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A rule of thumb, as you go deeper, number of filters increase and the size of filter remains same or increases. You don't follow both of them. This will help your network learn. Then, consider increasing the number of filters in proper fashion if still your network is not learning.


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have you already tried using only very little of the -ve cases? So for example to train your model on 900 points total, 600/300? Then stratified sampling should still work fine. Then I'd evaluate your model based on it's ability to predict -ve cases and just monitor the performance it get's on the (in your case) gigantic test dataset that the model hasn't ...


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95 is not number of samples, it equals to testset divided by batch_size: testset // batch_size in other words steps per epoch


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Random Model Classifier It feels like there's a word missing here, you probably mean "Random Forest [model] classifier" or "Random Fields classifier"? the confusion matrix This is an important piece of information, because if you have a new (annotated) sample this allows you to compare a few things: compare the distribution of the ...


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Welcome to the community. If I understood correctly: you have is a dataset whose target labels (aka ground truth) you don't know, so you figured these out by assigning the output of another model from someone else --> I guess this pre-trained model was built with the same dataset right? you built a new model on this dataset, using as target values the ...


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So turns out the answer was that the shape of the dense tensor was different across the training set and validation set. This was because the longest sequence differed in length between the two sets (same with the test set).


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I had a similar issue, but I realized that I had included my target variable while predicting the test outcomes. error: predict(object = model_nb, test[,]) void of error: predict(object = model_nb, test[,-16]) where the 16th column was for the dependent variable.


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