I'm trying to concatenate two tensors along their 3rd dimension.
param = T.concatenate(
[img_param[:, :, :, :3], cost.repeat(2, axis=3)],
axis=3
)
img_param has the shape (1, 128, 128, 5), cost is of shape (1, 128, 128, 1).
If I take cost directly from the input, this works perfectly fine.
Yet, if I first run T.nnet.conv2d with a constant filter (1, 1, 1, 1) I get:
ValueError: all the input array dimensions except for the concatenation
axis must match exactly
Apply node that caused the error: Join(TensorConstant{3}, Subtensor{::, ::,
::, :int64:}.0, Reshape{4}.0)
Toposort index: 12
Inputs types: [TensorType(int8, scalar), TensorType(float32, 4D),
TensorType(float32, 4D)]
Inputs shapes: [(), (1, 128, 128, 3), (1, 128, 1, 2)]
The conv2d call:
cost = T.nnet.conv2d(
input=input,
filters=self.unary_w,
input_shape=self.input_shp,
filter_shape=self.unary_filter_shp,
border_mode='valid'
). dimshuffle(0,2,3,1)
(I also made sure all dimensions are shuffled to the correct shape)
I tried returning both tensors to validate their shape and both return just
fine.
I've looked a bit into this and it seems to have something to do with a
sort of sparse-matrix conversion. Meaning Theano just automatically drops
dimensions which are close to 0.
Any ideas?
Thanks,
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