Comments (2)
Looks like the learning rate is too high. LR < 0.3 seems to work.
from playground.
Yes. This is the expected behavior. Whenever you get a NaN, we show it using a grey color.
In this case, you are using regression as a problem type, and the output of the last neuron is just a linear function (unbounded output, so the error derivative is unbounded). So, since your learning rate is too high, the network is going to escape the stable region and diverge.
from playground.
Related Issues (20)
- Feature selection broken HOT 1
- Magic number
- Why aren't you saying in the Title Area a statement something like this TensorFlow model is using CNN, RNN or whatever form of NNN your using. HOT 1
- visualization_Locally
- upload my own data
- Feature request: Cross-entropy error function
- i farded HOT 1
- Unable to see dataset options
- How feature visualisation works? HOT 1
- about the code update weight in the function updateWeights.
- Make it easier to understand with cats & dogs database
- Training loss graph is updated when "discretize output" is selected or deselected HOT 1
- our own data HOT 1
- Yes, here are two examples I am aware of:
- Invitation to Collaborate: Enhancing TensorFlow Playground for Beginners and Beyond.
- Final output values do not make mathematical sense. HOT 2
- Feature Request: "best observed" parameters for "best" classification output on test data and some accompanying commentary on why.
- [Question] On playground the parameter in entry are x and y, what about color ? HOT 1
- New Feature Request - Live update of weights upon placement of cursor on any weight HOT 1
- Model Compression
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from playground.