# -*- coding: utf-8 -*-
"""
Created on Tue Jul 19 16:22:22 2016@author: root
"""import matplotlib.pyplot as plt
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
# 使用SGDSolver，即随机梯度下降算法
solver = caffe.SGDSolver('/home/xxx/mnist/solver.prototxt')  # 等价于solver文件中的max_iter，即最大解算次数
niter = 9380
# 每隔100次收集一次数据
display= 100  # 每次测试进行100次解算，10000/100
test_iter = 100
# 每500次训练进行一次测试（100次解算），60000/64
test_interval =938  #初始化
train_loss = zeros(ceil(niter * 1.0 / display))
test_loss = zeros(ceil(niter * 1.0 / test_interval))
test_acc = zeros(ceil(niter * 1.0 / test_interval))  # iteration 0，不计入
solver.step(1)  # 辅助变量
_train_loss = 0; _test_loss = 0; _accuracy = 0
# 进行解算
for it in range(niter):  # 进行一次解算  solver.step(1)  # 每迭代一次，训练batch_size张图片  _train_loss += solver.net.blobs['SoftmaxWithLoss1'].data  if it % display == 0:  # 计算平均train loss  train_loss[it // display] = _train_loss / display  _train_loss = 0  if it % test_interval == 0:  for test_it in range(test_iter):  # 进行一次测试
            solver.test_nets[0].forward()  # 计算test loss  _test_loss += solver.test_nets[0].blobs['SoftmaxWithLoss1'].data  # 计算test accuracy  _accuracy += solver.test_nets[0].blobs['Accuracy1'].data  # 计算平均test loss  test_loss[it / test_interval] = _test_loss / test_iter  # 计算平均test accuracy  test_acc[it / test_interval] = _accuracy / test_iter  _test_loss = 0  _accuracy = 0  # 绘制train loss、test loss和accuracy曲线
print '\nplot the train loss and test accuracy\n'
_, ax1 = plt.subplots()
ax2 = ax1.twinx()  # train loss -> 绿色
ax1.plot(display * arange(len(train_loss)), train_loss, 'g')
# test loss -> 黄色
ax1.plot(test_interval * arange(len(test_loss)), test_loss, 'y')
# test accuracy -> 红色
ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')  ax1.set_xlabel('iteration')
ax1.set_ylabel('loss')
ax2.set_ylabel('accuracy')
plt.show()