diff --git a/src/nnet2bin/nnet-am-shrink.cc b/src/nnet2bin/nnet-am-shrink.cc
+++ /dev/null
@@ -1,102 +0,0 @@
-// nnet2bin/nnet-am-shrink.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 "hmm/transition-model.h"
-#include "nnet2/shrink-nnet.h"
-#include "nnet2/am-nnet.h"
-
-
-int main(int argc, char *argv[]) {
- try {
- using namespace kaldi;
- using namespace kaldi::nnet2;
- typedef kaldi::int32 int32;
- typedef kaldi::int64 int64;
-
- const char *usage =
- "Using a validation set, compute optimal scaling parameters for each\n"
- "class of neural network parameters (i.e. each updatable component), to\n"
- "maximize validation-set objective function.\n"
- "\n"
- "Usage: nnet-am-shrink [options] <model-in> <valid-examples-in> <model-out>\n"
- "\n"
- "e.g.:\n"
- " nnet-am-shrink 1.nnet ark:valid.egs 2.nnet\n";
-
- bool binary_write = true;
- NnetShrinkConfig shrink_config;
-
- ParseOptions po(usage);
- po.Register("binary", &binary_write, "Write output in binary mode");
-
- shrink_config.Register(&po);
-
- po.Read(argc, argv);
-
- if (po.NumArgs() != 3) {
- po.PrintUsage();
- exit(1);
- }
-
- std::string nnet_rxfilename = po.GetArg(1),
- valid_examples_rspecifier = po.GetArg(2),
- nnet_wxfilename = po.GetArg(3);
-
- TransitionModel trans_model;
- AmNnet am_nnet;
- {
- bool binary_read;
- Input ki(nnet_rxfilename, &binary_read);
- trans_model.Read(ki.Stream(), binary_read);
- am_nnet.Read(ki.Stream(), binary_read);
- }
-
- std::vector<NnetExample> validation_set; // stores validation
- // frames.
-
- { // This block adds samples to "validation_set".
- SequentialNnetExampleReader example_reader(
- valid_examples_rspecifier);
- for (; !example_reader.Done(); example_reader.Next())
- validation_set.push_back(example_reader.Value());
- KALDI_LOG << "Read " << validation_set.size() << " examples from the "
- << "validation set.";
- KALDI_ASSERT(validation_set.size() > 0);
- }
-
- ShrinkNnet(shrink_config,
- validation_set,
- &(am_nnet.GetNnet()));
-
- {
- Output ko(nnet_wxfilename, binary_write);
- trans_model.Write(ko.Stream(), binary_write);
- am_nnet.Write(ko.Stream(), binary_write);
- }
-
- KALDI_LOG << "Finished shrinking neural net, wrote model to "
- << nnet_wxfilename;
- return (validation_set.size() == 0 ? 1 : 0);
- } catch(const std::exception &e) {
- std::cerr << e.what() << '\n';
- return -1;
- }
-}