Update README.md

README.md

@@ -19,10 +19,30 @@ you want to classify and the folder will be use as label for images in it.
 ## Usage  
 ### 1. Run directly  
 You can run it directly after preparing those datasets, and it will automatically build and train a
-***3 lays convolutional neural network*** and a ***5 lays convolutional neural network*** and solve the best model as ***best_model3/5.hdf5***, after finishing, you could either choose some
+***3 lays convolutional neural network*** and a ***5 lays convolutional neural network*** and solve the best model as ***best_model3/5.hdf5***. After finishing, you could either choose some
 test images and show the prediction results using ***test_plot*** or plot the Training and validation loss and Training and validation accuracy's trend into line chart using ***evaluate_plot*** or both.
 ### 2. Use as API
 It can also be used as API. There're several functions: ***rename, one_hot, getlabel, flatten, convmodel3, convmodel5, test_plot and evaluate_plot***, most of them will have a introduction in the beginning, you can also use ***main***
 as API's example code.
 
+## Comparison
+### 1. Original model
+Here're two line charts about the trend of loss and accuracy during training process of 3/5 lays convolutional neural network ***(First for 3, second for 5)*** .
+![image](https://github.com/XiangkunYe/EC601_Deep-Learning/blob/master/Chart/Original_Chart3.png)
+![image](https://github.com/XiangkunYe/EC601_Deep-Learning/blob/master/Chart/Original_Chart5.png)
+I used about 700 images of car and cat to train them. As you can see, in this amount of datasets, the 3 lays convolutional neural network perform well while overfitting occured just after several epoches in the 5 lays convolutional neural network model.
+### 2. Optimal model
+To deal with the overfitting problem, I read several papers and tutorials and find a few approaches:
+* Get more training data.
+* Reduce the capacity of the network.
+* Add weight regularization.
+* Add early stop.
+* Add dropout.    
+
+I choosed to add an early stop and set the patience as 10 (since we only run 50 epoches). Also, I added a dropout in each model (the amount is 0.25 for 3 convolutional neural network and 0.5 for 5) and changed some parameters like learning rate. Here's line charts after change and it performed much better ***(Also first for 3, second for 5)*** .
+![image](https://github.com/XiangkunYe/EC601_Deep-Learning/blob/master/Chart/Optimal_Chart3.png)
+![image](https://github.com/XiangkunYe/EC601_Deep-Learning/blob/master/Chart/Optimal_Chart5.png)
+### 3. Conclusion
+After optimization, the 5 lays convolutional neural network perform better and the overfitting problem is less significant even if it still occur. Also, the accuracy arises faster and higher than 3 lays convolutional neural network. The advantages of 3 lays convolutional neural network is that it learns faster and basically no overfitting and more suitable for small dataset. ***So for large dataset, we should use 5 lays convolutional neural network, and for small dataset or in order to prevent overfitting, we could use 3 lays convolutional neural network.***
+
 ### Hope you have fun with it! If you find any bugs or anything else, please tell me!