How to recognise when to stop training based on Overfitting/Underfitting?2019 Community Moderator ElectionNeural Network: how to interpret this loss graph?How to improve loss and avoid overfittingHow to set input for proper fit with lstm?Neural Network Prediction regression task, output is a multiple factor of input with same peaksWhy does my LSTM perform better when randomizing training subset vs. standard batch training?Is my model over-fitting (LSTM,GRU)Value error in Merging two different models in kerasBias-variance tradeoff in practice (CNN)Network either overfits or underfits, but never generalizes - what to do?Remedies to CNN-LSTM overfitting on relatively small image dataset
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How to recognise when to stop training based on Overfitting/Underfitting?
2019 Community Moderator ElectionNeural Network: how to interpret this loss graph?How to improve loss and avoid overfittingHow to set input for proper fit with lstm?Neural Network Prediction regression task, output is a multiple factor of input with same peaksWhy does my LSTM perform better when randomizing training subset vs. standard batch training?Is my model over-fitting (LSTM,GRU)Value error in Merging two different models in kerasBias-variance tradeoff in practice (CNN)Network either overfits or underfits, but never generalizes - what to do?Remedies to CNN-LSTM overfitting on relatively small image dataset
$begingroup$
I am trying to train a LSTM network, over a total of 200 epochs, with hidden layer size of 100 and 1 dense layer after the LSTM layer. I have used a batch size of 10 for the same. Basically, I am confused as to why the loss curve which I get (with MAE as loss criteria and Adam Optimiser) is looking very different from what a good model generally gives. I believe that the likely reason may be that the training is occurring over more number of epochs than should be ideal, and it is underfitting/overfitting, but I am not sure that how to recognise the same.
The loss curve for the model is 
I would like to be sure of whether the model is overfitting or undercutting, and if I need to reduce the training epochs (say from 200 to 20?).Being new to this, is there any specific point to identify when to stop the training process (such as based on this loss curve). Any help in this regard is appreciated.
neural-network deep-learning lstm loss-function overfitting
asked 31 mins ago
JChatJChat
154
$endgroup$
add a comment |
$begingroup$
I am trying to train a LSTM network, over a total of 200 epochs, with hidden layer size of 100 and 1 dense layer after the LSTM layer. I have used a batch size of 10 for the same. Basically, I am confused as to why the loss curve which I get (with MAE as loss criteria and Adam Optimiser) is looking very different from what a good model generally gives. I believe that the likely reason may be that the training is occurring over more number of epochs than should be ideal, and it is underfitting/overfitting, but I am not sure that how to recognise the same.
The loss curve for the model is
I would like to be sure of whether the model is overfitting or undercutting, and if I need to reduce the training epochs (say from 200 to 20?).Being new to this, is there any specific point to identify when to stop the training process (such as based on this loss curve). Any help in this regard is appreciated.
neural-network deep-learning lstm loss-function overfitting
asked 31 mins ago
JChatJChat
154
$endgroup$
add a comment |
$begingroup$
I am trying to train a LSTM network, over a total of 200 epochs, with hidden layer size of 100 and 1 dense layer after the LSTM layer. I have used a batch size of 10 for the same. Basically, I am confused as to why the loss curve which I get (with MAE as loss criteria and Adam Optimiser) is looking very different from what a good model generally gives. I believe that the likely reason may be that the training is occurring over more number of epochs than should be ideal, and it is underfitting/overfitting, but I am not sure that how to recognise the same.
The loss curve for the model is
I would like to be sure of whether the model is overfitting or undercutting, and if I need to reduce the training epochs (say from 200 to 20?).Being new to this, is there any specific point to identify when to stop the training process (such as based on this loss curve). Any help in this regard is appreciated.
neural-network deep-learning lstm loss-function overfitting
asked 31 mins ago
JChatJChat
154
$endgroup$
I am trying to train a LSTM network, over a total of 200 epochs, with hidden layer size of 100 and 1 dense layer after the LSTM layer. I have used a batch size of 10 for the same. Basically, I am confused as to why the loss curve which I get (with MAE as loss criteria and Adam Optimiser) is looking very different from what a good model generally gives. I believe that the likely reason may be that the training is occurring over more number of epochs than should be ideal, and it is underfitting/overfitting, but I am not sure that how to recognise the same.
The loss curve for the model is
I would like to be sure of whether the model is overfitting or undercutting, and if I need to reduce the training epochs (say from 200 to 20?).Being new to this, is there any specific point to identify when to stop the training process (such as based on this loss curve). Any help in this regard is appreciated.
neural-network deep-learning lstm loss-function overfitting
neural-network deep-learning lstm loss-function overfitting
asked 31 mins ago
JChatJChat
154
asked 31 mins ago
JChatJChat
154
asked 31 mins ago
JChatJChat
154
asked 31 mins ago
JChatJChat
154
asked 31 mins ago
JChatJChat
154
154
add a comment |
add a comment |
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