6bij SrSSKrSSKrSSKrSSKJs Jr SSKJ r SSK J r SSK J r SSKJr SSKJr SSKJr SS KJr SS KJr SS KJr SS KJr SS KJr SSKJr \"S5"SS55r\"S/S9"SS\55r\"S5"SS\55r \"S5"SS\55r!\"S5"SS\55r"\"S5"S S!\55r#\"S"5"S#S$\55r$\"S%5"S&S'\55r%\"S(5"S)S*\55r&\"S+5"S,S-\55r'\"S.5"S/S0\55r(\"S15"S2S3\55r)\"S45"S5S6\55r*\"S75"S8S9\55r+\"S:5"S;S<\55r,\"S=5"S>S?\55r-\"S@5"SASB\55r.\"SC5"SDSE\55r/\"SF5"SGSH\55r0\"SISJSKSLSMSN5\RbR2RdSO55r3SSPjr4\Rj"\3\Rl5SQ5r7\"SRSSSTSUSVSW5\RbR2RdSX55r8\Rj"\8\Rl5SY5r9\"SZS[S\S]S^S_5\RbR2RdS`55r:\Rj"\:\Rl5Sa5r;\"SbScSdSeSfSg5\RbR2RdSh55r<\Rj"\<\Rl5Si5r=Sjr>\"SkSl5\RbR2RdSm55r?\"SnSo5\RbR2RdSp55r@\"Sq5\RbR2RdSr55rA\"Ss/S9\RbR2RdSStj55rB\"SuSvSwSx5\RbR2RdSy55rC\"SzS{5\RbR2RdSS|j55rD\Rj"\D\Rl5SS}j5rE\"S~S5\RbR2RdSSj55rF\Rj"\F\Rl5SSj5rG\"SS5\RbR2RdSSj55rH\Rj"\H\Rl5SSj5rI\"SS5\RbR2RdSSj55rJ\Rj"\J\Rl5SSj5rK\"SS5\RbR2RdSSj55rL\Rj"\L\Rl5SSj5rM\"SSSSSSSS5\RbR2RdS55rN\"SS5\RbR2RdS55rO\"S/SQS9\RbR2RdSSj55rP\J=rQrR\3=rSrT\8=rUrV\:=rWrX\<=rYrZ\N=r[=r\r]\Cr^\Br_Sr`\"S5SSj5ra\"S5SSj5rb\"S5S5rc\R RRRS\HS0rgg)zBuilt-in loss functions.N)backend) saving_lib) serialization)deserialize_keras_object)serialize_keras_object) losses_utils)tf_utils)ragged_map_ops) ragged_util)dispatch) keras_export) doc_controlszkeras.losses.Lossc\rSrSrSr\R RS4SjrSr S Sjr \ S5r Sr \R\R"S 55rS rS rg) Loss)a#Loss base class. To be implemented by subclasses: * `call()`: Contains the logic for loss calculation using `y_true`, `y_pred`. Example subclass implementation: ```python class MeanSquaredError(Loss): def call(self, y_true, y_pred): return tf.reduce_mean(tf.math.square(y_pred - y_true), axis=-1) ``` When using a Loss under a `tf.distribute.Strategy`, except passing it to `Model.compile()` for use by `Model.fit()`, please use reduction types 'SUM' or 'NONE', and reduce losses explicitly. Using 'AUTO' or 'SUM_OVER_BATCH_SIZE' will raise an error when calling the Loss object from a custom training loop or from user-defined code in `Layer.call()`. Please see this custom training [tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training) for more details on this. Nc[RRU5 XlX lSUlUR 5 g)aInitializes `Loss` class. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. FN)r ReductionV2validate reductionname_allow_sum_over_batch_size_set_name_scope)selfrrs M/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/losses.py__init__ Loss.__init__Ds8   )))4" +0' cURc+URRRS5UlgURS:XaSUlgURRS5Ulg)z"Creates a valid `name_scope` name.N_zlambda)r __class____name__strip _name_scopers rrLoss._set_name_scope\sT 99 #~~66<>IIr88KKMV,F"++F3G#,,V4H"x';)$%++-I(99tM 5554=9 1 199 1 1 1s#E2D E E2 E& "E22 FcU"S0UD6$)zInstantiates a `Loss` from its config (output of `get_config()`). Args: config: Output of `get_config()`. Returns: A `Loss` instance. rA)clsconfigs r from_configLoss.from_configs}V}rc4URURS.$)z4Returns the config dictionary for a `Loss` instance.rrrGr%s r get_configLoss.get_configs!^^TYY??rc[S5e)aqInvokes the `Loss` instance. Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`, except sparse loss functions such as sparse categorical crossentropy where shape = `[batch_size, d0, .. dN-1]` y_pred: The predicted values. shape = `[batch_size, d0, .. dN]` Returns: Loss values with the shape `[batch_size, d0, .. dN-1]`. z"Must be implemented in subclasses.)NotImplementedError)rr5r6s rr, Loss.calls""FGGrcUR(d~[RR5(a[UR[ R R:Xd(UR[ R R:Xa [S5eUR[ R R:Xa[ R R$UR$)z?Handles `AUTO` reduction cases and returns the reduction value.aZPlease use `tf.keras.losses.Reduction.SUM` or `tf.keras.losses.Reduction.NONE` for loss reduction when losses are used with `tf.distribute.Strategy`, except for specifying losses in `Model.compile()` for use by the built-in training looop `Model.fit()`. Please see https://www.tensorflow.org/tutorials/distribute/custom_training for more details.) rr* distribute has_strategyrrrAUTOSUM_OVER_BATCH_SIZE ValueErrorr%s rr2Loss._get_reductions// **,,,":":"?"??>>++??@@  >>\55:: :++?? ?~~r)rr$rrN)r" __module__ __qualname____firstlineno____doc__rrrPrrr> classmethodrDrHabcabstractmethodrfor_subclass_implementersr,r2__static_attributes__rArrrr)ss2".!9!9!>!>T04=~  @ ++ H, Hrrz-keras.__internal__.losses.LossFunctionWrapper)v1c~^\rSrSrSr\R RS4U4SjjrSr U4Sjr \ S5r Sr U=r$) LossFunctionWrapperz*Wraps a loss function in the `Loss` class.Nc 8>[TU]X#S9 XlX@lg)aAInitializes `LossFunctionWrapper` class. Args: fn: The loss function to wrap, with signature `fn(y_true, y_pred, **kwargs)`. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. **kwargs: The keyword arguments that are passed on to `fn`. rGN)superrfn _fn_kwargs)rrdrrkwargsr!s rrLossFunctionWrapper.__init__s* 98 rcx[R"U5(a3[R"U5(a[R"X!5up![RR R UR[RR R55nU"X40URD6$)zInvokes the `LossFunctionWrapper` instance. Args: y_true: Ground truth values. y_pred: The predicted values. Returns: Loss values per sample. ) r* is_tensorrsqueeze_or_expand_dimensionsr-r.r/rdr0re)rr5r6ag_fns rr,LossFunctionWrapper.calls <<  BLL$8$8)FFNF))44 GGR__..AAC V7t77rc>0nURR5H:up#[R"U5(a[R "U5OUX'M< [ R"5(aSSKJ n U"UR5US'[TU]15n[[UR55[UR55-5$)Nr)get_registered_namerd)reitemsr is_tensor_or_variablerevalrsaving_v3_enabledtf_keras.src.utilsrnrdrcrHdictlist)rrCkvrn base_configr!s rrHLossFunctionWrapper.get_configsOO))+DA#+#A#A!#D#D Q! I,  ' ' ) ) >.tww7F4Lg(* D**,-V\\^0DDEErc[R"5(a0URSS5nU(aU[La[ U5US'U"S0UD6$)zInstantiates a `Loss` from its config (output of `get_config()`). Args: config: Output of `get_config()`. Returns: A `keras.losses.Loss` instance. rdNrA)rrrpopr`get)rBrCfn_names rrDLossFunctionWrapper.from_configsG  ' ' ) )jjt,G3"55"7|t }V}r)rerd)r"rUrVrWrXrrrPrr,rHrYrDr] __classcell__r!s@rr`r`s=4)4499!28( F  rr`zkeras.losses.MeanSquaredErrorc\^\rSrSrSr\R RS4U4SjjrSr U=r $)MeanSquaredErrori0aComputes the mean of squares of errors between labels and predictions. `loss = mean(square(y_true - y_pred))` Standalone usage: >>> y_true = [[0., 1.], [0., 0.]] >>> y_pred = [[1., 1.], [1., 0.]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> mse = tf.keras.losses.MeanSquaredError() >>> mse(y_true, y_pred).numpy() 0.5 >>> # Calling with 'sample_weight'. >>> mse(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy() 0.25 >>> # Using 'sum' reduction type. >>> mse = tf.keras.losses.MeanSquaredError( ... reduction=tf.keras.losses.Reduction.SUM) >>> mse(y_true, y_pred).numpy() 1.0 >>> # Using 'none' reduction type. >>> mse = tf.keras.losses.MeanSquaredError( ... reduction=tf.keras.losses.Reduction.NONE) >>> mse(y_true, y_pred).numpy() array([0.5, 0.5], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.MeanSquaredError()) ``` mean_squared_errorc*>[TU][X!S9 g)aInitializes `MeanSquaredError` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'mean_squared_error'. rrN)rcrrrrrr!s rrMeanSquaredError.__init__Vs& +$LrrA r"rUrVrWrXrrrPrr]rrs@rrr0s)"J%0055>> y_true = [[0., 1.], [0., 0.]] >>> y_pred = [[1., 1.], [1., 0.]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> mae = tf.keras.losses.MeanAbsoluteError() >>> mae(y_true, y_pred).numpy() 0.5 >>> # Calling with 'sample_weight'. >>> mae(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy() 0.25 >>> # Using 'sum' reduction type. >>> mae = tf.keras.losses.MeanAbsoluteError( ... reduction=tf.keras.losses.Reduction.SUM) >>> mae(y_true, y_pred).numpy() 1.0 >>> # Using 'none' reduction type. >>> mae = tf.keras.losses.MeanAbsoluteError( ... reduction=tf.keras.losses.Reduction.NONE) >>> mae(y_true, y_pred).numpy() array([0.5, 0.5], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.MeanAbsoluteError()) ``` mean_absolute_errorc*>[TU][X!S9 g)aInitializes `MeanAbsoluteError` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'mean_absolute_error'. rN)rcrrrs rrMeanAbsoluteError.__init__s* ,4MrrArrs@rrrls)"L**// "NNrrz(keras.losses.MeanAbsolutePercentageErrorc\^\rSrSrSr\R RS4U4SjjrSr U=r $)MeanAbsolutePercentageErroriaBComputes the mean absolute percentage error between `y_true` & `y_pred`. Formula: `loss = 100 * abs((y_true - y_pred) / y_true)` Note that to avoid dividing by zero, a small epsilon value is added to the denominator. Standalone usage: >>> y_true = [[2., 1.], [2., 3.]] >>> y_pred = [[1., 1.], [1., 0.]