getParameters can return empty tensor #3
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If parameters() is not defined or returns an empty table, getParameters() will return an empty tensor.
clementfarabet
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Jan 7, 2014
getParameters can return empty tensor
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curious-attempt-bunny
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May 28, 2016
Prior to this fix I was getting: ``` /Users/home/torch/install/bin/luajit: /Users/home/torch/install/share/lua/5.1/nn/THNN.lua:109: bad argument torch#3 to 'v' (cannot convert 'struct THByteTensor *' to 'struct THDoubleTensor *') stack traceback: [C]: in function 'v' /Users/home/torch/install/share/lua/5.1/nn/THNN.lua:109: in function 'MSECriterion_updateOutput' /Users/home/torch/install/share/lua/5.1/nn/MSECriterion.lua:14: in function 'forward' sum_using_bit_represenation.lua:42: in function 'opfunc' /Users/home/torch/install/share/lua/5.1/optim/sgd.lua:44: in function 'sgd' sum_using_bit_represenation.lua:48: in main chunk [C]: in function 'dofile' ...home/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk [C]: at 0x010088d1f0 ``` The composed snippets that were causing this error for me were: ``` require 'nn' local model = nn.Sequential(); -- make a multi-layer perceptron local inputs = 2; local outputs = 1; local HUs = 20; -- parameters model:add(nn.Linear(inputs, HUs)) model:add(nn.Tanh()) model:add(nn.Linear(HUs, outputs)) local criterion = nn.MSECriterion() local batchSize = 128 local batchInputs = torch.Tensor(batchSize, inputs) local batchLabels = torch.ByteTensor(batchSize) for i=1,batchSize do local input = torch.randn(2) -- normally distributed example in 2d local label = 1 if input[1]*input[2]>0 then -- calculate label for XOR function label = -1; end batchInputs[i]:copy(input) batchLabels[i] = label end local params, gradParams = model:getParameters() local optimState = {learningRate=0.01} require 'optim' for epoch=1,50 do -- local function we give to optim -- it takes current weights as input, and outputs the loss -- and the gradient of the loss with respect to the weights -- gradParams is calculated implicitly by calling 'backward', -- because the models weight and bias gradient tensors -- are simply views onto gradParams local function feval(params) gradParams:zero() local outputs = model:forward(batchInputs) local loss = criterion:forward(outputs, batchLabels) local dloss_doutput = criterion:backward(outputs, batchLabels) model:backward(batchInputs, dloss_doutput) return loss,gradParams end optim.sgd(feval, params, optimState) end x = torch.Tensor(2) x[1] = 0.5; x[2] = 0.5; print(model:forward(x)) x[1] = 0.5; x[2] = -0.5; print(model:forward(x)) x[1] = -0.5; x[2] = 0.5; print(model:forward(x)) x[1] = -0.5; x[2] = -0.5; print(model:forward(x)) ```
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If parameters() is not defined or returns an empty table,
getParameters() will return an empty tensor.