make_nnet_config.pl
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#!/usr/bin/perl -w
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
# 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.
# These options can be useful if we want to splice the input
# features across time.
$input_left_context = 0;
$input_right_context = 0;
$param_stddev_factor = 1.0; # can be used to adjust initial variance
# of parameters.
$initial_num_hidden_layers = -1; # if >= 0, the number of hidden layers
# the model should start with, which may be less than the final number
# (the final number is used to calculate the #neurons).
$single_layer_config = "";
$bias_stddev = 2.0;
$learning_rate = 0.001;
$nobias = "";
for ($x = 1; $x < 10; $x++) {
if ($ARGV[0] eq "--input-left-context") {
$input_left_context = $ARGV[1];
shift; shift;
}
if ($ARGV[0] eq "--input-right-context") {
$input_right_context = $ARGV[1];
shift; shift;
}
if ($ARGV[0] eq "--param-stddev-factor") {
$param_stddev_factor = $ARGV[1];
shift; shift;
}
if ($ARGV[0] eq "--bias-stddev") {
$bias_stddev = $ARGV[1];
shift; shift;
}
if ($ARGV[0] eq "--nobias") {
$nobias = "Nobias";
shift;
}
if ($ARGV[0] eq "--learning-rate") {
$learning_rate = $ARGV[1];
shift; shift;
}
if ($ARGV[0] eq "--initial-num-hidden-layers") {
$initial_num_hidden_layers = $ARGV[1];
$single_layer_config = $ARGV[2];
shift; shift; shift;
}
}
if (@ARGV != 4) {
print STDERR "Usage: make_nnet_config.pl [options] <feat-dim> <num-leaves> <num-hidden-layers> <num-parameters> >config-file
Options:
--input-left-context <n> # #frames of left context for input features; default 0.
--input-right-context <n> # #frames of right context for input features; default 0.
--param-stdddev-factor <f> # Factor which can be used to modify the standard deviation of
# randomly initialized features (default, 1. Gets multiplied by
# 1/sqrt of number of inputs).
--initial-num-hidden-layers <n> <config-file> # If >0, number of hidden layers to initialize the network with.
# In this case, the positional parameter <num-hidden-layers> is only
# used to work out the number of units per hidden layer (based on
# parameter count), and we write to <config-file> the config corresponding
# to a single hidden layer.
--learning-rate <f> # Initial learning rate, default 0.001\n";
exit(1);
}
($feat_dim, $num_leaves, $num_hidden_layers, $num_params) = @ARGV;
($input_left_context < 0) && die "Invalid input left context $input_left_context";
($input_right_context < 0) && die "Invalid input right context $input_right_context";
($feat_dim <= 0) && die "Invalid feature dimension $feat_dim";
($num_leaves <= 0) && die "Invalid number of leaves $num_leaves";
($num_hidden_layers <= 0) && die "Invalid number of hidden layers $num_hidden_layers";
if ($initial_num_hidden_layers < 0) {
$initial_num_hidden_layers = $num_hidden_layers;
}
if ($initial_num_hidden_layers > $num_hidden_layers) {
print STDERR "Initial number of hidden layers is more than #hidden layers.\n" .
"This does not really make sense but continuing anyway.";
}
$context_size = 1 + $input_left_context + $input_right_context;
($num_params < ($num_leaves + ($feat_dim * $context_size) + $num_hidden_layers + 1))
&& die "Invalid number of params $num_params";
## num_params = hidden_layer_size^2 * (num_hidden_layers-1)
## + hidden_layer_size * (num_leaves + feat_dim * context_size)
## solve for hidden_layer_size = x.
## a x^2 + b + c, with
## a = num_hidden_layers - 1
## b = num_leaves + feat_dim * context_size
## c = -num_params
$a = $num_hidden_layers - 1;
$b = $num_leaves + $feat_dim * $context_size;
$c = -$num_params;
if ($a > 0) {
$hidden_layer_size = int((-$b + sqrt($b*$b - 4*$a*$c)) / (2*$a));
} else {
$hidden_layer_size = int(-$c/$b);
}
$actual_num_params = $hidden_layer_size * $hidden_layer_size * ($num_hidden_layers - 1)
+ $hidden_layer_size * ($num_leaves + $feat_dim * $context_size);
if (abs($actual_num_params - $num_params) > 0.1 * $num_params) {
print STDERR "Warning: make_nnet_config.pl: possible failure $actual_num_params != $num_params";
}
if ($input_left_context + $input_right_context != 0) {
# First component has to be splicing component...
# Note: we might be interested in decorrelating this e.g. with
# DCT layer at some point, but for now, splicing isn't seeming to be
# that useful.
print "SpliceComponent input-dim=$feat_dim left-context=$input_left_context right-context=$input_right_context\n";
}
$cur_input_dim = $feat_dim * (1 + $input_left_context + $input_right_context);
for ($hidden_layer = 0; $hidden_layer < $initial_num_hidden_layers; $hidden_layer++) {
$param_stddev = $param_stddev_factor * 1.0 / sqrt($cur_input_dim);
print "AffineComponent$nobias input-dim=$cur_input_dim output-dim=$hidden_layer_size " .
"learning-rate=$learning_rate param-stddev=$param_stddev bias-stddev=$bias_stddev\n";
$cur_input_dim = $hidden_layer_size;
print "TanhComponent dim=$cur_input_dim\n";
}
if ($single_layer_config ne "") {
# Create a config file we'll use to add new hidden layers.
open(F, ">$single_layer_config") || die "Error opening $single_layer_config for output";
$param_stddev = $param_stddev_factor * 1.0 / sqrt($hidden_layer_size);
print F "AffineComponent$nobias input-dim=$hidden_layer_size output-dim=$hidden_layer_size " .
"learning-rate=$learning_rate param-stddev=$param_stddev bias-stddev=$bias_stddev\n";
print F "TanhComponent dim=$hidden_layer_size\n";
close (F) || die "Closing config file";
}
## Now the output layer.
print "AffineComponent$nobias input-dim=$cur_input_dim output-dim=$num_leaves " .
"learning-rate=$learning_rate param-stddev=0 bias-stddev=0\n"; # we just set the parameters to zero for this layer.
## the softmax nonlinearity.
print "SoftmaxComponent dim=$num_leaves\n";
##