title: How to use numpy.amax() on list of ndarrays of different shape?
body: <p>I have a list of ndarrays of different shape, for example the shapes <code>(3072, 128), (128,), (128, 64)</code>.
  It is no coincidence, that the last dimension of a former array matches the first dimension of the latter array – if that helps in any way.
  I want to use numpy to calculate the overall maximum value of all arrays (so just one scalar). However, <code>numpy.amax(my_list)</code> returns</p>
  
  <pre class="lang-none prettyprint-override"><code>ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
  </code></pre>
  
  <p>I know that I can avoid the problem by using an explicit <code>for</code> loop like</p>
  
  <pre><code>for arr in my_list:
      max_so_far = numpy.amax(arr)
      ...
  </code></pre>
  
  <p>but I would like to take advantage of numpy's broadcasting/vectorizing feature, if possible. Is there a way to achieve that?</p>