle
20
from .. import functional as F
21
from paddle.fluid.framework import _varbase_creator, in_dygraph_mode, _in_legacy_dygraph
Z
zhiboniu 已提交
22
from .. import Layer
Z
zhiboniu 已提交
23
from paddle import in_dynamic_mode
24

25 26
__all__ = []

L
Leo Chen 已提交
27

Z
zhiboniu 已提交
28
class BCEWithLogitsLoss(Layer):
29
    r"""
30 31 32 33 34 35 36 37 38 39 40 41 42
    This operator combines the sigmoid layer and the :ref:`api_nn_loss_BCELoss` layer.
    Also, we can see it as the combine of ``sigmoid_cross_entropy_with_logits``
    layer and some reduce operations.

    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.

    First this operator calculate loss function as follows:

    .. math::
43
           Out = -Labels * \log(\sigma(Logit)) - (1 - Labels) * \log(1 - \sigma(Logit))
44

45
    We know that :math:`\sigma(Logit) = \frac{1}{1 + e^{-Logit}}`. By substituting this we get:
46 47

    .. math::
48
           Out = Logit - Logit * Labels + \log(1 + e^{-Logit})
49

50
    For stability and to prevent overflow of :math:`e^{-Logit}` when Logit < 0,
51 52 53
    we reformulate the loss as follows:

    .. math::
54
           Out = \max(Logit, 0) - Logit * Labels + \log(1 + e^{-\|Logit\|})
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131

    Then, if ``weight`` or ``pos_weight`` is not None, this operator 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, 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 labels ``label`` should be numbers between 0 and 1.

    Args:
        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`.

    Shapes:
        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.
        output (Tensor): If ``reduction`` is ``'none'``, the shape of output is
            same as ``logit`` , else the shape of output is scalar.

    Returns:
        A callable object of BCEWithLogitsLoss.

    Examples:

        .. code-block:: python
            import paddle
            logit = paddle.to_tensor([5.0, 1.0, 3.0], dtype="float32")
            label = paddle.to_tensor([1.0, 0.0, 1.0], dtype="float32")
            bce_logit_loss = paddle.nn.BCEWithLogitsLoss()
            output = bce_logit_loss(logit, label)
            print(output.numpy())  # [0.45618808]

    """

    def __init__(self,
                 weight=None,
                 reduction='mean',
                 pos_weight=None,
                 name=None):
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in BCEWithLogitsLoss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)

        super(BCEWithLogitsLoss, self).__init__()
        self.weight = weight
        self.reduction = reduction
        self.pos_weight = pos_weight
        self.name = name

    def forward(self, logit, label):
        out = paddle.nn.functional.binary_cross_entropy_with_logits(
            logit, label, self.weight, self.reduction, self.pos_weight,
            self.name)
        return out


Z
zhiboniu 已提交
132
class CrossEntropyLoss(Layer):
133
    r"""
134 135
    By default, 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
136
    to provide a more numerically stable computing.
S
swtkiwi 已提交
137

138
    This operator will calculate the cross entropy loss function without softmax when use_softmax=False.
139

140 141
    By default, this operator will 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
142
    parameters for details.
143

144
    This operator can be used to calculate the softmax cross entropy loss with soft and hard labels.
145
    Where, the hard labels mean the actual label value, 0, 1, 2, etc.  And the soft labels
146
    mean the probability of the actual label, 0.6, 0.8, 0.2, etc.
147

148
    The calculation of this operator includes the following two steps.
149

150
    -  **I.softmax cross entropy**
151

152
        1. Hard label (each sample can only be assigned into one category)
153

154
        1.1. when use_softmax=True
155

156 157
            .. math::
              \\loss_j=-\text{logits}_{label_j}+\log\left(\sum_{i=0}^{C}\exp(\text{logits}_i)\right) , j = 1,...,N
158

159
            where, N is the number of samples and C is the number of categories.
160

161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186
        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).



187
    -  **II.Weight and reduction processing**
188 189 190 191 192 193 194 195 196 197 198

        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::
199
                \\loss_j=loss_j*weight[label_j]
200

201

202 203 204 205 206 207 208
            1.2. Soft labels (soft_label = True)

             .. math::
                \\loss_j=loss_j*\sum_{i}\left(weight[label_i]*logits_i\right)

        2. reduction

209
            2.1 if the ``reduction`` parameter is ``none``
210 211 212

            Return the previous result directly

213
            2.2 if the ``reduction`` parameter is ``sum``
214 215 216 217 218 219

            Return the sum of the previous results

            .. math::
               \\loss=\sum_{j}loss_j

220 221
            2.3 if the ``reduction`` parameter is ``mean`` , it will be processed according to
            the ``weight`` parameter as follows.
222

223
            2.3.1. If the  ``weight``  parameter is ``None``
224 225 226 227 228 229 230 231 232 233 234 235 236

            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::
237
                \\loss=\sum_{j}loss_j/\sum_{j}weight[label_j]
238 239 240 241 242

            2. Soft labels (soft_label = True)

             .. math::
                \\loss=\sum_{j}loss_j/\sum_{j}\left(\sum_{i}weight[label_i]\right)
243 244


245
    Parameters:
246 247 248

        - **weight** (Tensor, optional)

249 250
            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.
251 252 253 254 255
            Default is ``'None'`` .

        - **ignore_index** (int64, optional)

            Specifies a target value that is ignored
256 257
            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.
258 259 260 261 262
            Default is ``-100`` .

        - **reduction** (str, optional)

            Indicate how to average the loss by batch_size,
263 264 265 266 267
            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'``.
268

269
        - **soft_label** (bool, optional)
270

271
            Indicate whether label is soft.
272 273
            If soft_label=False, the label is hard.  If soft_label=True, the label is soft.
            Default is ``False``.
274

275 276
        - **axis** (int, optional)

277 278 279
            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`.
280 281 282 283 284 285 286
            Default is ``-1`` .

