Blame view
src/nnet3bin/nnet3-init.cc
4.18 KB
8dcb6dfcb
first commit
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
// nnet3bin/nnet3-init.cc
// Copyright 2012-2015 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 "nnet3/nnet-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::nnet3;
typedef kaldi::int32 int32;
const char *usage =
"Initialize nnet3 neural network from a config file; outputs 'raw' nnet
"
"without associated information such as transition model and priors.
"
"Search for examples in scripts in /egs/wsj/s5/steps/nnet3/
"
"Can also be used to add layers to existing model (provide existing model
"
"as 1st arg)
"
"
"
"Usage: nnet3-init [options] [<existing-model-in>] <config-in> <raw-nnet-out>
"
"e.g.:
"
" nnet3-init nnet.config 0.raw
"
"or: nnet3-init 1.raw nnet.config 2.raw
"
"See also: nnet3-copy, nnet3-info
";
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.NumArgs() > 3) {
po.PrintUsage();
exit(1);
}
std::string raw_nnet_rxfilename = (po.NumArgs() == 3 ?
po.GetArg(1) : std::string("")),
config_rxfilename = po.GetArg(po.NumArgs() == 3 ? 2 : 1),
raw_nnet_wxfilename = po.GetArg(po.NumArgs() == 3 ? 3 : 2);
Nnet nnet;
if (po.NumArgs() == 3) {
ReadKaldiObject(raw_nnet_rxfilename, &nnet);
KALDI_LOG << "Read raw neural net from "
<< raw_nnet_rxfilename;
}
{
bool binary;
Input ki(config_rxfilename, &binary);
KALDI_ASSERT(!binary && "Expect config file to contain text.");
nnet.ReadConfig(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() << '
';
return -1;
}
}
/*
Test script:
cat <<EOF | nnet3-init --binary=false - foo.raw
component name=affine1 type=NaturalGradientAffineComponent input-dim=72 output-dim=59
component name=relu1 type=RectifiedLinearComponent dim=59
component name=final_affine type=NaturalGradientAffineComponent input-dim=59 output-dim=298
component name=logsoftmax type=SoftmaxComponent dim=298
input-node name=input dim=18
component-node name=affine1_node component=affine1 input=Append(Offset(input, -4), Offset(input, -3), Offset(input, -2), Offset(input, 0))
component-node name=nonlin1 component=relu1 input=affine1_node
component-node name=final_affine component=final_affine input=nonlin1
component-node name=output_nonlin component=logsoftmax input=final_affine
output-node name=output input=output_nonlin
EOF
cat <<EOF | nnet3-init --binary=false foo.raw - bar.raw
component name=affine2 type=NaturalGradientAffineComponent input-dim=59 output-dim=59
component name=relu2 type=RectifiedLinearComponent dim=59
component name=final_affine type=NaturalGradientAffineComponent input-dim=59 output-dim=298
component-node name=affine2 component=affine2 input=nonlin1
component-node name=relu2 component=relu2 input=affine2
component-node name=final_affine component=final_affine input=relu2
EOF
rm foo.raw bar.raw
*/
|