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com.intel.analytics.bigdl.models.inception.Options.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.models.inception
import scopt.OptionParser
object Options {
case class TrainParams(
folder: String = "./",
checkpoint: Option[String] = None,
modelSnapshot: Option[String] = None,
stateSnapshot: Option[String] = None,
classNumber: Int = 1000,
batchSize: Int = -1,
learningRate: Double = 0.01,
env: String = "local",
overWriteCheckpoint: Boolean = false,
maxEpoch: Option[Int] = None,
maxIteration: Int = 62000,
weightDecay: Double = 0.0001,
checkpointIteration: Int = 620,
graphModel: Boolean = false,
maxLr: Option[Double] = None,
warmupEpoch: Option[Int] = None,
gradientL2NormThreshold: Option[Double] = None,
gradientMin: Option[Double] = None,
gradientMax: Option[Double] = None
)
val trainParser = new OptionParser[TrainParams]("BigDL Inception Example") {
opt[String]('f', "folder")
.text("url of hdfs folder store the hadoop sequence files")
.action((x, c) => c.copy(folder = x))
opt[String]("model")
.text("model snapshot location")
.action((x, c) => c.copy(modelSnapshot = Some(x)))
opt[String]("state")
.text("state snapshot location")
.action((x, c) => c.copy(stateSnapshot = Some(x)))
opt[String]("checkpoint")
.text("where to cache the model")
.action((x, c) => c.copy(checkpoint = Some(x)))
opt[Int]('e', "maxEpoch")
.text("epoch numbers")
.action((x, c) => c.copy(maxEpoch = Some(x)))
opt[Int]('i', "maxIteration")
.text("iteration numbers")
.action((x, c) => c.copy(maxIteration = x))
opt[Double]('l', "learningRate")
.text("inital learning rate")
.action((x, c) => c.copy(learningRate = x))
.required()
opt[Int]('b', "batchSize")
.text("batch size")
.action((x, c) => c.copy(batchSize = x))
.required()
opt[Int]("classNum")
.text("class number")
.action((x, c) => c.copy(classNumber = x))
opt[Unit]("overWrite")
.text("overwrite checkpoint files")
.action( (_, c) => c.copy(overWriteCheckpoint = true) )
opt[Double]("weightDecay")
.text("weight decay")
.action((x, c) => c.copy(weightDecay = x))
opt[Int]("checkpointIteration")
.text("checkpoint interval of iterations")
.action((x, c) => c.copy(checkpointIteration = x))
opt[Unit]('g', "graphModel")
.text("use graph model")
.action((x, c) => c.copy(graphModel = true))
opt[Double]("maxLr")
.text("max Lr after warm up")
.action((x, c) => c.copy(maxLr = Some(x)))
opt[Int]("warmupEpoch")
.text("warm up epoch numbers")
.action((x, c) => c.copy(warmupEpoch = Some(x)))
opt[Double]("gradientL2NormThreshold")
.text("gradient L2-Norm threshold")
.action((x, c) => c.copy(gradientL2NormThreshold = Some(x)))
opt[Double]("gradientMax")
.text("max gradient clipping by")
.action((x, c) => c.copy(gradientMax = Some(x)))
opt[Double]("gradientMin")
.text("min gradient clipping by")
.action((x, c) => c.copy(gradientMin = Some(x)))
}
case class TestParams(
folder: String = "./",
model: String = "",
batchSize: Option[Int] = None
)
val testParser = new OptionParser[TestParams]("BigDL Inception Test Example") {
opt[String]('f', "folder")
.text("url of hdfs folder store the hadoop sequence files")
.action((x, c) => c.copy(folder = x))
opt[String]("model")
.text("model snapshot location")
.action((x, c) => c.copy(model = x))
.required()
opt[Int]('b', "batchSize")
.text("batch size")
.action((x, c) => c.copy(batchSize = Some(x)))
}
}