caffe的python介面繪製loss和accuracy曲線

# -*- 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，即隨機梯度下降演演算法  
solver = caffe.SGDSolver('/home/xxx/mnist/solver.prototxt')  
# 等價於solver檔案中的max_iter，即最大解算次數  
niter = 9380  
# 每隔100次收集一次資料  
display= 100  
# 每次測試進行100次解算，10000/100  
test_iter = 100  
# 每500次訓練進行一次測試（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，不計入  
solver.step(1)  
# 輔助變數  
_train_loss = 0; _test_loss = 0; _accuracy = 0  
# 進行解算  
for it in range(niter):  
    # 進行一次解算  
    solver.step(1)  
    # 每迭代一次，訓練batch_size張圖片  
    _train_loss += solver.net.blobs['SoftmaxWithLoss1'].data  
    if it % display == 0:  
        # 計算平均train loss  
        train_loss[it // display] = _train_loss / display  
        _train_loss = 0  
    if it % test_interval == 0:  
        for test_it in range(test_iter):  
            # 進行一次測試  
            solver.test_nets[0].forward()  
            # 計算test loss  
            _test_loss += solver.test_nets[0].blobs['SoftmaxWithLoss1'].data  
            # 計算test accuracy  
            _accuracy += solver.test_nets[0].blobs['Accuracy1'].data  
        # 計算平均test loss  
        test_loss[it / test_interval] = _test_loss / test_iter  
        # 計算平均test accuracy  
        test_acc[it / test_interval] = _accuracy / test_iter  
        _test_loss = 0  
        _accuracy = 0  
# 繪製train loss、test loss和accuracy曲線  
print 'nplot the train loss and test accuracyn'  
_, ax1 = plt.subplots()  
ax2 = ax1.twinx()  
# train loss -> 綠色  
ax1.plot(display * arange(len(train_loss)), train_loss, 'g')  
# test loss -> 黃色  
ax1.plot(test_interval * arange(len(test_loss)), test_loss, 'y')  
# test accuracy -> 紅色  
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()