Typically people use the reparameterization trick to handle this. See the
original variational autoencoder paper, and example lasagne implementation
here:
https://github.com/Lasagne/Recipes/blob/master/examples/variational_autoencoder/variational_autoencoder.py#L92
On Friday, June 16, 2017 at 6:43:44 PM UTC-4, Sunjeet Jena wrote:
>
> I am working on a code to implement a deep RL algorithm where the policy
> function is the sampled values from a normal distribution. But when I
> differentiate through the cost function(which of course depends upon the
> the distribution) show the following error:
>
> "theano.gradient.NullTypeGradError: tensor.grad encountered a NaN. This
> variable is Null because the grad method for input 2
> (Subtensor{int64:int64:}.0) of the RandomFunction{normal} op is
> mathematically undefined. No gradient defined through raw random numbers op"
>
>
> Is there anyway I can solve it?
>
>
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