title: Understanding input/output dimensions of neural networks
body: <p>Let's take a fully-connected neural network with one hidden layer as an example. The input layer consists of <strong>5 units</strong> that are each connected to all hidden neurons. In total there are <strong>10 hidden neurons</strong>.</p>
  
  <p>Libraries such as Theano and Tensorflow allow <em>multidimensional input/output shapes</em>. For example, we could use sentences of 5 words where each word is represented by a 300d vector. </p>
  
  <p>How is such an input mapped on the described neural network? I do not understand what an ouptut shape of (None, 5, 300) (just an example) means. In my imagination we just have a bunch of neurons through which single numbers flow. </p>
  
  <p>When I have an output shape of (None, 5, 300), how much neurons do I have in the corresponding network? How do I connect the words to my neural network?</p>