title: How to split a tensor column-wise in Keras to implement STFCN
body: <p>I'd like to implement the Spatiotemporal Fully Convolutional Network (STFCN) in Keras. I need to feed each depth column of a 3D convolutional output, e.g. a tensor with shape <code>(64, 16, 16)</code>, as input to a separate LSTM.</p>
  
  <p>To make this clear, I have a <code>(64 x 16 x 16)</code> tensor of dimensions <code>(channels, height, width)</code>. I need to split (explicitly or implicitly) the tensor into 16 * 16 = 256 tensors of shape <code>(64 x 1 x 1)</code>.</p>
  
  <p>Here is a diagram from the STFCN paper to illustrate the Spatio-Temporal module. What I described above is the arrow between 'Spatial Features' and 'Spatio-Temporal Module'.</p>
  
  <p><a href="https://i.stack.imgur.com/V253r.png" rel="noreferrer"><img src="https://i.stack.imgur.com/V253r.png" alt="The connection between FCn and Spatio-Temporal Module is the relevant part of the diagram."></a></p>
  
  <p>How would this idea be best implemented in Keras?</p>