Module keras.api.keras.losses
Public API for tf.keras.losses namespace.
Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.losses namespace.
"""
from __future__ import print_function as _print_function
import sys as _sys
from keras.losses import BinaryCrossentropy
from keras.losses import CategoricalCrossentropy
from keras.losses import CategoricalHinge
from keras.losses import CosineSimilarity
from keras.losses import Hinge
from keras.losses import Huber
from keras.losses import KLDivergence
from keras.losses import LogCosh
from keras.losses import Loss
from keras.losses import MeanAbsoluteError
from keras.losses import MeanAbsolutePercentageError
from keras.losses import MeanSquaredError
from keras.losses import MeanSquaredLogarithmicError
from keras.losses import Poisson
from keras.losses import SparseCategoricalCrossentropy
from keras.losses import SquaredHinge
from keras.losses import binary_crossentropy
from keras.losses import categorical_crossentropy
from keras.losses import categorical_hinge
from keras.losses import cosine_similarity
from keras.losses import cosine_similarity as cosine
from keras.losses import cosine_similarity as cosine_proximity
from keras.losses import deserialize
from keras.losses import get
from keras.losses import hinge
from keras.losses import kl_divergence
from keras.losses import kl_divergence as KLD
from keras.losses import kl_divergence as kld
from keras.losses import kl_divergence as kullback_leibler_divergence
from keras.losses import log_cosh
from keras.losses import log_cosh as logcosh
from keras.losses import mean_absolute_error
from keras.losses import mean_absolute_error as MAE
from keras.losses import mean_absolute_error as mae
from keras.losses import mean_absolute_percentage_error
from keras.losses import mean_absolute_percentage_error as MAPE
from keras.losses import mean_absolute_percentage_error as mape
from keras.losses import mean_squared_error
from keras.losses import mean_squared_error as MSE
from keras.losses import mean_squared_error as mse
from keras.losses import mean_squared_logarithmic_error
from keras.losses import mean_squared_logarithmic_error as MSLE
from keras.losses import mean_squared_logarithmic_error as msle
from keras.losses import poisson
from keras.losses import serialize
from keras.losses import sparse_categorical_crossentropy
from keras.losses import squared_hinge
del _print_function
from tensorflow.python.util import module_wrapper as _module_wrapper
if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
_sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
_sys.modules[__name__], "keras.losses", public_apis=None, deprecation=True,
has_lite=False)
Functions
def KLD(y_true, y_pred)
-
Computes Kullback-Leibler divergence loss between
y_true
andy_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 they_pred.dtype
.
Expand source code
@keras_export('keras.metrics.kl_divergence', 'keras.metrics.kullback_leibler_divergence', 'keras.metrics.kld', 'keras.metrics.KLD', 'keras.losses.kl_divergence', 'keras.losses.kullback_leibler_divergence', 'keras.losses.kld', 'keras.losses.KLD') @tf.__internal__.dispatch.add_dispatch_support def kl_divergence(y_true, y_pred): """Computes Kullback-Leibler divergence loss between `y_true` and `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`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) y_true = backend.clip(y_true, backend.epsilon(), 1) y_pred = backend.clip(y_pred, backend.epsilon(), 1) return tf.reduce_sum(y_true * tf.math.log(y_true / y_pred), axis=-1)
def MAE(y_true, y_pred)
-
Computes 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]
.Expand source code
@keras_export('keras.metrics.mean_absolute_error', 'keras.metrics.mae', 'keras.metrics.MAE', 'keras.losses.mean_absolute_error', 'keras.losses.mae', 'keras.losses.MAE') @tf.__internal__.dispatch.add_dispatch_support def mean_absolute_error(y_true, y_pred): """Computes 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.mean(tf.abs(y_pred - y_true), axis=-1)
def MAPE(y_true, y_pred)
-
Computes the mean absolute percentage error between
y_true
andy_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]
.Expand source code
@keras_export('keras.metrics.mean_absolute_percentage_error', 'keras.metrics.mape', 'keras.metrics.MAPE', 'keras.losses.mean_absolute_percentage_error', 'keras.losses.mape', 'keras.losses.MAPE') @tf.__internal__.dispatch.add_dispatch_support def mean_absolute_percentage_error(y_true, y_pred): """Computes the mean absolute percentage error between `y_true` and `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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) diff = tf.abs( (y_true - y_pred) / backend.maximum(tf.abs(y_true), backend.epsilon())) return 100. * backend.mean(diff, axis=-1)
def MSE(y_true, y_pred)
-
Computes 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]
.Expand source code
@keras_export('keras.metrics.mean_squared_error', 'keras.metrics.mse', 'keras.metrics.MSE', 'keras.losses.mean_squared_error', 'keras.losses.mse', 'keras.losses.MSE') @tf.__internal__.dispatch.add_dispatch_support def mean_squared_error(y_true, y_pred): """Computes 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
def MSLE(y_true, y_pred)
-
Computes the mean squared logarithmic error between
y_true
andy_pred
.loss = mean(square(log(y_true + 1) - log(y_pred + 1)), 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_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]
.Expand source code
