Caffe 配置文件详解

#训练样本418521，测试样本18715net: "face_recognizer.prototxt"# 测试多少iteration恢复训练，test_iter = 测试样本个数 / batch_size，也就是 。test_iter: 3# 训练多少iteration开始测试，test_interval=训练样本个数/batch_size，也就是整个训练集过一遍开始测试。test_interval: 52# 更新权重时候，原权重的比例momentum: 0.9# 防止过拟合的权重范数惩罚权重weight_decay: 0.0005# The learning rate decay policy. The currently implemented learning rate# policies are as follows:#    - fixed: always return base_lr.#    - step: return base_lr * gamma ^ (floor(iter / step))#    - exp: return base_lr * gamma ^ iter#    - inv: return base_lr * (1 + gamma * iter) ^ (- power)#    - multistep: similar to step but it allows non uniform steps defined by#      stepvalue#    - poly: the effective learning rate follows a polynomial decay, to be#      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)#    - sigmoid: the effective learning rate follows a sigmod decay#      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))## where base_lr, max_iter, gamma, step, stepvalue and power are defined# in the solver parameter protocol buffer, and iter is the current iteration.lr_policy: "step"base_lr: 0.001gamma: 0.95#step_size = n x (训练样本个数/batch_size)也就是所有样本过n次就降低lr。n=20stepsize: 5200# max_iter * batch_size =  200 × 训练样本数量max_iter: 1040000display: 200# 权重快照保存的间隔snapshot=训练样本数/batch_sizesnapshot: 5200snapshot_prefix: "face"solver_mode: GPU