Thanks for your reply. I'm afraid I might not have explained myself well.
> Under the hood, it is doing:
>
> >>> f = theano.function(inputs=[], outputs=E)
> >>> f()
>
>
I think that what you are describing here is what is the eval method doing.
That was not the question, I understand this. To put it simply, my question
is: how can I myself define a TensorVariable whose eval method takes no
arguments and returns a value?
For instance, let's say that I want a variable that represents the number
Pi. In my mind this is no less no more of a constant than the identity
matrix. So, I should be able to define a variable pi, for which I can call
pi.eval()
and get "3.14..."
>
> In that case, you can use the explicit version:
> >>> f = theano.function(inputs=[x], outputs=x)
> >>> f(3)
>
> or the "eval shortcut"
> >>> x.eval({x: 3})
>
This is unsatisfactory as I explained above. When you run x.eval({x : 3}) you
get array(3.0). In the following line, you could run x.eval({x : 999}) and
you would obtain array(999.0). This is NOT the behavior I want. The number
3 is nor permanently assigned to the variable x, the way the identity
matrix is assigned to E in my original example. I hope this makes my
question clearer.
> > Also, if someone knows of a reference that answers these basic questions
> that are not answered in the docs, I would be grateful.
>
> You can start with
> http://deeplearning.net/software/theano/tutorial/index.html
>
>
I have of course read that. And I find that it doesn't answer basic
questions, such as the one above, about how theano operates. It is just a
tutorial. I was asking if there were other resourced other than that.
Thanks! Daniel
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