<p>Kalchbrenner et. al (2015) implemented an CNN that accepts dynamic length input and pools them into k elements. If there are less than k elements to begin with, the remaining are zero-padded. Their experiments with sentence classification show that such networks successfully represent grammatical structures.</p>

<p>For details check out:</p>

<ul>
<li>the paper (<a href="http://arxiv.org/pdf/1404.2188v1.pdf" rel="nofollow">http://arxiv.org/pdf/1404.2188v1.pdf</a>)</li>
<li>Matlab code (link on page 2 of the paper)</li>
<li>suggestion for DCNNs for Theano/Keras (<a href="https://github.com/fchollet/keras/issues/373" rel="nofollow">https://github.com/fchollet/keras/issues/373</a>)</li>
</ul>
how to make the image_shape dynamic in the convolution in Theano
<p>I tried to process the tweets dataset using CNN in Theano. Different from images, the lenght of different tweets (corresponding to the image shape) is variable. So the shape of each tweet is different. However, in Theano, the convolution need that the shape information are constant values. So my question is that is there some way to make the image_shape dynamic?</p>