Classifying Car Data By Year The 2019 Stack Overflow Developer Survey Results Are In Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsClassifying Java exceptionsClassifying survey response text SVMClassifying Email in RPredicting car failures with machine learningClassifying / labeling polygonal meshesClassifying with certaintyClassifying time series data that overlapValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)Training Accuracy stuck in Keras“10-year-challenge” data for age algorithms?

Can the Right Ascension and Argument of Perigee of a spacecraft's orbit keep varying by themselves with time?

Do warforged have souls?

Can each chord in a progression create its own key?

How to politely respond to generic emails requesting a PhD/job in my lab? Without wasting too much time

Variable with quotation marks "$()"

The following signatures were invalid: EXPKEYSIG 1397BC53640DB551

What force causes entropy to increase?

How do spell lists change if the party levels up without taking a long rest?

Drawing vertical/oblique lines in Metrical tree (tikz-qtree, tipa)

What can I do if neighbor is blocking my solar panels intentionally?

Why don't hard Brexiteers insist on a hard border to prevent illegal immigration after Brexit?

How to make Illustrator type tool selection automatically adapt with text length

should truth entail possible truth

How do I design a circuit to convert a 100 mV and 50 Hz sine wave to a square wave?

Button changing its text & action. Good or terrible?

Am I ethically obligated to go into work on an off day if the reason is sudden?

How did the crowd guess the pentatonic scale in Bobby McFerrin's presentation?

What happens to a Warlock's expended Spell Slots when they gain a Level?

Does Parliament need to approve the new Brexit delay to 31 October 2019?

Why can't wing-mounted spoilers be used to steepen approaches?

Store Dynamic-accessible hidden metadata in a cell

Working through the single responsibility principle (SRP) in Python when calls are expensive

Is it ok to offer lower paid work as a trial period before negotiating for a full-time job?

Can I visit the Trinity College (Cambridge) library and see some of their rare books



Classifying Car Data By Year



The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsClassifying Java exceptionsClassifying survey response text SVMClassifying Email in RPredicting car failures with machine learningClassifying / labeling polygonal meshesClassifying with certaintyClassifying time series data that overlapValueError: Error when checking target: expected dense_2 to have shape (1,) but got array with shape (0,)Training Accuracy stuck in Keras“10-year-challenge” data for age algorithms?










3












$begingroup$


I have huge car photos.



I want to predict car's "brand-model-body type and production year"



data



First, I splitted data into train and validation, and I categorized them like this.



categorized_cars



Every category has about 1000 train and 900 validation images.



My plan was: I train my keras model with these categories after training, model can predict labels like below:



audi a3 sedan 2008 => %25



audi a3 sedan 2009 => %25



audi a3 sedan 2010 => %25



audi a3 sedan 2011 => %25



And I can tell user that: "This car is Audi A3 Sedan 2008-2011"



My problem is, some of these categories have very similar photos. For example: audi a3 2009 and audi a3 2010 have same body type and there is not much difference between photos (No difference in reality).
Because of that, train accuracy has improved to about 0.9 but validation accuracy hasn't improved above 0.55



When I try some predictions, it usually gives same label, "Ford Focus sedan 2009" :)



Here is my output:



epoch, acc, loss, val_acc, val_loss
27, 0.7965514530544776, 0.56618134500483, 0.5192149643316993, 1.729015349846447

28, 0.8058803490480816, 0.5408204138258657, 0.5176764522193236, 1.778763979018732

29, 0.8167710489770164, 0.5116128672937693, 0.523258489762041, 1.7806432932022545

30, 0.8256544639818643, 0.4872381848016096, 0.5207534764479939, 1.8059904007678271

31, 0.8355546238309248, 0.4629556378035959, 0.5237253032663666, 1.8191414148756815

32, 0.8424464767701014, 0.4444190686917562, 0.5242512903147193, 1.8496954914466912

33, 0.8508739288802705, 0.422022156655134, 0.5303593149032422, 1.8565427863780883

34, 0.8576819265745635, 0.40545297008116027, 0.5262894901236571, 1.909881308499735


My train code is here:



