人工神经网络基准 [英] Artificial neural networks benchmark
问题描述
是否有任何基准可用于检查ANN的实现是否正确?
我想要一些输入和输出数据，以及一些信息，例如:
-在90％的测试数据中，三层前馈神经网络的输出应该正确.
我需要此信息以确保这种ANN能够处理此类问题.
可能最好的方法是设计一个学习XOR函数的神经网络.这是一个显示示例运行的网站: http://www.generation5.org/content/2001/xornet.asp
我有一个家庭作业，其中的老师给了我们给定权重的神经网络的前几次运行...如果您将神经网络设置为相同的权重，那么您应该得到相同的结果(直接反向传播) .
如果您的神经网络具有1个输入层(具有2个输入神经元+ 1个常数)，1个隐藏层(具有2个神经元+ 1个常数)和1个输出层，则将所有权重初始化为0.6，然后将恒定的神经元总是返回-1，那么您应该在前10次运行中得到完全相同的结果:
* Data File: xor.csv
* Number of examples: 4
Number of input units: 2
Number of hidden units: 2
Maximum Epochs: 10
Learning Rate: 0.100000
Error Margin: 0.100000
==== Initial Weights ====
Input (3) --> Hidden (3) :
1 2
0 0.600000 0.600000
1 0.600000 0.600000
2 0.600000 0.600000
Hidden (3) --> Output:
0 0.600000
1 0.600000
2 0.600000
***** Epoch 1 *****
Maximum RMSE: 0.5435466682137927
Average RMSE: 0.4999991292217466
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.599691 0.599691
1 0.599987 0.599987
2 0.599985 0.599985
Hidden (3) --> Output:
0 0.599864
1 0.599712
2 0.599712
***** Epoch 2 *****
Maximum RMSE: 0.5435080531724404
Average RMSE: 0.4999982558452263
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.599382 0.599382
1 0.599973 0.599973
2 0.599970 0.599970
Hidden (3) --> Output:
0 0.599726
1 0.599425
2 0.599425
***** Epoch 3 *****
Maximum RMSE: 0.5434701135827593
Average RMSE: 0.4999973799942081
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.599072 0.599072
1 0.599960 0.599960
2 0.599956 0.599956
Hidden (3) --> Output:
0 0.599587
1 0.599139
2 0.599139
***** Epoch 4 *****
Maximum RMSE: 0.5434328258833577
Average RMSE: 0.49999650178769495
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.598763 0.598763
1 0.599948 0.599948
2 0.599941 0.599941
Hidden (3) --> Output:
0 0.599446
1 0.598854
2 0.598854
***** Epoch 5 *****
Maximum RMSE: 0.5433961673713259
Average RMSE: 0.49999562134010495
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.598454 0.598454
1 0.599936 0.599936
2 0.599927 0.599927
Hidden (3) --> Output:
0 0.599304
1 0.598570
2 0.598570
***** Epoch 6 *****
Maximum RMSE: 0.5433601161709642
Average RMSE: 0.49999473876144657
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.598144 0.598144
1 0.599924 0.599924
2 0.599914 0.599914
Hidden (3) --> Output:
0 0.599161
1 0.598287
2 0.598287
***** Epoch 7 *****
Maximum RMSE: 0.5433246512036478
Average RMSE: 0.49999385415748615
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.597835 0.597835
1 0.599912 0.599912
2 0.599900 0.599900
Hidden (3) --> Output:
0 0.599017
1 0.598005
2 0.598005
***** Epoch 8 *****
Maximum RMSE: 0.5432897521587884
Average RMSE: 0.49999296762990975
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.597526 0.597526
1 0.599901 0.599901
2 0.599887 0.599887
Hidden (3) --> Output:
0 0.598872
1 0.597723
2 0.597723
***** Epoch 9 *****
Maximum RMSE: 0.5432553994658493
Average RMSE: 0.49999207927647754
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.597216 0.597216
1 0.599889 0.599889
2 0.599874 0.599874
Hidden (3) --> Output:
0 0.598726
1 0.597443
2 0.597443
***** Epoch 10 *****
Maximum RMSE: 0.5432215742673802
Average RMSE: 0.4999911891911738
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.596907 0.596907
1 0.599879 0.599879
2 0.599862 0.599862
Hidden (3) --> Output:
0 0.598579
1 0.597163
2 0.597163
Input (3) --> Hidden (3) :
1 2
0 0.596907 0.596907
1 0.599879 0.599879
2 0.599862 0.599862
Hidden (3) --> Output:
0 0.598579
1 0.597163
2 0.597163
xor.csv包含以下数据:
0.000000,0.000000,0
0.000000,1.000000,1
1.000000,0.000000,1
1.000000,1.000000,0
您的神经网络应如下所示(不考虑权重，黄色是恒定输入神经元):
(来源: jtang.org )
Are there any benchmarks that can be used to check if implementation of ANN is correct?
