bp video

Neural network concepts and suitability fieldsThe earliest research of neural network was proposed by the 40 psychologist McCulloch and mathematician Pitts, and their MP model was the prelude of Neural Network research.The development of neural networks has gone through 3 stages: 1947-1969 years early, during which time the scientists put forward many neuron models and learning rules, such as MP model, Hebb learning rules and perceptron, etc. the neural network control is at a low ebb from the e

first, the concept of BP neural networkBP Neural Network is a multilayer feedforward neural network, its basic characteristics are: the signal is forward propagation, and the error is the reverse propagation. in detail. For example, a neural network model with only one hidden layer, such as the following:(three-layer BP neural network model)the process of BP neur

BP algorithm is about the error of the reverse propagation algorithm, that is, starting from the output layer, the results compared with the expected results, to find out the error, and then according to the gradient of the maximum descent direction, adjust the connection weights of the neurons, and then in turn to adjust the level of the connections between each layer, for the bulk learning method, repeating the process Until the error reaches a suff

The training process for the BP network consists of the following steps:Step One: Network initialization.Step two: Implicit layer output calculation.Step three: Output layer output calculation.Step four: Error calculation.Step five: Weight update.Step Six: Threshold updates.Step Seven: Determine whether the iteration of the algorithm ends, and if not, return to step two.Speech feature signal recognition:The modeling of Speech feature signal classifica

Building4.4.2.1 BP network modelBP networks (Back-propagation network), also known as the reverse propagation neural network, through the training of sample data, constantly revise the network weights and thresholds to make the error function down in the negative gradient direction, approaching the desired output. It is a widely used neural network model, which is applied to function approximation, model recognition classification, data compression a

before you write: Some of the previous articles, such as the decision tree, Bayesian algorithm, and other simple algorithms to the Neural Network (BP), Support vector Machine (SVM), AdaBoost and other more sophisticated machine learning algorithms (to which interested friends can forward blog look), various algorithms have advantages and disadvantages, Basically can deal with linear and non-linear sample sets, but concept viewing Mencius these algori

Neural networks have been very hot, there has been a period of depression, now because of deep learning reasons to continue to fire up. There are many kinds of neural networks: forward transmission network, reverse transmission network, recurrent neural network, convolution neural network and so on. This paper introduces the basic Reverse Transmission neural Network (backpropagation), mainly describes the basic flow of the algorithm and its own experience in training

BPBP command is a breakpoint at an address , BP 0x7783feb can also be BP myapp! SomeFunction.For the latter, WinDBG will automatically find myapp! SomeFunction the corresponding address and set a breakpoint. But the problem with BP is:1) When the code changes, the function address changes, the breakpoint remains in the same position, not necessarily continue to b

BP (Back Propagation) network is a multi-layer feed-forward Network trained by the error inverse propagation algorithm, which was proposed by a team of scientists led by Rumelhart and mccelland in 1986, it is one of the most widely used neural networks. The BP network can learn and store a large number of input-output mode ing relationships without revealing mathematical equations describing such ing relati

1. Realize the effect2. Related codeThreading class for implementing BP training model1 classWorkthread (qtcore.qthread):2Finish_trigger = Qtcore.pyqtsignal ()#Close Waiting_gif3Result_trigger = Qtcore.pyqtsignal (PD. Series)#transmitting the signal of the prediction result4Evaluate_trigger = qtcore.pyqtsignal (list)#transmit the correct rate signal5 6 def __int__(self):7Super (Workthread, self).__init__()8 9 definit (self, dataset, feature, l