Under market completeness assumptions, hedging a portfolio of derivatives is straightforward. In view of friction, transaction costs, liquidity and other factors, a framework is presented to extend the pricing and hedging with the hedging strategy treated as a neural network. We study the deep hedging model under incomplete market constraints such as frictions, traction cost, permanent impacts on the market and illiquidity. We discuss the limitations of certain models concerning the applications in deep hedging with constraints. After which, we analyse the advantages of different models and their joint models and find that the hedging strategy is close to the Black-Scholes delta hedging strategy. An example is also given when training after designing two hedging models. The Black-Scholes delta hedging is indeed approximated by unsupervised learning.