Image_width, Image_height = 224, 224
num_epoch = 5000
batch_size = 16
learning_rate = 0.0001
model = ResNet50(weights='imagenet', include_top=False, input_shape=(Image_width, Image_height, 3))
fc_neuron_count = 1024
output = model.output
output = GlobalAveragePooling2D()(output)
output = Dense(fc_neuron_count, activation='relu')(output)
predictions = Dense(num_classes, activation='softmax')(output)
model = Model(inputs=model.input, outputs=predictions)

model.compile(optimizer=opt.Adam(lr=learning_rate), loss=losses.categorical_crossentropy,
metrics=['accuracy'])

history_transfer_learning = model.fit_generator(
train_generator,
epochs=num_epoch,
steps_per_epoch=num_train_samples // batch_size,
validation_data=validation_generator,
validation_steps=num_validate_samples // batch_size,
class_weight='auto',
callbacks=callbacks_list)


  • Am I doing something wrong? How can I achieve this result?


  • Should I change validation accuracy calculation, or should I give more photos per category?










share|improve this question











$endgroup$




bumped to the homepage by Community 44 mins ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.



















    3












    $begingroup$


    I have huge car photos.



    I want to predict car's "brand-model-body type and production year"



    data



    First, I splitted data into train and validation, and I categorized them like this.



    categorized_cars



    Every category has about 1000 train and 900 validation images.



    My plan was: I train my keras model with these categories after training, model can predict labels like below:



    audi a3 sedan 2008 => %25



    audi a3 sedan 2009 => %25



    audi a3 sedan 2010 => %25



    audi a3 sedan 2011 => %25



    And I can tell user that: "This car is Audi A3 Sedan 2008-2011"



    My problem is, some of these categories have very similar photos. For example: audi a3 2009 and audi a3 2010 have same body type and there is not much difference between photos (No difference in reality).
    Because of that, train accuracy has improved to about 0.9 but validation accuracy hasn't improved above 0.55



    When I try some predictions, it usually gives same label, "Ford Focus sedan 2009" :)



    Here is my output:



    epoch, acc, loss, val_acc, val_loss
    27, 0.7965514530544776, 0.56618134500483, 0.5192149643316993, 1.729015349846447

    28, 0.8058803490480816, 0.5408204138258657, 0.5176764522193236, 1.778763979018732

    29, 0.8167710489770164, 0.5116128672937693, 0.523258489762041, 1.7806432932022545

    30, 0.8256544639818643, 0.4872381848016096, 0.5207534764479939, 1.8059904007678271

    31, 0.8355546238309248, 0.4629556378035959, 0.5237253032663666, 1.8191414148756815

    32, 0.8424464767701014, 0.4444190686917562, 0.5242512903147193, 1.8496954914466912

    33, 0.8508739288802705, 0.422022156655134, 0.5303593149032422, 1.8565427863780883

    34, 0.8576819265745635, 0.40545297008116027, 0.5262894901236571, 1.909881308499735


    My train code is here:



    Image_width, Image_height = 224, 224
    num_epoch = 5000
    batch_size = 16
    learning_rate = 0.0001
    model = ResNet50(weights='imagenet', include_top=False, input_shape=(Image_width, Image_height, 3))
    fc_neuron_count = 1024
    output = model.output
    output = GlobalAveragePooling2D()(output)
    output = Dense(fc_neuron_count, activation='relu')(output)
    predictions = Dense(num_classes, activation='softmax')(output)
    model = Model(inputs=model.input, outputs=predictions)

    model.compile(optimizer=opt.Adam(lr=learning_rate), loss=losses.categorical_crossentropy,
    metrics=['accuracy'])

    history_transfer_learning = model.fit_generator(
    train_generator,
    epochs=num_epoch,
    steps_per_epoch=num_train_samples // batch_size,
    validation_data=validation_generator,
    validation_steps=num_validate_samples // batch_size,
    class_weight='auto',
    callbacks=callbacks_list)


    • Am I doing something wrong? How can I achieve this result?


    • Should I change validation accuracy calculation, or should I give more photos per category?










    share|improve this question











    $endgroup$




    bumped to the homepage by Community 44 mins ago


    This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

















      3












      3








      3





      $begingroup$


      I have huge car photos.



