title: does inputs have to match weights in a neural network (forwardpropagation)?
body: <p>I want to a have 4 inputs to the model below, but i cant get it to work. Even if i remote the asset command, add one more input and run, then i get an error (btw the code i have posted workes fine).</p>
  <p>Can anyone explain to me, why i cant have more inputs than weights? I suspect that it has something to do with the vector dimensions, but i cant arrive at an answer.</p>
  <pre><code>def w_sum(a,b):
      assert(len(a) == len(b))
      output = 0
      for i in range(len(a)):
          output += (a[i] * b[i])
      return output
  
  weights = [0.1, 0.2, 0]
  def neural_network(input, weights):
      pred = w_sum(input,weights)
      return pred
  
  input1 = [8.5, 0,65, 1.2]
  
  input = [input1[0],input1[1],input1[2]]
  pred = neural_network(input,weights)
  print(pred)
  </code></pre>