Apr 17, 2018 · So in the end i want to sum each training accuracy in each epoch with the previous one and divided them by 200.. here is my code num = 200 total_sum = 0 for n in range(num): avg_train=np.array(model.fit(x_train,y_train, epochs=200, batch_size=64, verbose=2)) total_sum = avg_train + total_sum avg = total_sum/num score=model.evaluate(x_test, y_test, verbose=2) print(score) print('the average is',avg)
16.04.2018 · Assuming that this is not what you are trying to do, but you want simply the average accuracy during your training, the answer is to use the History object returned by model.fit; from the model.fit docs: Returns A History object.
Mar 03, 2021 · If you would like to calculate the loss for each epoch, divide the running_loss by the number of batches and append it to train_losses in each epoch. Accuracy is the number of correct classifications / the total amount of classifications .I am dividing it by the total number of the dataset because I have finished one epoch.
The sum of accuracies divided by 10 (number of folds). Then, you present averaged accuracy from CV. You are also able to discuss the variation of the accuracies ...
28.02.2018 · Your confusion matrix tells us how much it is overfitting, because your largest class makes up over 90% of the population. Assuming that you test and train set have a similar distribution, any useful model would have to score more than 90% accuracy: A simple 0R-model would. Your model scores just under 80% on the test set.
02.01.2019 · To get a valid accuracy between 0 and 100% you should divide correct_trainby the number of pixels in your batch. Try to calculate total_trainas total_train += mask.nelement(). 3 Likes Neda(Neda) January 2, 2019, 2:08pm #3 @ptrblckyes it works. Thanks a lot 1 Like singhvishal0209(Vishal Singh)
Dec 05, 2015 · Method 1 After creating a partitioned model using crossval function using the formula (1 - kfoldLoss(partitionedModel, 'LossFun', 'ClassifError') to calculate the accuracy. Method 2 Using the trained classifier after crossval to predict the responses for the predictors that I used for training.
Jan 02, 2019 · You are currently summing all correctly predicted pixels and divide it by the batch size. To get a valid accuracy between 0 and 100% you should divide correct_train by the number of pixels in your batch. Try to calculate total_train as total_train += mask.nelement().
05.12.2015 · In method 1, you calculate accuracy as 1-CV_Error which is reliable and makes sense. In method 2, you give samples to trained classifier for the second time. The obtained accuracy is not much reliable as the method 1. Of course, you get different accuracy from method 1 and method 2. And I think the first method for caculating accuracy is enough.
training accuracy is usually the accuracy you get if you apply the model on the training data, while testing accuracy is the accuracy for the testing data. It's sometimes useful to compare these to identify overtraining. ~Martin - Head of Data Science Services at RapidMiner - Dortmund, Germany IngoRM Posts: 1,751 RM Founder