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How to determine what's the optimal number of iterations in learning a neural network?
One way of doing it is to split your training data into a train and validation set. During training, the error on the training set should decrease steadily. The error on the validation set will decrease and at some point start to increase again. At this point the net starts to overfit to the training data. What that means is that the model adapts to the random variations in the data rather than learning the true regularities. You should retain the model with overall lowest validation error. This is called Early Stopping.
Alternatively, you can use Dropout. With a high enough Dropout probability, you can essentially train for as long as you want, and overfitting will not be a significant issue.
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I have a question regarding the model.fit method and overfitting from the scikit learn library in Pandas
Does the generic sklearn method model.fit(x---, y--) returns the score after applying the model to the specified training data?
Also, it is overfitting when performance on the test data degrades as more training data is used to learn the model?
model.fit(X, y) doesn't explicitly give you the score, if you assign a variable to it, it stores all the artifacts, training parameters. You can get the score by using model.score(X, y).
Overfitting in simple words is increasing the variance in your model by which your model fails to generalize. There are ways to reduce overfitting like feature engineering, normalization, regularization, ensemble methods etc.
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i have this model that i trained with 100 epochs :
Model with 100 Epoch
and then i save the model and train for another 100 epoch (total 200 epoch):
Model with additional 100 epoch (200 epoch)
my question is, is my model not overfitting ? is it optimal ?
Overfitting is when a model captures patterns that won't recur in the future. This leads to a decrease in prediction accuracy.
You need to test your model on data that has not been seen in training or validation to determine if it is overfitting or not.
Over fitting is when your model scores very highly on your training set and poorly on a validation test set (or real life post-training predictions).
When you are training your model make sure that you have split your training dataset into two subsets. One for training and one for validation. If you see that your validation accuracy is decreasing as training goes on it means that your CNN has "overfitted" to the training set specifically and should not be generalized.
There are many ways to combat overfitting that should be used while training your model. Seeking more data and using harsh dropout are popular ways to ensure that a model is not overfitting. Check out this article for a good description of your problem and possible solutions.
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I have written a ML-based Intrusion prediction. In the learning process, I used training and test data both labeled to evaluate the accuracy and generate confusion matrixes. I came up with good accuracy and now I want to test it with new data( Unlabeled data). How do I do that?
Okay so say you do test on unlabeled data and your algorithm predicts some X output. How can you check the accuracy, how can you check if this is correct or not? This is the only thing that matters in predictions, how your program works on data it has not seen before.
The short answer is, you can't. You need to split your data into:
Training 70%
Validation 10%
Test 20%
All of these should be labled and accuracy, confusion matrix, f measure and anything else should be computed on the labled test data that your program has not seen before. Your train on training data and every once in a while you check the performance on the validation data to see if it is doing well or if you need to do adjustments. In the very end you check on test data. This is supervised learning, you always need labeled data.
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I have a python Machine Learning script which learns from Firewall log data. I am using Random Forest Classifier in it. If I run it as a service is it possible to train the model, again and again, each day whenever last day's Firewall logs are received.
These are the estimators in sklearn that support what you want to do and unfortunately for you Random Forest isn't one of them, so you will have to refit every time you add data.
If you insist on sticking with Random Forest, one option would be to reduce the number of features (based on the classifier you currently have) to increase the speed of refitting your classifier.
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for iteration in range(NUM_ITERATIONS):
print()
print("="*50)
print("Iteration:",iteration)
model.fit(X,y,batch_size=BATCH_SIZE,epochs=NUM_EPOCH_ITERATION)
rand=np.random.randint(len(x_labels))
test="vikas n s "
print(test,end="")
for i in range(NUM_PREDICTION_PER_EPOCH):
Xtest=np.zeros((1,sqlen,nb_chars),dtype=np.bool)
for i,ch in enumerate(test):
Xtest[0,i,char2index[ch]]=1
pred=model.predict(Xtest)
temp=pred
pred=pred[0]
ypredict=index2char[np.argmax(pred)]
print(ypredict,end="")
test=test[1:]+ypredict
In this code,at every iteration I'm fitting the model.
My assumption is that as I'm fitting the model again the loss should be reset to original loss or something close to that.But what I'm finding is that the loss continues.
ie:
if the initial loss is 4
and after all the epoch in 1st iteration loss drops to 2.
Now when i once again fit the model in the next iteration i expect the loss to start from 4.Instead it continues from 2.
Why is this happening?
Because Keras model.fit method does exactly that, i.e. starts training from whatever state the model has been by then; hence, if the model has already undergone some training from a previous session/iteration, the new fit starts from that point indeed.
If you want to have a new fitting session for each iteration (i.e. "resetting" your model), you should wrap your model building in a convenience function, and call this function in each iteration before model.fit (or of course simply include the whole model building code in each iteration...).