Caffe学习：使用pycaffe定义网络

使用pycaffe定义网络：

参考链接：Learning LeNet

引入库：

import caffe

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

使用pycaffe定义Net：

n = caffe.NetSpec()

定义DataLayer：

n.data, n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB, source=lmdb,
transform_param=dict(scale=1. / 255), ntop=2)

# 效果如下：

layer {
name: "data"
type: "Data"
top: "data"
top: "label"
transform_param {
scale: 0.00392156862745
}
data_param {
source: "mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}

定义ConvolutionLayer：

n.conv1 = L.Convolution(n.data, kernel_size=5,
num_output=20, weight_filler=dict(type='xavier'))

# 效果如下：

layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 20
kernel_size: 5
weight_filler {
type: "xavier"
}
}
}

定义PoolingLayer：

n.pool1 = L.Pooling(n.conv1, kernel_size=2,
stride=2, pool=P.Pooling.MAX)

# 效果如下：

layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}

定义InnerProductLayer：

n.ip1 = L.InnerProduct(n.pool2, num_output=500,
weight_filler=dict(type='xavier'))

# 效果如下：

layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
}
}

定义ReLULayer：

n.relu1 = L.ReLU(n.ip1, in_place=True)

# 效果如下：

layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}

定义SoftmaxWithLossLayer：

n.loss = L.SoftmaxWithLoss(n.ip2, n.label)

# 效果如下：

layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}

定义minst网络：

import caffe

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

def lenet(lmdb, batch_size):
n = caffe.NetSpec()
n.data, n.label = L.Data(batch_size=batch_size,
backend=P.Data.LMDB, source=lmdb,
transform_param=dict(scale=1. / 255), ntop=2)
n.conv1 = L.Convolution(n.data, kernel_size=5,
num_output=20, 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=50, weight_filler=dict(type='xavier'))
n.pool2 = L.Pooling(n.conv2, kernel_size=2,
stride=2, pool=P.Pooling.MAX)
n.ip1 = L.InnerProduct(n.pool2, num_output=500,
weight_filler=dict(type='xavier'))
n.relu1 = L.ReLU(n.ip1, in_place=True)
n.ip2 = L.InnerProduct(n.relu1, num_output=10,
weight_filler=dict(type='xavier'))
n.loss = L.SoftmaxWithLoss(n.ip2, n.label)

return n.to_proto()

保存网络定义：

with open('mnist/lenet_auto_train.prototxt', 'w') as f:
f.write(str(lenet('mnist/mnist_train_lmdb', 64)))
with open('mnist/lenet_auto_test.prototxt', 'w') as f:
f.write(str(lenet('mnist/mnist_test_lmdb', 100)))

得

lenet_auto_train.prototxt

文件如下：（

lenet_auto_test.prototxt

文件类似）：

layer {
name: "data"
type: "Data"
top: "data"
top: "label"
transform_param {
scale: 0.00392156862745
}
data_param {
source: "mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 20
kernel_size: 5
weight_filler {
type: "xavier"
}
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
convolution_param {
num_output: 50
kernel_size: 5
weight_filler {
type: "xavier"
}
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}