        - **use_softmax** (bool, optional)

            Indicate whether compute softmax before cross_entropy.
            Default is ``True``.

Z
zhiboniu 已提交
287
        - **name** (str, optional)
288 289 290 291 292 293 294 295 296 297

            The name of the operator. Default is ``None`` .
            For more information, please refer to :ref:`api_guide_Name` .


    Shape:

        - **input** (Tensor)

            Input tensor, the data type is float32, float64. Shape is
298
        :math:`[N_1, N_2, ..., N_k, C]`, where C is number of classes ,  ``k >= 1`` .
299

300
            Note:
301

302
                1. when use_softmax=True, it expects unscaled logits. This operator should not be used with the
303 304 305
                output of softmax operator, which will produce incorrect results.

                2. when use_softmax=False, it expects the output of softmax operator.
306

307 308 309

        - **label** (Tensor)

310
            1. If soft_label=False, the shape is
311 312 313
            :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].

314
            2. If soft_label=True, the shape and data type should be same with ``input`` ,
315
            and the sum of the labels for each sample should be 1.
316

317 318 319 320 321 322 323 324 325 326
        - **output** (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'``:

327
            1. If soft_label = False, the dimension of return value is the same with ``label`` .
328

329
            2. if soft_label = True, the dimension of return value is :math:`[N_1, N_2, ..., N_k, 1]` .
330

331
    Examples:
332 333

        .. code-block:: python
334

335
            # hard labels
336 337 338 339 340
            import paddle
            paddle.seed(99999)
            N=100
            C=200
            reduction='mean'
341
            input =  paddle.rand([N, C], dtype='float64')
342
            label =  paddle.randint(0, C, shape=[N], dtype='int64')
343 344
            weight = paddle.rand([C], dtype='float64')

345 346 347 348 349 350 351
            cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
                weight=weight, reduction=reduction)
            dy_ret = cross_entropy_loss(
                                       input,
                                       label)
            print(dy_ret.numpy()) #[5.41993642]

352
        .. code-block:: python
353 354

            # soft labels
355
            import paddle
356 357 358 359 360 361 362 363 364 365 366 367
            paddle.seed(99999)
            axis = -1
            ignore_index = -100
            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(
368 369 370
                                                                  logits,
                                                                  labels,
                                                                  soft_label=True,
371 372 373 374 375
                                                                  axis=axis,
                                                                  weight=weight,
                                                                  reduction=reduction)
            print(paddle_loss_mean.numpy()) #[1.12908343]

376 377
    """

378 379 380 381 382 383
    def __init__(self,
                 weight=None,
                 ignore_index=-100,
                 reduction='mean',
                 soft_label=False,
                 axis=-1,
384
                 use_softmax=True,
385
                 name=None):
386 387 388
        super(CrossEntropyLoss, self).__init__()
        self.weight = weight
        self.reduction = reduction
389
        self.ignore_index = ignore_index
390 391
        self.soft_label = soft_label
        self.axis = axis
392
        self.use_softmax = use_softmax
393
        self.name = name
394 395

    def forward(self, input, label):
396 397 398 399 400 401 402 403 404
        ret = paddle.nn.functional.cross_entropy(input,
                                                 label,
                                                 weight=self.weight,
                                                 ignore_index=self.ignore_index,
                                                 reduction=self.reduction,
                                                 soft_label=self.soft_label,
                                                 axis=self.axis,
                                                 use_softmax=self.use_softmax,
                                                 name=self.name)
405 406

        return ret
407 408


Z
zhiboniu 已提交
409
class HSigmoidLoss(Layer):
410 411
    """
    Hierarchical Sigmoid Layer.
412

413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446
    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, the OP 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 OP supports default tree and custom tree. For the default tree, you can refer to `Hierarchical Probabilistic Neural
    Network Language Model <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>_`. 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:
        feature_size (int): The number of features.
        num_classes (int): The number of classes or the size of word dict, must be greater than 2.
            If the default tree is used (:attr:`is_custom` is set to False), :attr:`num_classes`
            should not be None. If the custom tree is used (:attr:`is_custom` is set to True),
            :attr:`num_classes` should be the number of non-leaf nodes, which indicates the num of
            classes using by the binary classifier.
        weight_attr (ParamAttr, optional): The parameter attribute for the learnable weights
            of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid will create a
            ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is
            initialized with Xavier. Default is None.
        bias_attr (ParamAttr|bool, optional): The parameter attribute for the bias of hsigmoid. If it
            is set to False, no bias will be added. If it is set to None or one attribute of ParamAttr,
            hsigmoid will create a ParamAttr as bias_attr. If the Initializer of the bias_attr is not
            set, the bias is initialized zero. Default is None.
447
        is_custom (bool, optional): Whether use custom binary tree. If it's True, `path_table` and
448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465
            `path_code` should be passed to its forward method, otherwise `path_table` and `path_code`
            should not be passed to its forward method. Default is False.
        is_sparse (bool, optional): Whether use sparse updating instead of dense updating, if it's 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`.

