Thanks Pascal,
I tried your approach
class TemporalDifference(object):
def __init__(self):
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):
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.
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