malzzz

//Warning: Uses Akka internal APIs (not binary compatible),
//can be written not to depend on that but implemented like this for brevity.

package akka.util

trait CallbackExecutionContext { this: akka.actor.Actor ⇒
  // Defines our signal for callback execution
  case object Execute
  // ``SerializedSuspendableExecutionContext`` is Akka-internal
  private[this] final val ssec: SerializedSuspendableExecutionContext = {

malzzz

import scala.concurrent.duration._
import scala.concurrent.ExecutionContext
import scala.concurrent.Future
import akka.pattern.after
import akka.actor.Scheduler

/**
 * Given an operation that produces a T, returns a Future containing the result of T, unless an exception is thrown,
 * in which case the operation will be retried after _delay_ time, if there are more possible retries, which is configured through
 * the _retries_ parameter. If the operation does not succeed and there is no retries left, the resulting Future will contain the last failure.

malzzz

0xb7BfB0d9c2c93a22FE911f871B0D9e4160FDCafE

malzzz

0xE0Bfce34f6e43b54D7C68dEc29675A92c60ECb6E

malzzz

## Java
sudo apt-get update
sudo apt-get install default-jdk

## Scala
sudo apt-get remove scala-library scala
sudo wget https://downloads.lightbend.com/scala/2.12.3/scala-2.12.3.deb
sudo dpkg -i scala-2.12.3.deb
sudo apt-get update
sudo apt-get install scala

malzzz

  def conv2[A: ClassTag](input: Vector[A], kernel: Vector[A], padding: Int, stride: Int)(implicit ev: Numeric[A]) = {
    // Static sizes
    val inputHW = sqrt(input.length).toInt
    val kernelHW = sqrt(kernel.length).toInt
    val rowSizeAfterPad = inputHW + kernelHW - 1
    val rowSizeStrided = rowSizeAfterPad + stride
    val opsPerPass = rowSizeAfterPad / kernelHW
    val passSize = kernelHW * rowSizeStrided
    val inputSizePaddedStrided = (rowSizeStrided * inputHW) + (rowSizeStrided * 2)

malzzz

trait NArray[I, A] { self =>
  def dims: List[Idx[I]]

  def coords: data.Vector[A]

  def resetCoords(v: data.Vector[A]): NArray[I, A] =
    if (coords.dim == v.dim) {
      new NArray[I, A] {
        val dims = self.dims
        val coords = v

malzzz

import org.deeplearning4j.nn.api.OptimizationAlgorithm
import org.deeplearning4j.nn.conf._
import org.deeplearning4j.nn.conf.graph.MergeVertex
import org.deeplearning4j.nn.conf.layers._
import org.deeplearning4j.nn.weights.WeightInit
import org.nd4j.linalg.lossfunctions.LossFunctions

/**
  * GoogLeNet: https://arxiv.org/abs/1409.4842
  */

malzzz

from tensorflow.models.embedding.word2vec_optimized import FLAGS, Options, Word2Vec

FLAGS1 = FLAGS
FLAGS2 = FLAGS

class MyOptions(Options):
  def __init__(self):
    super(MyOptions, self).__init__()
    self.train_data = FLAGS1.train_data = "new_train_data"