[DL]深度学习中的模型可视化工具，支持ONNX, Caffe, Keras, Darknet, pytorch, TensorFlow目前格式最全！！！！-马春杰杰

最新目录‹

目前支持的格式：
远程调用：

学习深度学习，经常会遇到一个问题，那就是如何对深度学习的模型进行可视化，以yolo为例，这是yolov2-tiny-voc.cfg 的配置文件：

点击展开查看更多

[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=2
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.001
max_batches = 40200
policy=steps
steps=-1,100,20000,30000
scales=.1,10,.1,.1

[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=1

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

###########

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=125
activation=linear

[region]
anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
bias_match=1
classes=20
coords=4
num=5
softmax=1
jitter=.2
rescore=1

object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1

absolute=1
thresh = .6
random=1

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

[net]

# Testing

batch=1

subdivisions=1

# Training

# batch=64

# subdivisions=2

width=416

height=416

channels=3

momentum=0.9

decay=0.0005

angle=0

saturation = 1.5

exposure = 1.5

hue=.1

learning_rate=0.001

max_batches = 40200

policy=steps

steps=-1,100,20000,30000

scales=.1,10,.1,.1

[convolutional]

batch_normalize=1

filters=16

size=3

stride=1

pad=1

activation=leaky

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=32

size=3

stride=1

pad=1

activation=leaky

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=64

size=3

stride=1

pad=1

activation=leaky

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=128

size=3

stride=1

pad=1

activation=leaky

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=256

size=3

stride=1

pad=1

activation=leaky

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

[maxpool]

size=2

stride=1

[convolutional]

batch_normalize=1

filters=1024

size=3

stride=1

pad=1

activation=leaky

###########

[convolutional]

batch_normalize=1

size=3

stride=1

pad=1

filters=1024

activation=leaky

[convolutional]

size=1

stride=1

pad=1

filters=125

activation=linear

[region]

anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52

bias_match=1

classes=20

coords=4

num=5

softmax=1

jitter=.2

rescore=1

object_scale=5

noobject_scale=1

class_scale=1

coord_scale=1

absolute=1

thresh = .6

random=1

光看这个配置文件，我们可能很难看出它到底是怎样的结构，这时，可以祭出这个模型可视化的神器了，地址：

https://www.machunjie.com/dl/Visualization/index.html

目前支持的格式：

ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Core ML (.mlmodel), Caffe (.caffemodel, .prototxt), Caffe2 (predict_net.pb, predict_net.pbtxt), MXNet (.model, -symbol.json), TorchScript (.pt, .pth), NCNN (.param) and TensorFlow Lite (.tflite).

PyTorch (.pt, .pth), Torch (.t7), CNTK (.model, .cntk), Deeplearning4j (.zip), PaddlePaddle (.zip, __model__), Darknet (.cfg), scikit-learn (.pkl), TensorFlow.js (model.json, .pb) and TensorFlow (.pb, .meta, .pbtxt).

远程调用：

https://www.machunjie.com/dl/Visualization/index.html?url=模型文件地址

如：https://www.machunjie.com/dl/Visualization/index.html?url=https://ibelem.github.io/webml-website/examples/image_classification/model/squeezenet1.1.onnx