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> mape = tf.keras.losses.MeanAbsolutePercentageError() >>> mape(y_true, y_pred).numpy() 50. >>> # Calling with 'sample_weight'. >>> mape(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy() 20. >>> # Using 'sum' reduction type. >>> mape = tf.keras.losses.MeanAbsolutePercentageError( ... reduction=tf.keras.losses.Reduction.SUM) >>> mape(y_true, y_pred).numpy() 100. >>> # Using 'none' reduction type. >>> mape = tf.keras.losses.MeanAbsolutePercentageError( ... reduction=tf.keras.losses.Reduction.NONE) >>> mape(y_true, y_pred).numpy() array([25., 75.], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.MeanAbsolutePercentageError()) ``` mean_absolute_percentage_errorc*>[TU][X!S9 g)aInitializes `MeanAbsolutePercentageError` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'mean_absolute_percentage_error'. rN)rcrrrs rr$MeanAbsolutePercentageError.__init__*  *  rrArrs@rrrs'(X**// -  rrz(keras.losses.MeanSquaredLogarithmicErrorc\^\rSrSrSr\R RS4U4SjjrSr U=r $)MeanSquaredLogarithmicErroriaComputes the mean squared logarithmic error between `y_true` & `y_pred`. `loss = square(log(y_true + 1.) - log(y_pred + 1.))` Standalone usage: >>> y_true = [[0., 1.], [0., 0.]] >>> y_pred = [[1., 1.], [1., 0.]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> msle = tf.keras.losses.MeanSquaredLogarithmicError() >>> msle(y_true, y_pred).numpy() 0.240 >>> # Calling with 'sample_weight'. >>> msle(y_true, y_pred, sample_weight=[0.7, 0.3]).numpy() 0.120 >>> # Using 'sum' reduction type. >>> msle = tf.keras.losses.MeanSquaredLogarithmicError( ... reduction=tf.keras.losses.Reduction.SUM) >>> msle(y_true, y_pred).numpy() 0.480 >>> # Using 'none' reduction type. >>> msle = tf.keras.losses.MeanSquaredLogarithmicError( ... reduction=tf.keras.losses.Reduction.NONE) >>> msle(y_true, y_pred).numpy() array([0.240, 0.240], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.MeanSquaredLogarithmicError()) ``` mean_squared_logarithmic_errorc*>[TU][X!S9 g)aInitializes `MeanSquaredLogarithmicError` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'mean_squared_logarithmic_error'. rN)rcrrrs rr$MeanSquaredLogarithmicError.__init__rrrArrs@rrrs'#N**// -  rrzkeras.losses.BinaryCrossentropycb^\rSrSrSrSSS\R RS4U4SjjrSr U=r $) BinaryCrossentropyi1aYComputes the cross-entropy loss between true labels and predicted labels. Use this cross-entropy loss for binary (0 or 1) classification applications. The loss function requires the following inputs: - `y_true` (true label): This is either 0 or 1. - `y_pred` (predicted value): This is the model's prediction, i.e, a single floating-point value which either represents a [logit](https://en.wikipedia.org/wiki/Logit), (i.e, value in [-inf, inf] when `from_logits=True`) or a probability (i.e, value in [0., 1.] when `from_logits=False`). **Recommended Usage:** (set `from_logits=True`) With `tf.keras` API: ```python model.compile( loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), .... ) ``` As a standalone function: >>> # Example 1: (batch_size = 1, number of samples = 4) >>> y_true = [0, 1, 0, 0] >>> y_pred = [-18.6, 0.51, 2.94, -12.8] >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True) >>> bce(y_true, y_pred).numpy() 0.865 >>> # Example 2: (batch_size = 2, number of samples = 4) >>> y_true = [[0, 1], [0, 0]] >>> y_pred = [[-18.6, 0.51], [2.94, -12.8]] >>> # Using default 'auto'/'sum_over_batch_size' reduction type. >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True) >>> bce(y_true, y_pred).numpy() 0.865 >>> # Using 'sample_weight' attribute >>> bce(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() 0.243 >>> # Using 'sum' reduction` type. >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True, ... reduction=tf.keras.losses.Reduction.SUM) >>> bce(y_true, y_pred).numpy() 1.730 >>> # Using 'none' reduction type. >>> bce = tf.keras.losses.BinaryCrossentropy(from_logits=True, ... reduction=tf.keras.losses.Reduction.NONE) >>> bce(y_true, y_pred).numpy() array([0.235, 1.496], dtype=float32) **Default Usage:** (set `from_logits=False`) >>> # Make the following updates to the above "Recommended Usage" section >>> # 1. Set `from_logits=False` >>> tf.keras.losses.BinaryCrossentropy() # OR ...('from_logits=False') >>> # 2. Update `y_pred` to use probabilities instead of logits >>> y_pred = [0.6, 0.3, 0.2, 0.8] # OR [[0.6, 0.3], [0.2, 0.8]] Fbinary_crossentropyc >>[TU][UUUUUS9 Xlg)aEInitializes `BinaryCrossentropy` instance. Args: from_logits: Whether to interpret `y_pred` as a tensor of [logit](https://en.wikipedia.org/wiki/Logit) values. By default, we assume that `y_pred` contains probabilities (i.e., values in [0, 1]). label_smoothing: Float in [0, 1]. When 0, no smoothing occurs. When > 0, we compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5. Larger values of `label_smoothing` correspond to heavier smoothing. axis: The axis along which to compute crossentropy (the features axis). Defaults to -1. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Name for the op. Defaults to 'binary_crossentropy'. rr from_logitslabel_smoothingaxisN)rcrrrrrrrrrr!s rrBinaryCrossentropy.__init__qs2D  #+  'rrrrs@rrr1s0<@ **// " *'*'rrz$keras.losses.BinaryFocalCrossentropyct^\rSrSrSrSSSSSS\R RS4U4S jjrU4S jr S r U=r $) BinaryFocalCrossentropyia?Computes focal cross-entropy loss between true labels and predictions. Binary cross-entropy loss is often used for binary (0 or 1) classification tasks. The loss function requires the following inputs: - `y_true` (true label): This is either 0 or 1. - `y_pred` (predicted value): This is the model's prediction, i.e, a single floating-point value which either represents a [logit](https://en.wikipedia.org/wiki/Logit), (i.e, value in [-inf, inf] when `from_logits=True`) or a probability (i.e, value in [0., 1.] when `from_logits=False`). According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it helps to apply a "focal factor" to down-weight easy examples and focus more on hard examples. By default, the focal tensor is computed as follows: `focal_factor = (1 - output) ** gamma` for class 1 `focal_factor = output ** gamma` for class 0 where `gamma` is a focusing parameter. When `gamma=0`, this function is equivalent to the binary crossentropy loss. With the `compile()` API: ```python model.compile( loss=tf.keras.losses.BinaryFocalCrossentropy(gamma=2.0, from_logits=True), .... ) ``` As a standalone function: >>> # Example 1: (batch_size = 1, number of samples = 4) >>> y_true = [0, 1, 0, 0] >>> y_pred = [-18.6, 0.51, 2.94, -12.8] >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=2, ... from_logits=True) >>> loss(y_true, y_pred).numpy() 0.691 >>> # Apply class weight >>> loss = tf.keras.losses.BinaryFocalCrossentropy( ... apply_class_balancing=True, gamma=2, from_logits=True) >>> loss(y_true, y_pred).numpy() 0.51 >>> # Example 2: (batch_size = 2, number of samples = 4) >>> y_true = [[0, 1], [0, 0]] >>> y_pred = [[-18.6, 0.51], [2.94, -12.8]] >>> # Using default 'auto'/'sum_over_batch_size' reduction type. >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=3, ... from_logits=True) >>> loss(y_true, y_pred).numpy() 0.647 >>> # Apply class weight >>> loss = tf.keras.losses.BinaryFocalCrossentropy( ... apply_class_balancing=True, gamma=3, from_logits=True) >>> loss(y_true, y_pred).numpy() 0.482 >>> # Using 'sample_weight' attribute with focal effect >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=3, ... from_logits=True) >>> loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() 0.133 >>> # Apply class weight >>> loss = tf.keras.losses.BinaryFocalCrossentropy( ... apply_class_balancing=True, gamma=3, from_logits=True) >>> loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() 0.097 >>> # Using 'sum' reduction` type. >>> loss = tf.keras.losses.BinaryFocalCrossentropy(gamma=4, ... from_logits=True, ... reduction=tf.keras.losses.Reduction.SUM) >>> loss(y_true, y_pred).numpy() 1.222 >>> # Apply class weight >>> loss = tf.keras.losses.BinaryFocalCrossentropy( ... apply_class_balancing=True, gamma=4, from_logits=True, ... reduction=tf.keras.losses.Reduction.SUM) >>> loss(y_true, y_pred).numpy() 0.914 >>> # Using 'none' reduction type. >>> loss = tf.keras.losses.BinaryFocalCrossentropy( ... gamma=5, from_logits=True, ... reduction=tf.keras.losses.Reduction.NONE) >>> loss(y_true, y_pred).numpy() array([0.0017 1.1561], dtype=float32) >>> # Apply class weight >>> loss = tf.keras.losses.BinaryFocalCrossentropy( ... apply_class_balancing=True, gamma=5, from_logits=True, ... reduction=tf.keras.losses.Reduction.NONE) >>> loss(y_true, y_pred).numpy() array([0.0004 0.8670], dtype=float32) Args: apply_class_balancing: A bool, whether to apply weight balancing on the binary classes 0 and 1. alpha: A weight balancing factor for class 1, default is `0.25` as mentioned in reference [Lin et al., 2018]( https://arxiv.org/pdf/1708.02002.pdf). The weight for class 0 is `1.0 - alpha`. gamma: A focusing parameter used to compute the focal factor, default is `2.0` as mentioned in the reference [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf). from_logits: Whether to interpret `y_pred` as a tensor of [logit](https://en.wikipedia.org/wiki/Logit) values. By default, we assume that `y_pred` are probabilities (i.e., values in `[0, 1]`). label_smoothing: Float in `[0, 1]`. When `0`, no smoothing occurs. When > `0`, we compute the loss between the predicted labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards `0.5`. Larger values of `label_smoothing` correspond to heavier smoothing. axis: The axis along which to compute crossentropy (the features axis). Defaults to `-1`. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Name for the op. Defaults to 'binary_focal_crossentropy'. F?@rrbinary_focal_crossentropyc h>[T U][UUUUUUUUS9 X@lXlX lX0lg)z/Initializes `BinaryFocalCrossentropy` instance.)apply_class_balancingalphagammarrrrrN)rcrrrrrr) rrrrrrrrrr!s rr BinaryFocalCrossentropy.__init__&sJ  %"7#+  '%:"  rc>URURURS.n[TU]5n[ [ UR55[ UR55-5$)N)rrr)rrrrcrHrtrurorrCrxr!s rrH"BinaryFocalCrossentropy.get_configBsY%)%?%?ZZZZ  g(* D**,-V\\^0DDEEr)rrrr r"rUrVrWrXrrrPrrHr]rrs@rrrsADP$ **// (8FFrrz$keras.losses.CategoricalCrossentropycb^\rSrSrSrSSS\R RS4U4SjjrSr U=r $) CategoricalCrossentropyiLa~Computes the crossentropy loss between the labels and predictions. Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided in a `one_hot` representation. If you want to provide labels as integers, please use `SparseCategoricalCrossentropy` loss. There should be `# classes` floating point values per feature. In the snippet below, there is `# classes` floating pointing values per example. The shape of both `y_pred` and `y_true` are `[batch_size, num_classes]`. Standalone usage: >>> y_true = [[0, 1, 0], [0, 0, 1]] >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> cce = tf.keras.losses.CategoricalCrossentropy() >>> cce(y_true, y_pred).numpy() 1.177 >>> # Calling with 'sample_weight'. >>> cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy() 0.814 >>> # Using 'sum' reduction type. >>> cce = tf.keras.losses.CategoricalCrossentropy( ... reduction=tf.keras.losses.Reduction.SUM) >>> cce(y_true, y_pred).numpy() 2.354 >>> # Using 'none' reduction type. >>> cce = tf.keras.losses.CategoricalCrossentropy( ... reduction=tf.keras.losses.Reduction.NONE) >>> cce(y_true, y_pred).numpy() array([0.0513, 2.303], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalCrossentropy()) ``` Frrcategorical_crossentropyc 2>[TU][UUUUUS9 g)aInitializes `CategoricalCrossentropy` instance. Args: from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: The axis along which to compute crossentropy (the features axis). Defaults to -1. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'categorical_crossentropy'. rN)rcrrrs rr CategoricalCrossentropy.__init__{s*@  $#+  rrArrs@rrrLs0+^ **// ' ' ' rrz)keras.losses.CategoricalFocalCrossentropycr^\rSrSrSrSSSSS\R RS4U4S jjrU4S jr S r U=r $) CategoricalFocalCrossentropyiuComputes the alpha balanced focal crossentropy loss. Use this crossentropy loss function when there are two or more label classes and if you want to handle class imbalance without using `class_weights`. We expect labels to be provided in a `one_hot` representation. According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it helps to apply a focal factor to down-weight easy examples and focus more on hard examples. The general formula for the focal loss (FL) is as follows: `FL(p_t) = (1 − p_t)^gamma * log(p_t)` where `p_t` is defined as follows: `p_t = output if y_true == 1, else 1 - output` `(1 − p_t)^gamma` is the `modulating_factor`, where `gamma` is a focusing parameter. When `gamma` = 0, there is no focal effect on the cross entropy. `gamma` reduces the importance given to simple examples in a smooth manner. The authors use alpha-balanced variant of focal loss (FL) in the paper: `FL(p_t) = −alpha * (1 − p_t)^gamma * log(p_t)` where `alpha` is the weight factor for the classes. If `alpha` = 1, the loss won't be able to handle class imbalance properly as all classes will have the same weight. This can be a constant or a list of constants. If alpha is a list, it must have the same length as the number of classes. The formula above can be generalized to: `FL(p_t) = alpha * (1 − p_t)^gamma * CrossEntropy(y_true, y_pred)` where minus comes from `CrossEntropy(y_true, y_pred)` (CE). Extending this to multi-class case is straightforward: `FL(p_t) = alpha * (1 − p_t)^gamma * CategoricalCE(y_true, y_pred)` In the snippet below, there is `# classes` floating pointing values per example. The shape of both `y_pred` and `y_true` are `[batch_size, num_classes]`. Standalone usage: >>> y_true = [[0., 1., 0.], [0., 0., 1.]] >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> cce = tf.keras.losses.CategoricalFocalCrossentropy() >>> cce(y_true, y_pred).numpy() 0.23315276 >>> # Calling with 'sample_weight'. >>> cce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy() 0.1632 >>> # Using 'sum' reduction type. >>> cce = tf.keras.losses.CategoricalFocalCrossentropy( ... reduction=tf.keras.losses.Reduction.SUM) >>> cce(y_true, y_pred).numpy() 0.46631 >>> # Using 'none' reduction type. >>> cce = tf.keras.losses.CategoricalFocalCrossentropy( ... reduction=tf.keras.losses.Reduction.NONE) >>> cce(y_true, y_pred).numpy() array([3.2058331e-05, 4.6627346e-01], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='adam', loss=tf.keras.losses.CategoricalFocalCrossentropy()) ``` Args: alpha: A weight balancing factor for all classes, default is `0.25` as mentioned in the reference. It can be a list of floats or a scalar. In the multi-class case, alpha may be set by inverse class frequency by using `compute_class_weight` from `sklearn.utils`. gamma: A focusing parameter, default is `2.0` as mentioned in the reference. It helps to gradually reduce the importance given to simple (easy) examples in a smooth manner. from_logits: Whether `output` is expected to be a logits tensor. By default, we consider that `output` encodes a probability distribution. label_smoothing: Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: The axis along which to compute crossentropy (the features axis). Defaults to -1. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'categorical_focal_crossentropy'. rrFrrcategorical_focal_crossentropyc Z>[TU][UUUUUUUS9 X0lXlX lg)z4Initializes `CategoricalFocalCrossentropy` instance.)rrrrrrrN)rcrrrrr) rrrrrrrrr!s rr%CategoricalFocalCrossentropy.__init__sA  *#+  '  rc>URURS.n[TU] 5n[ [ UR 55[ UR 55-5$)N)rr)rrrcrHrtrurors rrH'CategoricalFocalCrossentropy.get_config(sPZZZZ g(* D**,-V\\^0DDEEr)rrrrrs@rrrs>fT **// -2FFrrz*keras.losses.SparseCategoricalCrossentropyc`^\rSrSrSrSS\R RS4U4SjjrSr U=r $)SparseCategoricalCrossentropyi1a2Computes the crossentropy loss between the labels and predictions. Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using `one-hot` representation, please use `CategoricalCrossentropy` loss. There should be `# classes` floating point values per feature for `y_pred` and a single floating point value per feature for `y_true`. In the snippet below, there is a single floating point value per example for `y_true` and `# classes` floating pointing values per example for `y_pred`. The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is `[batch_size, num_classes]`. Standalone usage: >>> y_true = [1, 2] >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> scce = tf.keras.losses.SparseCategoricalCrossentropy() >>> scce(y_true, y_pred).numpy() 1.177 >>> # Calling with 'sample_weight'. >>> scce(y_true, y_pred, sample_weight=tf.constant([0.3, 0.7])).numpy() 0.814 >>> # Using 'sum' reduction type. >>> scce = tf.keras.losses.SparseCategoricalCrossentropy( ... reduction=tf.keras.losses.Reduction.SUM) >>> scce(y_true, y_pred).numpy() 2.354 >>> # Using 'none' reduction type. >>> scce = tf.keras.losses.SparseCategoricalCrossentropy( ... reduction=tf.keras.losses.Reduction.NONE) >>> scce(y_true, y_pred).numpy() array([0.0513, 2.303], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.SparseCategoricalCrossentropy()) ``` FNsparse_categorical_crossentropyc0>[TU][UUUUS9 g)aInitializes `SparseCategoricalCrossentropy` instance. Args: from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. ignore_class: Optional integer. The ID of a class to be ignored during loss computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (`ignore_class=None`), all classes are considered. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'sparse_categorical_crossentropy'. )rrr ignore_classN)rcrr)rrrrrr!s rr&SparseCategoricalCrossentropy.__init__bs&>  +#%  rrArrs@rrr1s--b**// . % % rrzkeras.losses.CosineSimilarityc^^\rSrSrSrS\R RS4U4SjjrSr U=r $)CosineSimilarityia Computes the cosine similarity between labels and predictions. Note that it is a number between -1 and 1. When it is a negative number between -1 and 0, 0 indicates orthogonality and values closer to -1 indicate greater similarity. The values closer to 1 indicate greater dissimilarity. This makes it usable as a loss function in a setting where you try to maximize the proximity between predictions and targets. If either `y_true` or `y_pred` is a zero vector, cosine similarity will be 0 regardless of the proximity between predictions and targets. `loss = -sum(l2_norm(y_true) * l2_norm(y_pred))` Standalone usage: >>> y_true = [[0., 1.], [1., 1.]] >>> y_pred = [[1., 0.], [1., 1.]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> cosine_loss = tf.keras.losses.CosineSimilarity(axis=1) >>> # l2_norm(y_true) = [[0., 1.], [1./1.414, 1./1.414]] >>> # l2_norm(y_pred) = [[1., 0.], [1./1.414, 1./1.414]] >>> # l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]] >>> # loss = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1)) >>> # = -((0. + 0.) + (0.5 + 0.5)) / 2 >>> cosine_loss(y_true, y_pred).numpy() -0.5 >>> # Calling with 'sample_weight'. >>> cosine_loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() -0.0999 >>> # Using 'sum' reduction type. >>> cosine_loss = tf.keras.losses.CosineSimilarity(axis=1, ... reduction=tf.keras.losses.Reduction.SUM) >>> cosine_loss(y_true, y_pred).numpy() -0.999 >>> # Using 'none' reduction type. >>> cosine_loss = tf.keras.losses.CosineSimilarity(axis=1, ... reduction=tf.keras.losses.Reduction.NONE) >>> cosine_loss(y_true, y_pred).numpy() array([-0., -0.999], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.CosineSimilarity(axis=1)) ``` Args: axis: The axis along which the cosine similarity is computed (the features axis). Defaults to -1. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'cosine_similarity'. rcosine_similarityc,>[TU][X#US9 g)N)rrr)rcrr)rrrrr!s rrCosineSimilarity.__init__s  D  rrArrs@rrrs*>D**//   rrzkeras.losses.Hingec\^\rSrSrSr\R RS4U4SjjrSr U=r $)HingeiaComputes the hinge loss between `y_true` & `y_pred`. `loss = maximum(1 - y_true * y_pred, 0)` `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1. Standalone usage: >>> y_true = [[0., 1.], [0., 0.]] >>> y_pred = [[0.6, 0.4], [0.4, 0.6]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> h = tf.keras.losses.Hinge() >>> h(y_true, y_pred).numpy() 1.3 >>> # Calling with 'sample_weight'. >>> h(y_true, y_pred, sample_weight=[1, 0]).numpy() 0.55 >>> # Using 'sum' reduction type. >>> h = tf.keras.losses.Hinge( ... reduction=tf.keras.losses.Reduction.SUM) >>> h(y_true, y_pred).numpy() 2.6 >>> # Using 'none' reduction type. >>> h = tf.keras.losses.Hinge( ... reduction=tf.keras.losses.Reduction.NONE) >>> h(y_true, y_pred).numpy() array([1.1, 1.5], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.Hinge()) ``` hingec*>[TU][X!S9 g)aInitializes `Hinge` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'hinge'. rN)rcrrrs rrHinge.__init__s T?rrArrs@rrrs(%N".!9!9!>!>W@@rrzkeras.losses.SquaredHingec\^\rSrSrSr\R RS4U4SjjrSr U=r $) SquaredHingeiaComputes the squared hinge loss between `y_true` & `y_pred`. `loss = square(maximum(1 - y_true * y_pred, 0))` `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1. Standalone usage: >>> y_true = [[0., 1.], [0., 0.]] >>> y_pred = [[0.6, 0.4], [0.4, 0.6]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> h = tf.keras.losses.SquaredHinge() >>> h(y_true, y_pred).numpy() 1.86 >>> # Calling with 'sample_weight'. >>> h(y_true, y_pred, sample_weight=[1, 0]).numpy() 0.73 >>> # Using 'sum' reduction type. >>> h = tf.keras.losses.SquaredHinge( ... reduction=tf.keras.losses.Reduction.SUM) >>> h(y_true, y_pred).numpy() 3.72 >>> # Using 'none' reduction type. >>> h = tf.keras.losses.SquaredHinge( ... reduction=tf.keras.losses.Reduction.NONE) >>> h(y_true, y_pred).numpy() array([1.46, 2.26], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.SquaredHinge()) ``` squared_hingec*>[TU][X!S9 g)aInitializes `SquaredHinge` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'squared_hinge'. rN)rcrrrs rrSquaredHinge.__init__<s$ TGrrArrs@rrrs(%P%0055OHHrrzkeras.losses.CategoricalHingec\^\rSrSrSr\R RS4U4SjjrSr U=r $)CategoricalHingeiQaComputes the categorical hinge loss between `y_true` & `y_pred`. `loss = maximum(neg - pos + 1, 0)` where `neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)` Standalone usage: >>> y_true = [[0, 1], [0, 0]] >>> y_pred = [[0.6, 0.4], [0.4, 0.6]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> h = tf.keras.losses.CategoricalHinge() >>> h(y_true, y_pred).numpy() 1.4 >>> # Calling with 'sample_weight'. >>> h(y_true, y_pred, sample_weight=[1, 0]).numpy() 0.6 >>> # Using 'sum' reduction type. >>> h = tf.keras.losses.CategoricalHinge( ... reduction=tf.keras.losses.Reduction.SUM) >>> h(y_true, y_pred).numpy() 2.8 >>> # Using 'none' reduction type. >>> h = tf.keras.losses.CategoricalHinge( ... reduction=tf.keras.losses.Reduction.NONE) >>> h(y_true, y_pred).numpy() array([1.2, 1.6], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalHinge()) ``` categorical_hingec*>[TU][X!S9 g)aInitializes `CategoricalHinge` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'categorical_hinge'. rN)rcrrrs rrCategoricalHinge.__init__xs& *KrrArrs@rrrQs)#L%0055>> y_true = [[0., 1.], [0., 0.]] >>> y_pred = [[1., 1.], [0., 0.]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> p = tf.keras.losses.Poisson() >>> p(y_true, y_pred).numpy() 0.5 >>> # Calling with 'sample_weight'. >>> p(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() 0.4 >>> # Using 'sum' reduction type. >>> p = tf.keras.losses.Poisson( ... reduction=tf.keras.losses.Reduction.SUM) >>> p(y_true, y_pred).numpy() 0.999 >>> # Using 'none' reduction type. >>> p = tf.keras.losses.Poisson( ... reduction=tf.keras.losses.Reduction.NONE) >>> p(y_true, y_pred).numpy() array([0.999, 0.], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.Poisson()) ``` poissonc*>[TU][X!S9 g)aInitializes `Poisson` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'poisson'. rN)rcrrrs rrPoisson.__init__s tArrArrs@rrrs("H".!9!9!>!>YBBrrzkeras.losses.LogCoshc\^\rSrSrSr\R RS4U4SjjrSr U=r $)LogCoshiaComputes the logarithm of the hyperbolic cosine of the prediction error. `logcosh = log((exp(x) + exp(-x))/2)`, where x is the error `y_pred - y_true`. Standalone usage: >>> y_true = [[0., 1.], [0., 0.]] >>> y_pred = [[1., 1.], [0., 0.]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> l = tf.keras.losses.LogCosh() >>> l(y_true, y_pred).numpy() 0.108 >>> # Calling with 'sample_weight'. >>> l(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() 0.087 >>> # Using 'sum' reduction type. >>> l = tf.keras.losses.LogCosh( ... reduction=tf.keras.losses.Reduction.SUM) >>> l(y_true, y_pred).numpy() 0.217 >>> # Using 'none' reduction type. >>> l = tf.keras.losses.LogCosh( ... reduction=tf.keras.losses.Reduction.NONE) >>> l(y_true, y_pred).numpy() array([0.217, 0.], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.LogCosh()) ``` log_coshc*>[TU][X!S9 g)aInitializes `LogCosh` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'log_cosh'. rN)rcrrrs rrLogCosh.