Rahul Raghavan raghavan-rahul
raghavan-rahul
/ dist_mnist.py
Created
March 22, 2018 23:38
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| from __future__ import print_function | |
| import tensorflow as tf | |
| import sys | |
| import time | |
| # cluster specification | |
| parameter_servers = ["localhost:2222"] | |
| workers = [ "localhost:2223", | |
| "localhost:2224"] |
raghavan-rahul
/ multiple_learning_rates.lua
Created
February 16, 2017 00:52
— forked from soumith/multiple_learning_rates.lua
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)) |