    Shape:
        input (Tensor): The input tensor. The shapes is [N, D], where N is batch size and D is feature size. It's data type should be float32, float64.
        label (Tensor): It's shapes is [N, 1]. It's data type should be int64.
        output (Tensor): The HSigmoid Loss of ``input`` and ``label``. Shape is [N, 1]

    Examples:
        .. code-block:: python

            import paddle
            paddle.set_device('cpu')

L
Linjie Chen 已提交
466 467 468 469 470
            input = paddle.uniform([4, 3])
            # [[0.56194401  -0.22450298  -0.10741806] # random
            #  [0.36136317  0.23556745  0.88748658] # random
            #  [0.18151939  0.80947340  -0.31078976] # random
            #  [0.68886101  -0.14239830  -0.41297770]] # random
471 472 473
            label = paddle.to_tensor([0, 1, 4, 5])
            m = paddle.nn.HSigmoidLoss(3, 5)
            out = m(input, label)
L
Linjie Chen 已提交
474 475 476 477
            # [[2.42524505]
            #  [1.74917245]
            #  [3.14571381]
            #  [2.34564662]]
478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
    """

    def __init__(self,
                 feature_size,
                 num_classes,
                 weight_attr=None,
                 bias_attr=None,
                 is_custom=False,
                 is_sparse=False,
                 name=None):
        super(HSigmoidLoss, self).__init__()
        if (num_classes < 2) and (not is_custom):
            raise ValueError(
                "num_classes must not be less than 2 with default tree")

        if (not is_custom) and (is_sparse):
            print("Sparse mode should not be used without custom tree")
            is_sparse = False

        self._feature_size = feature_size
        self._num_classes = num_classes
        self._is_custom = is_custom
        self._is_sparse = is_sparse

        self._weight_attr = weight_attr
        self._bias_attr = bias_attr

        self._name = name
        self._dtype = paddle.get_default_dtype()

        remote_prefetch = is_sparse
        print("With sparse mode, if your models has only"
              " small parameter prefetch may cause speed down")

        C = self._num_classes if is_custom else self._num_classes - 1
513 514 515 516 517 518 519 520
        self.weight = self.create_parameter([C, self._feature_size],
                                            attr=self._weight_attr,
                                            is_bias=False,
                                            dtype=self._dtype)
        self.bias = self.create_parameter([C, 1],
                                          attr=self._bias_attr,
                                          is_bias=True,
                                          dtype=self._dtype)
521 522

    def forward(self, input, label, path_table=None, path_code=None):
523 524 525 526 527 528 529 530 531
        out = F.hsigmoid_loss(input,
                              label,
                              self._num_classes,
                              self.weight,
                              self.bias,
                              path_table=path_table,
                              path_code=path_code,
                              is_sparse=self._is_sparse,
                              name=self._name)
532 533 534
        return out


Z
zhiboniu 已提交
535
class MSELoss(Layer):
536
    r"""
537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554
    **Mean Square Error Loss**
    Computes the mean square error (squared L2 norm) of given input and label.

    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)

555
    where `input` and `label` are `float32` tensors of same shape.
556 557 558 559

    Parameters:
        reduction (string, optional): The reduction method for the output,
            could be 'none' | 'mean' | 'sum'.
560 561 562
            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.
563 564
            Default is ``'mean'``.

B
Bai Yifan 已提交
565 566 567 568
    Shape:
        input (Tensor): Input tensor, the data type is float32 or float64
        label (Tensor): Label tensor, the data type is float32 or float64
        output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input.
569 570 571

    Examples:
        .. code-block:: python
572 573 574

            import paddle

B
Bai Yifan 已提交
575
            mse_loss = paddle.nn.loss.MSELoss()
576 577
            input = paddle.to_tensor([1.5])
            label = paddle.to_tensor([1.7])
B
Bai Yifan 已提交
578
            output = mse_loss(input, label)
579
            print(output)
B
Bai Yifan 已提交
580
            # [0.04000002]
581 582 583 584 585 586 587 588 589 590 591
    """

    def __init__(self, reduction='mean'):
        super(MSELoss, self).__init__()
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "'reduction' in 'MSELoss' should be 'sum', 'mean' or 'none', "
                "but received {}.".format(reduction))
        self.reduction = reduction

    def forward(self, input, label):
Z
zhiboniu 已提交
592
        if not in_dynamic_mode():
593 594 595 596 597 598
            fluid.data_feeder.check_variable_and_dtype(input, 'input',
                                                       ['float32', 'float64'],
                                                       'MSELoss')
            fluid.data_feeder.check_variable_and_dtype(label, 'label',
                                                       ['float32', 'float64'],
                                                       'MSELoss')
599

600
        if in_dygraph_mode():
601
            square_out = paddle._C_ops.square(paddle.subtract(input, label))
602 603
        else:
            square_out = paddle.square(paddle.subtract(input, label))
604 605 606 607 608 609 610 611 612 613
        if self.reduction == 'none':
            return square_out

        reduce_op = 'reduce_mean'
        if self.reduction == 'sum':
            reduce_op = 'reduce_sum'

        return getattr(fluid.layers, reduce_op)(square_out)


Z
zhiboniu 已提交
614
class L1Loss(Layer):
615
    r"""
616
    Construct a callable object of the ``L1Loss`` class.
617
    The L1Loss layer calculates the L1 Loss of ``input`` and ``label`` as follows.
618

619
    If `reduction` set to ``'none'``, the loss is:
L
Leo Chen 已提交
620 621

    .. math::
622
        Out = \lvert input - label\rvert
623

624
    If `reduction` set to ``'mean'``, the loss is:
625

L
Leo Chen 已提交
626
    .. math::
627
        Out = MEAN(\lvert input - label\rvert)
628

629
    If `reduction` set to ``'sum'``, the loss is:
630

L
Leo Chen 已提交
631
    .. math::
632
        Out = SUM(\lvert input - label\rvert)
L
Leo Chen 已提交
633

634

L
Leo Chen 已提交
635
    Parameters:
636
        reduction (str, optional): Indicate the reduction to apply to the loss,
L
Leo Chen 已提交
637
            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
638 639 640
            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.
L
Leo Chen 已提交
641
            Default is ``'mean'``.
642 643 644
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Shape:
645 646
        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.
647
        output (Tensor): The L1 Loss of ``input`` and ``label``.
648 649
            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 [1].
650

L
Leo Chen 已提交
651 652
    Examples:
        .. code-block:: python
653

L
Leo Chen 已提交
654
            import paddle
655

656 657
            input = paddle.to_tensor([[1.5, 0.8], [0.2, 1.3]])
            label = paddle.to_tensor([[1.7, 1], [0.4, 0.5]])
658