__init__s$ BrrArrs@rrrs(#L%0055JCCrrzkeras.losses.KLDivergencec\^\rSrSrSr\R RS4U4SjjrSr U=r $) KLDivergenceiaComputes Kullback-Leibler divergence loss between `y_true` & `y_pred`. `loss = y_true * log(y_true / y_pred)` See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence Standalone usage: >>> y_true = [[0, 1], [0, 0]] >>> y_pred = [[0.6, 0.4], [0.4, 0.6]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> kl = tf.keras.losses.KLDivergence() >>> kl(y_true, y_pred).numpy() 0.458 >>> # Calling with 'sample_weight'. >>> kl(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy() 0.366 >>> # Using 'sum' reduction type. >>> kl = tf.keras.losses.KLDivergence( ... reduction=tf.keras.losses.Reduction.SUM) >>> kl(y_true, y_pred).numpy() 0.916 >>> # Using 'none' reduction type. >>> kl = tf.keras.losses.KLDivergence( ... reduction=tf.keras.losses.Reduction.NONE) >>> kl(y_true, y_pred).numpy() array([0.916, -3.08e-06], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.KLDivergence()) ``` kl_divergencec*>[TU][X!S9 g)aInitializes `KLDivergence` instance. Args: reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'kl_divergence'. rN)rcrrrs rrKLDivergence.__init__+s& TGrrArrs@rrrs($N%0055OHHrrzkeras.losses.Huberc^^\rSrSrSrS\R RS4U4SjjrSr U=r $)HuberiAaComputes the Huber loss between `y_true` & `y_pred`. For each value x in `error = y_true - y_pred`: ``` loss = 0.5 * x^2 if |x| <= d loss = 0.5 * d^2 + d * (|x| - d) if |x| > d ``` where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss Standalone usage: >>> y_true = [[0, 1], [0, 0]] >>> y_pred = [[0.6, 0.4], [0.4, 0.6]] >>> # Using 'auto'/'sum_over_batch_size' reduction type. >>> h = tf.keras.losses.Huber() >>> h(y_true, y_pred).numpy() 0.155 >>> # Calling with 'sample_weight'. >>> h(y_true, y_pred, sample_weight=[1, 0]).numpy() 0.09 >>> # Using 'sum' reduction type. >>> h = tf.keras.losses.Huber( ... reduction=tf.keras.losses.Reduction.SUM) >>> h(y_true, y_pred).numpy() 0.31 >>> # Using 'none' reduction type. >>> h = tf.keras.losses.Huber( ... reduction=tf.keras.losses.Reduction.NONE) >>> h(y_true, y_pred).numpy() array([0.18, 0.13], dtype=float32) Usage with the `compile()` API: ```python model.compile(optimizer='sgd', loss=tf.keras.losses.Huber()) ``` ? huber_lossc,>[TU][X2US9 g)aInitializes `Huber` instance. Args: delta: A float, the point where the Huber loss function changes from a quadratic to linear. reduction: Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `AUTO` indicates that the reduction ption will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used under a `tf.distribute.Strategy`, except via `Model.compile()` and `Model.fit()`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training [tutorial]( https://www.tensorflow.org/tutorials/distribute/custom_training) for more details. name: Optional name for the instance. Defaults to 'huber_loss'. )rrdeltaN)rcrhuber)rrrrr!s rrHuber.__init__ms. TeLrrArrs@rrrAs,(X**//  MMrrz keras.metrics.mean_squared_errorzkeras.metrics.msezkeras.metrics.MSEzkeras.losses.mean_squared_errorzkeras.losses.msezkeras.losses.MSEc[R"U5n[R"XR5n[R "[R RX5SS9$)aComputes the mean squared error between labels and predictions. After computing the squared distance between the inputs, the mean value over the last dimension is returned. `loss = mean(square(y_true - y_pred), axis=-1)` Standalone usage: >>> y_true = np.random.randint(0, 2, size=(2, 3)) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.mean_squared_error(y_true, y_pred) >>> assert loss.shape == (2,) >>> assert np.array_equal( ... loss.numpy(), np.mean(np.square(y_true - y_pred), axis=-1)) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Mean squared error values. shape = `[batch_size, d0, .. dN-1]`. rr)r*convert_to_tensorcastdtypermeanmathsquared_differencer5r6s rrrsGB ! !& )F WWV\\ *F <<226B LLrc4^^ ^^SmSmU4Sjm U UUU4Sjn[U[R5(dT"XR55$URR 5SSn[ U5S:a[R"XRRS9nO[R"/URS9nX4Vs/sHowRPM nnU(a5UV s/sHn [ U 5PM n n U SU SS- :Xa USS SUS'[R"U[ U5S:S 9n [R"U5n [R"U 5 [ R""XU4US 9sS S S 5 $s snfs sn f!,(df  g =f) aApply a loss function on a per batch basis. Args: loss_fn: The loss function y_true: truth values (RaggedTensor) y_pred: predicted values (RaggedTensor) y_pred_extra_dim: whether y_pred has an additional dimension compared to y_true Returns: Loss-function result. A dense tensor if the output has a single dimension (per-batch loss value); a ragged tensor otherwise. cT[R"UR5Vs/sHtn[R"[RR [R "U[R"555[R"S/55PMv sn5$s snf)zReturns true if this RaggedTensor has the same row_lengths across all ragged dimensions and thus can be converted to a dense tensor without loss of information. Args: rt: RaggedTensor. r) r* reduce_allnested_row_lengthsequalrreduce_variancerrfloatxconstant)rtrow_lenss rrt_is_equiv_dense4_ragged_tensor_apply_loss..rt_is_equiv_denses}}!# 5 5 7 !8H GG++'..*:;KK&  !8    sA;B%c&[SU55$)Nc3# UH6n[U[R5(aUR5OUv M8 g7frT) isinstancer* RaggedTensor to_tensor).0rs r G_ragged_tensor_apply_loss.._convert_to_dense..s2 )R__==BLLN2 Es>A)tuple)inputss r_convert_to_dense4_ragged_tensor_apply_loss.._convert_to_denses    rc >T"U6nU(a@[U[R5(d![RRU5nU$U(d/[U[R5(aUR 5nU$)zAdapt the result to ragged or dense tensor according to the expected output type. This is done so that all the return values of the map operation have the same type. )rr*r  from_tensorr )r ragged_outputrloss_fns r _call_loss-_ragged_tensor_apply_loss.._call_losssb V  Ar!?!?++A.A:a#A#A Arc>^^Tup#[U[R5(a+[R"T"U5UUUU4SjUUU4Sj5$T"T6$)Nc">T"T"T5T5$rTrA)rrrrsr=_ragged_tensor_apply_loss.._wrapper..s #4V#T"TT5$rTrA)rrrsrrrs 6=9r)rr*r cond)rrrr6rrrrs`` r_wrapper+_ragged_tensor_apply_loss.._wrappersG  fboo . .77!&)L9  rrr)shaperN)r)elemsr)rr*r r r"as_listlenRaggedTensorSpecr TensorSpecnested_row_splits functoolspartialr assert_splits_matchcontrol_dependenciesr map_fn)rr5r6y_pred_extra_dimrlshapespecrnested_splits_listslistrdimsr-assertion_listrrrs` @@@r_ragged_tensor_apply_lossr5s_ *     fboo . .v//122 \\ ! ! #Ab )F 6{Q""||D}}2V\\::@9IJ9I2..9IJ);<);U);< 8uQx!| #$6q$9#2$> q !   xs6{Q GF 445GHN  0$$VF3C4P 1 0K= 1 0s>E? FF  Fc"[[X5$)aImplements support for handling RaggedTensors. Args: y_true: RaggedTensor truth values. shape = `[batch_size, d0, .. dN]`. y_pred: RaggedTensor predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Mean squared error values. shape = `[batch_size, d0, .. dN-1]`. When the number of dimensions of the batch feature vector [d0, .. dN] is greater than one the return value is a RaggedTensor. Otherwise, a Dense tensor with dimensions [batch_size] is returned. )r5rrs r_ragged_tensor_mser7s %%7 HHrz!keras.metrics.mean_absolute_errorzkeras.metrics.maezkeras.metrics.MAEz keras.losses.mean_absolute_errorzkeras.losses.maezkeras.losses.MAEc[R"U5n[R"XR5n[R "[R "X- 5SS9$)axComputes the mean absolute error between labels and predictions. `loss = mean(abs(y_true - y_pred), axis=-1)` Standalone usage: >>> y_true = np.random.randint(0, 2, size=(2, 3)) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.mean_absolute_error(y_true, y_pred) >>> assert loss.shape == (2,) >>> assert np.array_equal( ... loss.numpy(), np.mean(np.abs(y_true - y_pred), axis=-1)) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Mean absolute error values. shape = `[batch_size, d0, .. dN-1]`. rr)r*rrrrrabsrs rrrsB< ! !& )F WWV\\ *F <<v/b 99rc"[[X5$)z-RaggedTensor adapter for mean_absolute_error.)r5rrs r_ragged_tensor_maer;;s %%8& IIrz,keras.metrics.mean_absolute_percentage_errorzkeras.metrics.mapezkeras.metrics.MAPEz+keras.losses.mean_absolute_percentage_errorzkeras.losses.mapezkeras.losses.MAPEcL[R"U5n[R"XR5n[R"X- [ R "[R"U5[ R"55- 5nS[ R"USS9-$)aComputes the mean absolute percentage error between `y_true` & `y_pred`. `loss = 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)` Standalone usage: >>> y_true = np.random.random(size=(2, 3)) >>> y_true = np.maximum(y_true, 1e-7) # Prevent division by zero >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.mean_absolute_percentage_error(y_true, y_pred) >>> assert loss.shape == (2,) >>> assert np.array_equal( ... loss.numpy(), ... 