      I want to predict car's "brand-model-body type and production year"



      data



      First, I splitted data into train and validation, and I categorized them like this.



      categorized_cars



      Every category has about 1000 train and 900 validation images.



      My plan was: I train my keras model with these categories after training, model can predict labels like below:



      audi a3 sedan 2008 => %25



      audi a3 sedan 2009 => %25



      audi a3 sedan 2010 => %25



      audi a3 sedan 2011 => %25



      And I can tell user that: "This car is Audi A3 Sedan 2008-2011"



      My problem is, some of these categories have very similar photos. For example: audi a3 2009 and audi a3 2010 have same body type and there is not much difference between photos (No difference in reality).
      Because of that, train accuracy has improved to about 0.9 but validation accuracy hasn't improved above 0.55



      When I try some predictions, it usually gives same label, "Ford Focus sedan 2009" :)



      Here is my output:



      epoch, acc, loss, val_acc, val_loss
      27, 0.7965514530544776, 0.56618134500483, 0.5192149643316993, 1.729015349846447

      28, 0.8058803490480816, 0.5408204138258657, 0.5176764522193236, 1.778763979018732

      29, 0.8167710489770164, 0.5116128672937693, 0.523258489762041, 1.7806432932022545

      30, 0.8256544639818643, 0.4872381848016096, 0.5207534764479939, 1.8059904007678271

      31, 0.8355546238309248, 0.4629556378035959, 0.5237253032663666, 1.8191414148756815

      32, 0.8424464767701014, 0.4444190686917562, 0.5242512903147193, 1.8496954914466912

      33, 0.8508739288802705, 0.422022156655134, 0.5303593149032422, 1.8565427863780883

      34, 0.8576819265745635, 0.40545297008116027, 0.5262894901236571, 1.909881308499735


      My train code is here:



      Image_width, Image_height = 224, 224
      num_epoch = 5000
      batch_size = 16
      learning_rate = 0.0001
      model = ResNet50(weights='imagenet', include_top=False, input_shape=(Image_width, Image_height, 3))
      fc_neuron_count = 1024
      output = model.output
      output = GlobalAveragePooling2D()(output)
      output = Dense(fc_neuron_count, activation='relu')(output)
      predictions = Dense(num_classes, activation='softmax')(output)
      model = Model(inputs=model.input, outputs=predictions)

      model.compile(optimizer=opt.Adam(lr=learning_rate), loss=losses.categorical_crossentropy,
      metrics=['accuracy'])

      history_transfer_learning = model.fit_generator(
      train_generator,
      epochs=num_epoch,
      steps_per_epoch=num_train_samples // batch_size,
      validation_data=validation_generator,
      validation_steps=num_validate_samples // batch_size,
      class_weight='auto',
      callbacks=callbacks_list)


      • Am I doing something wrong? How can I achieve this result?


      • Should I change validation accuracy calculation, or should I give more photos per category?










      share|improve this question











      $endgroup$




      I have huge car photos.



      I want to predict car's "brand-model-body type and production year"



      data



      First, I splitted data into train and validation, and I categorized them like this.



      categorized_cars



      Every category has about 1000 train and 900 validation images.



      My plan was: I train my keras model with these categories after training, model can predict labels like below:



      audi a3 sedan 2008 => %25



      audi a3 sedan 2009 => %25



      audi a3 sedan 2010 => %25



      audi a3 sedan 2011 => %25



      And I can tell user that: "This car is Audi A3 Sedan 2008-2011"



      My problem is, some of these categories have very similar photos. For example: audi a3 2009 and audi a3 2010 have same body type and there is not much difference between photos (No difference in reality).
      Because of that, train accuracy has improved to about 0.9 but validation accuracy hasn't improved above 0.55



      When I try some predictions, it usually gives same label, "Ford Focus sedan 2009" :)



      Here is my output:



      epoch, acc, loss, val_acc, val_loss
      27, 0.7965514530544776, 0.56618134500483, 0.5192149643316993, 1.729015349846447

