y_true & y_pred.Formula:
loss <- mean(maximum(1 - y_true * y_pred, 0), axis=-1)
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.
loss_hinge(
y_true,
y_pred,
...,
reduction = "sum_over_batch_size",
name = "hinge",
dtype = NULL
)Hinge loss values with shape = [batch_size, d0, .. dN-1].
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 with shape = [batch_size, d0, .. dN].
The predicted values with shape = [batch_size, d0, .. dN].
For forward/backward compatability.
Type of reduction to apply to the loss. In almost all cases
this should be "sum_over_batch_size". Supported options are
"sum", "sum_over_batch_size", "mean",
"mean_with_sample_weight" or NULL. "sum" sums the loss,
"sum_over_batch_size" and "mean" sum the loss and divide by the
sample size, and "mean_with_sample_weight" sums the loss and
divides by the sum of the sample weights. "none" and NULL
perform no aggregation. Defaults to "sum_over_batch_size".
Optional name for the loss instance.
The dtype of the loss's computations. Defaults to NULL, which
means using config_floatx(). config_floatx() is a
"float32" unless set to different value
(via config_set_floatx()). If a keras$DTypePolicy is
provided, then the compute_dtype will be utilized.
y_true <- array(sample(c(-1,1), 6, replace = TRUE), dim = c(2, 3))
y_pred <- random_uniform(c(2, 3))
loss <- loss_hinge(y_true, y_pred)
loss
## tf.Tensor([1.0610152 0.93285507], shape=(2), dtype=float32)Other losses:
Loss()
loss_binary_crossentropy()
loss_binary_focal_crossentropy()
loss_categorical_crossentropy()
loss_categorical_focal_crossentropy()
loss_categorical_hinge()
loss_circle()
loss_cosine_similarity()
loss_ctc()
loss_dice()
loss_huber()
loss_kl_divergence()
loss_log_cosh()
loss_mean_absolute_error()
loss_mean_absolute_percentage_error()
loss_mean_squared_error()
loss_mean_squared_logarithmic_error()
loss_poisson()
loss_sparse_categorical_crossentropy()
loss_squared_hinge()
loss_tversky()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_hinge()
metric_huber()
metric_kl_divergence()
metric_log_cosh()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_poisson()
metric_sparse_categorical_crossentropy()
metric_squared_hinge()