100. * np.mean(np.abs((y_true - y_pred) / y_true), axis=-1)) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Mean absolute percentage error values. shape = `[batch_size, d0, .. dN-1]`. gY@rr) r*rrrr9rmaximumepsilonr)r5r6diffs rrrAsqB ! !& )F WWV\\ *F 66 GOOBFF6NGOO>> y_true = np.random.randint(0, 2, size=(2, 3)) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.mean_squared_logarithmic_error(y_true, y_pred) >>> assert loss.shape == (2,) >>> y_true = np.maximum(y_true, 1e-7) >>> y_pred = np.maximum(y_pred, 1e-7) >>> assert np.allclose( ... loss.numpy(), ... np.mean( ... np.square(np.log(y_true + 1.) - np.log(y_pred + 1.)), axis=-1)) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Mean squared logarithmic error values. shape = `[batch_size, d0, .. dN-1]`. rrr) r*rrrrlogrr=r>rr)r5r6 first_log second_logs rrrrsF ! !& )F WWV\\ *F GOOFGOO4EFLMIW__VW__5FG#MNJ << ""99 rc"[[X5$)z.Implements support for handling RaggedTensors.)r5rrs r_ragged_tensor_mslerHrBrc ^[R"TS5n[R"TS5n[R"[R"X55nU4Sjn[RR R X4U4Sj5nU$)z!Converts binary labels into -1/1.rr!c>ST-S- $)NrrrAr5sr_convert_binary_labels5_maybe_convert_labels.._convert_binary_labelssV|c!!rc>T$rTrArKsrr'_maybe_convert_labels..s6r)r*rr logical_orr- smart_cond)r5 are_zerosare_ones is_binaryrLupdated_y_trues` r_maybe_convert_labelsrVsh#Ixx"H bmmI@AI"__//::>N rzkeras.metrics.squared_hingezkeras.losses.squared_hingec  [R"U5n[R"XR5n[ U5n[ R "[R"[R"SX-- S55SS9$)a*Computes the squared hinge loss between `y_true` & `y_pred`. `loss = mean(square(maximum(1 - y_true * y_pred, 0)), axis=-1)` Standalone usage: >>> y_true = np.random.choice([-1, 1], size=(2, 3)) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.squared_hinge(y_true, y_pred) >>> assert loss.shape == (2,) >>> assert np.array_equal( ... loss.numpy(), ... np.mean(np.square(np.maximum(1. - y_true * y_pred, 0.)), axis=-1)) Args: y_true: The ground truth values. `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Squared hinge loss values. shape = `[batch_size, d0, .. dN-1]`. rrrr) r*rrrrVrrsquarer=rs rrrsa4 ! !& )F WWV\\ *F "6 *F << "**S6?2C89 rzkeras.metrics.hingezkeras.losses.hingec[R"U5n[R"XR5n[ U5n[ R "[R"SX-- S5SS9$)aComputes the hinge loss between `y_true` & `y_pred`. `loss = mean(maximum(1 - y_true * y_pred, 0), axis=-1)` Standalone usage: >>> y_true = np.random.choice([-1, 1], size=(2, 3)) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.hinge(y_true, y_pred) >>> assert loss.shape == (2,) >>> assert np.array_equal( ... loss.numpy(), ... np.mean(np.maximum(1. - y_true * y_pred, 0.), axis=-1)) Args: y_true: The ground truth values. `y_true` values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Hinge loss values. shape = `[batch_size, d0, .. dN-1]`. rrrr)r*rrrrVrrr=rs rrrsS4 ! !& )F WWV\\ *F "6 *F << 3#8#>R HHrzkeras.losses.categorical_hingecL[R"U5n[R"XR5n[R"X-SS9n[R "SU- U-SS9n[R"SUR5n[R "X2- S-U5$)a:Computes the categorical hinge loss between `y_true` & `y_pred`. `loss = maximum(neg - pos + 1, 0)` where `neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)` Standalone usage: >>> y_true = np.random.randint(0, 3, size=(2,)) >>> y_true = tf.keras.utils.to_categorical(y_true, num_classes=3) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.categorical_hinge(y_true, y_pred) >>> assert loss.shape == (2,) >>> pos = np.sum(y_true * y_pred, axis=-1) >>> neg = np.amax((1. - y_true) * y_pred, axis=-1) >>> assert np.array_equal(loss.numpy(), np.maximum(0., neg - pos + 1.)) Args: y_true: The ground truth values. `y_true` values are expected to be either `{-1, +1}` or `{0, 1}` (i.e. a one-hot-encoded tensor). y_pred: The predicted values. Returns: Categorical hinge loss values. rrrr)r*rrr reduce_sum reduce_maxr=)r5r6posnegzeros rrrs|6 ! !& )F WWV\\ *F --b 1C --v/b 9C 773 %D ::ci#ot ,,rzkeras.losses.huberc H[R"U[R"5S9n[R"U[R"5S9n[R"U[R"5S9n[R"X5n[R "U5n[R "SURS9n[R"[R"XB:*U[R"U5-X$-U[R"U5-- 5SS9$)aComputes Huber loss value. For each value x in `error = y_true - y_pred`: ``` loss = 0.5 * x^2 if |x| <= d loss = d * |x| - 0.5 * d^2 if |x| > d ``` where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss Args: y_true: tensor of true targets. y_pred: tensor of predicted targets. delta: A float, the point where the Huber loss function changes from a quadratic to linear. Returns: Tensor with one scalar loss entry per sample. r?rr) r*rrrsubtractr9rrrwhererX)r5r6rerror abs_errorhalfs rrrs,WWV7>>#3 4F WWV7>>#3 4F GGE!1 2E KK 'Eu I  9?? ;D <<    299U# #  ryy'7 7 7   rzkeras.losses.log_coshzkeras.losses.logcoshzkeras.metrics.log_coshzkeras.metrics.logcoshc[R"U5n[R"XR5nSn[R "U"X- 5SS9$)aLogarithm of the hyperbolic cosine of the prediction error. `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly like the mean squared error, but will not be so strongly affected by the occasional wildly incorrect prediction. Standalone usage: >>> y_true = np.random.random(size=(2, 3)) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.logcosh(y_true, y_pred) >>> assert loss.shape == (2,) >>> x = y_pred - y_true >>> assert np.allclose( ... loss.numpy(), ... np.mean(x + np.log(np.exp(-2. * x) + 1.) - tf.math.log(2.), ... axis=-1), ... atol=1e-5) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Logcosh error values. shape = `[batch_size, d0, .. dN-1]`. cU[RRSU-5-[R"[RR S5UR 5- $)Ngr)r*rsoftplusrrDr)xs r_logcoshlog_cosh.._logcoshgsC   * *RWWRWW[[5Eqww-O O rrr)r*rrrrr)r5r6rls rrrAsFF ! !& )F WWV\\ *F <<1 ;;rz&keras.metrics.categorical_crossentropyz%keras.losses.categorical_crossentropyc(^^^^[T[5(a[STS[T535e[R "T5m[R "TTR5m[R "TTRS9mTRSS:Xa([R"STRS3[SS 9 UUUU4S jn[RRRTUU4S j5m[R"TTUTS 9$) asComputes the categorical crossentropy loss. Standalone usage: >>> y_true = [[0, 1, 0], [0, 0, 1]] >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]] >>> loss = tf.keras.losses.categorical_crossentropy(y_true, y_pred) >>> assert loss.shape == (2,) >>> loss.numpy() array([0.0513, 2.303], dtype=float32) Args: y_true: Tensor of one-hot true targets. y_pred: Tensor of predicted targets. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: Defaults to -1. The dimension along which the entropy is computed. Returns: Categorical crossentropy loss value. -`axis` must be of type `int`. Received: axis= of type rarr!zIn loss categorical_crossentropy, expected y_pred.shape to be (batch_size, num_classes) with num_classes > 1. Received: y_pred.shape=B. Consider using 'binary_crossentropy' if you only have 2 classes. stacklevelc>[R"[R"T5TTR5nTST- -TU- -$)Nrr*rr"r) num_classesrrr6r5s r_smooth_labels0categorical_crossentropy.._smooth_labelssDggbhhv.t4fllC ./ k )  rc>T$rTrArKsrr*categorical_crossentropy..r)rr)rboolrRtyper*rrrr"warningswarn SyntaxWarningr-rQrrr5r6rrrrxs`` `` rrrosB$ "V9T$ZL :   ! !& )F WWVV\\ *F**?&,,OO ||B1  <JO O     __ ' ' 2 2F  + +Kd rcP[R"[UUUS9n[XPU5$)aHImplements support for handling RaggedTensors. Args: y_true: Tensor of one-hot true targets. y_pred: Tensor of predicted targets. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: The axis along which to compute crossentropy (the features axis). Defaults to -1. Returns: Categorical crossentropy loss value. Expected shape: (batch, sequence_len, n_classes) with sequence_len being variable per batch. Return shape: (batch, sequence_len). When used by CategoricalCrossentropy() with the default reduction (SUM_OVER_BATCH_SIZE), the reduction averages the loss over the number of elements independent of the batch. E.g. if the RaggedTensor has 2 batches with [2, 1] values respectively the resulting loss is the sum of the individual loss values divided by 3. rrr)r)r*rr5r5r6rrrrds r'_ragged_tensor_categorical_crossentropyrs/<    '   B %R 88rz,keras.metrics.categorical_focal_crossentropyz+keras.losses.categorical_focal_crossentropyc (^^^[U[5(a[SUS[U535e[R "T5m[R "TTR5m[R "TTRS9mTRSS:Xa([R"STRS3[SS 9 UUU4S jn[RRRTUU4S j5m[R"TTUUUUS 9$) aComputes the categorical focal crossentropy loss. Standalone usage: >>> y_true = [[0, 1, 0], [0, 0, 1]] >>> y_pred = [[0.05, 0.9, 0.05], [0.1, 0.85, 0.05]] >>> loss = tf.keras.losses.categorical_focal_crossentropy(y_true, y_pred) >>> assert loss.shape == (2,) >>> loss.numpy() array([2.63401289e-04, 6.75912094e-01], dtype=float32) Args: y_true: Tensor of one-hot true targets. y_pred: Tensor of predicted targets. alpha: A weight balancing factor for all classes, default is `0.25` as mentioned in the reference. It can be a list of floats or a scalar. In the multi-class case, alpha may be set by inverse class frequency by using `compute_class_weight` from `sklearn.utils`. gamma: A focusing parameter, default is `2.0` as mentioned in the reference. It helps to gradually reduce the importance given to simple examples in a smooth manner. When `gamma` = 0, there is no focal effect on the categorical crossentropy. