Validation loss increases and validation accuracy decreases












2












$begingroup$


I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is as below



# define model

model = Sequential()

model.add(Embedding(vocab_size, 100, input_length=max_length))

model.add(Conv1D(filters=32, kernel_size=8, activation='relu'))

model.add(MaxPooling1D(pool_size=2))

model.add(Flatten())

model.add(Dense(10, activation='relu'))

model.add(Dense(5, activation='softmax'))



# compile network

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])



# fit network

model.fit(final_X_train, final_Y_train, epochs=5, batch_size=50, validation_data=(final_X_val, final_Y_val), callbacks=callback)


Total params: 14,977,717



Trainable params: 14,977,717



Non-trainable params: 0



Train on 212135 samples, validate on 69472 samples



Epoch 1/5
loss: 0.9452 - acc: 0.5781 - val_loss: 0.8440 - val_acc: 0.6309



Epoch 2/5
loss: 0.7707 - acc: 0.6711 - val_loss: 0.8234 - val_acc: 0.6433



Epoch 3/5
loss: 0.5807 - acc: 0.7657 - val_loss: 0.9144 - val_acc: 0.6345



Epoch 4/5
loss: 0.3736 - acc: 0.8575 - val_loss: 1.1982 - val_acc: 0.6194



Epoch 5/5
loss: 0.2285 - acc: 0.9173 - val_loss: 1.5770 - val_acc: 0.6073



Training acc increases and loss decreases as expected. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s.



The total accuracy is : 0.6046845041714888




I've already cleaned, shuffled, down-sampled (all classes have 42427 number of data samples) and split the data properly to training(70%) / validation(10%) / testing(20%).




If you see any improvements to fix this problem, please let me know. :)






python deep-learning classification keras overfitting







share|improve this question







New contributor




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    2












    $begingroup$


    I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is as below



    # define model
    
model = Sequential()
    
model.add(Embedding(vocab_size, 100, input_length=max_length))
    
model.add(Conv1D(filters=32, kernel_size=8, activation='relu'))
    
model.add(MaxPooling1D(pool_size=2))
    
model.add(Flatten())
    
model.add(Dense(10, activation='relu'))
    
model.add(Dense(5, activation='softmax'))
    

    
# compile network
    
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    

    
# fit network
    
model.fit(final_X_train, final_Y_train, epochs=5, batch_size=50, validation_data=(final_X_val, final_Y_val), callbacks=callback)


    Total params: 14,977,717



    Trainable params: 14,977,717



    Non-trainable params: 0



    Train on 212135 samples, validate on 69472 samples



    Epoch 1/5
    loss: 0.9452 - acc: 0.5781 - val_loss: 0.8440 - val_acc: 0.6309



    Epoch 2/5
    loss: 0.7707 - acc: 0.6711 - val_loss: 0.8234 - val_acc: 0.6433



    Epoch 3/5
    loss: 0.5807 - acc: 0.7657 - val_loss: 0.9144 - val_acc: 0.6345



    Epoch 4/5
    loss: 0.3736 - acc: 0.8575 - val_loss: 1.1982 - val_acc: 0.6194



    Epoch 5/5
    loss: 0.2285 - acc: 0.9173 - val_loss: 1.5770 - val_acc: 0.6073



    Training acc increases and loss decreases as expected. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s.



    The total accuracy is : 0.6046845041714888




    I've already cleaned, shuffled, down-sampled (all classes have 42427 number of data samples) and split the data properly to training(70%) / validation(10%) / testing(20%).




    If you see any improvements to fix this problem, please let me know. :)






    python deep-learning classification keras overfitting







    share|improve this question







    New contributor




    stranger is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      2












      2








      2





      $begingroup$


      I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is as below



      # define model
      
model = Sequential()
      
model.add(Embedding(vocab_size, 100, input_length=max_length))
      
model.add(Conv1D(filters=32, kernel_size=8, activation='relu'))
      
model.add(MaxPooling1D(pool_size=2))
      
model.add(Flatten())
      
model.add(Dense(10, activation='relu'))
      
model.add(Dense(5, activation='softmax'))
      

      
# compile network
      
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
      

      
# fit network
      
model.fit(final_X_train, final_Y_train, epochs=5, batch_size=50, validation_data=(final_X_val, final_Y_val), callbacks=callback)


