Representing the network as a graph.
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- julia
- Domain
- machine-learning
Research direction
Start by inspecting the network object produced by the shown MXNet.jl chain and the existing mx.to_json(net) path. Determine whether a graph representation is already exposed without JSON parsing; done would be a documented, supported way to access the network graph for tasks such as identifying layers to freeze.
Written by the indexing model from the issue text.
Description
Is there any way besides parsing the json to work with the network as a graph?
using MXNet
using LightGraphs
using JSON
net = @mx.chain mx.Variable(:data) =>
mx.Convolution(num_filter = 64, kernel = (3,3), pad = (1, 1), stride=(1,1)) =>
mx.LeakyReLU(act_type = :prelu) =>
mx.Convolution(num_filter = 128, kernel = (3,3), pad = (1, 1), stride=(1,1)) =>
mx.LeakyReLU(act_type = :prelu) =>
mx.Convolution(num_filter = 2, kernel = (3,3), pad = (1, 1), stride=(1,1)) =>
mx.LeakyReLU(act_type = :prelu) =>
mx.SoftmaxOutput(name=:softmax, multi_output=true)
js_net = JSON.parse(mx.to_json(net))
nodes = js_net["nodes"]
graph = DiGraph(length(nodes))
for (idx, node) in enumerate(nodes)
for edge in node["inputs"]
x, _ = edge
add_edge!(graph, x+1, idx)
end
end
I am using this right now to determine which parts of the network should be frozen.
- Dominant language
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- Stars
- 370
- Forks
- 63
- PR merge metrics
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Getting set up
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First steps
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