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PHPExcel常用方法汇总(转载)
继续定义单元神经元
net.inputs{i}.range
This property defines the range of each element of the ith network input.
It can be set to any Ri x 2 matrix, where Ri is the number of elements in the input (net.inputs{i}.size), and each element in column 1 is less than the element next to it in column 2.
Each jth row defines the minimum and maximum values of the jth input element, in that order:
net.inputs{i}(j,:)
Uses. Some initialization functions use input ranges to find appropriate initial values for input weight matrices.
Side Effects. Whenever the number of rows in this property is altered, the input size, processedSize, and processedRange change to remain consistent. The sizes of any weights coming from this input and the dimensions of the weight matrices also change.
>> net.inputs{1}.range=[0 1;0 1]
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 2
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
numOutputs: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {2x1 cell} of layers
outputs: {1x2 cell} containing 1 output
biases: {2x1 cell} containing 2 biases
inputWeights: {2x1 cell} containing 1 input weight
layerWeights: {2x2 cell} containing 1 layer weight
functions:
adaptFcn: (none)
divideFcn: (none)
gradientFcn: (none)
initFcn: (none)
performFcn: (none)
plotFcns: {}
trainFcn: (none)
parameters:
adaptParam: (none)
divideParam: (none)
gradientParam: (none)
initParam: (none)
performParam: (none)
trainParam: (none)
weight and bias values:
IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell} containing 2 bias vectors
other:
name: ''
userdata: (user information)
>>
======
net.layers{i}.size
This property defines the number of neurons in the ith layer. It can be set to 0 or a positive integer.
Side Effects. Whenever this property is altered, the sizes of any input weights going to the layer (net.inputWeights{i,:}.size), any layer weights going to the layer (net.layerWeights{i,:}.size) or coming from the layer (net.inputWeights{i,:}.size), and the layer's bias (net.biases{i}.size), change.
The dimensions of the corresponding weight matrices (net.IW{i,:}, net.LW{i,:}, net.LW{:,i}), and biases (net.b{i}) also change.
Changing this property also changes the size of the layer's output (net.outputs{i}.size) and target (net.targets{i}.size) if they exist.
Finally, when this property is altered, the dimensions of the layer's neurons (net.layers{i}.dimension) are set to the same value. (This results in a one-dimensional arrangement of neurons. If another arrangement is required, set the dimensions property directly instead of using size.
=======
>> net.layers{1}.size=2
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 2
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
numOutputs: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {2x1 cell} of layers
outputs: {1x2 cell} containing 1 output
biases: {2x1 cell} containing 2 biases
inputWeights: {2x1 cell} containing 1 input weight
layerWeights: {2x2 cell} containing 1 layer weight
functions:
adaptFcn: (none)
divideFcn: (none)
gradientFcn: (none)
initFcn: (none)
performFcn: (none)
plotFcns: {}
trainFcn: (none)
parameters:
adaptParam: (none)
divideParam: (none)
gradientParam: (none)
initParam: (none)
performParam: (none)
trainParam: (none)
weight and bias values:
IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell} containing 2 bias vectors
other:
name: ''
userdata: (user information)
>>
=====
net.layers{i}.initFcn
This property defines which of the layer initialization functions are used to initialize the ith layer, if the network initialization function (net.initFcn) is initlay. If the network initialization is set to initlay, then the function indicated by this property is used to initialize the layer's weights and biases.
For a list of functions, type
help nninit
=====
>> net.layers{1}.initFcn='initnw'
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 2
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
numOutputs: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {2x1 cell} of layers
outputs: {1x2 cell} containing 1 output
biases: {2x1 cell} containing 2 biases
inputWeights: {2x1 cell} containing 1 input weight
layerWeights: {2x2 cell} containing 1 layer weight
functions:
adaptFcn: (none)
divideFcn: (none)
gradientFcn: (none)
initFcn: (none)
performFcn: (none)
plotFcns: {}
trainFcn: (none)
parameters:
adaptParam: (none)
divideParam: (none)
gradientParam: (none)
initParam: (none)
performParam: (none)
trainParam: (none)
weight and bias values:
IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell} containing 2 bias vectors
other:
name: ''
userdata: (user information)
>>
>> net.layers{2}.size=1
>> net.layers{2}.initFcn='initnw'
>> net.layers{2}.transferFcn='hardlim'
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 2
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
numOutputs: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {2x1 cell} of layers
outputs: {1x2 cell} containing 1 output
biases: {2x1 cell} containing 2 biases
inputWeights: {2x1 cell} containing 1 input weight
layerWeights: {2x2 cell} containing 1 layer weight
functions:
adaptFcn: (none)
divideFcn: (none)
gradientFcn: (none)
initFcn: (none)
performFcn: (none)
plotFcns: {}
trainFcn: (none)
parameters:
adaptParam: (none)
divideParam: (none)
gradientParam: (none)
initParam: (none)
performParam: (none)
trainParam: (none)
weight and bias values:
IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell} containing 2 bias vectors
other:
name: ''
userdata: (user information)
>>
=
net.layers{i}.transferFcn
This function defines which of the transfer functions is used to calculate the ith layer's output, given the layer's net input, during simulation and training.