      Total params: 14,977,717



      Trainable params: 14,977,717



      Non-trainable params: 0



      Train on 212135 samples, validate on 69472 samples



      Epoch 1/5
      loss: 0.9452 - acc: 0.5781 - val_loss: 0.8440 - val_acc: 0.6309



      Epoch 2/5
      loss: 0.7707 - acc: 0.6711 - val_loss: 0.8234 - val_acc: 0.6433



      Epoch 3/5
      loss: 0.5807 - acc: 0.7657 - val_loss: 0.9144 - val_acc: 0.6345



      Epoch 4/5
      loss: 0.3736 - acc: 0.8575 - val_loss: 1.1982 - val_acc: 0.6194



      Epoch 5/5
      loss: 0.2285 - acc: 0.9173 - val_loss: 1.5770 - val_acc: 0.6073



      Training acc increases and loss decreases as expected. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s.



      The total accuracy is : 0.6046845041714888




      I've already cleaned, shuffled, down-sampled (all classes have 42427 number of data samples) and split the data properly to training(70%) / validation(10%) / testing(20%).




      If you see any improvements to fix this problem, please let me know. :)






      python deep-learning classification keras overfitting







      share|improve this question







      New contributor




      stranger is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is as below



      # define model
      
model = Sequential()
      
model.add(Embedding(vocab_size, 100, input_length=max_length))
      
model.add(Conv1D(filters=32, kernel_size=8, activation='relu'))
      
model.add(MaxPooling1D(pool_size=2))
      
model.add(Flatten())
      
model.add(Dense(10, activation='relu'))
      
model.add(Dense(5, activation='softmax'))
      

      
# compile network
      
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
      

      
# fit network
      
model.fit(final_X_train, final_Y_train, epochs=5, batch_size=50, validation_data=(final_X_val, final_Y_val), callbacks=callback)


      Total params: 14,977,717



      Trainable params: 14,977,717



      Non-trainable params: 0



      Train on 212135 samples, validate on 69472 samples



      Epoch 1/5
      loss: 0.9452 - acc: 0.5781 - val_loss: 0.8440 - val_acc: 0.6309



      Epoch 2/5
      loss: 0.7707 - acc: 0.6711 - val_loss: 0.8234 - val_acc: 0.6433



      Epoch 3/5
      loss: 0.5807 - acc: 0.7657 - val_loss: 0.9144 - val_acc: 0.6345



      Epoch 4/5
      loss: 0.3736 - acc: 0.8575 - val_loss: 1.1982 - val_acc: 0.6194



      Epoch 5/5
      loss: 0.2285 - acc: 0.9173 - val_loss: 1.5770 - val_acc: 0.6073



      Training acc increases and loss decreases as expected. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s.



      The total accuracy is : 0.6046845041714888




      I've already cleaned, shuffled, down-sampled (all classes have 42427 number of data samples) and split the data properly to training(70%) / validation(10%) / testing(20%).




      If you see any improvements to fix this problem, please let me know. :)






      python deep-learning classification keras overfitting




      python deep-learning classification keras overfitting






      share|improve this question







      New contributor




      stranger is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      stranger is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




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      stranger is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 14 hours ago









      strangerstranger

      112




      112




      New contributor




      stranger is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      New contributor





      stranger is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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      Check out our Code of Conduct.






















          1 Answer
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          active

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          0












          $begingroup$

          What you are experiencing is known as overfitting, and it’s a common problem in machine learning and data science.



          Overfitting happens when a model begins to focus on the noise in the training dataset and extracts features based on it. This helps the model to improve its performance on the training set, but hurts its ability to generalize, so the accuracy of the validation set decreases.



          To deal with overfitting, you need to use regularization during the training. You ca try adding dropout layers or batch-normalization layers, adding weight regularization, or you can artificially increase the size of your training set by performing some data augmentation.



          You can read more about it in the following post:



          What are the possible approaches to fixing Overfitting on a CNN?






          share|improve this answer









          $endgroup$













          • $begingroup$
            Thanks, I tried adding regularizers to Conv1D and Dense layers as below. Conv1D(filters=64, kernel_size=8, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001)) AND Dense(10, activation='relu', kernel_regularizer=l2(0.001)) . But unfortunately none of them did any changes to val_acc and val_loss.
            $endgroup$
            – stranger
            11 hours ago











          Your Answer





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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          0












          $begingroup$

          What you are experiencing is known as overfitting, and it’s a common problem in machine learning and data science.



          Overfitting happens when a model begins to focus on the noise in the training dataset and extracts features based on it. This helps the model to improve its performance on the training set, but hurts its ability to generalize, so the accuracy of the validation set decreases.



