Your interpretation of what should happen is correct, I can't replicate
that issue on my machine. See the code below which runs to completion on my
machine.
import theano
import theano.tensor as T
import numpy as np
inp = T.tensor4()
w1 = theano.shared(np.random.rand(32,1,1,1).astype('float32'))
h1 = T.nnet.conv2d(inp, w1)
h1p = T.signal.pool.pool_2d(h1, ds=(2,2), ignore_border=True)
d = np.random.rand(64,1,28,28).astype('float32')
f = theano.function([inp], h1)
t = f(d).shape
assert t==(64L, 32L, 28L, 28L)
fp = theano.function([inp], h1p)
tp = fp(d).shape
assert tp==(64L, 32L, 14L, 14L)
On Monday, July 25, 2016 at 4:25:10 PM UTC-4, [email protected] wrote:
>
> Thanks @Doug,
> You were right about the stride. I just found that pool_2d() is returning
> a tensor of shape different from what I would expect which I don't know how
> to interpret. Meaning is my fault or theano?
>
> Using again my previous example:
> h1 = conv2d(X, w1, image_shape=(64, 1, 28, 28), filter_shape=(32, 1, 1, 1)
> )
> print(h1.shape)
> >>> (64,32,28,28)
> >>>
> >>>
> >>>
> h1p = pool_2d(h1, ds=(2,2), ignore_border=True)
> print(h1p.shape)
> >>> (64,1,14,14)
> >>>
> >>>
>
> I should be getting a shape of (64,32,14,14) for h1p right? and not a
> shape of (64,1,14,14). I don't know why pool_2d() is messing with number of
> feature maps?
> It shouldn't be doing that right? Or am I wrong?
>
> Thank you for helpful hint.
>
>
>
> On Monday, July 25, 2016 at 4:12:38 PM UTC+1, Doug wrote:
>>
>> I'd recommend compiling a function to see what the actual shape of h1p
>> is, that will help you understand if the problem is on your end or with
>> theano. In this specific case I think the issue is that you've specified
>> st=(1,1) for the pooling, so you aren't actually doing a traditional 2x2
>> maxpool. You need to either set st=(2,2), or leave it undefined in which
>> case it defaults to whatever ds is set to.
>>
>> On Sunday, July 24, 2016 at 7:28:21 PM UTC-4, [email protected]
>> wrote:
>>>
>>> Hello dear community members.
>>> I am facing this weird behavior of
>>> conv2d()
>>> function in a convent.
>>>
>>> I'll try to explain the situation with a simple example of a convent
>>> with only two convolutions.
>>> Imagine that I have the following filters and their corresponding size
>>> w1 = (32, 1, 1, 1)
>>> w2 = (64, 32, 3, 3)
>>>
>>> Then my convent would be something like the following:
>>> h1 = conv2d(X, w1, image_shape=(64, 1, 28, 28), filter_shape=w1.shape)
>>> h1p = pool_2d(h1, ds=(2,2), st=(1,1), ignore_border=True)
>>> h2 = conv2d(h1p, w2, image_shape=(64, 32, 14, 14), filter_shape=w2.shape
>>> )
>>> h2p = pool_2d(h2, ds(2,2), st=(1,1), ignore_border=True)
>>>
>>> In this case I get an error complaining about the image shape as an
>>> input regarding the second convolution. It says that should be 27 instead
>>> of 14.
>>> This where things start to get a bit unclear for me. By looking at the
>>> conv2d() documentation its says that if you use the valid mode which is the
>>> default
>>> in this case then the output image_shape from the convolution is
>>> computed as image_shape - filter_shape + 1.
>>> If we consider that then from our first image_shape=(64, 1, 28, 28) as
>>> input to the first convolution operation we would have the following
>>> image_shape dimension:
>>> image_height = 28 - 1 (filter_height) + 1 (Constant)
>>> new_image_height = 28 (unchanged from the above computations)
>>>
>>> now if we do a downsampling with filter size = (2,2)
>>>
>>> final_new_image_height = 28/2 = 14
>>>
>>> As I have exactly putted in my second convolution. Now why is theano
>>> complaining about that and is asking that the input should be 27 instead
>>> for the image height and width. It seems like the pooling is either being
>>> skipped or never considered by theano in this case. Why is that happening?
>>>
>>> Any developers of theano who can shed some light on this topic?
>>>
>>> Thanks!
>>>
>>>
>>>
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