def masterConfig():
from tensorlog import bpcompiler
from tensorlog import dataset
from tensorlog import funs
from tensorlog import learn
from tensorlog import matrixdb
from tensorlog import mutil
from tensorlog import ops
from tensorlog import program
from tensorlog import xcomp
master = config.Config()
master.bpcompiler = bpcompiler.conf
master.help.bpcompiler = 'config for tensorlog.bpcompiler'
master.dataset = dataset.conf
master.help.dataset = 'config for tensorlog.dataset'
master.funs = funs.conf
master.help.funs = 'config for tensorlog.funs'
master.learn = learn.conf
master.help.learn = 'config for tensorlog.learn'
master.matrixdb = matrixdb.conf
master.help.matrixdb = 'config for tensorlog.matrixdb'
master.mutil = mutil.conf
master.help.mutil = 'config for tensorlog.mutil'
master.ops = ops.conf
master.help.ops = 'config for tensorlog.ops'
master.program = program.conf
master.help.program = 'conf for tensorlog.program'
master.xcomp = xcomp.conf
master.help.xcomp = 'config for tensorlog.xcomp'
try:
from tensorlog import debug
master.debug = debug.conf
master.help.debug = 'config for tensorlog.debug'
except ImportError:
logging.warn('debug module not imported')
return master
import time
import math
import numpy as NP
import scipy.sparse as SS
import collections
from tensorlog import config
from tensorlog import dataset
from tensorlog import declare
from tensorlog import funs
from tensorlog import mutil
from tensorlog import opfunutil
# clip to avoid exploding gradients
conf = config.Config()
conf.minGradient = -100; conf.help.minGradient = "Clip gradients smaller than this to minGradient"
conf.maxGradient = +100; conf.help.minGradient = "Clip gradients larger than this to maxGradient"
##############################################################################
# helper classes
##############################################################################
class GradAccumulator(object):
"""Accumulate the sum gradients for perhaps many parameters, indexing
them by parameter name. Also maintains 'counter' statistics,
which are simply floats indexed by a counter name. Counters are
mostly updated by the Tracer functions. The only required counter
is the counter 'n', which is the size of the minibatch the
gradient was computed on.
"""
