tf.keras.losses.SquaredHinge
Computes the squared hinge loss between y_true and y_pred.
tf.keras.losses.SquaredHinge(
reduction=losses_utils.ReductionV2.AUTO, name='squared_hinge'
)
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.
Usage:
sh = tf.keras.losses.SquaredHinge()
loss = sh([-1., 1., 1.], [0.6, -0.7, -0.5])
# loss = (max(0, 1 - y_true * y_pred))^2 = [1.6^2 + 1.7^2 + 1.5^2] / 3
print('Loss: ', loss.numpy()) # Loss: 2.566666
Usage with the compile API:
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.SquaredHinge())
Methods
from_config
@classmethod
from_config(
config
)
Instantiates a Loss from its config (output of get_config()).
| Args | |
|---|---|
config |
Output of get_config(). |
| Returns | |
|---|---|
A Loss instance. |
get_config
get_config()
__call__
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss instance.
| Args | |
|---|---|
y_true |
Ground truth values. shape = [batch_size, d0, .. dN] |
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 ondN-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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/losses/SquaredHinge