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Showing posts with the label Convolutional Neural Network

96.24% accuracy with higher epoch numbers for Convolutional Neural Network

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For Convolutional Neural Network , there are a lots of factors affect the model accuracy. Factors included the structure of Convolutional Neural Network , hyper parameters values, over-fitting and etc. Only correct or suitable network structure can produce high model accuracy. If wrong network structure is used, whatever you tune the hyper parameters, the accuracy will still worse. So, after we found the right network structure, only we can start to tune the hyper parameters. However, over-fitting may happened on your model accuracy. There are several methods to prevent that. One of the over-fitting prevention is by using the dropout function. Dropout give the model a way to find alternative way to train the model by removed some characteristic according to the ratio. Another way to prevent over-fitting is by increasing the number of samples. Besides, we can also simplify the network structure. Some complicated network structure sometimes can cause over-fitting too.  Fi...