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janni_tutorial [2019/07/10 13:31] twagner [Training a model for your data] |
janni_tutorial [2019/09/16 09:08] twagner [Download and Installation] |
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You can find the download and installation instructions here: [[janni|Download and Installation]] | You can find the download and installation instructions here: [[janni|Download and Installation]] | ||
+ | ==== Start JANNI ==== | ||
+ | If you followed the installation instructions, | ||
+ | < | ||
+ | source activate janni | ||
+ | </ | ||
+ | |||
+ | You can use JANNI either by command line or with the GUI. Typically, most users prefer to use the GUI. You can start it with | ||
+ | < | ||
+ | janni_denoise.py | ||
+ | </ | ||
==== Training a model for your data ==== | ==== Training a model for your data ==== | ||
In case you want to use the general model ([[janni# | In case you want to use the general model ([[janni# | ||
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* **movie_dir**: | * **movie_dir**: | ||
* **even_dir**: | * **even_dir**: | ||
- | * **odd_dir**: | + | * **odd_dir**: |
* **batch_size**: | * **batch_size**: | ||
* **learning_rate**: | * **learning_rate**: | ||
Line 43: | Line 53: | ||
* **saved_weights_name**: | * **saved_weights_name**: | ||
In principle you only have to adapt the paths. The other could keep as they are. | In principle you only have to adapt the paths. The other could keep as they are. | ||
- | We typically use at least 30 movies to train the model. Less might also work, more work often much better. | + | We typically use at least 30 movies |
To run the training on gpu 0: | To run the training on gpu 0: |