Caffe学习笔记(五)：使用pycaffe生成solver.prototxt文件并进行训练

# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10

# The train/test net protocol buffer definition
net: "examples/cifar10/cifar10_quick_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.001
momentum: 0.9
weight_decay: 0.004
# The learning rate policy
lr_policy: "fixed"
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 4000
# snapshot intermediate results
snapshot: 4000
snapshot_format: HDF5
snapshot_prefix: "examples/cifar10/cifar10_quick"
# solver mode: CPU or GPU
solver_mode: GPU

# -*- coding: UTF-8 -*-
import caffe                                                     #导入caffe包

def write_sovler():
    my_project_root = "/home/Jack-Cui/caffe-master/my-caffe-project/"        #my-caffe-project目录
    sovler_string = caffe.proto.caffe_pb2.SolverParameter()                    #sovler存储
    solver_file = my_project_root + 'solver.prototxt'                        #sovler文件保存位置
    sovler_string.train_net = my_project_root + 'train.prototxt'            #train.prototxt位置指定
    sovler_string.test_net.append(my_project_root + 'test.prototxt')         #test.prototxt位置指定
    sovler_string.test_iter.append(100)                                        #测试迭代次数
    sovler_string.test_interval = 500                                        #每训练迭代test_interval次进行一次测试
    sovler_string.base_lr = 0.001                                            #基础学习率   
    sovler_string.momentum = 0.9                                            #动量
    sovler_string.weight_decay = 0.004                                        #权重衰减
    sovler_string.lr_policy = 'fixed'                                        #学习策略           
    sovler_string.display = 100                                                #每迭代display次显示结果
    sovler_string.max_iter = 4000                                            #最大迭代数
    sovler_string.snapshot = 4000                                             #保存临时模型的迭代数
    sovler_string.snapshot_format = 0                                        #临时模型的保存格式,0代表HDF5,1代表BINARYPROTO
    sovler_string.snapshot_prefix = 'examples/cifar10/cifar10_quick'        #模型前缀
    sovler_string.solver_mode = caffe.proto.caffe_pb2.SolverParameter.GPU    #优化模式

    with open(solver_file, 'w') as f:
        f.write(str(sovler_string))   

if __name__ == '__main__':
    write_sovler()

import caffe

my_project_root = "/home/Jack-Cui/caffe-master/my-caffe-project/"        #my-caffe-project目录
solver_file = my_project_root + 'solver.prototxt'                        #sovler文件保存位置
caffe.set_device(0)                                                      #选择GPU-0
caffe.set_mode_gpu()
solver = caffe.SGDSolver(solver_file)
solver.solve()