title: How Conv2d works in different input dimension and filter dimension?
body: <p>I wonder how TensorFlow conv2d works in different input dimensions and filter dimensions.
  For example, the input shape of a Conv2d layer is [1, 13, 13, 10] and the filter shape is
  [20, 3, 3, 10] (will use 20 3x3 filters, no pedding)).</p>
  <p>In this situation, how does the Filter works?
  As far as I understand, 20 filters do dot product on 10 inputs each.</p>
  <p>(The first filter does dot product on every 10 inputs, and next filter does same,,)
  So the output shape can be [1, 11, 11, 20].</p>
  <p>Am I right?</p>