C
Chen Long 已提交
659
            l1_loss = paddle.nn.L1Loss()
660
            output = l1_loss(input, label)
661
            print(output.numpy())
662 663
            # [0.35]

C
Chen Long 已提交
664
            l1_loss = paddle.nn.L1Loss(reduction='sum')
665
            output = l1_loss(input, label)
666
            print(output.numpy())
667 668
            # [1.4]

C
Chen Long 已提交
669
            l1_loss = paddle.nn.L1Loss(reduction='none')
670
            output = l1_loss(input, label)
C
Chen Long 已提交
671
            print(output)
672
            # [[0.20000005 0.19999999]
673
            # [0.2        0.79999995]]
L
Leo Chen 已提交
674 675
    """

676
    def __init__(self, reduction='mean', name=None):
L
Leo Chen 已提交
677 678 679 680 681 682
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in L1Loss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)
        super(L1Loss, self).__init__()
        self.reduction = reduction
683
        self.name = name
L
Leo Chen 已提交
684

685
    def forward(self, input, label):
686 687 688 689
        return paddle.nn.functional.l1_loss(input,
                                            label,
                                            self.reduction,
                                            name=self.name)
C
ceci3 已提交
690 691


Z
zhiboniu 已提交
692
class BCELoss(Layer):
C
ceci3 已提交
693
    """
C
ceci3 已提交
694
    This interface is used to construct a callable object of the ``BCELoss`` class.
695 696
    The BCELoss layer measures the binary_cross_entropy loss between input predictions ``input``
    and target labels ``label`` . The binary_cross_entropy loss can be described as:
C
ceci3 已提交
697

C
ceci3 已提交
698
    If :attr:`weight` is set, the loss is:
C
ceci3 已提交
699 700

    .. math::
C
ceci3 已提交
701
        Out = -1 * weight * (label * log(input) + (1 - label) * log(1 - input))
702

C
ceci3 已提交
703
    If :attr:`weight` is None, the loss is:
C
ceci3 已提交
704 705

    .. math::
C
ceci3 已提交
706 707
        Out = -1 * (label * log(input) + (1 - label) * log(1 - input))

708
    If :attr:`reduction` set to ``'none'``, the interface will return the original loss `Out`.
C
ceci3 已提交
709

C
ceci3 已提交
710
    If :attr:`reduction` set to ``'mean'``, the reduced mean loss is:
C
ceci3 已提交
711

C
ceci3 已提交
712 713
    .. math::
        Out = MEAN(Out)
714

C
ceci3 已提交
715
    If :attr:`reduction` set to ``'sum'``, the reduced sum loss is:
C
ceci3 已提交
716

C
ceci3 已提交
717 718
    .. math::
        Out = SUM(Out)
C
ceci3 已提交
719

720
    Note that the input predictions ``input`` always be the output of sigmoid, and the target labels ``label``
C
ceci3 已提交
721 722
    should be numbers between 0 and 1.

C
ceci3 已提交
723
    Parameters:
724 725
        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
C
ceci3 已提交
726
            is float32, float64. Default is ``'None'``.
727
        reduction (str, optional): Indicate how to average the loss by batch_size,
C
ceci3 已提交
728
            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
C
ceci3 已提交
729
            If :attr:`reduction` is ``'none'``, the unreduced loss is returned;
730
            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
C
ceci3 已提交
731
            If :attr:`reduction` is ``'sum'``, the summed loss is returned.
C
ceci3 已提交
732
            Default is ``'mean'``.
733 734 735 736
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Shape:
Z
Zhong Hui 已提交
737
        input (Tensor): 2-D tensor with shape: [N, *], N is batch_size, `*` means
738 739 740 741 742 743 744
            number of additional dimensions. The input ``input`` should always
            be the output of sigmod.  Available dtype is float32, float64.
        label (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 float32, float64.
        output (Tensor): If ``reduction`` is ``'none'``, the shape of output is
            same as ``input`` , else the shape of output is scalar.
C
ceci3 已提交
745

746
    Returns:
C
ceci3 已提交
747 748
        A callable object of BCELoss.

C
ceci3 已提交
749 750
    Examples:
        .. code-block:: python
C
ceci3 已提交
751

C
ceci3 已提交
752 753 754 755
            import numpy as np
            import paddle
            input_data = np.array([0.5, 0.6, 0.7]).astype("float32")
            label_data = np.array([1.0, 0.0, 1.0]).astype("float32")
756

Z
Zhong Hui 已提交
757 758
            input = paddle.to_tensor(input_data)
            label = paddle.to_tensor(label_data)
C
Chen Long 已提交
759
            bce_loss = paddle.nn.BCELoss()
760
            output = bce_loss(input, label)
C
Chen Long 已提交
761
            print(output)  # [0.65537095]
762

C
ceci3 已提交
763 764
    """

765
    def __init__(self, weight=None, reduction='mean', name=None):
C
ceci3 已提交
766 767 768 769 770 771 772 773
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in bce_loss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)

        super(BCELoss, self).__init__()
        self.weight = weight
        self.reduction = reduction
774
        self.name = name
C
ceci3 已提交
775 776

    def forward(self, input, label):
777 778 779 780
        out = paddle.nn.functional.binary_cross_entropy(input, label,
                                                        self.weight,
                                                        self.reduction,
                                                        self.name)
781
        return out
782 783


Z
zhiboniu 已提交
784
class NLLLoss(Layer):
785
    r"""
S
swtkiwi 已提交
786

787
    This class accepts input and target label and returns negative log likelihood
788
    cross error. It is useful to train a classification problem with C classes.
789

790
    The input for the loss is expected to contain log-probabilities of
791
    each classes. It has to be a Tensor of size either (batch_size, C) or
792 793 794 795
    (batch_size, C, d1, d2, ..., dK) with K >= 1 for the K-dimensional case.
    The label for the loss should be a class index in the range [0, C-1]
    where C is the number of classes. If ignore_index is specified, the
    specified target value does not contribute to the input gradient.
796

