Thanks Pascal,
I tried your approach
class TemporalDifference(object):
def __init__(self, shape):
self.old = None
def __call__(self, data):
if self.old is None:
self.old = theano.shared(np.zeros((1, )*data.ndim))
diff = data - self.old
add_update(self.old, data)
return diff
, but that leads to a different problem - that is, when I compile and run
on test data (shaped (3, 4, 5, 6)), I get:
ValueError: Input dimension mis-match. (input[0].shape[0] = 3, input[1].
shape[0] = 1)
Because (1, 1, 1, 1) doesn't match the shape of the data.
If I try to enable this by making the state variable broadcastable:
class TemporalDifference(object):
def __init__(self, shape):
self.old = None
def __call__(self, data):
if self.old is None:
self.old = theano.shared(np.zeros((1, )*data.ndim),
broadcastable=(True, )*data.ndim)
diff = data - self.old
add_update(self.old, data)
return diff
Then I get:
TypeError: ('An update must have the same type as the original shared
variable (shared_var=<TensorType(float64, (True, True, True, True))>,
shared_var.type=TensorType(float64, (True, True, True, True)),
update_val=<TensorType(float64, 4D)>, update_val.type=TensorType(float64,
4D)).', 'If the difference is related to the broadcast pattern, you can
call the tensor.unbroadcast(var, axis_to_unbroadcast[, ...]) function to
remove broadcastable dimensions.')
So now I'm wondering if there's any way to do this.
On Wednesday, November 9, 2016 at 5:53:58 PM UTC+1, Pascal Lamblin wrote:
>
> Hi,
>
> It is not possible to initialize a shared variable without a shape,
> since it needs a value.
>
> However, the shape of a shared variable is not fixed (only the number of
> dimensions and dtype are).
>
> So you could create a shared variable with shape (1, 1, ...) for
> instance, and then either call set_value() or a function with
> updates=... to properly initialize it when you actually know its shape.
>
> On Wed, Nov 09, 2016, Peter O'Connor wrote:
> > Hi all, I'm implementing a "temporal difference", which is just this:
> >
> > class TemporalDifference(object):
> >
> > def __init__(self, shape):
> > self.old = theano.shared(np.zeros(shape))
> >
> > def __call__(self, data):
> > diff = data - self.old
> > add_update(self.old, data)
> > return diff
> >
> >
> > It would be really nice not to have to pass in the shape in advance, as
> it
> > can be a bit difficult to figure out sometimes. Is there some way to do
> > this without having to know the shape in advance?
> >
> > Thanks.
> >
> > --
> >
> > ---
> > You received this message because you are subscribed to the Google
> Groups "theano-users" group.
> > To unsubscribe from this group and stop receiving emails from it, send
> an email to [email protected] <javascript:>.
> > For more options, visit https://groups.google.com/d/optout.
>
>
> --
> Pascal
>
--
---
You received this message because you are subscribed to the Google Groups
"theano-users" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to [email protected].
For more options, visit https://groups.google.com/d/optout.