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: Defaults to -1. The dimension along which the entropy is computed. Returns: Categorical focal crossentropy loss value. rorprarr!zIn loss categorical_focal_crossentropy, expected y_pred.shape to be (batch_size, num_classes) with num_classes > 1. Received: y_pred.shape=rqrrrsc>[R"[R"T5STR5nTST- -TU- -$)Nrrrv)rwrr6r5s rrx6categorical_focal_crossentropy.._smooth_labels sDggbhhv.r2FLLA ./ k )  rc>T$rTrArKsrr0categorical_focal_crossentropy.." r|r)targetoutputrrrr)rr}rRr~r*rrrr"rrrr-rQrr)r5r6rrrrrrxs`` ` rrrs^$ "V9T$ZL :   ! !& )F WWVV\\ *F**?&,,OO ||B1  <JO O    __ ' ' 2 2F  1 1   rc T[R"[UUUUUS9n[XpU5$)aImplements support for handling RaggedTensors. Expected shape: (batch, sequence_len, n_classes) with sequence_len being variable per batch. Return shape: (batch, sequence_len). When used by CategoricalFocalCrossentropy() with the default reduction (SUM_OVER_BATCH_SIZE), the reduction averages the loss over the number of elements independent of the batch. E.g. if the RaggedTensor has 2 batches with [2, 1] values respectively the resulting loss is the sum of the individual loss values divided by 3. Args: alpha: A weight balancing factor for all classes, default is `0.25` as mentioned in the reference. It can be a list of floats or a scalar. In the multi-class case, alpha may be set by inverse class frequency by using `compute_class_weight` from `sklearn.utils`. gamma: A focusing parameter, default is `2.0` as mentioned in the reference. It helps to gradually reduce the importance given to simple examples in a smooth manner. When `gamma` = 0, there is no focal effect on the categorical crossentropy. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: Defaults to -1. The dimension along which the entropy is computed. Returns: Categorical focal crossentropy loss value. )rrrrr)r)r*rr5)r5r6rrrrrrds r-_ragged_tensor_categorical_focal_crossentropyr/ s6R   &'   B %R 88rz-keras.metrics.sparse_categorical_crossentropyz,keras.losses.sparse_categorical_crossentropyc2[R"UUUUUS9$)a*Computes the sparse categorical crossentropy loss. Standalone usage: >>> y_true = [1, 2] >>> y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]] >>> loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred) >>> assert loss.shape == (2,) >>> loss.numpy() array([0.0513, 2.303], dtype=float32) >>> y_true = [[[ 0, 2], ... [-1, -1]], ... [[ 0, 2], ... [-1, -1]]] >>> y_pred = [[[[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]], ... [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]], ... [[[1.0, 0.0, 0.0], [0.0, 0.5, 0.5]], ... [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]]] >>> loss = tf.keras.losses.sparse_categorical_crossentropy( ... y_true, y_pred, ignore_class=-1) >>> loss.numpy() array([[[2.3841855e-07, 2.3841855e-07], [0.0000000e+00, 0.0000000e+00]], [[2.3841855e-07, 6.9314730e-01], [0.0000000e+00, 0.0000000e+00]]], dtype=float32) Args: y_true: Ground truth values. y_pred: The predicted values. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. axis: Defaults to -1. The dimension along which the entropy is computed. ignore_class: Optional integer. The ID of a class to be ignored during loss computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (`ignore_class=None`), all classes are considered. Returns: Sparse categorical crossentropy loss value. rrr)rr)r5r6rrrs rrrc s'f  2 2!   rcN[R"[UUUS9n[XPUSS9$)aImplements support for handling RaggedTensors. Expected y_pred shape: (batch, sequence_len, n_classes) with sequence_len being variable per batch. Return shape: (batch, sequence_len). When used by SparseCategoricalCrossentropy() with the default reduction (SUM_OVER_BATCH_SIZE), the reduction averages the loss over the number of elements independent of the batch. E.g. if the RaggedTensor has 2 batches with [2, 1] values respectively, the resulting loss is the sum of the individual loss values divided by 3. rT)r.)r)r*rr5)r5r6rrrrds r._ragged_tensor_sparse_categorical_crossentropyr s1   '!   B %R$ OOrz!keras.metrics.binary_crossentropyz keras.losses.binary_crossentropycn^^[R"U5n[R"TUR5m[R"TURS9mUU4Sjn[RR R TUU4Sj5m[ R"[ R"TXS9US9$)aComputes the binary crossentropy loss. Standalone usage: >>> y_true = [[0, 1], [0, 0]] >>> y_pred = [[0.6, 0.4], [0.4, 0.6]] >>> loss = tf.keras.losses.binary_crossentropy(y_true, y_pred) >>> assert loss.shape == (2,) >>> loss.numpy() array([0.916 , 0.714], dtype=float32) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels by squeezing them towards 0.5 That is, using `1. - 0.5 * label_smoothing` for the target class and `0.5 * label_smoothing` for the non-target class. axis: The axis along which the mean is computed. Defaults to -1. Returns: Binary crossentropy loss value. shape = `[batch_size, d0, .. dN-1]`. rac >TST- -ST--$NrrbrArr5srrx+binary_crossentropy.._smooth_labels ./#2GGGrc>T$rTrArKsrr%binary_crossentropy.. r|rrr) r*rrrr-rQrrrrs` ` rrr s@ ! !& )F WWVV\\ *F**?&,,OOH__ ' ' 2 2F <<##FFL  rcP[R"[UUUS9n[XPU5$)aImplements support for handling RaggedTensors. Args: y_true: Tensor of one-hot true targets. y_pred: Tensor of predicted targets. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: Axis along which to compute crossentropy. Returns: Binary crossentropy loss value. Expected shape: (batch, sequence_len) with sequence_len being variable per batch. Return shape: (batch,); returns the per batch mean of the loss values. When used by BinaryCrossentropy() with the default reduction (SUM_OVER_BATCH_SIZE), the reduction averages the per batch losses over the number of batches. r)r)r*rr5rs r"_ragged_tensor_binary_crossentropyr s/6   '   B %R 88rz'keras.metrics.binary_focal_crossentropyz&keras.losses.binary_focal_crossentropyc v^^[R"U5n[R"TUR5m[R"TURS9mUU4Sjn[RR R TUU4Sj5m[ R"[ R"TUUUUUS9US9$)a?Computes the binary focal crossentropy loss. According to [Lin et al., 2018](https://arxiv.org/pdf/1708.02002.pdf), it helps to apply a focal factor to down-weight easy examples and focus more on hard examples. By default, the focal tensor is computed as follows: `focal_factor = (1 - output)**gamma` for class 1 `focal_factor = output**gamma` for class 0 where `gamma` is a focusing parameter. When `gamma` = 0, there is no focal effect on the binary crossentropy loss. If `apply_class_balancing == True`, this function also takes into account a weight balancing factor for the binary classes 0 and 1 as follows: `weight = alpha` for class 1 (`target == 1`) `weight = 1 - alpha` for class 0 where `alpha` is a float in the range of `[0, 1]`. Standalone usage: >>> y_true = [[0, 1], [0, 0]] >>> y_pred = [[0.6, 0.4], [0.4, 0.6]] >>> loss = tf.keras.losses.binary_focal_crossentropy(y_true, y_pred, ... gamma=2) >>> assert loss.shape == (2,) >>> loss.numpy() array([0.330, 0.206], dtype=float32) Args: y_true: Ground truth values, of shape `(batch_size, d0, .. dN)`. y_pred: The predicted values, of shape `(batch_size, d0, .. dN)`. apply_class_balancing: A bool, whether to apply weight balancing on the binary classes 0 and 1. alpha: A weight balancing factor for class 1, default is `0.25` as mentioned in the reference. The weight for class 0 is `1.0 - alpha`. gamma: A focusing parameter, default is `2.0` as mentioned in the reference. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in `[0, 1]`. If higher than 0 then smooth the labels by squeezing them towards `0.5`, i.e., using `1. - 0.5 * label_smoothing` for the target class and `0.5 * label_smoothing` for the non-target class. axis: The axis along which the mean is computed. Defaults to `-1`. Returns: Binary focal crossentropy loss value. shape = `[batch_size, d0, .. dN-1]`. rac >TST- -ST--$rrArsrrx1binary_focal_crossentropy.._smooth_labelsQ rrc>T$rTrArKsrr+binary_focal_crossentropy..U r|r)rrrrrrr) r*rrrr-rQrrr) r5r6rrrrrrrxs ` ` rrr s@ ! !& )F WWVV\\ *F**?&,,OOH__ ' ' 2 2F <<))"7#    rc V[R"[UUUUUUS9n[XU5$)a:Implements support for handling RaggedTensors. Expected shape: `(batch, sequence_len)` with sequence_len being variable per batch. Return shape: `(batch,)`; returns the per batch mean of the loss values. When used by BinaryFocalCrossentropy() with the default reduction (SUM_OVER_BATCH_SIZE), the reduction averages the per batch losses over the number of batches. Args: y_true: Tensor of one-hot true targets. y_pred: Tensor of predicted targets. apply_class_balancing: A bool, whether to apply weight balancing on the binary classes 0 and 1. alpha: A weight balancing factor for class 1, default is `0.25` as mentioned in the reference [Lin et al., 2018]( https://arxiv.org/pdf/1708.02002.pdf). The weight for class 0 is `1.0 - alpha`. gamma: A focusing parameter, default is `2.0` as mentioned in the reference. from_logits: Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. label_smoothing: Float in `[0, 1]`. If > `0` then smooth the labels. For example, if `0.1`, use `0.1 / num_classes` for non-target labels and `0.9 + 0.1 / num_classes` for target labels. axis: Axis along which to compute crossentropy. Returns: Binary focal crossentropy loss value. )rrrrrr)r)r*rr5) r5r6rrrrrrrds r(_ragged_tensor_binary_focal_crossentropyre s9T   !3'  B %R 88rzkeras.metrics.kl_divergencez)keras.metrics.kullback_leibler_divergencezkeras.metrics.kldzkeras.metrics.KLDzkeras.losses.kl_divergencez(keras.losses.kullback_leibler_divergencezkeras.losses.kldzkeras.losses.KLDc[R"U5n[R"XR5n[R "U[R "5S5n[R "U[R "5S5n[R"U[RRX- 5-SS9$)a.Computes Kullback-Leibler divergence loss between `y_true` & `y_pred`. `loss = y_true * log(y_true / y_pred)` See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence Standalone usage: >>> y_true = np.random.randint(0, 2, size=(2, 3)).astype(np.float64) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.kullback_leibler_divergence(y_true, y_pred) >>> assert loss.shape == (2,) >>> y_true = tf.keras.backend.clip(y_true, 1e-7, 1) >>> y_pred = tf.keras.backend.clip(y_pred, 1e-7, 1) >>> assert np.array_equal( ... loss.numpy(), np.sum(y_true * np.log(y_true / y_pred), axis=-1)) Args: y_true: Tensor of true targets. y_pred: Tensor of predicted targets. Returns: A `Tensor` with loss. Raises: TypeError: If `y_true` cannot be cast to the `y_pred.dtype`. r!rr) r*rrrrclipr>r[rrDrs rrr s~N ! !& )F WWV\\ *F \\&'//"3Q 7F \\&'//"3Q 7F =="''++fo">>R HHrzkeras.metrics.poissonzkeras.losses.poissonc [R"U5n[R"XR5n[R "X[R RU[R"5-5-- SS9$)aComputes the Poisson loss between y_true and y_pred. The Poisson loss is the mean of the elements of the `Tensor` `y_pred - y_true * log(y_pred)`. Standalone usage: >>> y_true = np.random.randint(0, 2, size=(2, 3)) >>> y_pred = np.random.random(size=(2, 3)) >>> loss = tf.keras.losses.poisson(y_true, y_pred) >>> assert loss.shape == (2,) >>> y_pred = y_pred + 1e-7 >>> assert np.allclose( ... loss.numpy(), np.mean(y_pred - y_true * np.log(y_pred), axis=-1), ... atol=1e-5) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. Returns: Poisson loss value. shape = `[batch_size, d0, .. dN-1]`. Raises: InvalidArgumentError: If `y_true` and `y_pred` have incompatible shapes. rr) r*rrrrrrrDr>rs rrr s]: ! !& )F WWV\\ *F <<"''++fw/@&@AAA rkeras.losses.cosine_similarity)zkeras.metrics.cosine_proximityzkeras.metrics.cosinezkeras.losses.cosine_proximityzkeras.losses.cosinerc[RRXS9n[RRXS9n[R"X-US9*$)aComputes the cosine similarity between labels and predictions. Note that it is a number between -1 and 1. When it is a negative number between -1 and 0, 0 indicates orthogonality and values closer to -1 indicate greater similarity. The values closer to 1 indicate greater dissimilarity. This makes it usable as a loss function in a setting where you try to maximize the proximity between predictions and targets. If either `y_true` or `y_pred` is a zero vector, cosine similarity will be 0 regardless of the proximity between predictions and targets. `loss = -sum(l2_norm(y_true) * l2_norm(y_pred))` Standalone usage: >>> y_true = [[0., 1.], [1., 1.], [1., 1.]] >>> y_pred = [[1., 0.], [1., 1.], [-1., -1.]] >>> loss = tf.keras.losses.cosine_similarity(y_true, y_pred, axis=1) >>> loss.numpy() array([-0., -0.999, 0.999], dtype=float32) Args: y_true: Tensor of true targets. y_pred: Tensor of predicted targets. axis: Axis along which to determine similarity. Returns: Cosine similarity tensor. r)r*linalg l2_normalizer[)r5r6rs rrr sIRYY # #F # 6F YY # #F # 6F MM&/ 5 55rc[U[5=(d_ [U[5=(a UR[:H=(d. [ US5=(a UR S:H=(d US:HnU$)Nr"r)rrr`rdrhasattrr")lossresults ris_categorical_crossentropyr' sn401 0 t0 1 433 0 D* % < !;; 0 . .  Mrzkeras.losses.serializecUcg[U[5(d#[R"S[ U5S35 U(a[ R "U5$[ U5$)aSerializes loss function or `Loss` instance. Args: loss: A TF-Keras `Loss` instance or a loss function. use_legacy_format: Boolean, whether to use the legacy serialization format. Defaults to `False`. Returns: Loss configuration dictionary. NzzThe `keras.losses.serialize()` API should only be used for objects of type `keras.losses.Loss`. Found an instance of type z+, which may lead to improper serialization.)rrrrr~legacy_serializationr)ruse_legacy_formats r serializer7 s^ | dD ! !  NDzlE G #::4@@ !$ ''rzkeras.losses.deserializecvU(a[R"U[5USS9$[U[5USS9$)aDeserializes a serialized loss class/function instance. Args: name: Loss configuration. custom_objects: Optional dictionary mapping names (strings) to custom objects (classes and functions) to be considered during deserialization. use_legacy_format: Boolean, whether to use the legacy serialization format. Defaults to `False`. Returns: A TF-Keras `Loss` instance or a loss function. z loss function)module_objectscustom_objectsprintable_module_name)rrglobals)rrrs r deserializerP sE#<< "9)"1   $ y%-  rzkeras.losses.getcUcg[U[5(a[U5nSU;n[XS9$[U[5(a [U5$[ U5(aU$[ SU35e)aBRetrieves a TF-Keras loss as a `function`/`Loss` class instance. The `identifier` may be the string name of a loss function or `Loss` class. >>> loss = tf.keras.losses.get("categorical_crossentropy") >>> type(loss) >>> loss = tf.keras.losses.get("CategoricalCrossentropy") >>> type(loss) You can also specify `config` of the loss to this function by passing dict containing `class_name` and `config` as an identifier. Also note that the `class_name` must map to a `Loss` class >>> identifier = {"class_name": "CategoricalCrossentropy", ... "config": {"from_logits": True}} >>> loss = tf.keras.losses.get(identifier) >>> type(loss) Args: identifier: A loss identifier. One of None or string name of a loss function/class or loss configuration dictionary or a loss function or a loss class instance. Returns: A TF-Keras loss as a `function`/ `Loss` class instance. Raises: ValueError: If `identifier` cannot be interpreted. Nmodule)rz.Could not interpret loss function identifier: )rstrrrtcallablerR) identifierrs rr|r|n szD*c""_ $J6:KK*d##:&&   8 E rint32)F)r)Frr)rrFrr)FrN)FrrFrr)r)NF)hrXrZr)rtensorflow.compat.v2compatv2r* tf_keras.srcrtf_keras.src.savingrtf_keras.src.saving.legacyrr%tf_keras.src.saving.serialization_librrrsrr tensorflow.python.ops.raggedr r tensorflow.python.utilr tensorflow.python.util.tf_exportr tensorflow.tools.docsrrr`rrrrrrrrrrrrrrrrrr-add_dispatch_supportrr5dispatch_for_typesr r7rr;rrArrHrVrrrrrrrrrrrrrrrrrrbceBCEmseMSEmaeMAEmapeMAPEmsleMSLEkldKLDkullback_leibler_divergencelogcoshrrrrr|r^r:sparse_softmax_cross_entropyLABEL_DTYPES_FOR_LOSSESrArrrs   !! *LJH+'84+9.!"rr#rj="EM$MFM`-.8M*8M/8Mv./:N+:N0:Nz89B "5B :B J89= "5= := @/0i',i'1i'X45jF1jF6jFZ45U 1U 6U p9:HF#6HF;HFV:;U $7U <U p-.I *I /I X"#8@ 8@$8@v)*:H&:H+:Hz-.9L*9L/9Lx$%5B!5B&5Bp$%8C!8C&8Cv)*:H&:H+:Hz"#BM BM$BMJ&% ..M/M:VQr /AIBI"'& ..:/:4 0"//BJCJ 21 ..///@ ;R__MN21 .. / F ;R__MN  +-IJ../K@#%9:..I/;I<./..-/0-B"r*..!/+!H  ..$</ $../?D$ .. 6/  6J c c c,,t,,t*777c '     &'(((0()*: !-"-bIILL44g#Wr