Why loss and val_loss vary for same value of lr, epoch, batch size and lr-decay?Keras difference beetween val_loss and loss during trainingLSTM Timeseries recursive prediction converge to same valueKeras intuition/guidelines for setting epochs and batch sizeWhy might a neural network consistently underestimate its target?Why is my Keras model not learning image segmentation?Bidirectional GRU: validation loss stuck on plateau diverges from well performing training losscustom loss in keras, problem with batch sizeGeting batch size in keras custom losspredict gives the same output value for every image (Keras)InvalidArgumentError: Conv2DCustomBackpropInput: input and out_backprop must have the same batch size

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Why loss and val_loss vary for same value of lr, epoch, batch size and lr-decay?


Keras difference beetween val_loss and loss during trainingLSTM Timeseries recursive prediction converge to same valueKeras intuition/guidelines for setting epochs and batch sizeWhy might a neural network consistently underestimate its target?Why is my Keras model not learning image segmentation?Bidirectional GRU: validation loss stuck on plateau diverges from well performing training losscustom loss in keras, problem with batch sizeGeting batch size in keras custom losspredict gives the same output value for every image (Keras)InvalidArgumentError: Conv2DCustomBackpropInput: input and out_backprop must have the same batch size













0












$begingroup$


I am training a deep neural network in Keras. I have set the values of learning rate, learning rate decay, epochs and batch size.



(I am using fit_generator i.e data coming from a generator but without any kind of shuffling.)



Being said that my training shows highly differenttraining summary.



Sometimes it's like:
enter image description here



And other times it is



enter image description here



The two of the above images show the highly unpredictable behavior of model for the same values of all hyperparameters.



Can someone explain why this is happening?










share|improve this question











$endgroup$
















    0












    $begingroup$


    I am training a deep neural network in Keras. I have set the values of learning rate, learning rate decay, epochs and batch size.



    (I am using fit_generator i.e data coming from a generator but without any kind of shuffling.)



    Being said that my training shows highly differenttraining summary.



    Sometimes it's like:
    enter image description here



    And other times it is



    enter image description here



    The two of the above images show the highly unpredictable behavior of model for the same values of all hyperparameters.



    Can someone explain why this is happening?










    share|improve this question











    $endgroup$














      0












      0








      0





      $begingroup$


      I am training a deep neural network in Keras. I have set the values of learning rate, learning rate decay, epochs and batch size.



      (I am using fit_generator i.e data coming from a generator but without any kind of shuffling.)



      Being said that my training shows highly differenttraining summary.



      Sometimes it's like:
      enter image description here



      And other times it is



      enter image description here



      The two of the above images show the highly unpredictable behavior of model for the same values of all hyperparameters.



      Can someone explain why this is happening?










      share|improve this question











      $endgroup$




      I am training a deep neural network in Keras. I have set the values of learning rate, learning rate decay, epochs and batch size.



      (I am using fit_generator i.e data coming from a generator but without any kind of shuffling.)



      Being said that my training shows highly differenttraining summary.



      Sometimes it's like:
      enter image description here



      And other times it is



      enter image description here



      The two of the above images show the highly unpredictable behavior of model for the same values of all hyperparameters.



      Can someone explain why this is happening?







      keras predictive-modeling






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 43 secs ago







      yamini goel

















      asked 33 mins ago









      yamini goelyamini goel

      214




      214




















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