rasoolims

2020-04-07 14:56:15.927070 Epoch Step: 50 Loss: 10.338213 Tokens per Sec: 3368.013480
2020-04-07 14:57:20.718592 Epoch Step: 100 Loss: 10.167365 Tokens per Sec: 7114.368865
2020-04-07 14:58:23.048797 Epoch Step: 150 Loss: 10.049715 Tokens per Sec: 7365.751670
2020-04-07 14:59:27.724888 Epoch Step: 200 Loss: 10.009823 Tokens per Sec: 7001.795763
2020-04-07 15:00:33.312455 Epoch Step: 250 Loss: 10.029766 Tokens per Sec: 7038.528239
2020-04-07 15:01:37.124237 Epoch Step: 300 Loss: 9.958084 Tokens per Sec: 7132.142970
2020-04-07 15:02:41.631392 Epoch Step: 350 Loss: 10.029122 Tokens per Sec: 7037.992992
2020-04-07 15:03:47.031098 Epoch Step: 400 Loss: 10.011264 Tokens per Sec: 6991.174433
2020-04-07 15:04:50.035835 Epoch Step: 450 Loss: 9.939527 Tokens per Sec: 7247.814263
2020-04-07 15:05:54.265058 Epoch Step: 500 Loss: 10.037009 Tokens per Sec: 7080.542676

rasoolims

class avg_perceptron:
	def __init__(self,model_path='',label_path=''):
		self.avg_weights=self.load_model(model_path)
		self.labels=self.load_labels(label_path)
		self.weights=defaultdict(int)
		self.iteration=1

	def update_weight(self,feature,update):
		self.weights[feature]+=update
		self.avg_weights[feature]+=iteration*update

rasoolims

import dynet as dynet
import random
import matplotlib.pyplot as plt
import numpy as np

class Network:
    def __init__(self, vocab, properties):
        self.properties = properties
        self.vocab = vocab

rasoolims

from dynet import *
from depModel import DepModel
import random, time
import numpy as np

init()


class NNModel(DepModel):
    def __init__(self, words, pos_tags, labels, options):

rasoolims

import re,os,sys,codecs,traceback
from xml.dom import minidom

'''
	The input folder is the folder with Bible xml files from https://github.com/christos-c/bible-corpus/
	The output folder contains aligned files for all pairs of languages (src.target)
	It merges English and English-Web files.
'''
if len(sys.argv)<3:
	print 'input_folder output_folder'

rasoolims

arr = [1,2,3,4,5,6,8,9,10,12,13,15]

def binary_search(a, v):
    s = 0
    e = len(a) - 1
    mid = (s+e)/2
    
    while s <= e:
        if a[mid] == v:
            return mid

rasoolims

import time, random

def partition(myList, start, end):
    pivot = myList[start]
    left = start+1
    right = end
    done = False
    
    while not done:
        while left <= right and myList[left] <= pivot:

rasoolims

import time

def merge (a, b):
	p1 = 0
	p2 = 0
	
	res = list()
	while p1<len(a) and p2<len(b):
		if a[p1]<b[p2]:
			res.append(a[p1])

rasoolims

    private String trainWithAdam(List<String> trainSentencesInCONLLFormat,
                           IndexMap indexMap,  HashSet<String> labelSet,
                                 int numberOfTrainingIterations,
                           String modelDir, int numOfFeatures, String taskType, int batchSize)
            throws Exception {
        System.out.println("Training for task "+taskType);
        DecimalFormat format = new DecimalFormat("##.00");

rasoolims

package ml;

import java.io.*;
import java.text.SimpleDateFormat;
import java.util.*;
import java.util.zip.GZIPInputStream;
import java.util.zip.GZIPOutputStream;

/**
 * Created by Mohammad Sadegh Rasooli.