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/*
* 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.optim
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.utils.{File, T, Table}
import org.apache.commons.lang3.SerializationUtils
import scala.reflect.ClassTag
/**
* Similar to torch Optim method, which is used to update the parameter
*/
trait OptimMethod[@specialized(Float, Double) T] extends Serializable {
/**
* Optimize the model parameter
*
* @param feval a function that takes a single input (X), the point of a evaluation,
* and returns f(X) and df/dX
* @param parameter the initial point
* @return the new x vector and the function list, evaluated before the update
*/
def optimize(feval: (Tensor[T]) => (T, Tensor[T]), parameter: Tensor[T])
: (Tensor[T], Array[T])
/**
* Clear the history information in the OptimMethod state
*
* @return
*/
def clearHistory(): Unit
/**
* Update hyper parameter.
* We have updated hyper parameter in method optimize(). But in DistriOptimizer, the method
* optimize() is only called on the executor side, the driver's hyper parameter is unchanged.
* So this method is using to update hyper parameter on the driver side.
*
* @return A string.
*/
def updateHyperParameter(): Unit = {}
/**
* Get hyper parameter from config table.
*
*/
def getHyperParameter(): String = ""
/**
* clone OptimMethod
*
* @return
*/
override def clone(): OptimMethod[T] = SerializationUtils.clone(this)
/**
* get learning rate
*
* @return
*/
def getLearningRate(): Double
// a table describing the state of the optimizer; after each call the state is modified
private[bigdl] var state: Table = T(
"epoch" -> 1,
"neval" -> 1
)
/**
* save OptimMethod
*
* @param path path
* @param overWrite whether to overwrite
* @return
*/
def save(path: String, overWrite: Boolean = false): this.type = {
this.clearHistory()
File.save(this, path, overWrite)
this
}
/**
* load optimMethod parameters from Table
* @param config
* @return
*/
def loadFromTable(config: Table): this.type
/**
* Optimize the model parameter
*
* @param feval a function that takes a single input (X), the point of a evaluation,
* and returns f(X) and df/dX
* @param parameter the initial point
* @param config a table with configuration parameters for the optimizer
* @param state a table describing the state of the optimizer; after each call the state
* is modified
* @return the new x vector and the function list, evaluated before the update
*/
@deprecated(
"Please initialize OptimMethod with parameters when creating it instead of importing table",
"0.2.0")
def optimize(feval: (Tensor[T]) => (T, Tensor[T]), parameter: Tensor[T], config: Table,
state: Table = null): (Tensor[T], Array[T]) = {
val _config = if (config == null) T() else config
this.state = if (state == null) _config else state
this.loadFromTable(config)
val res = optimize(feval, parameter)
config.update("clr", this.getLearningRate())
res
}
/**
* Clear the history information in the state
*
* @param state
* @return
*/
@deprecated(
"Please use clearHistory() instead",
"0.2.0")
def clearHistory(state: Table): Table = {
this.state = state
this.clearHistory()
this.state
}
/**
* Update hyper parameter.
* We have updated hyper parameter in method optimize(). But in DistriOptimizer, the method
* optimize() is only called on the executor side, the driver's hyper parameter is unchanged.
* So this method is using to update hyper parameter on the driver side.
*
* @param config config table.
* @param state state Table.
* @return A string.
*/
@deprecated(
"Please use updateHyperParameter() instead",
"0.2.0")
def updateHyperParameter(config: Table, state: Table): Unit = {}
/**
* Get hyper parameter from config table.
*
* @param config a table contains the hyper parameter.
*/
@deprecated(
"Please use getHyperParameter() instead",
"0.2.0")
def getHyperParameter(config: Table): String = ""
}
object OptimMethod {
/**
* load optim method
* @param path file path
* @return
*/
def load[T: ClassTag](path : String) : OptimMethod[T] = {
File.load[OptimMethod[T]](path)
}
}
