lanking520 commented on a change in pull request #11292: [MXNET-531] CNN
Examples for Scala new API
URL: https://github.com/apache/incubator-mxnet/pull/11292#discussion_r199984082
##########
File path:
scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/CNNTextClassification.scala
##########
@@ -17,78 +17,81 @@
package org.apache.mxnetexamples.cnntextclassification
+import org.apache.mxnet.optimizer.RMSProp
+import org.apache.mxnet.{Context, Executor, Model, NDArray, Optimizer, Shape,
Symbol, Uniform}
import org.kohsuke.args4j.{CmdLineParser, Option}
import org.slf4j.LoggerFactory
+
import scala.collection.JavaConverters._
-import org.apache.mxnet.Uniform
-import org.apache.mxnet.Context
-import org.apache.mxnet.Symbol
-import org.apache.mxnet.Shape
-import org.apache.mxnet.NDArray
-import org.apache.mxnet.Executor
-import org.apache.mxnet.optimizer.RMSProp
-import org.apache.mxnet.Optimizer
-import org.apache.mxnet.Model
import scala.util.Random
/**
- * An Implementation of the paper
- * Convolutional Neural Networks for Sentence Classification
- * by Yoon Kim
- * @author Depeng Liang
- */
+ * An Implementation of the paper
+ * Convolutional Neural Networks for Sentence Classification
+ */
object CNNTextClassification {
private val logger = LoggerFactory.getLogger(classOf[CNNTextClassification])
case class CNNModel(cnnExec: Executor, symbol: Symbol, data: NDArray, label:
NDArray,
- argsDict: Map[String, NDArray], gradDict: Map[String, NDArray])
+ argsDict: Map[String, NDArray], gradDict: Map[String,
NDArray])
def makeTextCNN(sentenceSize: Int, numEmbed: Int, batchSize: Int,
- numLabel: Int = 2, filterList: Array[Int] = Array(3, 4, 5), numFilter: Int
= 100,
- dropout: Float = 0.5f): Symbol = {
+ numLabel: Int = 2, filterList: Array[Int] = Array(3, 4, 5),
numFilter: Int = 100,
+ dropout: Float = 0.5f): Symbol = {
val inputX = Symbol.Variable("data")
val inputY = Symbol.Variable("softmax_label")
val polledOutputs = filterList.map { filterSize =>
- val conv = Symbol.Convolution()()(
- Map("data" -> inputX, "kernel" -> s"($filterSize, $numEmbed)",
"num_filter" -> numFilter))
- val relu = Symbol.Activation()()(Map("data" -> conv, "act_type" ->
"relu"))
- val pool = Symbol.Pooling()()(Map("data" -> relu, "pool_type" -> "max",
- "kernel" -> s"(${sentenceSize - filterSize + 1}, 1)", "stride" ->
"(1,1)"))
+ val conv = Symbol.api.Convolution(data = Some(inputX),
+ kernel = new Shape(filterSize, numEmbed), num_filter = numFilter)
+ val relu = Symbol.api.Activation(data = Some(conv), act_type = "relu")
+ val pool = Symbol.api.Pooling(data = Some(relu), pool_type = Some("max"),
+ kernel = Some(new Shape(sentenceSize - filterSize + 1, 1)), stride =
Some(new Shape(1, 1)))
+ relu.dispose()
+ conv.dispose()
pool
}
val totalFilters = numFilter * filterList.length
- val concat = Symbol.Concat()(polledOutputs: _*)(Map("dim" -> 1))
- val hPool = Symbol.Reshape()()(Map("data" -> concat,
- "target_shape" -> s"($batchSize, $totalFilters)"))
+ // val concat = Symbol.Concat()(polledOutputs: _*)(Map("dim" -> 1))
+ val concat = Symbol.api.concat(data = polledOutputs,
+ num_args = polledOutputs.length, dim = Some(1))
+ val hPool = Symbol.api.reshape(data = Some(concat),
+ target_shape = Some(new Shape(batchSize, totalFilters)))
val hDrop = {
- if (dropout > 0f) Symbol.Dropout()()(Map("data" -> hPool, "p" ->
dropout))
+ if (dropout > 0f) Symbol.api.Dropout(data = Some(hPool), p =
Some(dropout))
else hPool
}
- val fc = Symbol.FullyConnected()()(Map("data" -> hDrop, "num_hidden" ->
numLabel))
- val sm = Symbol.SoftmaxOutput()()(Map("data" -> fc, "label" -> inputY))
+ val fc = Symbol.api.FullyConnected(data = Some(hDrop), num_hidden =
numLabel)
+ val sm = Symbol.api.SoftmaxOutput(data = Some(fc), label = Some(inputY))
+ fc.dispose()
Review comment:
Yes, I have. Not crashing
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