      28, 0.8058803490480816, 0.5408204138258657, 0.5176764522193236, 1.778763979018732

      29, 0.8167710489770164, 0.5116128672937693, 0.523258489762041, 1.7806432932022545

      30, 0.8256544639818643, 0.4872381848016096, 0.5207534764479939, 1.8059904007678271

      31, 0.8355546238309248, 0.4629556378035959, 0.5237253032663666, 1.8191414148756815

      32, 0.8424464767701014, 0.4444190686917562, 0.5242512903147193, 1.8496954914466912

      33, 0.8508739288802705, 0.422022156655134, 0.5303593149032422, 1.8565427863780883

      34, 0.8576819265745635, 0.40545297008116027, 0.5262894901236571, 1.909881308499735


      My train code is here:



      Image_width, Image_height = 224, 224
      num_epoch = 5000
      batch_size = 16
      learning_rate = 0.0001
      model = ResNet50(weights='imagenet', include_top=False, input_shape=(Image_width, Image_height, 3))
      fc_neuron_count = 1024
      output = model.output
      output = GlobalAveragePooling2D()(output)
      output = Dense(fc_neuron_count, activation='relu')(output)
      predictions = Dense(num_classes, activation='softmax')(output)
      model = Model(inputs=model.input, outputs=predictions)

      model.compile(optimizer=opt.Adam(lr=learning_rate), loss=losses.categorical_crossentropy,
      metrics=['accuracy'])

      history_transfer_learning = model.fit_generator(
      train_generator,
      epochs=num_epoch,
      steps_per_epoch=num_train_samples // batch_size,
      validation_data=validation_generator,
      validation_steps=num_validate_samples // batch_size,
      class_weight='auto',
      callbacks=callbacks_list)


      • Am I doing something wrong? How can I achieve this result?


      • Should I change validation accuracy calculation, or should I give more photos per category?







      machine-learning keras computer-vision






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Sep 10 '18 at 11:56









      ebrahimi

      75521022




      75521022










      asked Sep 10 '18 at 11:28









      ibrahimozgonibrahimozgon

      1212




      1212





      bumped to the homepage by Community 44 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







      bumped to the homepage by Community 44 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.






















          1 Answer
          1






          active

          oldest

          votes


















          0












          $begingroup$

          I want to say how I solved my problem for anyone who is looking for a similar question.



          My categorization was a mistake. I realized later that, I gave the same photos to my model and waited for the different results. For example, I had nearly same photos in Audi A3 Hatchback/5 2009 and Audi A3 Hatchback/5 2010. When the model starts training, first it learns data. Then it predicts and validates output itself. If the output is wrong, it tries a different way to success. But wait a minute, there was no mistake. I gave you the same photos and waited for different results! My categorization failed here.



          I categorized my cars by body changes like Audi A3 Hatchback 2008-2013. Except for categories that have the wrong photos, my results are great for now.



          Now, we will work on better photos and better year categorization.






          share|improve this answer









          $endgroup$













            Your Answer








            StackExchange.ready(function()
            var channelOptions =
            tags: "".split(" "),
            id: "557"
            ;
            initTagRenderer("".split(" "), "".split(" "), channelOptions);

            StackExchange.using("externalEditor", function()
            // Have to fire editor after snippets, if snippets enabled
            if (StackExchange.settings.snippets.snippetsEnabled)
            StackExchange.using("snippets", function()
            createEditor();
            );

            else
            createEditor();

            );

            function createEditor()
            StackExchange.prepareEditor(
            heartbeatType: 'answer',
            autoActivateHeartbeat: false,
            convertImagesToLinks: false,
            noModals: true,
            showLowRepImageUploadWarning: true,
            reputationToPostImages: null,
            bindNavPrevention: true,
            postfix: "",
            imageUploader:
            brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
            contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
            allowUrls: true
            ,
            onDemand: true,
            discardSelector: ".discard-answer"
            ,immediatelyShowMarkdownHelp:true
            );



            );













            draft saved

            draft discarded


















            StackExchange.ready(
            function ()
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f38042%2fclassifying-car-data-by-year%23new-answer', 'question_page');

            );

            Post as a guest















            Required, but never shown

























            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            0












            $begingroup$

            I want to say how I solved my problem for anyone who is looking for a similar question.



