#coding=utf-8
import matplotlib.pyplot as plt 
import caffe
import numpy as np

solver = caffe.SGDSolver("/home/capstone/Intel_face_detection_origin/cnn_train_models/train_front/mult_solver.prototxt")
#solver.solve()

# 等价于solver文件中的max_iter，即最大解算次数  
niter = 1086450
# 每隔500次收集一次数据  
display= 2000 
  
# 每次测试进行100次解算，70000/100  
test_iter = 3712
# 每500次训练进行一次测试（100次解算），60000/64  
test_interval =12071
  

print '\ninitialize plot\n'
#初始化 
train_loss = np.zeros(np.ceil(niter * 1.0 / display))   
test_loss = np.zeros(np.ceil(niter * 1.0 / test_interval))  
test_acc = np.zeros(np.ceil(niter * 1.0 / test_interval))  
  
# iteration 0，不计入  
solver.step(1)  
print '\n calculating \n'
# 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['loss'].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['loss'].data  
            # 计算test accuracy  
            _accuracy += solver.test_nets[0].blobs['accuracy'].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 * np.arange(len(train_loss)), train_loss, 'g')  
# test loss -> 黄色  
ax1.plot(test_interval * np.arange(len(test_loss)), test_loss, 'y')  
# test accuracy -> 红色  
ax2.plot(test_interval * np.arange(len(test_acc)), test_acc, 'r')  
  
ax1.set_xlabel('iteration')  
ax1.set_ylabel('loss')  
ax2.set_ylabel('accuracy')  
plt.show()  