@keras_export('keras.metrics.mean_squared_logarithmic_error', 'keras.metrics.msle', 'keras.metrics.MSLE', 'keras.losses.mean_squared_logarithmic_error', 'keras.losses.msle', 'keras.losses.MSLE') @tf.__internal__.dispatch.add_dispatch_support def mean_squared_logarithmic_error(y_true, y_pred): """Computes the mean squared logarithmic error between `y_true` and `y_pred`. `loss = mean(square(log(y_true + 1) - log(y_pred + 1)), 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_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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) first_log = tf.math.log(backend.maximum(y_pred, backend.epsilon()) + 1.) second_log = tf.math.log(backend.maximum(y_true, backend.epsilon()) + 1.) return backend.mean( tf.math.squared_difference(first_log, second_log), axis=-1)
def binary_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0, axis=-1)
-
Computes 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 thaty_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, using1. - 0.5 * label_smoothing
for the target class and0.5 * 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]
.Expand source code
@keras_export('keras.metrics.binary_crossentropy', 'keras.losses.binary_crossentropy') @tf.__internal__.dispatch.add_dispatch_support def binary_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0, axis=-1): """Computes 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) label_smoothing = tf.convert_to_tensor( label_smoothing, dtype=backend.floatx()) def _smooth_labels(): return y_true * (1.0 - label_smoothing) + 0.5 * label_smoothing y_true = tf.__internal__.smart_cond.smart_cond(label_smoothing, _smooth_labels, lambda: y_true) return backend.mean( backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits), axis=axis)
def categorical_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0, axis=-1)
-
Computes 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 thaty_pred
encodes a probability distribution. label_smoothing
- Float in [0, 1]. If >
0
then smooth the labels. For example, if0.1
, use0.1 / num_classes
for non-target labels and0.9 + 0.1 / num_classes
for target labels. axis
- Defaults to -1. The dimension along which the entropy is computed.
Returns
Categorical crossentropy loss value.
Expand source code
@keras_export('keras.metrics.categorical_crossentropy', 'keras.losses.categorical_crossentropy') @tf.__internal__.dispatch.add_dispatch_support def categorical_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0, axis=-1): """Computes 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. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) label_smoothing = tf.convert_to_tensor( label_smoothing, dtype=backend.floatx()) def _smooth_labels(): num_classes = tf.cast(tf.shape(y_true)[-1], y_pred.dtype) return y_true * (1.0 - label_smoothing) + (label_smoothing / num_classes) y_true = tf.__internal__.smart_cond.smart_cond(label_smoothing, _smooth_labels, lambda: y_true) return backend.categorical_crossentropy( y_true, y_pred, from_logits=from_logits, axis=axis)
def categorical_hinge(y_true, y_pred)
-
Computes the categorical hinge loss between
y_true
andy_pred
.loss = maximum(neg - pos + 1, 0)
whereneg=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.
Expand source code
@keras_export('keras.losses.categorical_hinge') @tf.__internal__.dispatch.add_dispatch_support def categorical_hinge(y_true, y_pred): """Computes the categorical hinge loss between `y_true` and `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. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) pos = tf.reduce_sum(y_true * y_pred, axis=-1) neg = tf.reduce_max((1. - y_true) * y_pred, axis=-1) zero = tf.cast(0., y_pred.dtype) return tf.maximum(neg - pos + 1., zero)
def cosine(y_true, y_pred, axis=-1)
-
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
ory_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.
Expand source code
@keras_export( 'keras.losses.cosine_similarity', v1=[ 'keras.metrics.cosine_proximity', 'keras.metrics.cosine', 'keras.losses.cosine_proximity', 'keras.losses.cosine', 'keras.losses.cosine_similarity', ]) @tf.__internal__.dispatch.add_dispatch_support def cosine_similarity(y_true, y_pred, axis=-1): """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.], [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. """ y_true = tf.linalg.l2_normalize(y_true, axis=axis) y_pred = tf.linalg.l2_normalize(y_pred, axis=axis) return -tf.reduce_sum(y_true * y_pred, axis=axis)
def cosine_proximity(y_true, y_pred, axis=-1)
-
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
ory_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.
Expand source code
@keras_export( 'keras.losses.cosine_similarity', v1=[ 'keras.metrics.cosine_proximity', 'keras.metrics.cosine', 'keras.losses.cosine_proximity', 'keras.losses.cosine', 'keras.losses.cosine_similarity', ]) @tf.__internal__.dispatch.add_dispatch_support def cosine_similarity(y_true, y_pred, axis=-1): """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.], [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. """ y_true = tf.linalg.l2_normalize(y_true, axis=axis) y_pred = tf.linalg.l2_normalize(y_pred, axis=axis) return -tf.reduce_sum(y_true * y_pred, axis=axis)
def cosine_similarity(y_true, y_pred, axis=-1)
-
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
ory_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.
Expand source code
@keras_export( 'keras.losses.cosine_similarity', v1=[ 'keras.metrics.cosine_proximity', 'keras.metrics.cosine', 'keras.losses.cosine_proximity', 'keras.losses.cosine', 'keras.losses.cosine_similarity', ]) @tf.__internal__.dispatch.add_dispatch_support def cosine_similarity(y_true, y_pred, axis=-1): """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.], [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. """ y_true = tf.linalg.l2_normalize(y_true, axis=axis) y_pred = tf.linalg.l2_normalize(y_pred, axis=axis) return -tf.reduce_sum(y_true * y_pred, axis=axis)
def deserialize(name, custom_objects=None)
-
Deserializes 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.
