com.intel.analytics.zoo.pipeline.api.keras.objectives.SquaredHinge.scala Maven / Gradle / Ivy
/*
* Copyright 2018 Analytics Zoo Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.zoo.pipeline.api.keras.objectives
import com.intel.analytics.bigdl.nn.MarginCriterion
import com.intel.analytics.bigdl.nn.abstractnn.AbstractCriterion
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.tensor.Tensor
import scala.reflect.ClassTag
/**
* Creates a criterion that optimizes a two-class classification
* squared hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y.
*
* @param margin if unspecified, is by default 1.
* @param sizeAverage Boolean. Whether losses are averaged over observations for each
* mini-batch. Default is true. If false, the losses are instead
* summed for each mini-batch.
*/
class SquaredHinge[@specialized(Float, Double) T: ClassTag]
(val margin: Double = 1.0, val sizeAverage: Boolean = true)
(implicit ev: TensorNumeric[T]) extends TensorLossFunction[T] {
override val loss: AbstractCriterion[Tensor[T], Tensor[T], T] =
MarginCriterion(margin, sizeAverage, true)
}
object SquaredHinge {
def apply[@specialized(Float, Double) T: ClassTag](
margin: Double = 1.0,
sizeAverage: Boolean = true)(implicit ev: TensorNumeric[T]) : SquaredHinge[T] = {
new SquaredHinge[T](margin, sizeAverage)
}
}