            My categorization was a mistake. I realized later that, I gave the same photos to my model and waited for the different results. For example, I had nearly same photos in Audi A3 Hatchback/5 2009 and Audi A3 Hatchback/5 2010. When the model starts training, first it learns data. Then it predicts and validates output itself. If the output is wrong, it tries a different way to success. But wait a minute, there was no mistake. I gave you the same photos and waited for different results! My categorization failed here.



            I categorized my cars by body changes like Audi A3 Hatchback 2008-2013. Except for categories that have the wrong photos, my results are great for now.



            Now, we will work on better photos and better year categorization.






            share|improve this answer









            $endgroup$

















              0












              $begingroup$

              I want to say how I solved my problem for anyone who is looking for a similar question.



              My categorization was a mistake. I realized later that, I gave the same photos to my model and waited for the different results. For example, I had nearly same photos in Audi A3 Hatchback/5 2009 and Audi A3 Hatchback/5 2010. When the model starts training, first it learns data. Then it predicts and validates output itself. If the output is wrong, it tries a different way to success. But wait a minute, there was no mistake. I gave you the same photos and waited for different results! My categorization failed here.



              I categorized my cars by body changes like Audi A3 Hatchback 2008-2013. Except for categories that have the wrong photos, my results are great for now.



              Now, we will work on better photos and better year categorization.






              share|improve this answer









              $endgroup$















                0












                0








                0





                $begingroup$

                I want to say how I solved my problem for anyone who is looking for a similar question.



                My categorization was a mistake. I realized later that, I gave the same photos to my model and waited for the different results. For example, I had nearly same photos in Audi A3 Hatchback/5 2009 and Audi A3 Hatchback/5 2010. When the model starts training, first it learns data. Then it predicts and validates output itself. If the output is wrong, it tries a different way to success. But wait a minute, there was no mistake. I gave you the same photos and waited for different results! My categorization failed here.



                I categorized my cars by body changes like Audi A3 Hatchback 2008-2013. Except for categories that have the wrong photos, my results are great for now.



                Now, we will work on better photos and better year categorization.






                share|improve this answer









                $endgroup$



                I want to say how I solved my problem for anyone who is looking for a similar question.



                My categorization was a mistake. I realized later that, I gave the same photos to my model and waited for the different results. For example, I had nearly same photos in Audi A3 Hatchback/5 2009 and Audi A3 Hatchback/5 2010. When the model starts training, first it learns data. Then it predicts and validates output itself. If the output is wrong, it tries a different way to success. But wait a minute, there was no mistake. I gave you the same photos and waited for different results! My categorization failed here.



                I categorized my cars by body changes like Audi A3 Hatchback 2008-2013. Except for categories that have the wrong photos, my results are great for now.



                Now, we will work on better photos and better year categorization.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Oct 14 '18 at 17:25









                ibrahimozgonibrahimozgon

                1212




                1212



























                    draft saved

                    draft discarded
















































                    Thanks for contributing an answer to Data Science Stack Exchange!


                    • Please be sure to answer the question. Provide details and share your research!

                    But avoid


                    • Asking for help, clarification, or responding to other answers.

                    • Making statements based on opinion; back them up with references or personal experience.

                    Use MathJax to format equations. MathJax reference.


                    To learn more, see our tips on writing great answers.




                    draft saved


                    draft discarded














                    StackExchange.ready(
                    function ()
                    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f38042%2fclassifying-car-data-by-year%23new-answer', 'question_page');

                    );

