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2019-03-06 11:23:39 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken Ya I ended up just making my own mlp from scratch haha, I need the control. It’s just excel dude |

2019-03-05 13:32:05 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken sjhalayka I am really confused why you gave me your code. This is a problem dealing with OpenCV? |

2019-03-05 12:28:20 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken Hey LBerger I rewrote the question to be more clear what the problem is. |

2019-03-05 12:23:32 -0500 | edited question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP using UPDATE_WEIGHTS to graph error - broken I ran into this problem while trying to make a learning curve of an |

2019-03-05 12:22:26 -0500 | edited question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP using UPDATE_WEIGHTS to graph error - broken I ran into this problem while trying to make a learning curve of an |

2019-03-05 12:03:53 -0500 | edited question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP using UPDATE_WEIGHTS to graph error - broke Doing some benchmarks and I want to make learning curves, showing er |

2019-03-05 12:03:03 -0500 | edited question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP using UPDATE_WEIGHTS to graph error - broke Doing some benchmarks and I want to make learning curves, showing er |

2019-03-05 12:00:52 -0500 | edited question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP using UPDATE_WEIGHTS to graph error vs number of training epochs Doing some benchmarks and I want to make learni |

2019-03-05 12:00:31 -0500 | edited question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP UPDATE_WEIGHTS wonky Doing some benchmarks and I want to make learning curves, showing error versus number of tr |

2019-03-05 03:07:24 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken Yes it’s a regression problem. The training data is still the same for both network1 and network2. Shouldn’t the random |

2019-03-05 02:52:30 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken Yes it’s a regression problem. berak contionous responses are fine, I think one of us confused? Could be me. The traini |

2019-03-05 02:51:09 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken Yes it’s a regression problem. berak contionous responses are fine, I think one of us confused? Could be me. The traini |

2019-03-05 02:39:28 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken Shuffling data differently shouldn’t have that big of an effect on how quickly the error converges? |

2019-03-05 02:27:38 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken What would be the point of trying the XOR problem? |

2019-03-05 02:25:53 -0500 | commented question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken It’s the same results if I comment out the random seed lines. |

2019-03-04 16:05:00 -0500 | edited question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP UPDATE_WEIGHTS wonky Doing some benchmarks and I want to make learning curves, showing error versus number of tr |

2019-03-04 15:45:53 -0500 | asked a question | ANN_MLP using UPDATE_WEIGHTS to graph error - broken ANN_MLP UPDATE_WEIGHTS wonky Doing some benchmarks and I want to make learning curves, showing error versus number of tr |

2019-03-01 12:33:18 -0500 | commented question | termination criteria broken if you want to specify number of epochs Thanks, I posted the issue on GitHub! |

2019-03-01 12:33:10 -0500 | commented question | termination criteria broken if you want to specify number of epochs Thanks, I posted the issue of GitHub! |

2019-02-28 19:30:57 -0500 | asked a question | termination criteria broken if you want to specify number of epochs termination criteria broken if you want to specify number of epochs cv::Ptr<cv::ml::ann_mlp> network = cv::ml::ANN |

2019-02-28 19:21:00 -0500 | commented answer | Max ANN_MLP size? Ya thanks dude. I found the issue, the termination criteria is BROKEN on OpenCV, See this line: https://github.com/openc |

2019-02-28 18:38:33 -0500 | commented answer | Max ANN_MLP size? I am not having any issues with number of nodes/layers. I am just using two layers with 40 nodes each. The problem is wh |

2019-02-28 17:50:26 -0500 | commented answer | Max ANN_MLP size? Ya my job right now is to determine if this problem is solvable with an MLP, sorry I can't talk about it too much becaus |

2019-02-28 16:59:25 -0500 | marked best answer | Max ANN_MLP size? Hello dev team, I had some general questions on max MLP parameters: Max number of layers? Max number of nodes per layer (and is this dependent on the number of layers used)? Max number of epochs you can set for training termination criteria? I know these numbers are directly limited by the size of the variable type used, but curious in implementation what the actual limits are. Thanks! -Tim |

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2019-02-28 16:57:18 -0500 | commented answer | Max ANN_MLP size? Thanks for the links to source code and response! From the train method here: https://github.com/opencv/opencv/blob/978 |

2019-02-28 16:44:18 -0500 | received badge | ● Student (source) |

2019-02-28 16:20:24 -0500 | edited question | Max ANN_MLP size? Max ANN_MLP size? Hello dev team, I had some general questions on max MLP parameters: Max number of layers? Max number |

2019-02-28 16:19:07 -0500 | asked a question | Max ANN_MLP size? Max ANN_MLP size? Hello dev team, I had some general questions on max MLP parameters: Max number of layers? Max number |

2017-10-04 17:57:48 -0500 | edited answer | MLP Activation Function - train vs predict Okay so tanh(x) = sigmoid function = (1+e^(-2x)) / (1-e^(-2x)) so the activation functions are the same. The sigmoid fun |

2017-10-04 17:56:48 -0500 | received badge | ● Editor (source) |

2017-10-04 17:56:48 -0500 | edited answer | MLP Activation Function - train vs predict Okay so tanh(x) = sigmoid function = (1+e^(-2x)) / (1-e^(-2x)) so the activation functions are the same. The sigmoid fun |

2017-10-04 17:55:39 -0500 | answered a question | MLP Activation Function - train vs predict Okay so tanh(x) = sigmoid function = (1+e^(-2x)) / (1-e^(-2x)) so the activation functions are the same. The sigmoid fun |

2017-10-02 02:48:29 -0500 | commented question | MLP Activation Function - train vs predict Ya you nailed it. Smart thinking diving into the source code. I posted an answer to this. |

2017-10-02 02:01:49 -0500 | commented question | MLP Activation Function - train vs predict In the docs, http://docs.opencv.org/2.4/modules/ml/doc/neural_networks.html The sigmoid function is described in the top |

2017-09-30 20:08:24 -0500 | asked a question | MLP Activation Function - train vs predict MLP Activation Function - train vs predict Hey, so my question was for the developers. In the docs, it says that train |

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