corochann.com Report : Visit Site


  • Ranking Alexa Global: # 404,704,Alexa Ranking in Japan is # 64,761

    Server:Apache...
    X-Powered-By:PHP/5.4.45

    The main IP address: 157.7.107.87,Your server Japan,Tokyo ISP:GMO Pepabo Inc.  TLD:com CountryCode:JP

    The description :deep learning, machine learning, android etc....

    This report updates in 01-Sep-2018

Created Date:2015-07-12
Changed Date:2016-06-27

Technical data of the corochann.com


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Latitude: 35.689506530762
Longitude: 139.69169616699
Country: Japan (JP)
City: Tokyo
Region: Tokyo
ISP: GMO Pepabo Inc.

HTTP Header Analysis


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Content-Length:15575
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Server:Apache
Connection:keep-alive
Link:; rel="https://api.w.org/"
Date:Sat, 01 Sep 2018 15:47:51 GMT
Content-Type:text/html; charset=UTF-8

DNS

soa:uns01.lolipop.jp. admin.madame.jp. 1436746894 3600 1200 1209600 600
txt:"v=spf1 include:_spf.lolipop.jp ~all"
ns:uns02.lolipop.jp.
uns01.lolipop.jp.
ipv4:IP:157.7.107.87
ASN:7506
OWNER:INTERQ GMO Internet,Inc, JP
Country:JP
mx:MX preference = 10, mail exchanger = mx01.lolipop.jp.

HtmlToText

deep learning, machine learning, android etc. menu skip to content home about deep learning tutorial with chainer android tv app tutorial projects apps hayya ‘alas salah seikeidenron for android tv theanonsr c/c++ single file execution plugin contact search for: define your own trainer extensions in chainer posted on august 28, 2017 updated on august 28, 2017 by corochann · leave a comment so how to implement custom extensions for trainer in chainer? there are mainly 3 approaches. define function use decorator, @chainer.training.extension.make_extension define class most of the case, 1. define function is the easiest way to quickly implement your extension. 1. define function just a function can be a trainer extension. simply, define a function […] continue reading → predict code for penn bank tree (ptb) dataset posted on august 17, 2017 updated on august 17, 2017 by corochann · leave a comment predict code is pretty much the same with predict code for simple sequence dataset, so i won’t explain in detail. code the code is on the github, predict_ptb.py. python """inference/predict code for simple_sequence dataset model must be trained before inference, train_simple_sequence.py must be executed beforehand. """ from __future__ import print_function import argparse import os import sys import matplotlib import numpy as np matplotlib.use('agg') import matplotlib.pyplot as plt import chainer import chainer.functions as f import chainer.links as l from chainer import training, iterators, serializers, optimizers, variable, cuda from chainer.training import extensions sys.path.append(os.pardir) from rnn import rnn from rnn2 import rnn2 from rnn3 import rnn3 from rnnforlm import rnnforlm def main(): archs = { 'rnn': rnn, 'rnn2': rnn2, 'rnn3': rnn3, 'lstm': rnnforlm } parser = argparse.argumentparser(description='simple_sequence rnn predict code') parser.add_argument('--arch', '-a', choices=archs.keys(), default='rnn', help='net architecture') #parser.add_argument('--batchsize', '-b', type=int, default=64, # help='number of images in each mini-batch') parser.add_argument('--unit', '-u', type=int, default=100, help='number of lstm units in each layer') parser.add_argument('--gpu', '-g', type=int, default=-1, help='gpu id (negative value indicates cpu)') parser.add_argument('--primeindex', '-p', type=int, default=1, help='base index data, used for sequence generation') parser.add_argument('--length', '-l', type=int, default=100, help='length of the generated sequence') parser.add_argument('--modelpath', '-m', default='', help='model path to be loaded') args = parser.parse_args() print('gpu: {}'.format(args.gpu)) #print('# minibatch-size: {}'.format(args.batchsize)) print('') train, val, test = chainer.datasets.get_ptb_words() n_vocab = max(train) + 1 # train is just an array of integers print('#vocab =', n_vocab) print('') # load vocabulary ptb_word_id_dict = chainer.datasets.get_ptb_words_vocabulary() ptb_id_word_dict = dict((v, k) for k, v in ptb_word_id_dict.items()) # model setup model = archs[args.arch](n_vocab=n_vocab, n_units=args.unit) classifier_model = l.classifier(model) if args.gpu >= 0: chainer.cuda.get_device(args.gpu).use() # make a specified gpu current classifier_model.to_gpu() # copy the model to the gpu xp = np if args.gpu < 0 else cuda.cupy if args.modelpath: serializers.load_npz(args.modelpath, model) else: serializers.load_npz('result/{}_ptb.model'.format(args.arch), model) # dataset preparation prev_index = args.primeindex # predict predicted_sequence = [prev_index] for i in range(args.length): prev = chainer.variable(xp.array([prev_index], dtype=xp.int32)) current = model(prev) current_index = np.argmax(cuda.to_cpu(current.data)) predicted_sequence.append(current_index) prev_index = current_index predicted_text_list = [ptb_id_word_dict[i] for i in predicted_sequence] print('predicted sequence: ', predicted_sequence) print('predicted text: ', ' '.join(predicted_text_list)) if __name__ == '__main__': main() 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 """inference/predict code for simple_sequence dataset model must be trained before inference, train_simple_sequence.py must be executed beforehand. """ from __future__ import print_function import argparse import os import sys import matplotlib import numpy as np matplotlib . use ( 'agg' ) import matplotlib . pyplot as plt import chainer import chainer . functions as f import chainer . links as l from chainer import training , iterators , serializers , optimizers , variable , cuda from chainer . training import extensions sys . path . append ( os . pardir ) from rnn import rnn from rnn2 import rnn2 from rnn3 import rnn3 from rnnforlm import rnnforlm def main ( ) : archs = { 'rnn' : rnn , 'rnn2' : rnn2 , 'rnn3' : rnn3 , 'lstm' : rnnforlm } parser = argparse . argumentparser ( description = 'simple_sequence rnn predict code' ) parser . add_argument ( '--arch' , '-a' , choices = archs . keys ( ) , default = 'rnn' , help = 'net architecture' ) #parser.add_argument('--batchsize', '-b', type=int, default=64, # help='number of images in each mini-batch') parser . add_argument ( '--unit' , '-u' , type = int , default = 100 , help = 'number of lstm units in each layer' ) parser . add_argument ( '--gpu' , '-g' , type = int , default = - 1 , help = 'gpu id (negative value indicates cpu)' ) parser . add_argument ( '--primeindex' , '-p' , type = int , default = 1 , help = 'base index data, used for sequence generation' ) parser . add_argument ( '--length' , '-l' , type = int , default = 100 , help = 'length of the generated sequence' ) parser . add_argument ( '--modelpath' , '-m' , default = '' , help = 'model path to be loaded' ) args = parser . parse_args ( ) print ( 'gpu: {}' . format ( args . gpu ) ) #print('# minibatch-size: {}'.format(args.batchsize)) print ( '' ) train , val , test = chainer . datasets . get_ptb_words ( ) n_vocab = max ( train ) + 1 # train is just an array of integers print ( '#vocab =' , n_vocab ) print ( '' ) # load vocabulary ptb_word_id_dict = chainer . datasets . get_ptb_words_vocabulary ( ) ptb_id_word_dict = dict ( ( v , k ) for k , v in ptb_word_id_dict . items ( ) ) # model setup model = archs [ args . arch ] ( n_vocab = n_vocab , n_units = args . unit ) classifier_model = l . classifier ( model ) if args . gpu >= 0 : chainer . cuda . get_device ( args . gpu ) . use ( ) # make a specified gpu current classifier_model . to_gpu ( ) # copy the model to the gpu xp = np if args . gpu < 0 else cuda . cupy if args . modelpath : serializers . load_npz ( args . modelpath , model ) else : serializers . load_npz ( 'result/{}_ptb.model' . format ( args . arch ) , model ) # dataset preparation prev_index = args . primeindex # predict predicted_sequence = [ prev_index ] for i in range ( args . length ) : prev = chainer . variable ( xp . array ( [ prev_index ] , dtype = xp . int32 ) ) current = model ( prev ) current_index = np . argmax ( cuda . to_cpu ( current . data ) ) predicted_sequence . append ( current_index ) prev_index = current_index predicted_text_list = [ ptb_id_word_dict [ i ] for i in predicted_sequence ] print ( 'predicted sequence: ' , predicted_sequence ) print ( 'predicted text: ' , ' ' . join ( predicted_text_list ) ) if __name__ == '__main__' : main ( ) given the first text by the index, args.primeindex, model will predict the following sequence as word id. the last three line converts the […] continue reading → training lstm model with penn bank tree (ptb) dataset posted on august 16, 2017 updated on august 16, 2017 by corochann · leave a comment this post mainly explains train_ptb.py, uploaded on github. we have already learned rnn and lstm network architecture, let’s apply it to ptb dataset. it is quite similar to