Returns
A Keras
Loss
instance or a loss function.Expand source code
@keras_export('keras.losses.deserialize') def deserialize(name, custom_objects=None): """Deserializes 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. Returns: A Keras `Loss` instance or a loss function. """ return deserialize_keras_object( name, module_objects=globals(), custom_objects=custom_objects, printable_module_name='loss function')
def get(identifier)
-
Retrieves a Keras loss as a
function
/Loss
class instance.The
identifier
may be the string name of a loss function orLoss
class.>>> loss = tf.keras.losses.get("categorical_crossentropy") >>> type(loss) <class 'function'> >>> loss = tf.keras.losses.get("CategoricalCrossentropy") >>> type(loss) <class '...keras.losses.CategoricalCrossentropy'>
You can also specify
config
of the loss to this function by passing dict containingclass_name
andconfig
as an identifier. Also note that theclass_name
must map to aLoss
class>>> identifier = {"class_name": "CategoricalCrossentropy", ... "config": {"from_logits": True}} >>> loss = tf.keras.losses.get(identifier) >>> type(loss) <class '...keras.losses.CategoricalCrossentropy'>
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 Keras loss as a
function
/Loss
class instance.Raises
ValueError
- If
identifier
cannot be interpreted.
Expand source code
@keras_export('keras.losses.get') def get(identifier): """Retrieves a 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) <class 'function'> >>> loss = tf.keras.losses.get("CategoricalCrossentropy") >>> type(loss) <class '...keras.losses.CategoricalCrossentropy'> 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) <class '...keras.losses.CategoricalCrossentropy'> 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 Keras loss as a `function`/ `Loss` class instance. Raises: ValueError: If `identifier` cannot be interpreted. """ if identifier is None: return None if isinstance(identifier, str): identifier = str(identifier) return deserialize(identifier) if isinstance(identifier, dict): return deserialize(identifier) if callable(identifier): return identifier raise ValueError( f'Could not interpret loss function identifier: {identifier}')
def hinge(y_true, y_pred)
-
Computes the hinge loss between
y_true
andy_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 they will be converted 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]
.Expand source code
@keras_export('keras.metrics.hinge', 'keras.losses.hinge') @tf.__internal__.dispatch.add_dispatch_support def hinge(y_true, y_pred): """Computes the hinge loss between `y_true` and `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 they will be converted 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) y_true = _maybe_convert_labels(y_true) return backend.mean(tf.maximum(1. - y_true * y_pred, 0.), axis=-1)
def kl_divergence(y_true, y_pred)
-
Computes Kullback-Leibler divergence loss between
y_true
andy_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 they_pred.dtype
.
Expand source code
@keras_export('keras.metrics.kl_divergence', 'keras.metrics.kullback_leibler_divergence', 'keras.metrics.kld', 'keras.metrics.KLD', 'keras.losses.kl_divergence', 'keras.losses.kullback_leibler_divergence', 'keras.losses.kld', 'keras.losses.KLD') @tf.__internal__.dispatch.add_dispatch_support def kl_divergence(y_true, y_pred): """Computes Kullback-Leibler divergence loss between `y_true` and `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`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) y_true = backend.clip(y_true, backend.epsilon(), 1) y_pred = backend.clip(y_pred, backend.epsilon(), 1) return tf.reduce_sum(y_true * tf.math.log(y_true / y_pred), axis=-1)
def kld(y_true, y_pred)
-
Computes Kullback-Leibler divergence loss between
y_true
andy_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 they_pred.dtype
.
Expand source code
@keras_export('keras.metrics.kl_divergence', 'keras.metrics.kullback_leibler_divergence', 'keras.metrics.kld', 'keras.metrics.KLD', 'keras.losses.kl_divergence', 'keras.losses.kullback_leibler_divergence', 'keras.losses.kld', 'keras.losses.KLD') @tf.__internal__.dispatch.add_dispatch_support def kl_divergence(y_true, y_pred): """Computes Kullback-Leibler divergence loss between `y_true` and `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`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) y_true = backend.clip(y_true, backend.epsilon(), 1) y_pred = backend.clip(y_pred, backend.epsilon(), 1) return tf.reduce_sum(y_true * tf.math.log(y_true / y_pred), axis=-1)
def kullback_leibler_divergence(y_true, y_pred)
-
Computes Kullback-Leibler divergence loss between
y_true
andy_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 they_pred.dtype
.
