pycaffe_draw

#!/usr/bin/env pythonfrom pylab import *import matplotlib.pyplot as pltimport caffecaffe.set_device(0)caffe.set_mode_gpu()solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')niter = 10000display_iter = 100test_iter = 100test_interval = 500# train losstrain_loss = zeros(ceil(niter * 1.0 / display_iter))# test losstest_loss = zeros(ceil(niter * 1.0 / test_interval))# test accuracytest_acc = zeros(ceil(niter * 1.0 / test_interval))solver.step(1)_train_loss = 0; _test_loss = 0; _accuracy = 0_, ax1 = plt.subplots()ax2 = ax1.twinx()for it in range(niter):    solver.step(1)    _train_loss += solver.net.blobs['loss'].data    if it % display_iter == 0:        train_loss[it // display_iter] = _train_loss / display_iter        _train_loss = 0    if it % test_interval == 0:        for test_it in range(test_iter):             solver.test_nets[0].forward()                     _test_loss += solver.test_nets[0].blobs['loss'].data            _accuracy += solver.test_nets[0].blobs['accuracy'].data        test_loss[it / test_interval] = _test_loss / test_iter        test_acc[it / test_interval] = _accuracy / test_iter        _test_loss = 0        _accuracy = 0    if it % display_iter == 0:        print '\nplot the train loss and test accuracy\n'           #_, ax1 = plt.subplots()        #ax2 = ax1.twinx()        ax1.plot(display_iter * arange(len(train_loss)), train_loss, 'g')                ax1.plot(test_interval * arange(len(test_loss)), test_loss, 'y')        #plt.plot(test_interval * arange(len(test_acc)), test_acc, 'r')        ax1.set_xlabel('iteration')        ax1.set_ylabel('loss')        ax2.set_ylabel('accuracy')        #plt.show()        plt.pause(0.000001)