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train.py
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787 lines (637 loc) · 44 KB
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import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
import getpass
import argparse
import time
import os
import numpy.ma as MA
import sys
import pickle
import random
import math
from time import gmtime, strftime
from utils import DataLoader
import utils
from model import Model
from model import get_pi_idx
from stats import Stats
import resource
def memusage( point = "") :
usage = resource.getrusage( resource.RUSAGE_SELF)
return '''%s: usertime = %s systime = %s mem = %s mb
'''%( point, usage[ 0 ], usage[ 1 ],
( usage[ 2 ]*resource.getpagesize( ) ) /1000000.0 )
def main( ) :
print_input = 0
user = getpass.getuser( )
print ( "user: ", user)
#logdir = "/home/"+user+"/code/digits/rnn/log/"
logdir = '~/projects/incremental-sequence-learning/log/'
parser = argparse.ArgumentParser( )
parser.add_argument( '--rnn_size', type = int, default = 256,
help = 'size of RNN hidden state')
parser.add_argument( '--num_layers', type = int, default = 2,
help = 'number of layers in the RNN')
parser.add_argument( '--model', type = str, default = 'basiclstm',
help = 'rnn, gru, or lstm, or ffnn')
parser.add_argument( '--nrseq_per_batch', type = int, default = 50,
help = 'minibatch size')
parser.add_argument( '--nrseq_per_batch_test', type = int, default = 50,
help = 'minibatch size')
parser.add_argument( '--nrpoints_per_batch', type = int, default = 0,
help = 'Number of points ( sequence steps) per batch')
parser.add_argument( '--num_epochs', type = int, default = 30,
help = 'number of epochs')
parser.add_argument( '--report_every', type = int, default = 50,
help = 'report frequency')
parser.add_argument( '--save_every_nrbatches', type = int, default = 200,
help = 'save frequency')
parser.add_argument( '--save_maxnrmodels_keep', type = int, default = 5,
help = 'Max nr of models to keep')
parser.add_argument( '--eval_every', type = int, default = 50,
help = 'evaluation frequency')
parser.add_argument( '--test_every_nrbatches', type = int, default = 0,
help = 'testing frequency')
parser.add_argument( '--grad_clip', type = float, default = 10.,
help = 'clip gradients at this value')
parser.add_argument( '--learning_rate', type = float, default = 0.005,
help = 'learning rate')
parser.add_argument( '--decay_rate', type = float, default = 0.95,
help = 'decay rate for rmsprop')
parser.add_argument( '--num_mixture', type = int, default = 20,
help = 'number of gaussian mixtures')
parser.add_argument( '--keep_prob', type = float, default = 0.8,
help = 'dropout keep probability')
parser.add_argument( '--predict', type = int, default = 0,
help = 'predict instead of training')
parser.add_argument( '--predictideal', type = int, default = 0,
help = 'predict given ideal input')
parser.add_argument( '--evaluate', type = int, default = 0,
help = 'Run evaluation process that monitors the checkpoint files of a concurrently running training process.')
parser.add_argument( '--nrinputfiles_train', type = int, default = 0,
help = 'number of training input data files to use')
parser.add_argument( '--nrinputfiles_test', type = int, default = 0,
help = 'number of test input data files to use')
parser.add_argument( '--explabel', type = str, default = 0,
help = 'experiment label')
parser.add_argument( '--max_seq_length', type = int, default = 0,
help = 'max amount of points per sequence that will be used')
parser.add_argument( '--file_label', type = str, default = "",
help = 'input file label')
parser.add_argument( '--train_on_own_output_method', type = int, default = 0,
help = 'Various methods for training on own output, governed by current network performance')
parser.add_argument( '--model_checkpointfile', type = str, default = "",
help = 'checkpoint file to load')
parser.add_argument( '--sample_from_output', type = int, default = 0,
help = 'If set, when using train_on_own_output_method, the output will be sampled from first before passing it on as the next input; if not set, the output is used directly.')
parser.add_argument( '--regularization_factor', type = float, default = .01)
parser.add_argument( '--l2_weight_regularization', type = int, default = 1, help = 'Use the average of all weights as a regularization component')
parser.add_argument( '--max_weight_regularization', type = int, default = 0, help = 'Use the maximum of all weights as a basis for regularization')
parser.add_argument( '--discard_inputs', type = int, default = 0, help = 'Discard the input data; network must produce the output balistically.')
parser.add_argument( '--discard_classvar_inputs', type = int, default = 0, help = 'Discard the 10 boolean class indicators indicating the current class as input.')
parser.add_argument( '--nrClassOutputVars', type = int, default = 0, help = 'Use up to 10 binary class indicator outputs that the model has to predict at each step')
parser.add_argument( '--useStrokeOutputVars', type = int, default = 1, help = 'Generate strokes.')
