很简单：
from caffe import layers as L, params as P
def custom_net(lmdb, batch_size):
# define your own net!
n = caffe.NetSpec()
if lmdb is None: # "deploy" flavor
# assuming your data is of shape 3x224x224
n.data = L.Input(input_param={'shape':{'dim':[1,3,224,224]}})
else:
# keep this data layer for all networks
n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,
ntop=2, transform_param=dict(scale=1. / 255))
# the other layers common to all flavors: train/val/deploy...
n.conv1 = L.Convolution(n.data, kernel_size=6,
num_output=48, weight_filler=dict(type='xavier'))
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=48, weight_filler=dict(type='xavier'))
n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.conv3 = L.Convolution(n.pool2, kernel_size=4,
num_output=48, weight_filler=dict(type='xavier'))
n.pool3 = L.Pooling(n.conv3, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.conv4 = L.Convolution(n.pool3, kernel_size=2,
num_output=48, weight_filler=dict(type='xavier'))
n.pool4 = L.Pooling(n.conv4, kernel_size=2, stride=2, pool=P.Pooling.MAX)
n.fc1 = L.InnerProduct(n.pool4, num_output=50,
weight_filler=dict(type='xavier'))
# do you "drop" i deploy as well? up to you to decide...
n.drop1 = L.Dropout(n.fc1, dropout_param=dict(dropout_ratio=0.5))
n.score = L.InnerProduct(n.drop1, num_output=2,
weight_filler=dict(type='xavier'))
if lmdb is None:
n.prob = L.softmax(n.score)
else:
# keep this loss layer for all networks apart from "Deploy"
n.loss = L.softmaxWithLoss(n.score, n.label)
return n.to_proto()
with open('net_deploy.prototxt', 'w') as f:
f.write(str(custom_net(None, None)))
正如你可以看到有两处修改到prototxt（条件上lmdb是None）： 第一个，而不是"Data"一层，你必须声明"Input"层只声明"data"，不"label"。 第二个变化是输出层：您有一个预测层（而不是损失层）。
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