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require 'totem'
require 'nngraph'
local test = {}
local tester = totem.Tester()
function test.test_call()
local prediction = nn.Identity()()
local target = nn.Identity()()
local mse = nn.MSECriterion()({prediction, target})
local costBits = nn.MulConstant(1/math.log(2))(mse)
local net = nn.gModule({prediction, target}, {costBits})
local input = {torch.randn(3, 5), torch.rand(3, 5)}
local criterion = nn.MSECriterion()
local output = net:forward(input)
criterion:forward(input[1], input[2])
tester:eq(output[1], criterion.output/math.log(2), "output", 1e-14)
local gradOutput = torch.randn(1)
local gradInput = net:backward(input, gradOutput)
criterion:backward(input[1], input[2])
tester:eq(gradInput[1], criterion.gradInput:clone():mul(gradOutput[1]/math.log(2)), "gradPrediction", 1e-14)
tester:eq(gradInput[2], torch.zeros(input[2]:size()), "gradTarget")
end
function test.test_grad()
local prediction = nn.Identity()()
local zero = nn.MulConstant(0)(prediction)
-- The target is created inside of the nngraph
-- to ignore the zero gradTarget.
local target = nn.AddConstant(1.23)(zero)
local mse = nn.MSECriterion()({prediction, target})
local net = nn.gModule({prediction}, {mse})
local input = torch.randn(4, 7)
totem.nn.checkGradients(tester, net, input)
end
local function module()
local module = nn.ModuleFromCriterion(nn.MSECriterion())
local input = {torch.randn(3, 5), torch.randn(3, 5)}
return module, input
end
function test.test_serializable()
local module, input = module()
totem.nn.checkSerializable(tester, module, input)
end
function test.test_typeCastable()
local module, input = module()
totem.nn.checkTypeCastable(tester, module, input)
end
tester:add(test):run()
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