Visualizing CNN architectures side by side with mxnet

Convolutional Neural Networks can be visualized as computation graphs with input nodes where the computation starts and output nodes where the result can be read. Here the models that are provided with mxnet are compared using the mx.viz.plot_network method. The output node is at the top and the input node is at the bottom.

import find_mxnet
import mxnet as mx
import importlib

name = "inception-v3"
net = importlib.import_module("symbol_" + name).get_symbol(2)
a = mx.viz.plot_network(net, shape={"data":(1, 1, 299, 299)}, node_attrs={"shape":'rect',"fixedsize":'false'})
a.render(name)

 

LeNet
28×28
(1998)
AlexNet
224×224
(2012)
VGG
224×224
(9/2014)
GoogLeNet
224×224
(9/2014)
Inception BN
224×224
(2/2015)
Inception V3
299×299
(12/2015)
Resnet (n=9, 56 Layers)
28×28
(12/2015)
a a a a a a a

1 Comment

  1. Thank you, I search for Inception v3 diagram.

    But I found there are different input sizes and last convolution layers feature map. Is this different topology of standard nets?

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