          To deal with overfitting, you need to use regularization during the training. You ca try adding dropout layers or batch-normalization layers, adding weight regularization, or you can artificially increase the size of your training set by performing some data augmentation.



          You can read more about it in the following post:



          What are the possible approaches to fixing Overfitting on a CNN?






          share|improve this answer









          $endgroup$













          • $begingroup$
            Thanks, I tried adding regularizers to Conv1D and Dense layers as below. Conv1D(filters=64, kernel_size=8, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001)) AND Dense(10, activation='relu', kernel_regularizer=l2(0.001)) . But unfortunately none of them did any changes to val_acc and val_loss.
            $endgroup$
            – stranger
            11 hours ago
















          0












          $begingroup$

          What you are experiencing is known as overfitting, and it’s a common problem in machine learning and data science.



          Overfitting happens when a model begins to focus on the noise in the training dataset and extracts features based on it. This helps the model to improve its performance on the training set, but hurts its ability to generalize, so the accuracy of the validation set decreases.



          To deal with overfitting, you need to use regularization during the training. You ca try adding dropout layers or batch-normalization layers, adding weight regularization, or you can artificially increase the size of your training set by performing some data augmentation.



          You can read more about it in the following post:



          What are the possible approaches to fixing Overfitting on a CNN?






          share|improve this answer









          $endgroup$













          • $begingroup$
            Thanks, I tried adding regularizers to Conv1D and Dense layers as below. Conv1D(filters=64, kernel_size=8, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001)) AND Dense(10, activation='relu', kernel_regularizer=l2(0.001)) . But unfortunately none of them did any changes to val_acc and val_loss.
            $endgroup$
            – stranger
            11 hours ago














          0












          0








          0





          $begingroup$

          What you are experiencing is known as overfitting, and it’s a common problem in machine learning and data science.



          Overfitting happens when a model begins to focus on the noise in the training dataset and extracts features based on it. This helps the model to improve its performance on the training set, but hurts its ability to generalize, so the accuracy of the validation set decreases.



          To deal with overfitting, you need to use regularization during the training. You ca try adding dropout layers or batch-normalization layers, adding weight regularization, or you can artificially increase the size of your training set by performing some data augmentation.



          You can read more about it in the following post:



          What are the possible approaches to fixing Overfitting on a CNN?






          share|improve this answer









          $endgroup$



          What you are experiencing is known as overfitting, and it’s a common problem in machine learning and data science.



          Overfitting happens when a model begins to focus on the noise in the training dataset and extracts features based on it. This helps the model to improve its performance on the training set, but hurts its ability to generalize, so the accuracy of the validation set decreases.



          To deal with overfitting, you need to use regularization during the training. You ca try adding dropout layers or batch-normalization layers, adding weight regularization, or you can artificially increase the size of your training set by performing some data augmentation.



          You can read more about it in the following post:



          What are the possible approaches to fixing Overfitting on a CNN?







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 14 hours ago









          Mark.FMark.F

          9661418




          9661418












          • $begingroup$
            Thanks, I tried adding regularizers to Conv1D and Dense layers as below. Conv1D(filters=64, kernel_size=8, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001)) AND Dense(10, activation='relu', kernel_regularizer=l2(0.001)) . But unfortunately none of them did any changes to val_acc and val_loss.
            $endgroup$
            – stranger
            11 hours ago


















          • $begingroup$
            Thanks, I tried adding regularizers to Conv1D and Dense layers as below. Conv1D(filters=64, kernel_size=8, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001)) AND Dense(10, activation='relu', kernel_regularizer=l2(0.001)) . But unfortunately none of them did any changes to val_acc and val_loss.
            $endgroup$
            – stranger
            11 hours ago
















          $begingroup$
          Thanks, I tried adding regularizers to Conv1D and Dense layers as below. Conv1D(filters=64, kernel_size=8, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001)) AND Dense(10, activation='relu', kernel_regularizer=l2(0.001)) . But unfortunately none of them did any changes to val_acc and val_loss.
          $endgroup$
          – stranger
          11 hours ago




          $begingroup$
          Thanks, I tried adding regularizers to Conv1D and Dense layers as below. Conv1D(filters=64, kernel_size=8, activation='relu', kernel_regularizer=l2(0.001), bias_regularizer=l2(0.001)) AND Dense(10, activation='relu', kernel_regularizer=l2(0.001)) . But unfortunately none of them did any changes to val_acc and val_loss.
          $endgroup$
          – stranger
          11 hours ago










          stranger is a new contributor. Be nice, and check out our Code of Conduct.










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