URL analysis for corochann.com


http://corochann.com/category/app
http://corochann.com/predict-code-for-penn-bank-tree-ptb-dataset-1466.html#respond
http://corochann.com/category/wordpress
http://corochann.com/training-lstm-model-with-penn-bank-tree-ptb-dataset-1454.html
http://corochann.com/setup-python-environment-1395.html#respond
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http://corochann.com/date/2017/04
http://corochann.com/date/2017/05
http://corochann.com/category/python
http://corochann.com/penn-tree-bank-ptb-dataset-introduction-1456.html#respond
http://corochann.com/projects/apps/hayya-alas-salah
http://corochann.com/penn-tree-bank-ptb-dataset-introduction-1456.html

Whois Information


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Domain Name: COROCHANN.COM
Registry Domain ID: 1946365550_DOMAIN_COM-VRSN
Registrar WHOIS Server: whois.godaddy.com
Registrar URL: http://www.godaddy.com
Updated Date: 2016-06-27T22:48:42Z
Creation Date: 2015-07-12T07:09:00Z
Registry Expiry Date: 2018-07-12T07:09:00Z
Registrar: GoDaddy.com, LLC
Registrar IANA ID: 146
Registrar Abuse Contact Email: [email protected]
Registrar Abuse Contact Phone: 480-624-2505
Domain Status: clientDeleteProhibited https://icann.org/epp#clientDeleteProhibited
Domain Status: clientRenewProhibited https://icann.org/epp#clientRenewProhibited
Domain Status: clientTransferProhibited https://icann.org/epp#clientTransferProhibited
Domain Status: clientUpdateProhibited https://icann.org/epp#clientUpdateProhibited
Name Server: UNS01.LOLIPOP.JP
Name Server: UNS02.LOLIPOP.JP
DNSSEC: unsigned
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  REGISTRAR GoDaddy.com, LLC

SERVERS

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  ARGS domain =corochann.com

  PORT 43

  TYPE domain

DOMAIN

  NAME corochann.com

  CHANGED 2016-06-27

  CREATED 2015-07-12

STATUS
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clientTransferProhibited https://icann.org/epp#clientTransferProhibited
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