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I trained a model with these parameters:
conv1_kernel = 10
conv2_kernel = 10
min_rt = 0
max_rt = 61
time_scale = 60
max_length = 51
Is this model now restricted to input in the scale of the parameters also when predicting? e.g. RT in 0-61min, time_scale in sec and max length of the peptides = 51?
When predicting (python prediction_emb.py 100 model.pt 10 input_file.txt), what does the RT_scale mean?
The observed values in the prediction_file are different from the RTs in the input_file, because they are multiplied with the RT_scale parameter?
Hi, yes, the min_rt, max_rt, and max_length need to be the same in training and prediction.
The parameter RT_scale means the max_rt (e.g. 61 in your training data). Because before training we use min_rt and max_rt to normalize all RT values to (0,1), when predicting we multiply max_rt to change them back to their original range (min_rt is normally 0).
So just use max_rt here. And all predicted RT values will be within (0,1) before multiplying this parameter.
I trained a model with these parameters:
conv1_kernel = 10
conv2_kernel = 10
min_rt = 0
max_rt = 61
time_scale = 60
max_length = 51
Is this model now restricted to input in the scale of the parameters also when predicting? e.g. RT in 0-61min, time_scale in sec and max length of the peptides = 51?
When predicting (python prediction_emb.py 100 model.pt 10 input_file.txt), what does the RT_scale mean?
The observed values in the prediction_file are different from the RTs in the input_file, because they are multiplied with the RT_scale parameter?
I would like the output RTs to be between 0-100, independent of the input time.
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