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-- usage: nerv config_file nerv_param_input tnet_output
dofile(arg[1])
param_repo = nerv.ParamRepo()
param_repo:import({arg[2], gconf.initialized_param[2]}, nil, gconf)
layer_repo = make_layer_repo(param_repo)
f = assert(io.open(arg[3], "w"))
function print_tnet_matrix(cumat)
local strs = {}
collectgarbage()
if cumat:nrow() == 1 then
local mat = nerv.MMatrixFloat(1, cumat:ncol())
cumat:copy_toh(mat)
table.insert(strs, "[ ")
for j = 0, mat:ncol() - 1 do
table.insert(strs, string.format("%.8f ", mat[0][j]))
end
table.insert(strs, " ]\n")
f:write(table.concat(strs))
else
cumat = cumat:trans()
local mat = nerv.MMatrixFloat(cumat:nrow(), cumat:ncol())
cumat:copy_toh(mat)
table.insert(strs, string.format(" [\n", mat:nrow(), mat:ncol()))
for i = 0, mat:nrow() - 1 do
local row = mat[i]
for j = 0, mat:ncol() - 1 do
table.insert(strs, string.format("%.8f ", row[j]))
end
if i == mat:nrow() - 1 then
table.insert(strs, " ]\n")
else
table.insert(strs, "\n")
end
f:write(table.concat(strs))
strs = {}
end
end
end
local lnames = {"affine0", "sigmoid0",
"affine1", "sigmoid1",
"affine2", "sigmoid2",
"affine3", "sigmoid3",
"affine4", "sigmoid4",
"affine5", "sigmoid5",
"affine6", "ce_crit"}
f:write("<Nnet>\n")
for i, name in ipairs(lnames) do
local layer = layer_repo:get_layer(name)
local layer_type = layer.__typename
if layer_type == "nerv.AffineLayer" then
f:write(string.format("<AffineTransform> %d %d\n<LearnRateCoef> 1 <BiasLearnRateCoef> 1 <MaxNorm> 0",
layer.dim_out[1], layer.dim_in[1]))
print_tnet_matrix(layer.ltp.trans)
print_tnet_matrix(layer.bp.trans)
elseif layer_type == "nerv.SigmoidLayer" then
f:write(string.format("<Sigmoid> %d %d\n", layer.dim_out[1], layer.dim_in[1]))
elseif layer_type == "nerv.SoftmaxCELayer" then
f:write(string.format("<Softmax> %d %d\n", layer.dim_in[1], layer.dim_in[1]))
else
nerv.error("unknown layer type %s", layer_type)
end
end
f:write("</Nnet>\n")
f:close()
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