Expand source code
@keras_export('keras.metrics.kl_divergence', 'keras.metrics.kullback_leibler_divergence', 'keras.metrics.kld', 'keras.metrics.KLD', 'keras.losses.kl_divergence', 'keras.losses.kullback_leibler_divergence', 'keras.losses.kld', 'keras.losses.KLD') @tf.__internal__.dispatch.add_dispatch_support def kl_divergence(y_true, y_pred): """Computes Kullback-Leibler divergence loss between `y_true` and `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`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) y_true = backend.clip(y_true, backend.epsilon(), 1) y_pred = backend.clip(y_pred, backend.epsilon(), 1) return tf.reduce_sum(y_true * tf.math.log(y_true / y_pred), axis=-1)
def log_cosh(y_true, y_pred)
-
Logarithm of the hyperbolic cosine of the prediction error.
log(cosh(x))
is approximately equal to(x ** 2) / 2
for smallx
and toabs(x) - log(2)
for largex
. 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.) - math_ops.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]
.Expand source code
@keras_export('keras.losses.log_cosh', 'keras.losses.logcosh', 'keras.metrics.log_cosh', 'keras.metrics.logcosh') @tf.__internal__.dispatch.add_dispatch_support def log_cosh(y_true, y_pred): """Logarithm 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.) - math_ops.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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) def _logcosh(x): return x + tf.math.softplus(-2. * x) - tf.cast( tf.math.log(2.), x.dtype) return backend.mean(_logcosh(y_pred - y_true), axis=-1)
def logcosh(y_true, y_pred)
-
Logarithm of the hyperbolic cosine of the prediction error.
log(cosh(x))
is approximately equal to(x ** 2) / 2
for smallx
and toabs(x) - log(2)
for largex
. 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.) - math_ops.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]
.Expand source code
@keras_export('keras.losses.log_cosh', 'keras.losses.logcosh', 'keras.metrics.log_cosh', 'keras.metrics.logcosh') @tf.__internal__.dispatch.add_dispatch_support def log_cosh(y_true, y_pred): """Logarithm 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.) - math_ops.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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) def _logcosh(x): return x + tf.math.softplus(-2. * x) - tf.cast( tf.math.log(2.), x.dtype) return backend.mean(_logcosh(y_pred - y_true), axis=-1)
def mae(y_true, y_pred)
-
Computes 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]
.Expand source code
@keras_export('keras.metrics.mean_absolute_error', 'keras.metrics.mae', 'keras.metrics.MAE', 'keras.losses.mean_absolute_error', 'keras.losses.mae', 'keras.losses.MAE') @tf.__internal__.dispatch.add_dispatch_support def mean_absolute_error(y_true, y_pred): """Computes 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.mean(tf.abs(y_pred - y_true), axis=-1)
def mape(y_true, y_pred)
-
Computes the mean absolute percentage error between
y_true
andy_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]
.Expand source code
@keras_export('keras.metrics.mean_absolute_percentage_error', 'keras.metrics.mape', 'keras.metrics.MAPE', 'keras.losses.mean_absolute_percentage_error', 'keras.losses.mape', 'keras.losses.MAPE') @tf.__internal__.dispatch.add_dispatch_support def mean_absolute_percentage_error(y_true, y_pred): """Computes the mean absolute percentage error between `y_true` and `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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) diff = tf.abs( (y_true - y_pred) / backend.maximum(tf.abs(y_true), backend.epsilon())) return 100. * backend.mean(diff, axis=-1)
def mean_absolute_error(y_true, y_pred)
-
Computes 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]
.Expand source code
@keras_export('keras.metrics.mean_absolute_error', 'keras.metrics.mae', 'keras.metrics.MAE', 'keras.losses.mean_absolute_error', 'keras.losses.mae', 'keras.losses.MAE') @tf.__internal__.dispatch.add_dispatch_support def mean_absolute_error(y_true, y_pred): """Computes 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.mean(tf.abs(y_pred - y_true), axis=-1)
def mean_absolute_percentage_error(y_true, y_pred)
-
Computes the mean absolute percentage error between
y_true
andy_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]
.Expand source code
@keras_export('keras.metrics.mean_absolute_percentage_error', 'keras.metrics.mape', 'keras.metrics.MAPE', 'keras.losses.mean_absolute_percentage_error', 'keras.losses.mape', 'keras.losses.MAPE') @tf.__internal__.dispatch.add_dispatch_support def mean_absolute_percentage_error(y_true, y_pred): """Computes the mean absolute percentage error between `y_true` and `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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) diff = tf.abs( (y_true - y_pred) / backend.maximum(tf.abs(y_true), backend.epsilon())) return 100. * backend.mean(diff, axis=-1)
def mean_squared_error(y_true, y_pred)
-
Computes 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]
.Expand source code
@keras_export('keras.metrics.mean_squared_error', 'keras.metrics.mse', 'keras.metrics.MSE', 'keras.losses.mean_squared_error', 'keras.losses.mse', 'keras.losses.MSE') @tf.__internal__.dispatch.add_dispatch_support def mean_squared_error(y_true, y_pred): """Computes 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
def mean_squared_logarithmic_error(y_true, y_pred)
-
Computes the mean squared logarithmic error between
y_true
andy_pred
.loss = mean(square(log(y_true + 1) - log(y_pred + 1)), 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_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]
.Expand source code
@keras_export('keras.metrics.mean_squared_logarithmic_error', 'keras.metrics.msle', 'keras.metrics.MSLE', 'keras.losses.mean_squared_logarithmic_error', 'keras.losses.msle', 'keras.losses.MSLE') @tf.__internal__.dispatch.add_dispatch_support def mean_squared_logarithmic_error(y_true, y_pred): """Computes the mean squared logarithmic error between `y_true` and `y_pred`. `loss = mean(square(log(y_true + 1) - log(y_pred + 1)), 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_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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) first_log = tf.math.log(backend.maximum(y_pred, backend.epsilon()) + 1.) second_log = tf.math.log(backend.maximum(y_true, backend.epsilon()) + 1.) return backend.mean( tf.math.squared_difference(first_log, second_log), axis=-1)
