Hello,
I'm not sure if my implementation is correct so I'm here to ask for
recommendation.
my input is of the size (66,100,100,3)
66 = batch size
100,100 width and height
3 is the image depth since it is colored.
so the first I do is to shuffle the input into
self.layer0_input = input.reshape((66, 3, 100, 100))
I'll just talk about the first convolution. I'm using as a first layer
convolution of 3 by 3 dimension with depth 32 so my result is of size
(66,32,98,98) without pooling
later to add the bias I have to shuffle the vector b from 1D 32 into
self.b.dimshuffle('x', 0, 'x', 'x')
I don't know if the implementation is correct so each b is added and
broadcasted into each kernel. In order to check the result
I printed the result of the convolution and the result after adding the bias or
in a different way I implemented this
print conv[0][0] - conv_b[0][0]
so here I check if the same bias is added to the full batch or single depth in
the first layer. the result is good so the b is added correctly to kernel.
I wanted to check to which bias this result belong by printing the the value of
the bias and it turned out to be different non of the values belong to the list
of the biases
here is the values of the biases
[ -1.44519465e-04 1.74123124e-06 -1.13637732e-04 1.43587783e-06
3.36924654e-06 5.05619437e-06 -1.06570985e-09 -4.12313739e-06
-2.31869606e-04 -4.95991553e-05 1.45750237e-05 -4.66647077e-09
-1.09766654e-04 1.57191480e-05 1.55315167e-04 2.15575733e-06 7.00626856e-07
3.62427163e-05 9.70169058e-05 7.42816774e-05 -7.28896484e-05 4.93809648e-06
-8.88103386e-06 -2.78137827e-06 -1.84110595e-05 -3.87128166e-05
-1.27837466e-05 3.16912156e-06
-2.48807328e-06 9.45689771e-06 2.58873297e-05 5.42514499e-06]
and here is the result of
or idx in xrange(32):
print conv[0][idx][0][0] - conv_b[0][idx][0][0]
0.000137329 0.0 0.000114441 -1.90735e-06 0.0 0.0 0.0 0.0 0.000228882
4.95911e-05 -1.52588e-05 0.0 0.000106812 -1.52588e-05 -0.000152588 0.0 0.0
-3.62396e-05 -9.15527e-05 -7.62939e-05 7.24792e-05 -7.62939e-06 8.58307e-06
0.0 1.83582e-05 3.8147e-05 1.33514e-05 0.0 0.0 -1.52588e-05 -3.05176e-05
0.0
I think there might be a mistake in the implementation.
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