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def eval_style_net(weights, test_iters=10):
    test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)
    accuracy = 0
    for it in xrange(test_iters):
        accuracy += test_net.forward()['acc']
    accuracy /= test_iters
    return test_net, accuracy
    
    
test_net, accuracy = eval_style_net(style_weights)
print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )
scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)
print 'Accuracy, trained from   random initialization: %3.1f%%' % (100*scratch_accuracy, )

    # run a full test every so often
    # (Caffe can also do this for us and write to a log, but we show here
    #  how to do it directly in Python, where more complicated things are easier.)
    if it % test_interval == 0:
        print 'Iteration', it, 'testing...'
        correct = 0
        for test_it in range(100):
            solver.test_nets[0].forward()
            correct += sum( our_res #solver.test_nets[0].blobs['score'].data.argmax(1)
                           == label #solver.test_nets[0].blobs['label'].data)
        test_acc[it // test_interval] = correct / 1e4

_, ax1 = subplots()
ax2 = ax1.twinx()
ax1.plot(arange(niter), train_loss)
ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')
ax1.set_xlabel('iteration')
ax1.set_ylabel('train loss')
ax2.set_ylabel('test accuracy')
ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))