def mse(y_true, y_pred)
-
Computes 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]
.Expand source code
@keras_export('keras.metrics.mean_squared_error', 'keras.metrics.mse', 'keras.metrics.MSE', 'keras.losses.mean_squared_error', 'keras.losses.mse', 'keras.losses.MSE') @tf.__internal__.dispatch.add_dispatch_support def mean_squared_error(y_true, y_pred): """Computes 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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
def msle(y_true, y_pred)
-
Computes the mean squared logarithmic error between
y_true
andy_pred
.loss = mean(square(log(y_true + 1) - log(y_pred + 1)), 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_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]
.Expand source code
@keras_export('keras.metrics.mean_squared_logarithmic_error', 'keras.metrics.msle', 'keras.metrics.MSLE', 'keras.losses.mean_squared_logarithmic_error', 'keras.losses.msle', 'keras.losses.MSLE') @tf.__internal__.dispatch.add_dispatch_support def mean_squared_logarithmic_error(y_true, y_pred): """Computes the mean squared logarithmic error between `y_true` and `y_pred`. `loss = mean(square(log(y_true + 1) - log(y_pred + 1)), 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_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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) first_log = tf.math.log(backend.maximum(y_pred, backend.epsilon()) + 1.) second_log = tf.math.log(backend.maximum(y_true, backend.epsilon()) + 1.) return backend.mean( tf.math.squared_difference(first_log, second_log), axis=-1)
def poisson(y_true, y_pred)
-
Computes 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
andy_pred
have incompatible shapes.
Expand source code
@keras_export('keras.metrics.poisson', 'keras.losses.poisson') @tf.__internal__.dispatch.add_dispatch_support def poisson(y_true, y_pred): """Computes 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. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.mean( y_pred - y_true * tf.math.log(y_pred + backend.epsilon()), axis=-1)
def serialize(loss)
-
Serializes loss function or
Loss
instance.Args
loss
- A Keras
Loss
instance or a loss function.
Returns
Loss configuration dictionary.
Expand source code
@keras_export('keras.losses.serialize') def serialize(loss): """Serializes loss function or `Loss` instance. Args: loss: A Keras `Loss` instance or a loss function. Returns: Loss configuration dictionary. """ return serialize_keras_object(loss)
def sparse_categorical_crossentropy(y_true, y_pred, from_logits=False, axis=-1)
-
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)
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 thaty_pred
encodes a probability distribution. axis
- Defaults to -1. The dimension along which the entropy is computed.
Returns
Sparse categorical crossentropy loss value.
Expand source code
@keras_export('keras.metrics.sparse_categorical_crossentropy', 'keras.losses.sparse_categorical_crossentropy') @tf.__internal__.dispatch.add_dispatch_support def sparse_categorical_crossentropy(y_true, y_pred, from_logits=False, axis=-1): """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) 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. Returns: Sparse categorical crossentropy loss value. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return backend.sparse_categorical_crossentropy( y_true, y_pred, from_logits=from_logits, axis=axis)
def squared_hinge(y_true, y_pred)
-
Computes the squared hinge loss between
y_true
andy_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]
.Expand source code
@keras_export('keras.metrics.squared_hinge', 'keras.losses.squared_hinge') @tf.__internal__.dispatch.add_dispatch_support def squared_hinge(y_true, y_pred): """Computes the squared hinge loss between `y_true` and `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]`. """ y_pred = tf.convert_to_tensor(y_pred) y_true = tf.cast(y_true, y_pred.dtype) y_true = _maybe_convert_labels(y_true) return backend.mean( tf.square(tf.maximum(1. - y_true * y_pred, 0.)), axis=-1)
Classes
class BinaryCrossentropy (from_logits=False, label_smoothing=0, axis=-1, reduction='auto', name='binary_crossentropy')
-
Computes 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, (i.e, value in [-inf, inf] whenfrom_logits=True
) or a probability (i.e, value in [0., 1.] whenfrom_logits=False
).
Recommended Usage: (set
from_logits=True
)With
tf.keras
API: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 <code>y\_pred</code> 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]]
Initializes
BinaryCrossentropy
instance.Args
from_logits
- Whether to interpret
y_pred
as a tensor of logit values. By default, we assume thaty_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 isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Name for the op. Defaults to 'binary_crossentropy'.
Expand source code
class BinaryCrossentropy(LossFunctionWrapper): """Computes 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]] """ def __init__(self, from_logits=False, label_smoothing=0, axis=-1, reduction=losses_utils.ReductionV2.AUTO, name='binary_crossentropy'): """Initializes `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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__( binary_crossentropy, name=name, reduction=reduction, from_logits=from_logits, label_smoothing=label_smoothing, axis=axis) self.from_logits = from_logits
Ancestors
Inherited members
class CategoricalCrossentropy (from_logits=False, label_smoothing=0, axis=-1, reduction='auto', name='categorical_crossentropy')
-
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 useSparseCategoricalCrossentropy
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 bothy_pred
andy_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:model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalCrossentropy())
Initializes
CategoricalCrossentropy
instance.Args
from_logits
- Whether
y_pred
is expected to be a logits tensor. By default, we assume thaty_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
, use0.1 / num_classes
for non-target labels and0.9 + 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 isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'categorical_crossentropy'.
