本人也是入门小白一枚，写博客是为了加深自己的映像。如有错误或不妥之处请多多指教
环境准备
caffe安装及其Python接口编译请自行查找教程。caffe使用自己的Python层在编译pycaffe之前需要额外修改Makefile.config.
# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1 # 删除前面的#号取消注释修改之后make编译一下：make pycaffe -j4
编译成功后在命令行中进入Python环境，然后输入import caffe,显示如下则说明caffe和python_layer都编译成功了。
zhangguoxiong@zhangguoxiong-Inspiron-5448:~$ python
Python 2.7.13 |Anaconda 4.4.0 (64-bit)| (default, Dec 20 2016, 23:09:15)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> import caffe
>>>
查看/添加路径
在命令行输入vim ~/.bashrc查看你的caffe_python的系统路径,我的python路径如下：
export PYTHONPATH=~/caffe/python
我们自己定义的python层的文件要放在python路径下。
定义自己的Python层
在Python的文件中，需要定义类，类的里面包括几个必须包含的部分：
setup( ): 用于检查输入的参数是否存在异常，初始化的功能.
reshape( ): 也是初始化，设定一下参数的size
forward( ): 前向传播
backward( ): 反向传播
在这里我们简单的实现一个恒等层来进行测试，代码如下。
Mylayer.py
import caffe
class MyLayer(caffe.Layer):
def setup(self, bottom, top):
pass
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
top[0].data[...] = bottom[0].data[:]
def backward(self, top, propagate_down, bottom):
for i in range(len(propagate_down)):
if not propagate_down[i]:
continue
bottom[i].diff[...] = top[i].diff[:]python层定义好之后我们就需要在自己的网络里使用他。我在examples/mnist下新建了一个网络来实现手写体识别，并将SoftmaxWithLoss层分解成softmax层和MultinomialLogisticLoss层。因为根据Caffe提供的官方文档，我们知SoftmaxWithLoss是softmax层和MultinomialLogisticLoss的合并。
网络文件如下：
lenet_lr.prototxt
name: "LeNet"
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_test_lmdb"
batch_size: 100
backend: LMDB
}
}
layer {
name: "ip"
type: "InnerProduct"
bottom: "data"
top: "ip"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "output_1"
type: "Softmax"
bottom: "ip"
top: "output_1"
}
layer {
type: "Python"
name: "output"
bottom: "output_1"
top: "output"
python_param {
module: "Mylayer"
layer: "MyLayer"
param_str: '{"x1": 1, "x2": 2 }'
}
}
layer {
name: "loss"
type: "MultinomialLogisticLoss"
bottom: "output"
bottom: "label"
top: "loss"
}配置文件如下：
lenet_lr_solver.prototxt
# The train/test net protocol buffer definition
net: "examples/mnist/lenet_lr.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 10000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"
# solver mode: CPU or GPU
solver_mode: CPU由于这个网络比较小，用CPU跑也很快，所以用了CPU。
接下来在caffe根目录下输入：
Inspiron-5448:~/caffe$ ./build/tools/caffe train --solver=examples/mnist/lenet_lr_solver.prototxt十几秒就训练完了，准确率也挺高。
mnist手写体识别的数据获取可以参考《21天实战caffe》第6天：运行手写体数字识别例程
参考文献
https://www.jianshu.com/p/e05d1b210fcb?utm_campaign=hugo&utm_medium=reader_share&utm_content=note&utm_source=weibo