In `linear regression` we use the following cost function which is a convex function:<br /> [![enter image description here][1]][1]
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We Use the following cost function<br /> 
[![enter image description here][2]][2]
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in `logistic regression` because the preceding cost function is not `convex` whenever the hypothesis (h) is logistic function. We have changed the equation of cost function to have a convex shape to find its global (the only one which exists). There is a fact that I can not understand. In `Multi Layer Perceptrons` ANNs I have seen a lot that they can be stuck in local minimums. Why is that? We have used this cost function for each perceptron and gotten the rules for updating the values for the weights in back propagation algorithm; So why do we stuck?


  [1]: https://i.sstatic.net/aFBeP.png
  [2]: https://i.sstatic.net/JX0jv.png