                    Post as a guest















                    Required, but never shown





















































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown

































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown







                    Popular posts from this blog

                    Францішак Багушэвіч Змест Сям'я | Біяграфія | Творчасць | Мова Багушэвіча | Ацэнкі дзейнасці | Цікавыя факты | Спадчына | Выбраная бібліяграфія | Ушанаванне памяці | У філатэліі | Зноскі | Літаратура | Спасылкі | НавігацыяЛяхоўскі У. Рупіўся дзеля Бога і людзей: Жыццёвы шлях Лявона Вітан-Дубейкаўскага // Вольскі і Памідораў з песняй пра немца Адвакат, паэт, народны заступнік Ашмянскі веснікВ Минске появится площадь Богушевича и улица Сырокомли, Белорусская деловая газета, 19 июля 2001 г.Айцец беларускай нацыянальнай ідэі паўстаў у бронзе Сяргей Аляксандравіч Адашкевіч (1918, Мінск). 80-я гады. Бюст «Францішак Багушэвіч».Яўген Мікалаевіч Ціхановіч. «Партрэт Францішка Багушэвіча»Мікола Мікалаевіч Купава. «Партрэт зачынальніка новай беларускай літаратуры Францішка Багушэвіча»Уладзімір Іванавіч Мелехаў. На помніку «Змагарам за родную мову» Барэльеф «Францішак Багушэвіч»Памяць пра Багушэвіча на Віленшчыне Страчаная сталіца. Беларускія шыльды на вуліцах Вільні«Krynica». Ideologia i przywódcy białoruskiego katolicyzmuФранцішак БагушэвічТворы на knihi.comТворы Францішка Багушэвіча на bellib.byСодаль Уладзімір. Францішак Багушэвіч на Лідчыне;Луцкевіч Антон. Жыцьцё і творчасьць Фр. Багушэвіча ў успамінах ягоных сучасьнікаў // Запісы Беларускага Навуковага таварыства. Вільня, 1938. Сшытак 1. С. 16-34.Большая российская1188761710000 0000 5537 633Xn9209310021619551927869394п

