Classifying ImageNet: the instant Caffe way

这是一个测试，用于演示使用iPython NoteBook进行代码运行和说明。很适合Python的教学辅助。

原始路径 点我访问

这里应该是支持Latex的，测试一下吧：\(A(x)=sum^1_n(x)\), Perfect!

至于颜色，我想应该可以支持html代码，似乎还支持CSS，试试：这里是红色加粗18px号字体！ navyBlue 海蓝色？

下面是Caffe的例子代码片段！

In [11]: 
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# Make sure that caffe is on the python path:
caffe_root = '/home/ouxinyu/caffe-master/'  # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')

import caffe

# Set the right path to your model definition file, pretrained model weights,
# and the image you would like to classify.
MODEL_FILE = 'models/bvlc_reference_caffenet/deploy.prototxt'
PRETRAINED = 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
IMAGE_FILE = 'examples/images/cat.jpg'

import os
if not os.path.isfile(PRETRAINED):
    print("Downloading pre-trained CaffeNet model...")
    !scripts/download_model_binary.py models/bvlc_reference_caffenet

这一步，要注意的是修改原始案例中的一些地址路径，还有下载caffemodel之类的。

In [12]: 
caffe.set_mode_cpu()
net = caffe.Classifier(MODEL_FILE, PRETRAINED,
                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),
                       channel_swap=(2,1,0),
                       raw_scale=255,
                       image_dims=(256, 256))
In [13]: 
input_image = caffe.io.load_image(IMAGE_FILE)
plt.imshow(input_image)
Out[13]:
<matplotlib.image.AxesImage at 0x7f1a28ba0550>
In []: 
好啦，显示出图片啦，演示就此结束。