caffe solver参数解析

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/zhjm07054115/article/details/52072698

# 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 definition
net: "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 iterations
max_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: 0.001
momentum: 0.9
weight_decay: 0.004

# 学习率更新策略
# The learning rate policy
lr_policy: "fixed"

# 显示频率：每迭代x次，打印一次结果
# Display every 200 iterations
display: 200

# 保存频率： 每迭代x次，保存一次中间模型
# snapshot intermediate results
snapshot: 10000
snapshot_format: HDF5
snapshot_prefix: "examples/cifar10/cifar10_full"

# 调用设备
# solver mode: CPU or GPU
solver_mode: GPU