For a list of functions type: help nntransfer
=
>> net.adapFcn='trans'
===
net.adaptFcn
This property defines the function to be used when the network adapts. It can be set to the name of any network adapt function. The network adapt function is used to perform adaption whenever adapt is called.
[net,Y,E,Pf,Af] = adapt(NET,P,T,Pi,Ai)
For a list of functions type help nntrain.
Side Effects. Whenever this property is altered, the network's adaption parameters (net.adaptParam) are set to contain the parameters and default values of the new function.
===
>> net.adaptFcn='trains'
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 2
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
numOutputs: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {2x1 cell} of layers
outputs: {1x2 cell} containing 1 output
biases: {2x1 cell} containing 2 biases
inputWeights: {2x1 cell} containing 1 input weight
layerWeights: {2x2 cell} containing 1 layer weight
functions:
adaptFcn: 'trains'
divideFcn: (none)
gradientFcn: (none)
initFcn: (none)
performFcn: (none)
plotFcns: {}
trainFcn: (none)
parameters:
adaptParam: .passes
divideParam: (none)
gradientParam: (none)
initParam: (none)
performParam: (none)
trainParam: (none)
weight and bias values:
IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell} containing 2 bias vectors
other:
name: ''
userdata: (user information)
>>
==========
net.performFcn
This property defines the function used to measure the network's performance. You can set it to the name of any of the performance functions. The performance function is used to calculate network performance during training whenever train is called.
[net,tr] = train(NET,P,T,Pi,Ai)
For a list of functions, type
help nnperformance
Side Effects. Whenever this property is altered, the network's performance parameters (net.performParam) are set to contain the parameters and default values of the new function
==========
net.performFcn='mse'
======
======
>> net.trainFcn='trainlm'
发表评论
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R语言与数据分析
2015-05-15 20:58 2161当今计算机系统要处理的数据类型变得多种多样,并且为了深入理 ... -
机器学习实践指南:案例应用解析
2014-04-17 19:53 1005试读及购买链接 《机器 ... -
matlab-矩阵合并
2013-06-10 13:56 3230a = 1 2 3 2 -
人工智能与数据分析所需要的知识
2013-04-30 18:27 292想较好得在数据分析和人工智能相关领域发展,最好具备以下基础: ... -
麦哈普的AI乐园【myhaspl@qq.com】我的另一个博客(机器学习、数据分析、智能计算的原创)
2013-04-28 10:52 11http://blog.csdn.net/u0102556 ... -
R-并行计算
2013-04-28 10:50 6125啊。。。找了一下,R 居然真的有办法可以多cpu平行运算!! ... -
谱聚类
2013-04-11 10:44 27291. 谱聚类 给你博客园上若干个博客,让你将它 ... -
对变化建模-用差分方程-动力系统及常数解
2013-04-09 15:24 1385差分表示在一个时间周期里考察对象的变化量。 差分表示在一个时 ... -
逻辑斯蒂映射-伪随机数
2013-04-04 15:28 3310逻辑斯蒂映射的形式为 x_(n+1)=ax_n( ... -
matlab-多项式乘除法及式子和导数
2013-03-21 15:06 4703>> a=[22 12 4 54] ... -
matlab-数组-元胞数据与结构数组
2013-03-20 17:45 3296y、z是元胞数组,num2cell完成由数值数组到元胞数组的 ... -
矩阵-范数
2013-03-13 17:30 1926>> a a = 12 33 ... -
向量-范数
2013-03-13 16:06 2374>> b=a(3,:) b = 22 ... -
矩阵-求逆
2013-02-27 15:51 2524设R是一个交换环,A是 ... -
lisp-猜数字算法与全局函数、变量
2013-01-30 17:55 1608* (defvar *big* 100) *BIG* ... -
开源 Lisp 相关项目
2013-01-19 22:38 3931IOLib 项目 (http://common-lisp.n ... -
四分位数求法
2012-11-22 20:18 2793四分位数间距:是上四分位数与下四分位数之差,用四分位数间距可反 ... -
matlab-神经网络-自定义多层感知器解决异或(1)
2012-10-09 22:41 5250>> net=network net = ... -
matlab-模态对话框
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matlab-gui activex
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