trunk,nnet: setting output of python scripts to use buffering, adding apache headers

git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@3256 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
This commit is contained in:
Karel Vesely 2013-12-03 22:14:55 +00:00
Родитель a9c8b965cc
Коммит 55f5c00c4c
8 изменённых файлов: 90 добавлений и 28 удалений

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@ -1,5 +1,5 @@
#!/bin/bash
# Copyright 2012 Karel Vesely
# Copyright 2012 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.

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@ -1,5 +1,5 @@
#!/bin/bash
# Copyright 2012 Karel Vesely
# Copyright 2012 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.

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@ -1,10 +1,24 @@
#!/usr/bin/python -u
#!/usr/bin/python
# Copyright 2012 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.
# ./gen_dct_mat.py
# script generates matrix with DCT transform
#
# author: Karel Vesely
#
# script generates matrix with DCT transform, which is sparse
# and takes into account that data-layout is along frequency axis,
# while DCT is done along temporal axis.
from math import *
import sys
@ -34,7 +48,7 @@ M_PI = 3.1415926535897932384626433832795
M_SQRT2 = 1.4142135623730950488016887
#generate small DCT matrix
#generate sparse DCT matrix
print '['
for k in range(dct_basis):
for m in range(dim):

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@ -1,10 +1,22 @@
#!/usr/bin/python -u
#!/usr/bin/python
# Copyright 2012 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.
# ./gen_hamm_mat.py
# script generates diagonal matrix with hamming window values
#
# author: Karel Vesely
#
from math import *
import sys

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@ -1,10 +1,22 @@
#!/usr/bin/python -u
#!/usr/bin/python
# Copyright 2010-2013 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.
# ./gen_mlp_init.py
# script generateing NN initialization
#
# author: Karel Vesely
#
import math, random
import sys
@ -42,6 +54,7 @@ for i in range(len(dimStrL)):
#print dimL,'linBN',options.linBNdim
print '<Nnet>'
for layer in range(len(dimL)-1):
print '<affinetransform>', dimL[layer+1], dimL[layer]
@ -86,6 +99,8 @@ for layer in range(len(dimL)-1):
#print '<sigmoid>', dimL[layer+1], dimL[layer+1]
print options.activation, dimL[layer+1], dimL[layer+1]
print '</Nnet>'

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@ -1,13 +1,22 @@
#!/usr/bin/python -u
#!/usr/bin/python
# egs/wsj/s2/scripts/gen_rbm_init.py
# Copyright 2012 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
#
# Copyright 2012 Karel Vesely
# http://www.apache.org/licenses/LICENSE-2.0
#
# Initializes the RBM Neural Network
# 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.
#
# calling example:
# python gen_mlp_init.py --dimIn=598 --dimOut=135 --dimHid=1024:1024:1024
# Initialization of single RBM (``layer'')
#
import math, random

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@ -1,11 +1,22 @@
#!/usr/bin/python -u
#!/usr/bin/python
# Copyright 2012 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.
# ./gen_splice.py
# script generates splice nnet transfrom
#
#
# author: Karel Vesely
#
# generates <splice> Component
from math import *
import sys

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@ -35,6 +35,7 @@ in_dim=$1
out_dim=$2
nnet_out=$3
dir=$(dirname $nnet_out)
[ ! -d $dir ] && mkdir -p $dir
###
### What is the topology? Straight or bottleneck?