How to set the learning rate strategy in Caffe?

In Caffe, you can set the learning rate strategy by modifying the solver.prototxt file. The specific steps are as follows:

1. Open the solver.prototxt file with a text editor.
2. In the file, locate the base_lr parameter, which is the value of the initial learning rate that can be adjusted as needed.
3. You can add a learning rate schedule in the file, using several common learning rate strategies such as:
4. Stepsize strategy: Add the stepsize parameter and gamma parameter in the solver.prototxt file, where stepsize indicates after how many iterations the learning rate will decrease, and gamma represents the rate of decrease. For example:
5. stepping size: 100,000
   gamma value: 0.1
6. The learning rate decays to 0.1 times its original value every 100,000 iterations.
7. Multistep strategy: Add the parameters stepvalue and gamma to the solver.prototxt file. The stepvalue parameter represents a list of iteration numbers, where the learning rate will decay at each iteration specified in the list, and gamma represents the decay rate. For example:
8. Step values are set at increments of 100,000, with a gamma of 0.1.
9. Indicate that the learning rate will decrease by a factor of 0.1 after every 100,000, 200,000, and 300,000 iterations.
10. Strategy: Add the gamma parameter and the power parameter in the solver.prototxt file, where gamma represents the initial learning rate decay rate, and power represents the power of the learning rate adjustment. For example:
11. gamma value is 0.0001 and power value is 0.75.
12. The learning rate decreases by a factor of 0.0001 each time.
13. Save the file, and then restart training the model, so that the learning rate policy will be adjusted according to the rules set.

By following the above steps, you can set different learning rate strategies in Caffe to optimize the model training performance.