diff options
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala | 10 |
1 files changed, 4 insertions, 6 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala index 88909a9fb9..e7e0dae0b5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala @@ -545,7 +545,9 @@ private[ann] object FeedForwardModel { * @return model */ def apply(topology: FeedForwardTopology, weights: Vector): FeedForwardModel = { - // TODO: check that weights size is equal to sum of layers sizes + val expectedWeightSize = topology.layers.map(_.weightSize).sum + require(weights.size == expectedWeightSize, + s"Expected weight vector of size ${expectedWeightSize} but got size ${weights.size}.") new FeedForwardModel(weights, topology) } @@ -559,11 +561,7 @@ private[ann] object FeedForwardModel { def apply(topology: FeedForwardTopology, seed: Long = 11L): FeedForwardModel = { val layers = topology.layers val layerModels = new Array[LayerModel](layers.length) - var totalSize = 0 - for (i <- 0 until topology.layers.length) { - totalSize += topology.layers(i).weightSize - } - val weights = BDV.zeros[Double](totalSize) + val weights = BDV.zeros[Double](topology.layers.map(_.weightSize).sum) var offset = 0 val random = new XORShiftRandom(seed) for (i <- 0 until layers.length) { |