官术网_书友最值得收藏!

Mean squared error

The mean squared error (MSE) is also called a quadratic cost function as it uses the squared difference to measure the magnitude of the error:

Here, the following applies:

  • a is the output from the ANN
  • y is the expected output
  • n is the number of samples used

The cost function is pretty straightforward. For exampleconsider a single neuron with just one sample, (n=1). If the expected output is 2 (y=2) and the neuron outputs 3 (a=3), then the MSE is as follows:

Similarly, if the expected output is 3 (y=3) and the neuron outputs 2 (a=2), then the MSE is as follows:

Therefore, the MSE quantifies the magnitude of the error made by the neuron. One of the issues with MSE is that when the values in the network get large, the learning becomes slow. In other words, when the weights (w) and bias (b) or z get large, the learning becomes very slow. Keep in mind that we are talking about thousands of neurons in an ANN, which is why the learning slows down and eventually stagnates with no further learning.

主站蜘蛛池模板: 蚌埠市| 义乌市| 馆陶县| 鱼台县| 正镶白旗| 晋江市| 隆林| 孟津县| 容城县| 兴山县| 铁岭市| 云梦县| 和顺县| 吉安县| 那曲县| 棋牌| 阳朔县| 辽宁省| 牟定县| 天台县| 灵山县| 凯里市| 手机| 新乡县| 绥滨县| 五大连池市| 宁强县| 敦化市| 滁州市| 东丽区| 土默特右旗| 宝清县| 广平县| 祥云县| 阳新县| 石门县| 揭东县| 淮滨县| 长春市| 阿瓦提县| 陆良县|