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egs/wsj/s5/utils/nnet/make_lstm_proto.py
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#!/usr/bin/env python
# Copyright 2015-2016 Brno University of Technology (author: Karel Vesely)
# 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.
# Generated Nnet prototype, to be initialized by 'nnet-initialize'.
from __future__ import print_function
import sys
###
### Parse options
###
from optparse import OptionParser
usage="%prog [options] <feat-dim> <num-leaves> >nnet-proto-file"
parser = OptionParser(usage)
# Required,
parser.add_option('--cell-dim', dest='cell_dim', type='int', default=320,
help='Number of cells for one direction in LSTM [default: %default]');
parser.add_option('--proj-dim', dest='proj_dim', type='int', default=400,
help='Number of LSTM recurrent units [default: %default]');
parser.add_option('--num-layers', dest='num_layers', type='int', default=2,
help='Number of LSTM layers [default: %default]');
# Optional (default == 'None'),
parser.add_option('--lstm-param-range', dest='lstm_param_range', type='float',
help='Range of initial LSTM parameters [default: %default]');
parser.add_option('--param-stddev', dest='param_stddev', type='float',
help='Standard deviation for initial weights of Softmax layer [default: %default]');
parser.add_option('--cell-clip', dest='cell_clip', type='float',
help='Clipping cell values during propagation (per-frame) [default: %default]');
parser.add_option('--diff-clip', dest='diff_clip', type='float',
help='Clipping partial-derivatives during BPTT (per-frame) [default: %default]');
parser.add_option('--cell-diff-clip', dest='cell_diff_clip', type='float',
help='Clipping partial-derivatives of "cells" during BPTT (per-frame, those accumulated by CEC) [default: %default]');
parser.add_option('--grad-clip', dest='grad_clip', type='float',
help='Clipping the accumulated gradients (per-updates) [default: %default]');
#
(o,args) = parser.parse_args()
if len(args) != 2 :
parser.print_help()
sys.exit(1)
(feat_dim, num_leaves) = [int(i) for i in args];
# Original prototype from Jiayu,
#<NnetProto>
#<Transmit> <InputDim> 40 <OutputDim> 40
#<LstmProjectedStreams> <InputDim> 40 <OutputDim> 512 <CellDim> 800 <ParamScale> 0.01 <NumStream> 4
#<AffineTransform> <InputDim> 512 <OutputDim> 8000 <BiasMean> 0.000000 <BiasRange> 0.000000 <ParamStddev> 0.04
#<Softmax> <InputDim> 8000 <OutputDim> 8000
#</NnetProto>
lstm_extra_opts=""
if None != o.lstm_param_range: lstm_extra_opts += "<ParamRange> %f " % o.lstm_param_range
if None != o.cell_clip: lstm_extra_opts += "<CellClip> %f " % o.cell_clip
if None != o.diff_clip: lstm_extra_opts += "<DiffClip> %f " % o.diff_clip
if None != o.cell_diff_clip: lstm_extra_opts += "<CellDiffClip> %f " % o.cell_diff_clip
if None != o.grad_clip: lstm_extra_opts += "<GradClip> %f " % o.grad_clip
softmax_affine_opts=""
if None != o.param_stddev: softmax_affine_opts += "<ParamStddev> %f " % o.param_stddev
# The LSTM layers,
print("<LstmProjected> <InputDim> %d <OutputDim> %d <CellDim> %s" % (feat_dim, o.proj_dim, o.cell_dim) + lstm_extra_opts)
for l in range(o.num_layers - 1):
print("<LstmProjected> <InputDim> %d <OutputDim> %d <CellDim> %s" % (o.proj_dim, o.proj_dim, o.cell_dim) + lstm_extra_opts)
# Adding <Tanh> for more stability,
print("<Tanh> <InputDim> %d <OutputDim> %d" % (o.proj_dim, o.proj_dim))
# Softmax layer,
print("<AffineTransform> <InputDim> %d <OutputDim> %d <BiasMean> 0.0 <BiasRange> 0.0" % (o.proj_dim, num_leaves) + softmax_affine_opts)
print("<Softmax> <InputDim> %d <OutputDim> %d" % (num_leaves, num_leaves))
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