Closed Ellebkey closed 8 years ago
Hi! What have you specified as the activation functions? If you're able to post a MVC of your data? If so it'll be even easier to help you out.
I used the same example main.py on the settings I put
settings = {
"n_inputs" : 48,
"layers" : [ (30, sigmoid_function), (2, sigmoid_function) ],
"weights_low" : -1.0,
"weights_high" : 1.0,
}
My entries look like this: Instance([0,0,0,13,5,0,0,0,0,0,0,59,44,0,0,0,0,0,2,99,88,0,0,0,0,0,1,90,80,0,0,0,0,0,0,63,54,0,0,0,0,0,0,44,44,0,0,0], [0,1]) . . . and the same for 100 more entries.
But when I run the code stop on the line: network.check_gradient( training_data, cost_function ) and gives me that error
Ok, it looks like the error was located in the preprocessors. I guess you've used the standarize
preprocessor?
If that was the case, both the Github repo and the pip
-installable version has been fixed!
Yes, I used this:
preprocess = construct_preprocessor( dataset, [standarize] ) training_data = preprocess( dataset ) test_data = preprocess( dataset )
I'll try to use the new fix
Thanks you so much! It worked
Good to hear! Thank you for making me aware of the issue!
Hello I'm checking the library and I got a problem when I set my own data.
I'm creating a network with 48inputs, 30hidden layes and 2 for output. My inputs consist on values between 0-100. When I run the examples with my training data, the programs stops working and I get the error: The numeric gradient check failed!. I printed the diferrents values on gradient() and check_gradient() and I got nan on many values.
Hope you can help me.