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README.md
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@ -33,12 +33,13 @@ https://www.tensorflow.org/install/pip
## DOWNLOAD CODE FROM THIS GIT
Into the sequences folder of your local labscript installation create a dedicated folder for NNDy where you copy:
• NNDy.py
• NNDy_Interface.py
• runmanager_remote.py
• experiment_remote.py - not necessary, used only for testing and debugging
• DA/newRun_NNDy.py
<ul>
<li>NNDy.py</li>
<li>NNDy_Interface.py</li>
<li>runmanager_remote.py</li>
<li>experiment_remote.py - not necessary, used only for testing and debugging</li>
<li>DA/newRun_NNDy.py</li>
</ul>
## RUNMANAGER_REMOTE.PY
Modify the path where it will look for the sequences, this will be the shared_drive path of the specified in the during configuration of the labscript software (see https://labscriptsuite.org/en/latest/setup/configuration/ for more details)
@ -56,9 +57,11 @@ def cost(hdf_file):
This is the function that will be transferred to the internal structure of the interface (see NNDy_Interface.py) and be called after the experiment sequence is finished
The function reads the hdf_file of the shot and returns a dictionary with
• cost
• bad flag
• uncertainity
<ul>
<li>cost</li>
<li>bad flag</li>
<li>uncertainity</li>
</ul>
Within this cost function the analysis routine is initiated and handled to return the desired observable from which you want to estimate the cost
The analysis can be customized in any way you desire, as long as it relies of the hdf file to read the results of the shot and the cost function returns the dictionary as explained above
@ -68,13 +71,17 @@ The analysis can be customized in any way you desire, as long as it relies of th
Read carefully through the NNDy.py before running the optimizer as all the degrees of freedom offered and the details that you need to take care of are explained in the comments
As a recap:
• Indicate the name of the routine that you want to run - aka the experimet
• Indicate the name of the cost/DA script that - aka the cost function
• Set at least one halting condition
• Set the names, the initial values and the boundaries of the input parameters
<ul>
<li>Indicate the name of the routine that you want to run - aka the experimet</li>
<li>Indicate the name of the cost/DA script that - aka the cost function</li>
<li>Set at least one halting condition</li>
<li>Set the names, the initial values and the boundaries of the input parameters</li>
</ul>
Optional functionalities
• Set values of fixed, global parameters
• Recall as training dataset the data of past runs
• Handle trust region
• Activate update of hyperparameters
<ul>
<li>Set values of fixed, global parameters</li>
<li>Recall as training dataset the data of past runs</li>
<li>Handle trust region</li>
<li>Activate update of hyperparameters</li>
</ul>