How to extract Weights, bias from trained model, and how to choose a error function ?

[automataIA]

1. How to extract Weights and bias from trained model?
2. How to set, or choose a error function for model?

im using the same code in the example:

 IMPORT DATASET -----------------------------------
X = pd.read_feather('Input.file') ; 
Y = pd.read_feather('Target.file') ;

TRASFORMATION DATASET IN ARRAY -------------------
X = X.values.T
Y = Y.values.T
print(X.shape,Y.shape)
 NETWORK ------------------------------
net = prn.CreateNN([3,6,8])

net = prn.train_LM(X,Y,net,verbose=True,k_max=1000,E_stop=1e-5)

ADD : why with command :

W0 = net['w0']; print(W0.shape) ;
W1 = net['w']; print(W1.shape) ; 

3. give me a shape for W0 and W1 of [80,] and not : [6x3] and [8x6] ?
4. and normP and normY are bias, i think?

[yabata]

HI Zawarud, you can save weights as descibed here: https://pyrenn.readthedocs.io/en/latest/save.html Pyrenn only supports tanh as transfer function. Best regards Dennis Von: Zawarud [mailto:[email protected]] Gesendet: Mittwoch, 29. Januar 2020 15:26 An: yabata/pyrenn <[email protected]> Cc: Subscribed <[email protected]> Betreff: [yabata/pyrenn] How to extract Weights, bias from trained model, and how to choose a error function ? (#10) 1. How to extract Weights and bias from trained model? 2. How to set, or choose a error function for model? im using the same code in the example: `# IMPORT DATASET ----------------------------------- X = pd.read_feather('Input.file') ; Y = pd.read_feather('Target.file') ; TRASFORMATION DATASET IN ARRAY ------------------- X = X.values.T Y = Y.values.T print(X.shape,Y.shape) NETWORK ------------------------------ net = prn.CreateNN([3,6,8]) net = prn.train_LM(X,Y,net,verbose=True,k_max=1000,E_stop=1e-5)` — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#10?email_source=notifications&email_token=ACGZMJX4VNLNOPLLTGJ7D6TRAGGYNA5CNFSM4KNFQ34KYY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4IJRSORQ> , or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACGZMJT5RNV3MUABAUNJNK3RAGGYNANCNFSM4KNFQ34A> . <https://github.com/notifications/beacon/ACGZMJRRG5XIX7DSSLPG44LRAGGYNA5CNFSM4KNFQ34KYY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4IJRSORQ.gif>