Expand source code
class CategoricalCrossentropy(LossFunctionWrapper): """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()) ``` """ def __init__(self, from_logits=False, label_smoothing=0, axis=-1, reduction=losses_utils.ReductionV2.AUTO, name='categorical_crossentropy'): """Initializes `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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__( categorical_crossentropy, name=name, reduction=reduction, from_logits=from_logits, label_smoothing=label_smoothing, axis=axis)
Ancestors
Inherited members
class CategoricalHinge (reduction='auto', name='categorical_hinge')
-
Computes the categorical hinge loss between
y_true
andy_pred
.loss = maximum(neg - pos + 1, 0)
whereneg=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:model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalHinge())
Initializes
CategoricalHinge
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'categorical_hinge'.
Expand source code
class CategoricalHinge(LossFunctionWrapper): """Computes the categorical hinge loss between `y_true` and `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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='categorical_hinge'): """Initializes `CategoricalHinge` 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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(categorical_hinge, name=name, reduction=reduction)
Ancestors
Inherited members
class CosineSimilarity (axis=-1, reduction='auto', name='cosine_similarity')
-
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
ory_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: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 isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_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.
Initializes
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 isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance.
**kwargs
- The keyword arguments that are passed on to
fn
.
Expand source code
class CosineSimilarity(LossFunctionWrapper): """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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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. """ def __init__(self, axis=-1, reduction=losses_utils.ReductionV2.AUTO, name='cosine_similarity'): super().__init__( cosine_similarity, reduction=reduction, name=name, axis=axis)
Ancestors
Inherited members
class Hinge (reduction='auto', name='hinge')
-
Computes the hinge loss between
y_true
andy_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:model.compile(optimizer='sgd', loss=tf.keras.losses.Hinge())
Initializes
Hinge
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'hinge'.
Expand source code
class Hinge(LossFunctionWrapper): """Computes the hinge loss between `y_true` and `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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='hinge'): """Initializes `Hinge` 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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(hinge, name=name, reduction=reduction)
Ancestors
Inherited members
class Huber (delta=1.0, reduction='auto', name='huber_loss')
-
Computes the Huber loss between
y_true
andy_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_lossStandalone 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:model.compile(optimizer='sgd', loss=tf.keras.losses.Huber())
Initializes
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 isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'huber_loss'.
Expand source code
class Huber(LossFunctionWrapper): """Computes the Huber loss between `y_true` and `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()) ``` """ def __init__(self, delta=1.0, reduction=losses_utils.ReductionV2.AUTO, name='huber_loss'): """Initializes `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 option will be determined by the usage context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(huber, name=name, reduction=reduction, delta=delta)
Ancestors
Inherited members
class KLDivergence (reduction='auto', name='kl_divergence')
-
Computes Kullback-Leibler divergence loss between
y_true
andy_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:model.compile(optimizer='sgd', loss=tf.keras.losses.KLDivergence())
Initializes
KLDivergence
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'kl_divergence'.
Expand source code
class KLDivergence(LossFunctionWrapper): """Computes Kullback-Leibler divergence loss between `y_true` and `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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='kl_divergence'): """Initializes `KLDivergence` 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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(kl_divergence, name=name, reduction=reduction)
Ancestors
Inherited members
class LogCosh (reduction='auto', name='log_cosh')
-
Computes the logarithm of the hyperbolic cosine of the prediction error.
logcosh = log((exp(x) + exp(-x))/2)
, where x is the errory_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:model.compile(optimizer='sgd', loss=tf.keras.losses.LogCosh())
Initializes
LogCosh
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'log_cosh'.
Expand source code
class LogCosh(LossFunctionWrapper): """Computes 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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='log_cosh'): """Initializes `LogCosh` 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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(log_cosh, name=name, reduction=reduction)
Ancestors
Inherited members
class Loss (reduction='auto', name=None)
-
Loss base class.
To be implemented by subclasses: *
call()
: Contains the logic for loss calculation usingy_true
,y_pred
.Example subclass implementation:
class MeanSquaredError(Loss): def call(self, y_true, y_pred): y_pred = tf.convert_to_tensor_v2(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return tf.reduce_mean(math_ops.square(y_pred - y_true), axis=-1)
When used with
tf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, please use 'SUM' or 'NONE' reduction types, and reduce losses explicitly in your training loop. Using 'AUTO' or 'SUM_OVER_BATCH_SIZE' will raise an error.Please see this custom training tutorial for more details on this.
You can implement 'SUM_OVER_BATCH_SIZE' using global batch size like:
with strategy.scope(): loss_obj = tf.keras.losses.CategoricalCrossentropy( reduction=tf.keras.losses.Reduction.NONE) .... loss = (tf.reduce_sum(loss_obj(labels, predictions)) * (1. / global_batch_size))
Initializes
Loss
class.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance.
