com.intel.analytics.bigdl.visualization.TrainSummary.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.visualization
import com.intel.analytics.bigdl.optim.Trigger
import com.intel.analytics.bigdl.visualization.tensorboard.{FileReader, FileWriter}
import scala.collection.mutable
/**
* Train logger for tensorboard.
* Use optimize.setTrainSummary to enable train logger. Then the log will be saved to
* logDir/appName/train.
*
* @param logDir log dir.
* @param appName application Name.
*/
class TrainSummary(
logDir: String,
appName: String) extends Summary(logDir, appName) {
protected val folder = s"$logDir/$appName/train"
protected override val writer = new FileWriter(folder)
private val triggers: mutable.HashMap[String, Trigger] = mutable.HashMap(
"Loss" -> Trigger.severalIteration(1),
"Throughput" -> Trigger.severalIteration(1))
/**
* Read scalar values to an array of triple by tag name.
* First element of the triple is step, second is value, third is wallClockTime.
* @param tag tag name. Supported tag names is "LearningRate", "Loss", "Throughput"
* @return an array of triple.
*/
override def readScalar(tag: String): Array[(Long, Float, Double)] = {
FileReader.readScalar(folder, tag)
}
/**
* Supported tag name are LearningRate, Loss, Throughput, Parameters.
* Parameters contains weight, bias, gradWeight, gradBias, and some running status(eg.
* runningMean and runningVar in BatchNormalization).
*
* Notice: By default, we record LearningRate, Loss and Throughput each iteration, while
* recording parameters is disabled. The reason is getting parameters from workers is a
* heavy operation when the model is very big.
*
* @param tag tag name
* @param trigger trigger
* @return
*/
def setSummaryTrigger(tag: String, trigger: Trigger): this.type = {
require(tag.equals("LearningRate") || tag.equals("Loss") ||
tag.equals("Throughput") | tag.equals("Parameters"),
s"TrainSummary: only support LearningRate, Loss, Parameters and Throughput")
triggers(tag) = trigger
this
}
/**
* Get a trigger by tag name.
* @param tag
* @return
*/
def getSummaryTrigger(tag: String): Option[Trigger] = {
if (triggers.contains(tag)) {
Some(triggers(tag))
} else {
None
}
}
private[bigdl] def getScalarTriggers(): Iterator[(String, Trigger)] = {
triggers.filter(!_._1.equals("Parameters")).toIterator
}
}
object TrainSummary{
def apply(logDir: String,
appName: String): TrainSummary = {
new TrainSummary(logDir, appName)
}
}