全连接层由于长度给定错误报错

全连接层参数计算
博客提及根据给定长度775除以最后通道数来计算全连接层所需参数,涉及深度学习相关计算。

 前面长度是对的,根据这个长度775除以最后得通道数就是全连接层所需参数。

已知模型如下 layers = [ %imageInputLayer([num_dim num_seq 1],Name="img_in") sequenceInputLayer([num_dim num_seq 1],Name="input") % 卷積層 %convolution2dLayer([3, 3], 32, "Padding", "same", "Name", "conv1") % 第一個卷積層 %reluLayer("Name", "relu1") %maxPooling2dLayer(2, "Stride", 2, "Name", "maxpool1") % 最大池化層 %convolution2dLayer([3, 3], 64, "Padding", "same", "Name", "conv2") % 第二個卷積層 %reluLayer("Name", "relu2") %maxPooling2dLayer(2, "Stride", 2, "Name", "maxpool2") % 最大池化層 % 特征提取模块 convolution2dLayer([1 3], 2,"Padding", "same", "Name", "conv_1") % 建立卷积层, 卷积核大小[2, 1], 32个特征图 reluLayer("Name", "relu_1") % Relu 激活层 convolution2dLayer([1 3], 4, "Padding", "same","Name", "conv_2") reluLayer("Name", "relu_2") %reshapeLayer([13,1,1], "Name", "a_reshape", OperationDimension="spatial-channel" ); % 增加 S 維度 %globalAveragePooling2dLayer %globalAveragePooling1dLayer %functionLayer(squeezeDim, "Name", "a_squ") %globalAveragePooling1dLayer % fullyConnectedLayer(8,"Name","fc") %fullyConnectedLayer(4,"Name","a_fc_1") % reluLayer("Name","a_relu") % fullyConnectedLayer(13,"Name","a_fc_2") % sigmoidLayer("Name","a_sigmoid") % reshapeLayer([13,1,1], "Name", "a_reshape", OperationDimension="spatial-channel" ); % 增加 S 維度 % multiplicationLayer(2,"Name","a_mul"); %--------------- flattenLayer("Name", "flatten_in"), %positionEmbeddingLayer(13,maxPosition,Name="pos-emb"), positionEmbeddingLayer(num_dim*num_seq*4,maxPosition,Name="pos-emb"), additionLayer(2, Name="add"), selfAttentionLayer(numHeads,numKeyChannels,'AttentionMask','causal'), selfAttentionLayer(numHeads,numKeyChannels), indexing1dLayer("last"), fullyConnectedLayer(num_class), % 全连接层 softmaxLayer("Name", "softmax") % 這裡是輸出層 classificationLayer("Name", "classification") % 這裡是最終的分類層 ]; % 添加層到網絡 lgraph = layerGraph(layers);
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