删除参数

Remove parameter fromONNXParametersobject

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Syntax

params = removeParameter(params,name)

描述

example

params = removeParameter(params,name)删除由name来自ONNXParametersobjectparams。

例子

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Remove Parameters from Imported ONNX Model Function

此示例使用：

打开实时脚本

导入以ONNX格式保存的网络作为函数，并修改网络参数。

进口预处理simplenet3fc.onnxnetwork as a function.simplenet3fc是一个简单的卷积神经网络，该神经网络对数字图像数据进行了训练。有关如何创建类似于类似的网络的更多信息simplenet3fc, seeCreate Simple Image Classification Network。

Importsimplenet3fc.onnxusingimportONNXFunction, which returns anONNXParametersobject that contains the network parameters. The function also creates a new model function in the current folder that contains the network architecture. Specify the name of the model function asSimpleEnetFCN。

params = importONNXFunction('SimpleNet3fc.onnx','simplenetFcn');

A function containing the imported ONNX network has been saved to the file simplenetFcn.m. To learn how to use this function, type: help simplenetFcn.

Display the parameters that are updated during training (params.learnables) and the parameters that remain unchanged during training (params.Nonlearnables).

params.learnables

ans =带有字段的结构：imageInput_mean：[1×1 dlarray] conv_w：[5×5×1×20 dlarray] conv_b：[20×1 dlarray] batchnorm_scale：[20×1 dlarray] batchnorm_b：[20×1 dlarray] fc_1_w：[24×24×24×20×20 dlarray] fc_1_b：[20×1 dlarray] fc_2_w：[1×1×1×20×20 dlarray] fc_2_b：[20×1 dlarray] fc_3_w：[1×1×1×1×20×10×10×10 dlarray]×1 dlarray]

params.Nonlearnables

ans =带有字段的结构：ConvStride1004: [2×1 dlarray] ConvDilationFactor1005: [2×1 dlarray] ConvPadding1006: [4×1 dlarray] ConvStride1007: [2×1 dlarray] ConvDilationFactor1008: [2×1 dlarray] ConvPadding1009: [4×1 dlarray] ConvStride1010:[2×1 dlarray] ConvdilationFactor1011：[2×1 dlarray] Convpadding1012：[4×1 dlarray] Convstride1013：[2×1 dlarray] ConvdilationFactor1014：[2×1 Dlarray] Corvpadding1015：[4×1 Dlarray：[4×1 Dlarray]

The network has parameters that represent three fully connected layers. To see the parameters of the convolutional layersfc_1,fc_2，和fc_3, open the model functionSimpleEnetFCN。

openSimpleEnetFCN

向下滚动到功能中的图层定义SimpleEnetFCN。下面的代码显示了层的定义fc_1,fc_2，和fc_3。

% Conv:[weights, bias, stride, dilationFactor, padding, dataFormat, NumDims.fc_1] = prepareConvArgs(Vars.fc_1_W, Vars.fc_1_B, Vars.ConvStride1007, Vars.ConvDilationFactor1008, Vars.ConvPadding1009, 1, NumDims.relu1001, NumDims.fc_1_W); Vars.fc_1 = dlconv(Vars.relu1001, weights, bias,'Stride'，大步“扩张量”, dilationFactor,'Padding', padding,“ dataformat'，dataformat）;% Conv:[weights, bias, stride, dilationFactor, padding, dataFormat, NumDims.fc_2] = prepareConvArgs(Vars.fc_2_W, Vars.fc_2_B, Vars.ConvStride1010, Vars.ConvDilationFactor1011, Vars.ConvPadding1012, 1, NumDims.fc_1, NumDims.fc_2_W);vars.fc_2 = dlconv（vars.fc_1，striges，bias，'Stride'，大步“扩张量”, dilationFactor,'Padding', padding,“ dataformat'，dataformat）;% Conv:[权重，偏见，大步，扩张量，填充，填充，dataFormat，numdims.fc_3] = PrepareConvargs（vars.fc_3_w，vars.fc_3_b，vars.convstride1013，vars.convdilationfactor1014;Vars.fc_3 = dlconv(Vars.fc_2, weights, bias,'Stride'，大步“扩张量”, dilationFactor,'Padding', padding,“ dataformat'，dataformat）;

You can remove the parameters of the fully connected layerfc_2降低计算复杂性。检查上一层的输出尺寸和后续层的输入维度params。In this case, the output size of the previous layerfc_1为20，后续层的输入大小fc_3也是20。

Remove the parameters of layerfc_2通过使用删除参数。

params = removeParameter(params,'fc_2_B'); params = removeParameter(params,'fc_2_w'); params = removeParameter(params,'ConvStride1010'); params = removeParameter(params,'ConvDilationFactor1011'); params = removeParameter(params,'ConvPadding1012');

Display the updated learnable and nonlearnable parameters.

params.learnables

ans =带有字段的结构：imageInput_mean：[1×1 dlarray] conv_w：[5×5×1×20 dlarray] conv_b：[20×1 dlarray] batchnorm_scale：[20×1 dlarray] batchnorm_b：[20×1 dlarray] fc_1_w：[24×24×24×20×20 dlarray] fc_1_b：[20×1 dlarray] fc_3_w：[1×1×1×20×10 dlarray] fc_3_b：[10×1 dlarray]

params.Nonlearnables

ans =带有字段的结构：ConvStride1004: [2×1 dlarray] ConvDilationFactor1005: [2×1 dlarray] ConvPadding1006: [4×1 dlarray] ConvStride1007: [2×1 dlarray] ConvDilationFactor1008: [2×1 dlarray] ConvPadding1009: [4×1 dlarray] ConvStride1013: [2×1 dlarray] ConvDilationFactor1014: [2×1 dlarray] ConvPadding1015: [4×1 dlarray]

Modify the architecture of the model function to reflect the changes inparamsso you can use the network for prediction with the new parameters or retrain the network. Open the model functionSimpleEnetFCN。Then, remove the fully connected layerfc_2，和change the input data of the convolution operationDLCONVfor layerfc_3tovars.fc_1。

openSimpleEnetFCN

The code below shows layersfc_1和fc_3。

% Conv:[weights, bias, stride, dilationFactor, padding, dataFormat, NumDims.fc_1] = prepareConvArgs(Vars.fc_1_W, Vars.fc_1_B, Vars.ConvStride1007, Vars.ConvDilationFactor1008, Vars.ConvPadding1009, 1, NumDims.relu1001, NumDims.fc_1_W); Vars.fc_1 = dlconv(Vars.relu1001, weights, bias,'Stride'，大步“扩张量”, dilationFactor,'Padding', padding,“ dataformat'，dataformat）;% Conv:[权重，偏见，大步，扩张量，填充，填充，dataFormat，numdims.fc_3] = PrepareConvargs（vars.fc_3_w，vars.fc_3_b，vars.convstride1013，vars.convdilationfactor1014;vars.fc_3 = dlconv（vars.fc_1，striges，bias，'Stride'，大步“扩张量”, dilationFactor,'Padding', padding,“ dataformat'，dataformat）;