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com.intel.analytics.bigdl.nn.tf.package.scala Maven / Gradle / Ivy
/*
* Copyright 2016 The BigDL 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.bigdl.nn
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity}
import com.intel.analytics.bigdl.nn.tf.TensorModuleWrapper
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
package object tf {
object Mean {
def apply[T: ClassTag, D: ClassTag](dimension: Int = 1,
nInputDims: Int = -1,
squeeze: Boolean = true)
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.Mean(dimension, nInputDims, squeeze))
}
object Abs {
def apply[T: ClassTag, D: ClassTag]()
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.Abs[D]())
}
object Clamp {
def apply[T: ClassTag, D: ClassTag](min: Int, max: Int)
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.Clamp[D](min, max))
}
object ReLU6 {
def apply[T: ClassTag, D: ClassTag]()
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.ReLU6[D]())
}
object ELU {
def apply[T: ClassTag, D: ClassTag]()
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.ELU[D]())
}
object Log {
def apply[T: ClassTag, D: ClassTag]()
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.Log[D]())
}
object Power {
def apply[T: ClassTag, D: ClassTag](power: Double,
scale : Double = 1,
shift : Double = 0)
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.Power[D](power, scale, shift))
}
object SoftPlus {
def apply[T: ClassTag, D: ClassTag]()
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.SoftPlus[D]())
}
object SoftSign {
def apply[T: ClassTag, D: ClassTag]()
(implicit ev: TensorNumeric[T], evd: TensorNumeric[D]):
AbstractModule[Activity, Activity, T]
= TensorModuleWrapper[T, D](
com.intel.analytics.bigdl.nn.SoftSign[D]())
}
}
