// nnet2bin/nnet-init.cc // Copyright 2012 Johns Hopkins University (author: Daniel Povey) // See ../../COPYING for clarification regarding multiple authors // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, // MERCHANTABLITY OR NON-INFRINGEMENT. // See the Apache 2 License for the specific language governing permissions and // limitations under the License. #include "base/kaldi-common.h" #include "util/common-utils.h" #include "nnet2/am-nnet.h" #include "hmm/transition-model.h" #include "tree/context-dep.h" int main(int argc, char *argv[]) { try { using namespace kaldi; using namespace kaldi::nnet2; typedef kaldi::int32 int32; const char *usage = "Initialize the nnet2 neural network from a config file with a line for each\n" "component. Note, this only outputs the neural net itself, not the associated\n" "information such as the transition-model; you'll probably want to pipe\n" "the output into something like nnet-am-init.\n" "\n" "Usage: nnet-init [options] \n" "e.g.:\n" " nnet-init nnet.config 1.raw\n"; bool binary_write = true; int32 srand_seed = 0; ParseOptions po(usage); po.Register("binary", &binary_write, "Write output in binary mode"); po.Register("srand", &srand_seed, "Seed for random number generator"); po.Read(argc, argv); srand(srand_seed); if (po.NumArgs() != 2) { po.PrintUsage(); exit(1); } std::string config_rxfilename = po.GetArg(1), raw_nnet_wxfilename = po.GetArg(2); Nnet nnet; { bool binary; Input ki(config_rxfilename, &binary); KALDI_ASSERT(!binary && "Expect config file to contain text."); nnet.Init(ki.Stream()); } WriteKaldiObject(nnet, raw_nnet_wxfilename, binary_write); KALDI_LOG << "Initialized raw neural net and wrote it to " << raw_nnet_wxfilename; return 0; } catch(const std::exception &e) { std::cerr << e.what() << '\n'; return -1; } }