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) |
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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?
You are amazing…
Thanks for these beautiful diagrams of CNN models. I was searching for Inception 3 architecture on the internet. You helped me!
thank you this diagrams are so educational this amazing
Thank you for the diagrams, they are helpful.
Thank you
Thank you for the diagrams. they are very helpful.