I want to have some input and output data, and some information like:
- The output of Feedforward neural network with 3 layers should be correct in 90% of test data.
I need this information to be sure that this kind of ANN is able to deal with such problem.
Probably the best thing you can do is design a neural network that learns the XOR function. Here is a web site that shows sample runs: http://www.generation5.org/content/2001/xornet.asp
I had a homework in which our teacher gave us the first few runs of the neural network with given weights... if you set your neural network with the same weights, then you should get the same results (with straight backpropagation).
If you have a neural network with 1 input layer (with 2 input neurons + 1 constant), 1 hidden layer (with 2 neurons + 1 constant) and 1 output layer and you initialize all your weights to 0.6, and make your constant neurons always return -1, then you should get the exact same results in your first 10 runs:
* Data File: xor.csv
* Number of examples: 4
Number of input units: 2
Number of hidden units: 2
Maximum Epochs: 10
Learning Rate: 0.100000
Error Margin: 0.100000
==== Initial Weights ====
Input (3) --> Hidden (3) :
1 2
0 0.600000 0.600000
1 0.600000 0.600000
2 0.600000 0.600000
Hidden (3) --> Output:
0 0.600000
1 0.600000
2 0.600000
***** Epoch 1 *****
Maximum RMSE: 0.5435466682137927
Average RMSE: 0.4999991292217466
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.599691 0.599691
1 0.599987 0.599987
2 0.599985 0.599985
Hidden (3) --> Output:
0 0.599864
1 0.599712
2 0.599712
***** Epoch 2 *****
Maximum RMSE: 0.5435080531724404
Average RMSE: 0.4999982558452263
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.599382 0.599382
1 0.599973 0.599973
2 0.599970 0.599970
Hidden (3) --> Output:
0 0.599726
1 0.599425
2 0.599425
***** Epoch 3 *****
Maximum RMSE: 0.5434701135827593
Average RMSE: 0.4999973799942081
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.599072 0.599072
1 0.599960 0.599960
2 0.599956 0.599956
Hidden (3) --> Output:
0 0.599587
1 0.599139
2 0.599139
***** Epoch 4 *****
Maximum RMSE: 0.5434328258833577
Average RMSE: 0.49999650178769495
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.598763 0.598763
1 0.599948 0.599948
2 0.599941 0.599941
Hidden (3) --> Output:
0 0.599446
1 0.598854
2 0.598854
***** Epoch 5 *****
Maximum RMSE: 0.5433961673713259
Average RMSE: 0.49999562134010495
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.598454 0.598454
1 0.599936 0.599936
2 0.599927 0.599927
Hidden (3) --> Output:
0 0.599304
1 0.598570
2 0.598570
***** Epoch 6 *****
Maximum RMSE: 0.5433601161709642
Average RMSE: 0.49999473876144657
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.598144 0.598144
1 0.599924 0.599924
2 0.599914 0.599914
Hidden (3) --> Output:
0 0.599161
1 0.598287
2 0.598287
***** Epoch 7 *****
Maximum RMSE: 0.5433246512036478
Average RMSE: 0.49999385415748615
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.597835 0.597835
1 0.599912 0.599912
2 0.599900 0.599900
Hidden (3) --> Output:
0 0.599017
1 0.598005
2 0.598005
***** Epoch 8 *****
Maximum RMSE: 0.5432897521587884
Average RMSE: 0.49999296762990975
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.597526 0.597526
1 0.599901 0.599901
2 0.599887 0.599887
Hidden (3) --> Output:
0 0.598872
1 0.597723
2 0.597723
***** Epoch 9 *****
Maximum RMSE: 0.5432553994658493
Average RMSE: 0.49999207927647754
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.597216 0.597216
1 0.599889 0.599889
2 0.599874 0.599874
Hidden (3) --> Output:
0 0.598726
1 0.597443
2 0.597443
***** Epoch 10 *****
Maximum RMSE: 0.5432215742673802
Average RMSE: 0.4999911891911738
Percent Correct: 0%
Input (3) --> Hidden (3) :
1 2
0 0.596907 0.596907
1 0.599879 0.599879
2 0.599862 0.599862
Hidden (3) --> Output:
0 0.598579
1 0.597163
2 0.597163
Input (3) --> Hidden (3) :
1 2
0 0.596907 0.596907
1 0.599879 0.599879
2 0.599862 0.599862
Hidden (3) --> Output:
0 0.598579
1 0.597163
2 0.597163
xor.csv contains the following data:
0.000000,0.000000,0
0.000000,1.000000,1
1.000000,0.000000,1
1.000000,1.000000,0
Your neural network should look like this (disregard the weights, yellow is the constant input neuron):
(source: jtang.org)
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