bias-neuron | 易学教程

BackPropagation Neuron Network Approach - Design

阅读更多关于 BackPropagation Neuron Network Approach - Design

问题 I am trying to make a digit recognition program. I shall feed a white/black image of a digit and my output layer will fire the corresponding digit (one neuron shall fire, out of the 0 -> 9 neurons in the Output Layer). I finished implementing a Two-dimensional BackPropagation Neuron Network. My topology sizes are [5][3] -> [3][3] -> 1[10]. So it's One 2-D Input Layer, One 2-D Hidden Layer and One 1-D Output Layer. However I am getting weird and wrong results (Average Error and Output Values).

Why the BIAS is necessary in ANN? Should we have separate BIAS for each layer?

阅读更多关于 Why the BIAS is necessary in ANN? Should we have separate BIAS for each layer?

问题 I want to make a model which predicts the future response of the input signal, the architecture of my network is [3, 5, 1]: 3 inputs, 5 neurons in the hidden layer, and 1 neuron in output layer. My questions are: Should we have separate BIAS for each hidden and output layer? Should we assign weight to BIAS at each layer (as BIAS becomes extra value to our network and cause the over burden the network)? Why BIAS is always set to one? If eta has different values, why we don't set the BIAS with

Why the BIAS is necessary in ANN? Should we have separate BIAS for each layer?

阅读更多关于 Why the BIAS is necessary in ANN? Should we have separate BIAS for each layer?

Does bias in the convolutional layer really make a difference to the test accuracy?

阅读更多关于 Does bias in the convolutional layer really make a difference to the test accuracy?

问题 I understand that bias are required in small networks, to shift the activation function. But in the case of Deep network that has multiple layers of CNN, pooling, dropout and other non -linear activations, is Bias really making a difference? The convolutional filter is learning local features and for a given conv output channel same bias is used. This is not a dupe of this link. The above link only explains role of bias in small neural network and does not attempt to explain role of bias in

Equation that compute a Neural Network in Matlab

阅读更多关于 Equation that compute a Neural Network in Matlab

问题 I created a neural network matlab. This is the script: load dati.mat; inputs=dati(:,1:8)'; targets=dati(:,9)'; hiddenLayerSize = 10; net = patternnet(hiddenLayerSize); net.inputs{1}.processFcns = {'removeconstantrows','mapminmax', 'mapstd','processpca'}; net.outputs{2}.processFcns = {'removeconstantrows','mapminmax', 'mapstd','processpca'}; net = struct(net); net.inputs{1}.processParams{2}.ymin = 0; net.inputs{1}.processParams{4}.maxfrac = 0.02; net.outputs{2}.processParams{4}.maxfrac = 0.02;

Clarification on bias of a perceptron

阅读更多关于 Clarification on bias of a perceptron

问题 Isn't it true that if a bias is not present, a line passing through origin should be able to linearly separate the two data sets?? But the most popular answer in this -->> question says y ^ | - + \\ + | - +\\ + + | - - \\ + | - - + \\ + ---------------------> x stuck like this I am confused about it. Do you mean the origins in figure above are somewhere in middle of x-axis and y-axis? Can somebody please help me and clarify this? 回答1: Alright, so perhaps the original ASCII graph was not 100%

Does bias in the convolutional layer really make a difference to the test accuracy?

阅读更多关于 Does bias in the convolutional layer really make a difference to the test accuracy?

I understand that bias are required in small networks, to shift the activation function. But in the case of Deep network that has multiple layers of CNN, pooling, dropout and other non -linear activations, is Bias really making a difference? The convolutional filter is learning local features and for a given conv output channel same bias is used. This is not a dupe of this link . The above link only explains role of bias in small neural network and does not attempt to explain role of bias in deep-networks containing multiple CNN layers, drop-outs, pooling and non-linear activation functions. I

Neural Net Bias per Layer or per Node (non-input node)

阅读更多关于 Neural Net Bias per Layer or per Node (non-input node)

问题 I am looking to implement a generic Neural Net, with 1 Input Layer consisting of Input Nodes, 1 Output Layer consisting of Output Nodes, and N Hidden Layers consisting of Hidden Nodes. Nodes are organized into Layers, with the rule that Nodes in the same Layer cannot be connected. I mostly understand the concept of the Bias, and my question is this: Should there be one Bias value per Layer (shared by all nodes in that Layer) or should each Node (except Nodes in the Input Layer) have their own

Neural Net Bias per Layer or per Node (non-input node)

阅读更多关于 Neural Net Bias per Layer or per Node (non-input node)

I am looking to implement a generic Neural Net, with 1 Input Layer consisting of Input Nodes, 1 Output Layer consisting of Output Nodes, and N Hidden Layers consisting of Hidden Nodes. Nodes are organized into Layers, with the rule that Nodes in the same Layer cannot be connected. I mostly understand the concept of the Bias, and my question is this: Should there be one Bias value per Layer (shared by all nodes in that Layer) or should each Node (except Nodes in the Input Layer) have their own Bias value? I have a feeling it could be done both ways, and would like to understand the trade offs

Why is a bias neuron necessary for a backpropagating neural network that recognizes the XOR operator?

阅读更多关于 Why is a bias neuron necessary for a backpropagating neural network that recognizes the XOR operator?

I posted a question yesterday regarding issues that I was having with my backpropagating neural network for the XOR operator. I did a little more work and realized that it may have to do with not having a bias neuron. My question is, what is the role of the bias neuron in general, and what is its role in a backpropagating neural network that recognizes the XOR operator? Is it possible to create one without a bias neuron? Kiril It's possible to create a neural network without a bias neuron... it would work just fine, but for more information I would recommend you see the answers to this