What's the difference between keras.datasets.mnist and tensorflow.examples.tutorials.mnist?












1















I am analysing this DCGAN. When I use input_data from tensorflow.examples.tutorials.mnist, as seen in line 144:



self.x_train = input_data.read_data_sets("mnist",
one_hot=True).train.images


I obtain reasonably good results:
enter image description here
Though when I use mnist from keras.datasets and the 144th line looks like this:



(xtr, ytr), (xte, yte) = mnist.load_data();
self.x_train = xtr


I get horribly bad results:
enter image description here
I have checked manually a few images from both datasets and they are quite similar.



So what is the difference between keras.datasets.mnist and tensorflow.examples.tutorials.mnist? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist?










share|improve this question



























    1















    I am analysing this DCGAN. When I use input_data from tensorflow.examples.tutorials.mnist, as seen in line 144:



    self.x_train = input_data.read_data_sets("mnist",
    one_hot=True).train.images


    I obtain reasonably good results:
    enter image description here
    Though when I use mnist from keras.datasets and the 144th line looks like this:



    (xtr, ytr), (xte, yte) = mnist.load_data();
    self.x_train = xtr


    I get horribly bad results:
    enter image description here
    I have checked manually a few images from both datasets and they are quite similar.



    So what is the difference between keras.datasets.mnist and tensorflow.examples.tutorials.mnist? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist?










    share|improve this question

























      1












      1








      1








      I am analysing this DCGAN. When I use input_data from tensorflow.examples.tutorials.mnist, as seen in line 144:



      self.x_train = input_data.read_data_sets("mnist",
      one_hot=True).train.images


      I obtain reasonably good results:
      enter image description here
      Though when I use mnist from keras.datasets and the 144th line looks like this:



      (xtr, ytr), (xte, yte) = mnist.load_data();
      self.x_train = xtr


      I get horribly bad results:
      enter image description here
      I have checked manually a few images from both datasets and they are quite similar.



      So what is the difference between keras.datasets.mnist and tensorflow.examples.tutorials.mnist? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist?










      share|improve this question














      I am analysing this DCGAN. When I use input_data from tensorflow.examples.tutorials.mnist, as seen in line 144:



      self.x_train = input_data.read_data_sets("mnist",
      one_hot=True).train.images


      I obtain reasonably good results:
      enter image description here
      Though when I use mnist from keras.datasets and the 144th line looks like this:



      (xtr, ytr), (xte, yte) = mnist.load_data();
      self.x_train = xtr


      I get horribly bad results:
      enter image description here
      I have checked manually a few images from both datasets and they are quite similar.



      So what is the difference between keras.datasets.mnist and tensorflow.examples.tutorials.mnist? Why are the resulting images so different? What am I doing wrong with keras.datasets.mnist?







      tensorflow keras neural-network mnist dcgan






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Dec 31 '18 at 11:20









      LeftismerLeftismer

      528




      528
























          1 Answer
          1






          active

          oldest

          votes


















          1














          It is very likely that the images in tensorflow.examples.tutorials.mnist have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:



          (xtr, ytr), (xte, yte) = mnist.load_data()
          xtr = xtr.astype('float32') / 255.0
          xte = xte.astype('float32') / 255.0





          share|improve this answer

























            Your Answer






            StackExchange.ifUsing("editor", function () {
            StackExchange.using("externalEditor", function () {
            StackExchange.using("snippets", function () {
            StackExchange.snippets.init();
            });
            });
            }, "code-snippets");

            StackExchange.ready(function() {
            var channelOptions = {
            tags: "".split(" "),
            id: "1"
            };
            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: true,
            noModals: true,
            showLowRepImageUploadWarning: true,
            reputationToPostImages: 10,
            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%2fstackoverflow.com%2fquestions%2f53986848%2fwhats-the-difference-between-keras-datasets-mnist-and-tensorflow-examples-tutor%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









            1














            It is very likely that the images in tensorflow.examples.tutorials.mnist have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:



            (xtr, ytr), (xte, yte) = mnist.load_data()
            xtr = xtr.astype('float32') / 255.0
            xte = xte.astype('float32') / 255.0





            share|improve this answer






























              1














              It is very likely that the images in tensorflow.examples.tutorials.mnist have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:



              (xtr, ytr), (xte, yte) = mnist.load_data()
              xtr = xtr.astype('float32') / 255.0
              xte = xte.astype('float32') / 255.0





              share|improve this answer




























                1












                1








                1







                It is very likely that the images in tensorflow.examples.tutorials.mnist have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:



                (xtr, ytr), (xte, yte) = mnist.load_data()
                xtr = xtr.astype('float32') / 255.0
                xte = xte.astype('float32') / 255.0





                share|improve this answer















                It is very likely that the images in tensorflow.examples.tutorials.mnist have been normalized to the range [0, 1] and therefore you obtain better results. Whereas, the values in MNIST dataset in Keras are in the range [0, 255] and you are expected to normalize them (if needed, of course). Try this:



                (xtr, ytr), (xte, yte) = mnist.load_data()
                xtr = xtr.astype('float32') / 255.0
                xte = xte.astype('float32') / 255.0






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Dec 31 '18 at 12:10

























                answered Dec 31 '18 at 11:50









                todaytoday

                10.8k21837




                10.8k21837






























                    draft saved

                    draft discarded




















































                    Thanks for contributing an answer to Stack Overflow!


                    • 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.


                    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%2fstackoverflow.com%2fquestions%2f53986848%2fwhats-the-difference-between-keras-datasets-mnist-and-tensorflow-examples-tutor%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

                    Monofisismo

                    Angular Downloading a file using contenturl with Basic Authentication

                    Olmecas