TrainDimensionalityReduction

训练降维模型

描述

降维算法(自动编码器、主成分分析、自组织映射)的训练器。与其他机器学习模型一样，所有输入样本都用于计算模型。该模型可用于 ImageDimensionalityReduction 和 VectorDimensionalityReduction 申请。

参数

输入和输出数据

这组参数允许设置输入和输出数据。

Input Vector Data -io.vd vectorfile Mandatory
Input geometries used for training (note: all geometries from the layer will be used)

Output model -io.out filename [dtype] Mandatory
Output file containing the estimated model (.txt format).

Input XML image statistics file -io.stats filename [dtype]
XML file containing mean and variance of each feature.

Field names to be used for training -feat string1 string2...
List of field names in the input vector data used as features for training.

algorithm to use for the training -algorithm [som|autoencoder|pca] Default value: som
Choice of the dimensionality reduction algorithm to use for the training.

  • OTB SOM
    This group of parameters allows setting SOM parameters.
  • Shark Autoencoder
    This group of parameters allows setting Shark autoencoder parameters.
  • Shark PCA
    This group of parameters allows setting Shark PCA parameters.

OTB SOM选项

Map size -algorithm.som.s string1 string2... Default value: 10 10
Sizes of the SOM map (one per dimension). For instance, [12;15] means a 2D map of size 12x15. Support2D to 5D maps.

Neighborhood sizes -algorithm.som.n string1 string2... Default value: 3 3
Sizes of the initial neighborhood in the SOM map (one per dimension). The number of sizes should be the same as the map sizes

NumberIteration -algorithm.som.ni int Default value: 5
Number of iterations for SOM learning

BetaInit -algorithm.som.bi float Default value: 1
Initial learning coefficient

BetaFinal -algorithm.som.bf float Default value: 0.1
Final learning coefficient

InitialValue -algorithm.som.iv float Default value: 10
Maximum initial neuron weight

Shark自动编码器选项

Maximum number of iterations during training -algorithm.autoencoder.nbiter int Default value: 100
The maximum number of iterations used during training.

Maximum number of iterations during training -algorithm.autoencoder.nbiterfinetuning int Default value: 0
The maximum number of iterations used during fine tuning of the whole network.

Epsilon -algorithm.autoencoder.epsilon float Default value: 0
Epsilon

Weight initialization factor -algorithm.autoencoder.initfactor float Default value: 1
Parameter that control the weight initialization of the autoencoder

Size -algorithm.autoencoder.nbneuron string1 string2... Mandatory
The number of neurons in each hidden layer.

Strength of the regularization -algorithm.autoencoder.regularization string1 string2... Mandatory
Strength of the L2 regularization used during training

Strength of the noise -algorithm.autoencoder.noise string1 string2... Mandatory
Strength of the noise

Sparsity parameter -algorithm.autoencoder.rho string1 string2... Mandatory
Sparsity parameter

Sparsity regularization strength -algorithm.autoencoder.beta string1 string2... Mandatory
Sparsity regularization strength

Learning curve -algorithm.autoencoder.learningcurve filename [dtype]
Learning error values

Shark PCA选项

Dimension of the output of the pca transformation -algorithm.pca.dim int Default value: 10
Dimension of the output of the pca transformation.

Available RAM (MB) -ram int Default value: 256
Available memory for processing (in MB).

实例

从命令行执行以下操作：

otbcli_TrainDimensionalityReduction -io.vd cuprite_samples.sqlite -io.out model.som -algorithm som -algorithm.som.s 10 10 -algorithm.som.n 3 3 -algorithm.som.ni 5 -algorithm.som.bi 1 -algorithm.som.bf 0.1 -algorithm.som.iv 10 -feat value_0 value_1 value_2 value_3 value_4 value_5 value_6 value_7 value_8 value_9

来自Python的评论：

import otbApplication

app = otbApplication.Registry.CreateApplication("TrainDimensionalityReduction")

app.SetParameterString("io.vd", "cuprite_samples.sqlite")
app.SetParameterString("io.out", "model.som")
app.SetParameterString("algorithm","som")
app.SetParameterStringList("algorithm.som.s", ['10', '10'])
app.SetParameterStringList("algorithm.som.n", ['3', '3'])
app.SetParameterInt("algorithm.som.ni", 5)
app.SetParameterFloat("algorithm.som.bi", 1)
app.SetParameterFloat("algorithm.som.bf", 0.1)
app.SetParameterFloat("algorithm.som.iv", 10)
app.SetParameterStringList("feat", ['value_0', 'value_1', 'value_2', 'value_3', 'value_4', 'value_5', 'value_6', 'value_7', 'value_8', 'value_9'])

app.ExecuteAndWriteOutput()