caffe solver参数解析

# caffe solver.prototxt # 求解器说明# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10# then another factor of 10 after 10 more epochs (5000 iters)# 60000次迭代后 lr/10 ; 每增加500次迭代，再lr/10# 网络定义# The train/test net protocol buffer definitionnet: "examples/cifar10/cifar10_full_train_test.prototxt"# 训练批量数: 训练时，每次取多少张图像，只在网络文件中定义# trainBatchSize = 100# 训练迭代数：训练过程遍历多少个batch, # 训练回合数: epochs = max_iter × batchSize / totalImgs# The maximum number of iterationsmax_iter: 60000# 测试批量数：测试时，每个批次取多少张图像, 只在网络文件中定义# batchSize = 100 # 测试迭代数：每次测试遍历多少个batch# test_iter specifies how many forward passes the test should carry out.# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,# covering the full 10,000 testing images.# 每个batch的大小为batchSize=100; 测试中共迭代test_iters=100个batch;  刚好覆盖cifar10的10000张测试图片test_iter: 100# 测试间隔，每训练多少次插入一次测试# Carry out testing every 1000 training iterations.test_interval: 1000# 学习率，动量，衰减系数# The base learning rate, momentum and the weight decay of the network.base_lr: 0.001momentum: 0.9weight_decay: 0.004# 学习率更新策略# The learning rate policylr_policy: "fixed"# 显示频率：每迭代x次，打印一次结果# Display every 200 iterationsdisplay: 200# 保存频率： 每迭代x次，保存一次中间模型# snapshot intermediate resultssnapshot: 10000snapshot_format: HDF5snapshot_prefix: "examples/cifar10/cifar10_full"# 调用设备# solver mode: CPU or GPUsolver_mode: GPU