snakers4

class BertEmbeddingBag(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings.
    """
    def __init__(self, config):
        super(BertEmbeddingBag, self).__init__()

        # self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
        ngram_matrix=np.load(config.ngram_matrix_path)

        self.old_bag = config.old_bag

asanakoy

#Proxy list graper
# https://github.com/abdallahelsokary/Proxy-Collector-/blob/master/Proxy_Collector.py
import urllib.request
import urllib.error
import time

def proxy_list():
    try:

zeyademam

Troubleshooting Convolutional Neural Networks

Intro

This is a list of hacks gathered primarily from prior experiences as well as online sources (most notably Stanford's CS231n course notes) on how to troubleshoot the performance of a convolutional neural network . We will focus mainly on supervised learning using deep neural networks. While this guide assumes the user is coding in Python3.6 using tensorflow (TF), it can still be helpful as a language agnostic guide.

Suppose we are given a convolutional neural network to train and evaluate and assume the evaluation results are worse than expected. The following are steps to troubleshoot and potentially improve performance. The first section corresponds to must-do's and generally good practices before you start troubleshooting. Every subsequent section header corresponds to a problem and the section is devoted to solving it. The sections are ordered to reflect "more common" issues first and under each header the "most-eas

rzykov

import _root_.ml.dmlc.xgboost4j.scala.spark.XGBoost
import org.apache.spark.ml.feature.LabeledPoint

def encodeFeaturesToLabeledPoint(features: RDD[Feature], relevance: Option[RDD[Relevance]], workers: Int)
    (implicit parallel: Int): (RDD[LabeledPoint], Seq[String], Seq[Seq[Int]]) = {

    val missingValue = Double.NaN

    val names = features
      .map { _.name }