nnet-train-ensemble.cc
4.77 KB
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
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
// nnet2bin/nnet-train-ensemble.cc
// Copyright 2012 Johns Hopkins University (author: Daniel Povey)
// 2014 Xiaohui Zhang
// 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/train-nnet-ensemble.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 =
"Train an ensemble of neural networks with backprop and stochastic\n"
"gradient descent using minibatches. Modified version of nnet-train-simple.\n"
"Implements parallel gradient descent with a term that encourages the nnets to\n"
"produce similar outputs.\n"
"\n"
"Usage: nnet-train-ensemble [options] <model-in-1> <model-in-2> ... <model-in-n>"
" <training-examples-in> <model-out-1> <model-out-2> ... <model-out-n> \n"
"\n"
"e.g.:\n"
" nnet-train-ensemble 1.1.nnet 2.1.nnet ark:egs.ark 2.1.nnet 2.2.nnet \n";
bool binary_write = true;
bool zero_stats = true;
int32 srand_seed = 0;
std::string use_gpu = "yes";
NnetEnsembleTrainerConfig train_config;
ParseOptions po(usage);
po.Register("binary", &binary_write, "Write output in binary mode");
po.Register("zero-stats", &zero_stats, "If true, zero occupation "
"counts stored with the neural net (only affects mixing up).");
po.Register("srand", &srand_seed, "Seed for random number generator "
"(relevant if you have layers of type AffineComponentPreconditioned "
"with l2-penalty != 0.0");
po.Register("use-gpu", &use_gpu,
"yes|no|optional|wait, only has effect if compiled with CUDA");
train_config.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() <= 3) {
po.PrintUsage();
exit(1);
}
srand(srand_seed);
#if HAVE_CUDA==1
CuDevice::Instantiate().SelectGpuId(use_gpu);
#endif
int32 num_nnets = (po.NumArgs() - 1) / 2;
std::string nnet_rxfilename = po.GetArg(1);
std::string examples_rspecifier = po.GetArg(num_nnets + 1);
std::string nnet1_rxfilename = po.GetArg(1);
TransitionModel trans_model;
std::vector<AmNnet> am_nnets(num_nnets);
{
bool binary_read;
Input ki(nnet1_rxfilename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
KALDI_LOG << nnet1_rxfilename;
am_nnets[0].Read(ki.Stream(), binary_read);
}
std::vector<Nnet*> nnets(num_nnets);
nnets[0] = &(am_nnets[0].GetNnet());
for (int32 n = 1; n < num_nnets; n++) {
TransitionModel trans_model;
bool binary_read;
Input ki(po.GetArg(1 + n), &binary_read);
trans_model.Read(ki.Stream(), binary_read);
am_nnets[n].Read(ki.Stream(), binary_read);
nnets[n] = &am_nnets[n].GetNnet();
}
int64 num_examples = 0;
{
if (zero_stats) {
for (int32 n = 1; n < num_nnets; n++)
nnets[n]->ZeroStats();
}
{ // want to make sure this object deinitializes before
// we write the model, as it does something in the destructor.
NnetEnsembleTrainer trainer(train_config,
nnets);
SequentialNnetExampleReader example_reader(examples_rspecifier);
for (; !example_reader.Done(); example_reader.Next(), num_examples++)
trainer.TrainOnExample(example_reader.Value()); // It all happens here!
}
{
for (int32 n = 0; n < num_nnets; n++) {
Output ko(po.GetArg(po.NumArgs() - num_nnets + n + 1), binary_write);
trans_model.Write(ko.Stream(), binary_write);
am_nnets[n].Write(ko.Stream(), binary_write);
}
}
}
#if HAVE_CUDA==1
CuDevice::Instantiate().PrintProfile();
#endif
KALDI_LOG << "Finished training, processed " << num_examples
<< " training examples.";
return (num_examples == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '\n';
return -1;
}
}