diff options
| -rw-r--r-- | htk_io/tools/nerv_to_tnet.lua | 60 | ||||
| -rw-r--r-- | htk_io/tools/tnet_to_nerv.c | 2 |
2 files changed, 61 insertions, 1 deletions
diff --git a/htk_io/tools/nerv_to_tnet.lua b/htk_io/tools/nerv_to_tnet.lua new file mode 100644 index 0000000..c0ac76b --- /dev/null +++ b/htk_io/tools/nerv_to_tnet.lua @@ -0,0 +1,60 @@ +-- 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, string.format("v %d\n", mat:ncol())) + 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("m %d %d\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 + table.insert(strs, "\n") + f:write(table.concat(strs)) + strs = {} + end + end +end +local lnames = {"affine0", "sigmoid0", + "affine1", "sigmoid1", + "affine2", "sigmoid2", + "affine3", "sigmoid3", + "affine4", "sigmoid4", + "affine5", "sigmoid5", + "affine6", "sigmoid6", + "affine7", "ce_crit"} +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("<biasedlinearity> %d %d\n", 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:close() diff --git a/htk_io/tools/tnet_to_nerv.c b/htk_io/tools/tnet_to_nerv.c index f781236..5774819 100644 --- a/htk_io/tools/tnet_to_nerv.c +++ b/htk_io/tools/tnet_to_nerv.c @@ -41,7 +41,7 @@ int main() { fprintf(fout, "%16d", 0); fprintf(fout, "{type=\"nerv.BiasParam\",id=\"affine%d_bp\"}\n", cnt); - fprintf(fout, "1 %d\n", nrow, ncol); + fprintf(fout, "1 %d\n", ncol); for (j = 0; j < ncol; j++) fprintf(fout, "%.8f ", mat[0][j]); fprintf(fout, "\n"); |