當(dāng)前位置：首頁(yè) > 人文社科 > 生活经验 >内容正文

生活经验

Caffe实现概述

發(fā)布時(shí)間：2023/11/28 生活经验 29 豆豆

生活随笔收集整理的這篇文章主要介紹了 Caffe实现概述小編覺得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

Caffe實(shí)現(xiàn)概述
目錄
一、caffe配置文件介紹
二、標(biāo)準(zhǔn)層的定義
三、網(wǎng)絡(luò)微調(diào)技巧
四、Linux腳本使用及LMDB文件生成
五、帶你設(shè)計(jì)一個(gè)Caffe網(wǎng)絡(luò)，用于分類任務(wù)
一、caffe配置文件介紹

二、標(biāo)準(zhǔn)層的定義

三、網(wǎng)絡(luò)微調(diào)技巧

其中，multistep最為常用

四、Linux腳本使用及LMDB文件生成

五、帶你設(shè)計(jì)一個(gè)Caffe網(wǎng)絡(luò)，用于分類任務(wù)

下面：
使用pycaffe生成solver配置
使用pycaffe生成caffe測(cè)試網(wǎng)絡(luò)和訓(xùn)練網(wǎng)絡(luò)

數(shù)據(jù)集下載：

demoCaffe

數(shù)據(jù)集下載，cifar mnist:
百度云盤:
鏈接: https://pan.baidu.com/s/1bHFQUz7Q6BMBZv25AhsXKQ 密碼: dva9
鏈接: https://pan.baidu.com/s/1rPRjf2hanlYYjBQQDmIjNQ 密碼: 5nhv

lmdb數(shù)據(jù)制作：
手動(dòng)實(shí)現(xiàn)： https://blog.csdn.net/yx2017/article/details/72953537
https://www.jianshu.com/p/9d7ed35960cb
代碼實(shí)現(xiàn)：https://www.cnblogs.com/leemo-o/p/4990021.html
https://www.jianshu.com/p/ef84715e0fdc
以下僅供對(duì)比閱讀：
demo_lmdb.py：生成lmdb格式數(shù)據(jù)
import lmdb
import numpy as np
import cv2
import caffe
from caffe.proto import caffe_pb2
def write():
```
 # basic setting
```
```
lmdb_file = 'lmdb_data'
```
```
batch_size = 256
```

lmdb_env = lmdb.open(lmdb_file, map_size = int(1e12))

lmdb_txn = lmdb_env.begin(write = True)

```
for x in range(batch_size):
```

    data = np.ones((3, 64, 64), np.uint8)

```
    label = x
```

    datum = caffe.io.array_to_datum(data,label)

```
    keystr = "{:0>8d}".format(x)
```

    lmdb_txn.put(keystr, datum.SerializeToString())

```
lmdb_txn.commit()
```
def read():
```
lmdb_env = lmdb.open('lmdb_data')
```
```
lmdb_txt = lmdb_env.begin()
```
```
datum = caffe_pb2.Datum()
```
```
for key, value in lmdb_txt.cursor():
```
```
    datum.ParseFromString(value)
```
```
    label = datum.label
```

    data = caffe.io.datum_to_array(datum)

```
    print(label)
```
```
    print(data)
```
if name == ‘main’:
```
write()
```
```
read()
```

demo_create_solver.py: 生成solver配置文件

from caffe.proto import caffe_pb2
s = caffe_pb2.SolverParameter()
s.train_net = “train.prototxt”
s.test_net.append(“test.prototxt”)
s.test_interval = 100
s.test_iter.append(10)
s.max_iter = 1000
s.base_lr = 0.1
s.weight_decay = 5e-4
s.lr_policy = “step”
s.display = 10
s.snapshot = 10
s.snapshot_prefix = “model”
s.type = “SGD”
s.solver_mode = caffe_pb2.SolverParameter.GPU
with open(“net/s.prototxt”, “w”) as f:
```
f.write(str(s))
```

結(jié)果如下：

train_net: “/home/kuan/PycharmProjects/demo_cnn_net/net/train.prototxt”
test_net: “/home/kuan/PycharmProjects/demo_cnn_net/net/test.prototxt”
test_iter: 1000
test_interval: 100
base_lr: 0.10000000149
display: 100
max_iter: 100000
lr_policy: “step”
weight_decay: 0.000500000023749
snapshot: 100
snapshot_prefix: “/home/kuan/PycharmProjects/demo_cnn_net/cnn_model/mnist/lenet/”
solver_mode: GPU
type: “SGD”
demo_creat_net.py: 創(chuàng)建網(wǎng)絡(luò)
import caffe
def create_net():
```
 net = caffe.NetSpec()
```

 net.data, net.label = caffe.layers.Data(source="data.lmdb",

                                         backend=caffe.params.Data.LMDB,

                                         batch_size=32,

                                         ntop=2,  #數(shù)據(jù)層數(shù)據(jù)個(gè)數(shù)，分別為data,label

                                        transform_param=dict(crop_size=40, mirror=True)

net.conv1 = caffe.layers.Convolution(net.data, num_output=20, kernel_size=5,

                                     weight_filler={"type": "xavier"},

                                     bias_filler={"type":"xavier"})  #卷積核參數(shù)

net.relu1 = caffe.layers.ReLU(net.conv1, in_place=True)