797 798 799
    If the optional argument `weight` is provided, it should be a 1D Tensor
    assigning weight to each of the classed. This is particularly useful
    when you have an unbalanced training set.
800

801 802 803 804
    The loss is calculated as follows.
    The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as:

    .. math::
805 806

        \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
807
        l_n = - w_{y_n} x_{n,y_n}, \quad
808
        w_{c} = \text{weight}[c] \cdot \mathbb{1}\{c \not= \text{ignore_index}\},
809 810 811 812 813

    where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'``
    (default ``'mean'``), then

    .. math::
814 815 816 817 818 819 820 821 822 823

        \ell(x, y) =
        \left\{
            \begin{array}{lcl}
            \sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n}} l_n, &
            \text{if  reduction} = \text{'mean';}\\
            \sum_{n=1}^N l_n,  &
            \text{if  reduction} = \text{'sum'.}
            \end{array}
        \right.
824 825

    Parameters:
826 827
        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,
828
            it treated as if having all ones. the data type is
829
            float32, float64, Default is ``'None'``.
830
        ignore_index (int, optional): Specifies a target value that is ignored
831
            and does not contribute to the input gradient.
832
        reduction (str, optional): Indicate how to average the loss,
833
            the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
834 835 836
            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.
837
            Default is ``'mean'``.
838
         name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
839

840
    Shape:
841
        - input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes.
842 843
            But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`.
            The data type is float32, float64.
844
        - label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`.
845
            The data type is int64.
846
        - output (Tensor): the `negative log likelihood loss` between input `x` and `label`.
847 848
            If `reduction` is `'none'`, the shape is `[N, *]`.
            If `reduction` is `'sum'` or `'mean'`, the shape is `[1]`.
849 850 851 852

    Examples:
        .. code-block:: python

853
                import paddle
854

855
                nll_loss = paddle.nn.loss.NLLLoss()
856
                log_softmax = paddle.nn.LogSoftmax(axis=1)
857

858 859 860 861 862
                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")
863
                log_out = log_softmax(input)
864
                label = paddle.to_tensor([0, 2, 1, 1, 0], "int64")
865
                result = nll_loss(log_out, label)
866
                print(result) # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True, [1.07202101])
867

868
    """
869

870 871 872 873 874 875
    def __init__(self,
                 weight=None,
                 ignore_index=-100,
                 reduction='mean',
                 name=None):
        if reduction not in ['sum', 'mean', 'none']:
876
            raise ValueError(
877 878 879 880 881 882 883
                "The value of 'reduction' in nll_loss should be 'sum', 'mean' or "
                "'none', but received %s, which is not allowed." % reduction)
        super(NLLLoss, self).__init__()
        self._weight = weight
        self._ignore_index = ignore_index
        self._reduction = reduction
        self._name = name
884

885
    def forward(self, input, label):
886 887 888 889 890 891
        return F.nll_loss(input,
                          label,
                          weight=self._weight,
                          ignore_index=self._ignore_index,
                          reduction=self._reduction,
                          name=self._name)
892 893


Z
zhiboniu 已提交
894
class KLDivLoss(Layer):
895
    r"""
896 897 898 899
    Generate a callable object of 'KLDivLoss' to 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.
900 901 902 903 904 905

    KL divergence loss is calculated as follows:

    $$l(x, y) = y * (\log(y) - x)$$

    Parameters:
L
LielinJiang 已提交
906 907 908 909 910 911 912
        reduction (Tensor): 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'``.
913 914

    Shape:
915 916 917 918 919 920

        - input (Tensor): (N, *), where * means, any number of additional dimensions.

        - label (Tensor): (N, *), same shape as input.

        - output (Tensor): tensor with shape: [1] by default.
921 922 923 924 925 926 927


    Examples:
        .. code-block:: python

            import paddle
            import paddle.nn as nn
928

929
            shape = (5, 20)
930 931
            x = paddle.uniform(shape, min=-10, max=10).astype('float32')
            target = paddle.uniform(shape, min=-10, max=10).astype('float32')
932

L
LielinJiang 已提交
933
            # 'batchmean' reduction, loss shape will be [1]
934
            kldiv_criterion = nn.KLDivLoss(reduction='batchmean')
935
            pred_loss = kldiv_criterion(x, target)
L
LielinJiang 已提交
936
            # shape=[1]
937

938 939
            # 'mean' reduction, loss shape will be [1]
            kldiv_criterion = nn.KLDivLoss(reduction='mean')
940
            pred_loss = kldiv_criterion(x, target)
941 942 943 944
            # shape=[1]

            # 'sum' reduction, loss shape will be [1]
            kldiv_criterion = nn.KLDivLoss(reduction='sum')
945
            pred_loss = kldiv_criterion(x, target)
946 947 948 949
            # shape=[1]

            # 'none' reduction, loss shape is same with X shape
            kldiv_criterion = nn.KLDivLoss(reduction='none')
950
            pred_loss = kldiv_criterion(x, target)
951 952 953 954 955 956 957 958
            # shape=[5, 20]
    """

    def __init__(self, reduction='mean'):
        super(KLDivLoss, self).__init__()
        self.reduction = reduction

    def forward(self, input, label):
L
LielinJiang 已提交
959
        out = F.kl_div(input, label, self.reduction)
960 961 962
        return out


Z
zhiboniu 已提交
963
class MarginRankingLoss(Layer):
964
    r"""
965 966

    This interface is used to construct a callable object of the ``MarginRankingLoss`` class.
967
    The MarginRankingLoss layer calculates the margin rank loss between the input, other and label
968 969
    , use the math function as follows.

970
    .. math::
971
        margin\_rank\_loss = max(0, -label * (input - other) + margin)
972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989

    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:
        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`.

990
    Shape:
991

N
Noel 已提交
992 993
        input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64.

