网络结构可视化步骤

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: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
}
}
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"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
}
}
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"
param {
    lr_mult: 1
}
param {
    lr_mult: 2
}
inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
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: "ip2"
bottom: "label"
top: "accuracy"
include {
    phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"

}

如何直观地的看这个结构呢？

网络结构可视化：

ImportError: No module named _caffe出现这种问题后怎么解决？
1.>>> exit()--目的：退出到caffe路径下
2.cd ${CAFFE_ROOT}下输入： make pycaffe
3.>>> import caffe--目的：验证错误是否还存在
4.>>> exit()--目的：退出到caffe路径下
[email protected]:~/Documents/caffe-master/python$ python draw_net.py ../models/bvlc_reference_caffenet/train_val.prototxt vis.png

操作步骤如下PS：>>> import caffe
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/zhangxiangwei/Documents/caffe-master/python/caffe/__init__.py", line 1, in <module>
from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
File "/home/zhangxiangwei/Documents/caffe-master/python/caffe/pycaffe.py", line 13, in <module>
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
ImportError: No module named _caffe
>>> exit()
[email protected]:~/Documents/caffe-master$ make pycaffe
CXX/LD -o python/caffe/_caffe.so python/caffe/_caffe.cpp
touch python/caffe/proto/__init__.py
PROTOC (python) src/caffe/proto/caffe.proto
[email protected]:~/Documents/caffe-master$ python
Python 2.7.12 (default, Nov 19 2016, 06:48:10)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import caffe
>>> exit()
[email protected]:~/Documents/caffe-master$ cd python/
[email protected]:~/Documents/caffe-master/python$ python draw_net.py ../models/bvlc_reference_caffenet/train_val.prototxt vis.png
Drawing net to vis.png

网络结构可视化步骤

命令行命令：

sudo python ./python/draw_net.py ./examples/mnist/lenet_train_test.prototxt ./examples/mnist/lenet_train_test.jpeg
或者

sudo python ./python/draw_net.py --rankdir BT ./examples/mnist/lenet_train_test.prototxt ./examples/mnist/lenet_train_test.jpeg

网络结构可视化步骤