net.pool1 = caffe.layers.Pooling(net.relu1, pool=caffe.params.Pooling.MAX,

                                 kernel_size=3, stride=2)

net.conv2 = caffe.layers.Convolution(net.pool1, num_output=32, kernel_size=3,

                                     pad=1,

                                     weight_filler={"type": "xavier"},

                                     bias_filler={"type": "xavier"})

net.relu2 = caffe.layers.ReLU(net.conv2, in_place=True)

net.pool2 = caffe.layers.Pooling(net.relu2, pool=caffe.params.Pooling.MAX,

                                 kernel_size=3, stride=2)

```
#下面為全連接層
```

net.fc3 = caffe.layers.InnerProduct(net.pool2, num_output=1024, weight_filler=dict(type='xavier'))

net.relu3 = caffe.layers.ReLU(net.fc3, in_place=True)

```
##drop
```

net.drop = caffe.layers.Dropout(net.relu3, dropout_param=dict(dropout_ratio=0.5))

net.fc4 = caffe.layers.InnerProduct(net.drop, num_output=10, weight_filler=dict(type='xavier'))

net.loss = caffe.layers.SoftmaxWithLoss(net.fc4, net.label)

with open("net/tt.prototxt", 'w') as f:

```
    f.write(str(net.to_proto()))
```
if name == ‘main’:
```
create_net()
```

生成結(jié)果如下：

layer {
name: “data”
type: “Data”
top: “data”
top: “l(fā)abel”
transform_param {
```
 mirror: true
```
```
 crop_size: 40
```
}
data_param {

source: "/home/kuan/PycharmProjects/demo_cnn_net/lmdb_data"

```
batch_size: 32
```
```
backend: LMDB
```
}
}
layer {
name: “conv1”
type: “Convolution”
bottom: “data”
top: “conv1”
convolution_param {
```
num_output: 20
```
```
kernel_size: 5
```
```
weight_filler {
```
```
  type: "xavier"
```
```
}
```
```
bias_filler {
```
```
  type: "xavier"
```
```
}
```
}
}
layer {
name: “relu1”
type: “ReLU”
bottom: “conv1”
top: “conv1”
}
layer {
name: “pool1”
type: “Pooling”
bottom: “conv1”
top: “pool1”
pooling_param {
```
pool: MAX
```
```
kernel_size: 3
```
```
stride: 2
```
}
}
layer {
name: “conv2”
type: “Convolution”
bottom: “pool1”
top: “conv2”
convolution_param {
```
num_output: 32
```
```
pad: 1
```
```
kernel_size: 3
```
```
weight_filler {
```
```
  type: "xavier"
```
```
}
```
```
bias_filler {
```
```
  type: "xavier"
```
```
}
```
}
}
layer {
name: “relu2”
type: “ReLU”
bottom: “conv2”
top: “conv2”
}
layer {
name: “pool2”
type: “Pooling”
bottom: “conv2”
top: “pool2”
pooling_param {
```
pool: MAX
```
```
kernel_size: 3
```
```
stride: 2
```
}
}
layer {
name: “fc3”
type: “InnerProduct”
bottom: “pool2”
top: “fc3”
inner_product_param {
```
num_output: 1024
```
```
weight_filler {
```
```
  type: "xavier"
```
```
}
```
}
}
layer {
name: “relu3”
type: “ReLU”
bottom: “fc3”
top: “fc3”
}
layer {
```
 name: "drop"
```
```
 type: "Dropout"
```
```
 bottom: "fc3"
```
```
 top: "drop"
```
```
 dropout_param {
```
```
   dropout_ratio: 0.5
```
```
 }
```
}
layer {
```
 name: "fc4"
```
```
 type: "InnerProduct"
```
```
 bottom: "drop"
```
```
 top: "fc4"
```
```
 inner_product_param {
```
```
   num_output: 10
```
```
   weight_filler {
```
```
     type: "xavier"
```
```
   }
```
```
 }
```
}
layer {
```
 name: "loss"
```
```
 type: "SoftmaxWithLoss"
```
```
 bottom: "fc4"
```
```
 bottom: "label"
```
```
 top: "loss"
```
}
demo_train.py訓(xùn)練網(wǎng)絡(luò)：
import sys
sys.path.append(’/home/kuan/AM-softmax_caffe/python’)
import caffe
solver = caffe.SGDSolver("/home/kuan/PycharmProjects/demo_cnn_net/cnn_net/alexnet/solver.prototxt")
solver.solve()
demo_test.py:測(cè)試網(wǎng)絡(luò)
import sys
sys.path.append(’/home/kuan/AM-softmax_caffe/python’)
import caffe
import numpy as np
##caffemodel deploy.prototxt
deploy = “/home/kuan/PycharmProjects/demo_cnn_net/cnn_net/alexnet/deploy.prototxt”
model = “/home/kuan/PycharmProjects/demo_cnn_net/cnn_model/cifar/alexnet/alexnet_iter_110.caffemodel”
net = caffe.Net(deploy, model, caffe.TEST)
net.blobs[“data”].data[…] = np.ones((3,32,32),np.uint8)
net.forward()
prob = net.blobs[“prob”].data[0]
print(prob)

總結(jié)

以上是生活随笔為你收集整理的Caffe实现概述的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。

Caffe