994
        other: N-D Tensor, `other` have the same shape and dtype as `input`.
N
Noel 已提交
995

996
        label: N-D Tensor, label have the same shape and dtype as `input`.
N
Noel 已提交
997

998
        output: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor.
999 1000 1001 1002 1003 1004 1005 1006

    Returns:
        A callable object of MarginRankingLoss.

    Examples:

        .. code-block:: python

1007 1008
            import paddle

C
Chen Long 已提交
1009 1010
            input = paddle.to_tensor([[1, 2], [3, 4]], dtype="float32")
            other = paddle.to_tensor([[2, 1], [2, 4]], dtype="float32")
Z
Zhong Hui 已提交
1011
            label = paddle.to_tensor([[1, -1], [-1, -1]], dtype="float32")
1012
            margin_rank_loss = paddle.nn.MarginRankingLoss()
1013
            loss = margin_rank_loss(input, other, label)
1014 1015 1016

            print(loss)
            # [0.75]
1017 1018 1019 1020 1021
    """

    def __init__(self, margin=0.0, reduction='mean', name=None):
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
1022
                "The value of 'reduction' in MarginRankingLoss should be 'sum', 'mean' or 'none', but "
1023 1024 1025 1026 1027 1028
                "received %s, which is not allowed." % reduction)
        super(MarginRankingLoss, self).__init__()
        self.margin = margin
        self.reduction = reduction
        self.name = name

1029
    def forward(self, input, other, label):
1030 1031 1032 1033
        out = paddle.nn.functional.margin_ranking_loss(input, other, label,
                                                       self.margin,
                                                       self.reduction,
                                                       self.name)
1034
        return out
1035 1036


Z
zhiboniu 已提交
1037
class CTCLoss(Layer):
1038 1039
    """

1040 1041 1042
    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
1043 1044 1045 1046 1047 1048 1049
    is interated to the Warp-CTC library to normalize values for each row of the input tensor.

    Parameters:
        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 is 0.
        reduction (string, 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 is ``'mean'``.

    Shape:
1050
        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.
1051 1052 1053
        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.
1054
        norm_by_times (bool, default false) – 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'.
1055 1056 1057

    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 [1]. Data type is the same as ``log_probs``.
1058

1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095
    Examples:

        .. code-block:: python

            # declarative mode
            import numpy as np
            import paddle

            # 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

            np.random.seed(1)
            log_probs = 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]]]).astype("float32")
            labels = np.array([[1, 2, 2],
                            [1, 2, 2]]).astype("int32")
            input_lengths = np.array([5, 5]).astype("int64")
            label_lengths = np.array([3, 3]).astype("int64")

1096 1097 1098 1099
            log_probs = paddle.to_tensor(log_probs)
            labels = paddle.to_tensor(labels)
            input_lengths = paddle.to_tensor(input_lengths)
            label_lengths = paddle.to_tensor(label_lengths)
1100

1101 1102
            loss = paddle.nn.CTCLoss(blank=0, reduction='none')(log_probs, labels,
                input_lengths,
1103
                label_lengths)
1104
            print(loss)  #[3.9179852 2.9076521]
1105

1106 1107
            loss = paddle.nn.CTCLoss(blank=0, reduction='mean')(log_probs, labels,
                input_lengths,
1108
                label_lengths)
1109
            print(loss)  #[1.1376063]
1110 1111 1112 1113 1114 1115 1116
    """

    def __init__(self, blank=0, reduction='mean'):
        super(CTCLoss, self).__init__()
        self.blank = blank
        self.reduction = reduction

1117 1118 1119 1120 1121
    def forward(self,
                log_probs,
                labels,
                input_lengths,
                label_lengths,
H
Hui Zhang 已提交
1122
                norm_by_times=False):
1123 1124 1125 1126 1127 1128 1129
        return paddle.nn.functional.ctc_loss(log_probs,
                                             labels,
                                             input_lengths,
                                             label_lengths,
                                             self.blank,
                                             self.reduction,
                                             norm_by_times=norm_by_times)
1130 1131


Z
zhiboniu 已提交
1132
class SmoothL1Loss(Layer):
1133
    r"""
1134 1135 1136 1137 1138 1139 1140
    This operator calculates 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::

1141
         loss(x,y) = \frac{1}{n}\sum_{i}z_i
1142 1143 1144 1145 1146

    where z_i is given by:

    .. math::

1147 1148
        \mathop{z_i} = \left\{\begin{array}{rcl}
        0.5(x_i - y_i)^2 & & {if |x_i - y_i| < delta} \\
1149
        delta * |x_i - y_i| - 0.5 * delta^2 & & {otherwise}
1150
        \end{array} \right.
1151 1152 1153 1154 1155 1156 1157 1158

    Parameters:
        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'``.
1159
        delta (float, optional): Specifies the hyperparameter delta to be used.
1160 1161 1162 1163 1164 1165 1166 1167
            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): Name for the operation (optional, default is
            None). For more information, please refer to :ref:`api_guide_Name`.

    Call Parameters:

1168 1169
        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,
1170 1171
        this is (N, C, D1, D2,..., Dk), k >= 1.

1172
        label (Tensor): Label tensor, the data type is float32 or float64.
1173
        The shape of label is the same as the shape of input.
1174

1175 1176
    Returns:
        Tensor, The tensor storing the smooth_l1_loss of input and label.
1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188

    Examples:
        .. code-block:: python

            import paddle
            import numpy as np
            input_data = np.random.rand(3,3).astype("float32")
            label_data = np.random.rand(3,3).astype("float32")
            input = paddle.to_tensor(input_data)
            label = paddle.to_tensor(label_data)
            loss = paddle.nn.SmoothL1Loss()
            output = loss(input, label)
G
Guanghua Yu 已提交
1189
            print(output)
1190 1191 1192 1193 1194 1195 1196 1197 1198
    """

    def __init__(self, reduction='mean', delta=1.0, name=None):
        super(SmoothL1Loss, self).__init__()
        self.reduction = reduction
        self.delta = delta
        self.name = name

    def forward(self, input, label):
1199 1200 1201 1202 1203
        return F.smooth_l1_loss(input,
                                label,
                                reduction=self.reduction,
                                delta=self.delta,
                                name=self.name)
1204 1205


Y
yangguohao 已提交
1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283
class MultiLabelSoftMarginLoss(Layer):
    r"""Creates a criterion that optimizes 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:
	        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`.

