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#!/usr/bin/python -u
# egs/wsj/s2/scripts/gen_rbm_init.py
#
# Copyright 2012 Karel Vesely
#
# Initializes the RBM Neural Network
#
# calling example:
# python gen_mlp_init.py --dimIn=598 --dimOut=135 --dimHid=1024:1024:1024
#
import math, random
import sys
from optparse import OptionParser
parser = OptionParser()
parser.add_option('--dim', dest='dim', help='d1:d2 layer dimensions in the network')
parser.add_option('--gauss', dest='gauss', help='use gaussian noise for weights', action='store_true', default=False)
parser.add_option('--gauss-scale', dest='gauss_scale', help='standard deviation of the gaussain noise', default='0.1')
parser.add_option('--negbias', dest='negbias', help='use uniform [-4.1,-3.9] for bias (default all 0.0)', action='store_true', default=False)
parser.add_option('--hidtype', dest='hidtype', help='gauss/bern', default='bern')
parser.add_option('--vistype', dest='vistype', help='gauss/bern', default='bern')
parser.add_option('--cmvn-nnet', dest='cmvn_nnet', help='cmvn_nnet to parse mean activation used in visible bias initialization', default='')
(options, args) = parser.parse_args()
if(options.dim == None):
parser.print_help()
sys.exit(1)
dimStrL = options.dim.split(':')
assert(len(dimStrL) == 2) #only single layer to initialize
dimL = []
for i in range(len(dimStrL)):
dimL.append(int(dimStrL[i]))
gauss_scale=float(options.gauss_scale)
#generate RBM
print '<rbm>', dimL[1], dimL[0]
print options.vistype, options.hidtype
#init weight matrix
print '['
for row in range(dimL[1]):
for col in range(dimL[0]):
if(options.gauss):
print gauss_scale * random.gauss(0.0,1.0),
else:
print (random.random()-0.5)/5.0,
print
print ']'
#init visbias
if len(options.cmvn_nnet)>0:
### use the formula log(p/(1-p) for visible biases, where p is mean activity of the neuron
f = open(options.cmvn_nnet)
#make sure file starts by <addshift>
line = f.readline()
arr = line.split(' ')
if arr[0] != '<addshift>':
raise Exception('missing <addshift> in '+options.cmvn_nnet)
#get the p's
line = f.readline()
arr = line.strip().split(' ')
assert(len(arr)-2 == dimL[0])
#print the values
print '[',
for i in range(1,len(arr)-1):
p = -float(arr[i])
#p must be after sigmoid
if(not (p >= 0.0 and p <= 1.0)):
raise Exception('Negative addshifts from '+options.cmvn_nnet+' must be 0..1, ie. the sigmoid outputs')
#limit the bias to +/- 8, we will modify the p values accordingly
if(p < 0.00033535):
p = 0.00033535
if(p > 0.99966465):
p = 0.99966465
#use the inverse sigmoid to get biases from the mean activations
print math.log(p/(1-p)),
print ']'
f.close()
else:
print '[',
for idx in range(dimL[0]):
if(options.vistype=='gauss'):
print '0.0',
elif(options.negbias):
print random.random()/5.0-4.1,
else:
print '0.0',
print ']'
#init hidbias
print '[',
for idx in range(dimL[1]):
if(options.hidtype=='gauss'):
print '0.0',
elif(options.negbias):
print random.random()/5.0-4.1,
else:
print '0.0',
print ']'
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