self.Proto[i, :] = self.momentum * feature + self.Proto[i, :] * (1 - self.momentum)

This line of code updates the i-th row of the self.Proto matrix with a weighted average of the previous value and the current features vector.

The momentum parameter controls the weight given to the previous value, while (1 - momentum) controls the weight given to the current features vector.

This update rule is commonly used in stochastic gradient descent optimization algorithms to smooth out the updates and prevent oscillations in the training process. By gradually incorporating the new information while retaining some of the previous values, the algorithm can converge to a more stable solution.

相关问题

C# MyCaffe 调用mean.binaryproto train.caffemodel prototxt

在C#中使用MyCaffe调用train.caffemodel和prototxt文件的步骤与使用mean.binaryproto文件类似。您可以使用MyCaffe提供的BlobProto和NetParameter类来加载这些文件，并将它们转换为C#中的对象。

以下是一个示例代码片段，展示了如何在C#中使用MyCaffe加载train.caffemodel和prototxt文件：

using MyCaffe;
using MyCaffe.basecode;
using MyCaffe.common;
using MyCaffe.param;
using MyCaffe.proto;
using System.Collections.Generic;

namespace MyCaffeTest
{
    class Program
    {
        static void Main(string[] args)
        {
            // Load the prototxt file
            NetParameter net_param = new NetParameter();
            net_param.CopyFrom(LayerParameter.ParseFrom(File.ReadAllBytes("train.prototxt")));

            // Load the caffemodel file
            BlobProto blob_proto = new BlobProto();
            blob_proto.Load("train.caffemodel");

            // Convert the BlobProto object to a Blob object
            BlobCollection<float> colBlobs = new BlobCollection<float>();
            Blob<float> blob = new Blob<float>(new List<int> { 1, 3, 256, 256 });
            blob.FromProto(blob_proto);
            colBlobs.Add(blob);

            // Create the solver
            SolverParameter solver_param = new SolverParameter();
            solver_param.net_param = net_param;
            solver_param.test_iter.Add(0);
            solver_param.test_interval = 1;
            solver_param.base_lr = 0.01;
            solver_param.momentum = 0.9;
            solver_param.weight_decay = 5e-4;
            solver_param.lr_policy = "inv";
            solver_param.gamma = 0.1;
            solver_param.power = 0.75;
            solver_param.display = 1;
            solver_param.max_iter = 1;
            solver_param.snapshot = 1;
            solver_param.snapshot_prefix = "snapshot";
            solver_param.type = SolverParameter.SolverType.SGD;

            // Create the solver and solve the net
            MyCaffeControl<float> mycaffe = new MyCaffeControl<float>(null, null, null, colBlobs, null, solver_param, null, null, null, null);
            mycaffe.Solve();

            System.Console.ReadKey();
        }
    }
}

在上面的代码中，我们使用了MyCaffe提供的NetParameter和BlobProto类，分别加载了train.prototxt和train.caffemodel文件。我们还创建了一个SolverParameter对象，并使用NetParameter对象初始化它。最后，我们使用这些对象创建了一个MyCaffeControl对象，并使用Solve函数来训练网络。

希望这可以帮助您解决问题。