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I have a unique use case where I am attempting to isolate subjects in each run. I have 56 such runs that I want to automate. To achieve this, I created files such as 1_train.csv, 2_train.csv, ..., and 56_train.csv, along with corresponding 1_test.csv, 2_test.csv, ..., and 56_test.csv.
Contents of 1_train.csv would look like this;
image_path
label
is_valid
multi/image1.png
2
FALSE
multi/image2.png
2
FALSE
multi/image3.png
2
FALSE
1_test.csv;
image_path
label
multi/image11.png
2
multi/image12.png
2
Now, I am working on creating and exporting 56 models and their respective metrics to a common folder. I am currently following a specific approach, but if there is a more efficient way to accomplish this, please let me know.
Question: Each time I loop over the training, testing, and the learners, I assume the learners are distinct, and the weights are re-initialized. Is this correct?
I have a unique use case where I am attempting to isolate subjects in each run. I have 56 such runs that I want to automate. To achieve this, I created files such as 1_train.csv, 2_train.csv, ..., and 56_train.csv, along with corresponding 1_test.csv, 2_test.csv, ..., and 56_test.csv.
Contents of
1_train.csv
would look like this;1_test.csv
;Now, I am working on creating and exporting 56 models and their respective metrics to a common folder. I am currently following a specific approach, but if there is a more efficient way to accomplish this, please let me know.
Code:
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