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Gabriele Angeletti gabrieleangeletti
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farrajota
/ multiple_learning_rates.lua
Last active
April 10, 2018 16:47
Example code for how to set different learning rates per layer. Note that when calling :parameters(), the weights and bias of a given layer are separate, consecutive tensors. Therefore, when calling :parameters(), a network with N layers will output a table with N*2 tensors, where the i'th and i'th+1 tensors belong to the same layer.
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| -- multiple learning rates per network. Optimizes two copies of a model network and checks if the optimization steps (2) and (3) produce the same weights/parameters. | |
| require 'torch' | |
| require 'nn' | |
| require 'optim' | |
| torch.setdefaulttensortype('torch.FloatTensor') | |
| -- (1) Define a model for this example. | |
| local model = nn.Sequential() | |
| model:add(nn.Linear(10,20)) |
