在python中编写caffe的prototxt文件

利用python可以创建caffe的网络定义的prototxt文件，利用这种方法的一个好处就是：可以保证training，testing和deploy网络的一致性！

下面是利用python编写train和test的prototxt文件的一个小事例：

import caffe
from caffe import layers as L
from caffe import params as P

# Function: Set lenet net
def init_net_lenet(netName,netType,batch_size,prototxt_root,data_root):

# --------
# set the type and name of the net, e.g., train_lenet, or test_lenet
NetTypeName = netType + '_'+netName

# --------
n = caffe.NetSpec()

# --------
# set the input layer
n.data, n.label = L.ImageData(
image_data_param={"batch_size": batch_size, "is_color" : False,"shuffle": True},
source= data_root+netType+".data", transform_param=dict(scale=1./255), ntop=2)

# ------
# set other layers
n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=6, weight_filler=dict(type='xavier'))
n.tanh1 = L.TanH(n.conv1, in_place=True)
n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)

n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=16, weight_filler=dict(type='xavier'))
n.tanh2 = L.TanH(n.conv2, in_place=True)
n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)

n.ip3 = L.InnerProduct(n.pool2, num_output=120, weight_filler=dict(type='xavier'))
n.tanh3 = L.TanH(n.ip3, in_place=True)

n.ip4 = L.InnerProduct(n.ip3, num_output=84, weight_filler=dict(type='xavier'))
n.tanh4 = L.TanH(n.ip4, in_place=True)

n.ip5 = L.InnerProduct(n.ip4, num_output=2, weight_filler=dict(type='xavier'))
n.loss = L.SoftmaxWithLoss(n.ip5, n.label)

# --------
# write the prototxt file
print('Writing net to %s' % prototxt_root+NetTypeName+'.prototxt')
with open(prototxt_root+ NetTypeName+'.prototxt', 'w') as f:
f.write(str(n.to_proto()))
print 'done...'

# --------
# return the name of the output layer (used for predicting)
return 'ip5'