Expand source code
class Loss: """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): y_pred = tf.convert_to_tensor_v2(y_pred) y_true = tf.cast(y_true, y_pred.dtype) return tf.reduce_mean(math_ops.square(y_pred - y_true), axis=-1) ``` When used with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `fit`, please use 'SUM' or 'NONE' reduction types, and reduce losses explicitly in your training loop. 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 on this. You can implement 'SUM_OVER_BATCH_SIZE' using global batch size like: ```python with strategy.scope(): loss_obj = tf.keras.losses.CategoricalCrossentropy( reduction=tf.keras.losses.Reduction.NONE) .... loss = (tf.reduce_sum(loss_obj(labels, predictions)) * (1. / global_batch_size)) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name=None): """Initializes `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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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. """ losses_utils.ReductionV2.validate(reduction) self.reduction = reduction self.name = name # SUM_OVER_BATCH is only allowed in losses managed by `fit` or # CannedEstimators. self._allow_sum_over_batch_size = False self._set_name_scope() def _set_name_scope(self): """Creates a valid `name_scope` name.""" if self.name is None: self._name_scope = self.__class__.__name__ elif self.name == '<lambda>': self._name_scope = 'lambda' else: # E.g. '_my_loss' => 'my_loss' self._name_scope = self.name.strip('_') def __call__(self, y_true, y_pred, sample_weight=None): """Invokes 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]` sample_weight: Optional `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each loss element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss functions reduce by 1 dimension, usually axis=-1.) Returns: Weighted loss float `Tensor`. If `reduction` is `NONE`, this has shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1` because all loss functions reduce by 1 dimension, usually axis=-1.) Raises: ValueError: If the shape of `sample_weight` is invalid. """ # If we are wrapping a lambda function strip '<>' from the name as it is not # accepted in scope name. graph_ctx = tf_utils.graph_context_for_symbolic_tensors( y_true, y_pred, sample_weight) with backend.name_scope(self._name_scope), graph_ctx: if tf.executing_eagerly(): call_fn = self.call else: call_fn = tf.__internal__.autograph.tf_convert(self.call, tf.__internal__.autograph.control_status_ctx()) losses = call_fn(y_true, y_pred) return losses_utils.compute_weighted_loss( losses, sample_weight, reduction=self._get_reduction()) @classmethod def from_config(cls, config): """Instantiates a `Loss` from its config (output of `get_config()`). Args: config: Output of `get_config()`. Returns: A `Loss` instance. """ return cls(**config) def get_config(self): """Returns the config dictionary for a `Loss` instance.""" return {'reduction': self.reduction, 'name': self.name} @abc.abstractmethod @doc_controls.for_subclass_implementers def call(self, y_true, y_pred): """Invokes 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]`. """ raise NotImplementedError('Must be implemented in subclasses.') def _get_reduction(self): """Handles `AUTO` reduction cases and returns the reduction value.""" if (not self._allow_sum_over_batch_size and tf.distribute.has_strategy() and (self.reduction == losses_utils.ReductionV2.AUTO or self.reduction == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE)): raise ValueError( 'Please use `tf.keras.losses.Reduction.SUM` or ' '`tf.keras.losses.Reduction.NONE` for loss reduction when losses are ' 'used with `tf.distribute.Strategy` outside of the built-in training ' 'loops. You can implement ' '`tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` using global batch ' 'size like:\n```\nwith strategy.scope():\n' ' loss_obj = tf.keras.losses.CategoricalCrossentropy(' 'reduction=tf.keras.losses.Reduction.NONE)\n....\n' ' loss = tf.reduce_sum(loss_obj(labels, predictions)) * ' '(1. / global_batch_size)\n```\nPlease see ' 'https://www.tensorflow.org/tutorials/distribute/custom_training' ' for more details.') if self.reduction == losses_utils.ReductionV2.AUTO: return losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE return self.reduction
Subclasses
Static methods
def from_config(config)
-
Instantiates a
Loss
from its config (output ofget_config()
).Args
config
- Output of
get_config()
.
Returns
A
Loss
instance.Expand source code
@classmethod def from_config(cls, config): """Instantiates a `Loss` from its config (output of `get_config()`). Args: config: Output of `get_config()`. Returns: A `Loss` instance. """ return cls(**config)
Methods
def call(self, y_true, y_pred)
-
Invokes 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]
.Expand source code
@abc.abstractmethod @doc_controls.for_subclass_implementers def call(self, y_true, y_pred): """Invokes 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]`. """ raise NotImplementedError('Must be implemented in subclasses.')
def get_config(self)
-
Returns the config dictionary for a
Loss
instance.Expand source code
def get_config(self): """Returns the config dictionary for a `Loss` instance.""" return {'reduction': self.reduction, 'name': self.name}
class MeanAbsoluteError (reduction='auto', name='mean_absolute_error')
-
Computes the mean of absolute difference between labels and predictions.
loss = abs(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. >>> 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:model.compile(optimizer='sgd', loss=tf.keras.losses.MeanAbsoluteError())
Initializes
MeanAbsoluteError
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'mean_absolute_error'.