        Call 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 containing 1 or -1, the data type is float32 or float64. The shape of label is the same as the shape of input.

        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.
            output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input.

        Returns:
            A callable object of MultiLabelSoftMarginLoss.

        Examples:
            .. code-block:: python

                import paddle
                import paddle.nn as nn

                input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
                label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)

                multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='none')
                loss = multi_label_soft_margin_loss(input, label)
                print(loss)
                # Tensor([3.49625897, 0.71111226, 0.43989015])

                multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='mean')
                loss = multi_label_soft_margin_loss(input, label)
                print(loss)
                # Tensor([1.54908717])
        """

    def __init__(self, weight=None, reduction="mean", name=None):
        super(MultiLabelSoftMarginLoss, self).__init__()
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "'reduction' in 'MultiLabelSoftMarginloss' should be 'sum', 'mean' or 'none', "
                "but received {}.".format(reduction))
        self.weight = weight
        self.reduction = reduction
        self.name = name

    def forward(self, input, label):
        return F.multi_label_soft_margin_loss(input,
                                              label,
                                              weight=self.weight,
                                              reduction=self.reduction,
                                              name=self.name)


1284 1285
class HingeEmbeddingLoss(Layer):
    r"""
1286
    Create a callable object of `HingeEmbeddingLoss` to calculates hinge_embedding_loss. Measures the loss given an input tensor :math:`x` and a labels tensor :math:`y`(containing 1 or -1).
1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370
    This is usually used for measuring whether two inputs are similar or dissimilar, e.g. using the L1 pairwise distance as :math:`x`,
    and is typically used for learning nonlinear embeddings or semi-supervised learning.

    The loss function for :math:`n`-th sample in the mini-batch is

    .. math::
        l_n = \begin{cases}
            x_n, & \text{if}\; y_n = 1,\\
            \max \{0, \Delta - x_n\}, & \text{if}\; y_n = -1,
        \end{cases}

    and the total loss functions is

    .. math::
        \ell(x, y) = \begin{cases}
            \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\
            \operatorname{sum}(L),  & \text{if reduction} = \text{'sum'.}
        \end{cases}

    where :math:`L = \{l_1,\dots,l_N\}^\top`.

    Parameters:

        margin (float, optional): Specifies the hyperparameter margin to be used.
            The value determines how large the input need to be to calculate in
            hinge_embedding_loss. When label is -1, Input smaller than margin are minimized with hinge_embedding_loss.
            Default = 1.0
        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: ``'mean'``
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Call 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 containing 1 or -1, the data type is float32 or float64. The shape of label is the same as the shape of input.

    Shape:

        input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64. The sum operationoperates over all the elements.

        label: N-D Tensor, same shape as the input.

        output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input.

    Returns:

        Tensor, The tensor variable storing the hinge_embedding_loss of input and label.

    Examples:
        .. code-block:: python

            import paddle
            import paddle.nn as nn

            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)

            hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='none')
            loss = hinge_embedding_loss(input, label)
            print(loss)
            # Tensor([[0., -2., 0.],
            #         [0., -1., 2.],
            #         [1., 1., 1.]])

            hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='mean')
            loss = hinge_embedding_loss(input, label)
            print(loss)
            # Tensor([0.22222222])
    """

    def __init__(self, margin=1.0, reduction="mean", name=None):
        super(HingeEmbeddingLoss, self).__init__()
        self.margin = margin
        self.reduction = reduction
        self.name = name

    def forward(self, input, label):
1371 1372 1373 1374 1375
        return F.hinge_embedding_loss(input,
                                      label,
                                      reduction=self.reduction,
                                      margin=self.margin,
                                      name=self.name)
1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465


class CosineEmbeddingLoss(Layer):
    r"""
    This interface is used to construct a callable object of the ``CosineEmbeddingLoss`` class.
    The CosineEmbeddingLoss layer measures the cosine_embedding loss between input predictions ``input1``, ``input2``
    and target labels ``label`` with values 1 or 0. This is used for measuring whether two inputs are similar or
    dissimilar and is typically used for learning nonlinear embeddings or semi-supervised learning.
    The cosine embedding loss can be described as:

    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:
        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`.

    Shape:
        input1 (Tensor): tensor with shape: [N, M] or [M], 'N' means batch size, '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, 'M' means the length of input array.
                         Available dtypes are float32, float64.
        label (Tensor): tensor with shape: [N] or [1]. The target labels values should be -1 or 1.
                         Available dtypes are int32, int64, float32, float64.
        output (Tensor): 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 [1].

    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')

            cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='mean')
            output = cosine_embedding_loss(input1, input2, label)
            print(output) # [0.21155193]

            cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='sum')
            output = cosine_embedding_loss(input1, input2, label)
            print(output) # [0.42310387]

            cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='none')
            output = cosine_embedding_loss(input1, input2, label)
            print(output) # [0.42310387, 0.        ]

    """

    def __init__(self, margin=0, reduction='mean', name=None):
        if margin > 1 or margin < -1:
            raise ValueError(
                "The value of 'margin' should be in the interval of [-1, 1], but received %f, which is not allowed."
                % margin)
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' should be 'sum', 'mean' or "
                "'none', but received %s, which is not allowed." % reduction)
        super(CosineEmbeddingLoss, self).__init__()
        self.margin = margin
        self.reduction = reduction
        self.name = name

    def forward(self, input1, input2, label):
        return F.cosine_embedding_loss(input1,
                                       input2,
                                       label,
                                       margin=self.margin,
                                       reduction=self.reduction,
                                       name=self.name)
Y
yangguohao 已提交
1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482


class TripletMarginWithDistanceLoss(Layer):
    r"""
    Creates a criterion that 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`
1483