                    Беларусь Змест Назва Гісторыя Геаграфія Сімволіка Дзяржаўны лад Палітычныя партыі Міжнароднае становішча і знешняя палітыка Адміністрацыйны падзел Насельніцтва Эканоміка Культура і грамадства Сацыяльная сфера Узброеныя сілы Заўвагі Літаратура Спасылкі НавігацыяHGЯOiТоп-2011 г. (па версіі ej.by)Топ-2013 г. (па версіі ej.by)Топ-2016 г. (па версіі ej.by)Топ-2017 г. (па версіі ej.by)Нацыянальны статыстычны камітэт Рэспублікі БеларусьШчыльнасць насельніцтва па краінахhttp://naviny.by/rubrics/society/2011/09/16/ic_articles_116_175144/А. Калечыц, У. Ксяндзоў. Спробы засялення краю неандэртальскім чалавекам.І ў Менску былі мамантыА. Калечыц, У. Ксяндзоў. Старажытны каменны век (палеаліт). Першапачатковае засяленне тэрыторыіГ. Штыхаў. Балты і славяне ў VI—VIII стст.М. Клімаў. Полацкае княства ў IX—XI стст.Г. Штыхаў, В. Ляўко. Палітычная гісторыя Полацкай зямліГ. Штыхаў. Дзяржаўны лад у землях-княствахГ. Штыхаў. Дзяржаўны лад у землях-княствахБеларускія землі ў складзе Вялікага Княства ЛітоўскагаЛюблінская унія 1569 г."The Early Stages of Independence"Zapomniane prawdy25 гадоў таму было аб'яўлена, што Язэп Пілсудскі — беларус (фота)Наша вадаДакументы ЧАЭС: Забруджванне тэрыторыі Беларусі « ЧАЭС Зона адчужэнняСведения о политических партиях, зарегистрированных в Республике Беларусь // Министерство юстиции Республики БеларусьСтатыстычны бюлетэнь „Полаўзроставая структура насельніцтва Рэспублікі Беларусь на 1 студзеня 2012 года і сярэднегадовая колькасць насельніцтва за 2011 год“Индекс человеческого развития Беларуси — не было бы нижеБеларусь занимает первое место в СНГ по индексу развития с учетом гендерного факцёраНацыянальны статыстычны камітэт Рэспублікі БеларусьКанстытуцыя РБ. Артыкул 17Трансфармацыйныя задачы БеларусіВыйсце з крызісу — далейшае рэфармаванне Беларускі рубель — сусветны лідар па дэвальвацыяхПра змену коштаў у кастрычніку 2011 г.Бядней за беларусаў у СНД толькі таджыкіСярэдні заробак у верасні дасягнуў 2,26 мільёна рублёўЭканомікаГаласуем за ТОП-100 беларускай прозыСучасныя беларускія мастакіАрхитектура Беларуси BELARUS.BYА. Каханоўскі. Культура Беларусі ўсярэдзіне XVII—XVIII ст.Анталогія беларускай народнай песні, гуказапісы спеваўБеларускія Музычныя IнструментыБеларускі рок, які мы страцілі. Топ-10 гуртоў«Мясцовы час» — нязгаслая легенда беларускай рок-музыкіСЯРГЕЙ БУДКІН. МЫ НЯ ЗНАЕМ СВАЁЙ МУЗЫКІМ. А. Каладзінскі. НАРОДНЫ ТЭАТРМагнацкія культурныя цэнтрыПублічная дыскусія «Беларуская новая пьеса: без беларускай мовы ці беларуская?»Беларускія драматургі па-ранейшаму лепш ставяцца за мяжой, чым на радзіме«Працэс незалежнага кіно пайшоў, і дзяржаву турбуе яго непадкантрольнасць»Беларускія філосафы ў пошуках прасторыВсе идём в библиотекуАрхіваванаАб Нацыянальнай праграме даследавання і выкарыстання касмічнай прасторы ў мірных мэтах на 2008—2012 гадыУ космас — разам.У суседнім з Барысаўскім раёне пабудуюць Камандна-вымяральны пунктСвяты і абрады беларусаў«Мірныя бульбашы з малой краіны» — 5 непраўдзівых стэрэатыпаў пра БеларусьМ. Раманюк. Беларускае народнае адзеннеУ Беларусі скарачаецца колькасць злачынстваўЛукашэнка незадаволены мінскімі ўладамі Крадзяжы складаюць у Мінску каля 70% злачынстваў Узровень злачыннасці ў Мінскай вобласці — адзін з самых высокіх у краіне Генпракуратура аналізуе стан са злачыннасцю ў Беларусі па каэфіцыенце злачыннасці У Беларусі стабілізавалася крымінагеннае становішча, лічыць генпракурорЗамежнікі сталі здзяйсняць у Беларусі больш злачынстваўМУС Беларусі турбуе рост рэцыдыўнай злачыннасціЯ з ЖЭСа. Дазволіце вас абкрасці! Рэйтынг усіх службаў і падраздзяленняў ГУУС Мінгарвыканкама вырасАб КДБ РБГісторыя Аператыўна-аналітычнага цэнтра РБГісторыя ДКФРТаможняagentura.ruБеларусьBelarus.by — Афіцыйны сайт Рэспублікі БеларусьСайт урада БеларусіRadzima.org — Збор архітэктурных помнікаў, гісторыя Беларусі«Глобус Беларуси»Гербы и флаги БеларусиАсаблівасці каменнага веку на БеларусіА. Калечыц, У. Ксяндзоў. Старажытны каменны век (палеаліт). Першапачатковае засяленне тэрыторыіУ. Ксяндзоў. Сярэдні каменны век (мезаліт). Засяленне краю плямёнамі паляўнічых, рыбакоў і збіральнікаўА. Калечыц, М. Чарняўскі. Плямёны на тэрыторыі Беларусі ў новым каменным веку (неаліце)А. Калечыц, У. Ксяндзоў, М. Чарняўскі. Гаспадарчыя заняткі ў каменным векуЭ. Зайкоўскі. Духоўная культура ў каменным векуАсаблівасці бронзавага веку на БеларусіФарміраванне супольнасцей ранняга перыяду бронзавага векуФотографии БеларусиРоля беларускіх зямель ва ўтварэнні і ўмацаванні ВКЛВ. Фадзеева. З гісторыі развіцця беларускай народнай вышыўкіDMOZGran catalanaБольшая российскаяBritannica (анлайн)Швейцарскі гістарычны15325917611952699xDA123282154079143-90000 0001 2171 2080n9112870100577502ge128882171858027501086026362074122714179пппппп

                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result