Belinda,
I don't think that a layer should be Undefined. Sounds like that layer
hasn't been .connect() ed to another layer yet (when the layers get
connected, there is logic there to determine if each of the layers are
Input, Hidden, or Output types). Could there be a mistake in the topology?
FYI, there are bias units (aka, .weight[]) on Input and Context layers,
even though they are not used.
Also, did you know that you can refer to weights with this syntax:
A single weight:
net["fromLayerName", "toLayerName"][fromPos][toPos]
net["input", "hidden"][0][1]
and the entire matrix:
net["fromLayerName", "toLayerName"].weight
net["input", "hidden"].weight
If none of these bits help, feel free to send me some code and I'll take
a look at it.
-Doug
belinda thom wrote:
Hi,
I'd borrowed and then modified some code to store weights in a dictionary:
for layer in layers:
if layer.type != 'Input':
for i in range(layer.size):
debug_here()
d['%s[%d][0]'%(layer.name[0:3],i+1)]=layer.weight[i]
for connection in connections:
for i in range(connection.fromLayer.size):
for j in range(connection.toLayer.size):
d['%s[%d]%s[%d]'%(connection.fromLayer.name[0:3],i+1, \
connection.toLayer.name[0:3],j+1)] =
connection.weight[i][j]
w/the hope that putting these things in a dictionary would make the
actual weight values easier to understand/explain in class.
My plan was to have connection weights available via, e.g.
d['inp[1]hid[1]'] where this would access the 1st input to 1st hidden
unit weight. For biases, I would do, e.g. d['hid[1][0]'] which would
access 1st hidden unit's bias.
My problem is with the "if layer.type != 'Input'" check (part of the
code I inherited), as I'm getting more bias weights than make sense. So
I started poking around in the debugger and noticed an 'Undefined' layer
type. What is this? I think its the thing that's producing, e.g.
d['inp[1][0]'], where I would expect the input layers to have no useful
bias.
Thanks,
--b
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