Y
yangguohao 已提交
1484 1485
    .. math::
    	d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_2
1486 1487

    or user can define their own distance function. `margin` is a nonnegative margin representing the minimum difference
Y
yangguohao 已提交
1488 1489 1490 1491 1492
    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:
        distance_function (Callable, Optional): Quantifies the distance between two tensors. if not specified, 2 norm functions will be used.
1493

Y
yangguohao 已提交
1494 1495 1496 1497
        margin (float, Optional):Default: :math:`1`.A nonnegative margin representing the minimum difference
                between the positive and negative distances required for the loss to be 0. Larger
                margins penalize cases where the negative examples are not distant enough from the
                anchors, relative to the positives.
1498

Y
yangguohao 已提交
1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509
        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`.
1510

Y
yangguohao 已提交
1511 1512 1513 1514 1515 1516 1517 1518 1519
    Shapes:
        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.
1520

Y
yangguohao 已提交
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572
	    output(Tensor): The tensor variable storing the triplet_margin_with_distance_loss of input and positive and negative.

    Return:
        A callable object of TripletMarginWithDistanceLoss

    Examples:
        .. code-block:: python

            import paddle
            from paddle.nn import TripletMarginWithDistanceLoss

            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)
            triplet_margin_with_distance_loss = TripletMarginWithDistanceLoss(reduction='none')
            loss = triplet_margin_with_distance_loss(input, positive, negative,)
            print(loss)
            # Tensor([0.        , 0.57496738, 0.        ])

            triplet_margin_with_distance_loss = TripletMarginWithDistanceLoss(reduction='mean')
            loss = triplet_margin_with_distance_loss(input, positive, negative,)
            print(loss)
            # Tensor([0.19165580])

    """

    def __init__(self,
                 distance_function=None,
                 margin=1.0,
                 swap=False,
                 reduction: str = 'mean',
                 name=None):
        super(TripletMarginWithDistanceLoss, self).__init__()
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in TripletMarginWithDistanceLoss "
                "should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)
        self.margin = margin
        self.swap = swap
        self.reduction = reduction
        self.distance_function = distance_function
        self.name = name

    def forward(self, input, positive, negative):
        return F.triplet_margin_with_distance_loss(input,
                                                   positive,
                                                   negative,
                                                   margin=self.margin,
                                                   swap=self.swap,
                                                   reduction=self.reduction,
                                                   name=self.name)
Y
yangguohao 已提交
1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641


class TripletMarginLoss(Layer):
    r"""
    Creates a criterion that 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:
        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`.

    Call 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.

    Returns:
        Tensor. The tensor variable storing the triplet_margin_loss of input and positive and negative.

    Examples:
        .. code-block:: python

            import paddle

            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)
            triplet_margin_loss = paddle.nn.TripletMarginLoss(reduction='none')
            loss = triplet_margin_loss(input, positive, negative)
            print(loss)
            # Tensor([0.        , 0.57496738, 0.        ])
1642

Y
yangguohao 已提交
1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678
            triplet_margin_loss = paddle.nn.TripletMarginLoss(margin=1.0, swap=True, reduction='mean', )
            loss = triplet_margin_loss(input, positive, negative,)
            print(loss)
            # Tensor([0.19165580])

    """

    def __init__(self,
                 margin=1.0,
                 p=2.,
                 epsilon=1e-6,
                 swap=False,
                 reduction='mean',
                 name=None):
        super(TripletMarginLoss, self).__init__()
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in TripletMarginLoss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)
        self.margin = margin
        self.p = p
        self.epsilon = epsilon
        self.swap = swap
        self.reduction = reduction
        self.name = name

    def forward(self, input, positive, negative):
        return F.triplet_margin_loss(input,
                                     positive,
                                     negative,
                                     margin=self.margin,
                                     p=self.p,
                                     epsilon=self.epsilon,
                                     swap=self.swap,
                                     reduction=self.reduction,
                                     name=self.name)
1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749


class SoftMarginLoss(Layer):
    r"""
    Creates a criterion that measures a two-class soft margin loss between input predictions ``input``
    and target labels ``label`` . It can be described as:

    .. math::
        Out = log(1 + exp((-label * input)))

    Parameters:

        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`.

    Shapes:

        Input (Tensor): The input 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.

        Output (Tensor): If ``reduction`` is ``'none'``, the shape of output is
            same as ``input`` , else the shape of output is [1].

    Returns:
        A callable object of SoftMarginLoss.

    Examples:
        .. code-block:: python

            import paddle
            import numpy as np

            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')
            soft_margin_loss = paddle.nn.SoftMarginLoss()
            output = soft_margin_loss(input, label)

            input_np = np.random.uniform(0.1, 0.8, size=(5, 5)).astype(np.float64)
            label_np = np.random.randint(0, 2, size=(5, 5)).astype(np.int64)
            label_np[label_np==0]=-1
            input = paddle.to_tensor(input_np)
            label = paddle.to_tensor(label_np)
            soft_margin_loss = paddle.nn.SoftMarginLoss(reduction='none')
            output = soft_margin_loss(input, label)
    """

    def __init__(self, reduction='mean', name=None):
        if reduction not in ['sum', 'mean', 'none']:
            raise ValueError(
                "The value of 'reduction' in SoftMarginLoss should be 'sum', 'mean' or 'none', but "
                "received %s, which is not allowed." % reduction)

        super(SoftMarginLoss, self).__init__()
        self.reduction = reduction
        self.name = name

    def forward(self, input, label):
        out = paddle.nn.functional.soft_margin_loss(input, label,
                                                    self.reduction, self.name)
        return out