Expand source code
class MeanAbsoluteError(LossFunctionWrapper): """Computes the mean of absolute difference between labels and predictions. `loss = abs(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. >>> 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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='mean_absolute_error'): """Initializes `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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(mean_absolute_error, name=name, reduction=reduction)
Ancestors
Inherited members
class MeanAbsolutePercentageError (reduction='auto', name='mean_absolute_percentage_error')
-
Computes the mean absolute percentage error between
y_true
andy_pred
.loss = 100 * abs(y_true - y_pred) / y_true
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:model.compile(optimizer='sgd', loss=tf.keras.losses.MeanAbsolutePercentageError())
Initializes
MeanAbsolutePercentageError
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'mean_absolute_percentage_error'.
Expand source code
class MeanAbsolutePercentageError(LossFunctionWrapper): """Computes the mean absolute percentage error between `y_true` and `y_pred`. `loss = 100 * abs(y_true - y_pred) / y_true` 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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='mean_absolute_percentage_error'): """Initializes `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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__( mean_absolute_percentage_error, name=name, reduction=reduction)
Ancestors
Inherited members
class MeanSquaredError (reduction='auto', name='mean_squared_error')
-
Computes the mean of squares of errors between labels and predictions.
loss = 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:model.compile(optimizer='sgd', loss=tf.keras.losses.MeanSquaredError())
Initializes
MeanSquaredError
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'mean_squared_error'.
Expand source code
class MeanSquaredError(LossFunctionWrapper): """Computes the mean of squares of errors between labels and predictions. `loss = 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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='mean_squared_error'): """Initializes `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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(mean_squared_error, name=name, reduction=reduction)
Ancestors
Inherited members
class MeanSquaredLogarithmicError (reduction='auto', name='mean_squared_logarithmic_error')
-
Computes the mean squared logarithmic error between
y_true
andy_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:model.compile(optimizer='sgd', loss=tf.keras.losses.MeanSquaredLogarithmicError())
Initializes
MeanSquaredLogarithmicError
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'mean_squared_logarithmic_error'.
Expand source code
class MeanSquaredLogarithmicError(LossFunctionWrapper): """Computes the mean squared logarithmic error between `y_true` and `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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='mean_squared_logarithmic_error'): """Initializes `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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__( mean_squared_logarithmic_error, name=name, reduction=reduction)
Ancestors
Inherited members
class Poisson (reduction='auto', name='poisson')
-
Computes the Poisson loss between
y_true
andy_pred
.loss = y_pred - y_true * log(y_pred)
Standalone usage:
>>> 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:model.compile(optimizer='sgd', loss=tf.keras.losses.Poisson())
Initializes
Poisson
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'poisson'.
Expand source code
class Poisson(LossFunctionWrapper): """Computes the Poisson loss between `y_true` and `y_pred`. `loss = y_pred - y_true * log(y_pred)` Standalone usage: >>> 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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='poisson'): """Initializes `Poisson` 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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(poisson, name=name, reduction=reduction)
Ancestors
Inherited members
class SparseCategoricalCrossentropy (from_logits=False, reduction='auto', name='sparse_categorical_crossentropy')
-
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 as integers. If you want to provide labels using
one-hot
representation, please useCategoricalCrossentropy
loss. There should be# classes
floating point values per feature fory_pred
and a single floating point value per feature fory_true
.In the snippet below, there is a single floating point value per example for
y_true
and# classes
floating pointing values per example fory_pred
. The shape ofy_true
is[batch_size]
and the shape ofy_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:model.compile(optimizer='sgd', loss=tf.keras.losses.SparseCategoricalCrossentropy())
Initializes
SparseCategoricalCrossentropy
instance.Args
from_logits
- Whether
y_pred
is expected to be a logits tensor. By default, we assume thaty_pred
encodes a probability distribution. reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'sparse_categorical_crossentropy'.
Expand source code
class SparseCategoricalCrossentropy(LossFunctionWrapper): """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 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()) ``` """ def __init__(self, from_logits=False, reduction=losses_utils.ReductionV2.AUTO, name='sparse_categorical_crossentropy'): """Initializes `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. 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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__( sparse_categorical_crossentropy, name=name, reduction=reduction, from_logits=from_logits)
Ancestors
Inherited members
class SquaredHinge (reduction='auto', name='squared_hinge')
-
Computes the squared hinge loss between
y_true
andy_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:model.compile(optimizer='sgd', loss=tf.keras.losses.SquaredHinge())
Initializes
SquaredHinge
instance.Args
reduction
- Type of
tf.keras.losses.Reduction
to apply to loss. Default value isAUTO
.AUTO
indicates that the reduction option will be determined by the usage context. For almost all cases this defaults toSUM_OVER_BATCH_SIZE
. When used withtf.distribute.Strategy
, outside of built-in training loops such astf.keras
compile
andfit
, usingAUTO
orSUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training tutorial for more details. name
- Optional name for the instance. Defaults to 'squared_hinge'.
Expand source code
class SquaredHinge(LossFunctionWrapper): """Computes the squared hinge loss between `y_true` and `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()) ``` """ def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name='squared_hinge'): """Initializes `SquaredHinge` 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 with `tf.distribute.Strategy`, outside of built-in training loops such as `tf.keras` `compile` and `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'. """ super().__init__(squared_hinge, name=name, reduction=reduction)
Ancestors
Inherited members