parser.add_argument( '--useStrokeLoss', type = int, default = 1, help = 'Use loss component based on stroke output.')
parser.add_argument( '--useClassInputVars', type = int, default = 1, help = 'Use 10 binary input variable representing the digit class ( one-hot representation) .')
parser.add_argument( '--incremental_min_nrpoints', type = int, default = 5000, help = 'Min number of points to evaluate before considering next increment')
parser.add_argument( '--useInitializers', type = int, default = 0, help = 'Use initializers for network parameters to ensure reproducibility')
parser.add_argument( '--usePreviousEndState', type = int, default = 0, help = 'Use end state after previous batch as initial state for next batch')
parser.add_argument( '--print_length_correct', type = int, default = 0, help = 'Use initializers for network parameters to ensure reproducibility')
parser.add_argument( '--startingpoint', type = str, default = '', help = 'Start from saved state.')
parser.add_argument( '--randomizeSequenceOrder', type = int, default = 1, help = 'Randomize order of sequences to prevent learning order.')
parser.add_argument( '--classweightfactor', type = float, default = 10, help = 'weight of classvar loss')
parser.add_argument( '--curnrtrainexamples', type = float, default = 10)
parser.add_argument( '--current_seq_length', type = int, default = 0,
help = 'Used in combination with incremental_seq_length')
parser.add_argument( '--curnrdigits', type = int, default = 10)
parser.add_argument( '--correctfrac_threshold_inc_nrtrainex', type = float, default = .8)
parser.add_argument( '--threshold_rmse_stroke', type = float, default = 2)
parser.add_argument( '--usernn', type = int, default = 0)
parser.add_argument( '--fileselection', type = str, default = '', nargs = '+') #representative 10 digits: 1, 3, 25, 7, 89, 0, 62, 96, 85, 43
parser.add_argument( '--incremental_nr_trainexamples', type = int, default = 0)
parser.add_argument( '--incremental_seq_length', type = int, default = 0)
parser.add_argument( '--incremental_nr_digits', type = int, default = 0)
parser.add_argument( '--runnr', type = int, default = 1)
parser.add_argument( '--maxnrpoints', type = int, default = 0)
parser.add_argument( '--stat_windowsize_nrsequences', type = int, default = 1000)
parser.add_argument( '--firsttrainstep', type = int, default = 0, help = 'Loss is calculated from this sequence step onwards; preceding points are ignored ( fed, but not contributing to loss) ')
parser.add_argument( '--stopcrit_threshold_stroke_rmse_train', type = float, default = 0)
parser.add_argument( '--testovertrain', type = int, default = 0, help = 'Control experiment to check that overtraining can occur. Uses digits 0-4 for training, 5-9 for testing.')
parser.add_argument( '--reportstate', type = int, default = 0, help = 'report complete internal state ( weights, state) before/after each train/test batch')
parser.add_argument( '--reportmixture', type = int, default = 0, help = 'report mixture')
#arguments set internally:
parser.add_argument( '--maxdigitlength_nrpoints', type = int, default = 0, help = 'max sequence length ( nr points) that was encountered in the training data; calculated parameter.' )
parser.add_argument( '--rangemin', type = float, default = -22.6) #determined based on full MNIST stroke sequence data set
parser.add_argument( '--rangelen', type = float, default = 55.2) #determined based on full MNIST stroke sequence data set
parser.add_argument( '--seq_length', type = int, default = 0)
parser.add_argument( '--nroutputvars', type = int, default = 0)
parser.add_argument( '--nrtargetvars', type = int, default = 0)
parser.add_argument( '--nrauxinputvars', type = int, default = 0)
parser.add_argument( '--debuginfo', type = int, default = 0)
#variable sizes:
#o_pi: nrrowsperbatch x nrmixtures, i.e. pointnr x mixturenr
#targetdata: nrseq x seqlen x nrinputvars_network
#input: dx dy eos eod
#output: eos eod nr_mixtures*distribution-params classvars
args = parser.parse_args( )
file_label = args.file_label
explabel = args.explabel
outputdir = "./results/"+explabel+"r"+str( args.runnr) +"/"
if args.incremental_nr_trainexamples:
args.curnrtrainexamples = min( args.curnrtrainexamples, args.nrinputfiles_train )
args.incremental_min_nrpoints = 50 * args.curnrtrainexamples
else:
args.curnrtrainexamples = args.nrinputfiles_train
# datadir = "/home/"+user+"/code/digits/sequences/"
datadir = './data/sequences/'
print( "using data dir: ", datadir )
seqlenarg = 0
trainarg = 1
dataloader_train = DataLoader( datadir, args, args.nrinputfiles_train, args.curnrtrainexamples, seqlenarg, trainarg, file_label, print_input, args.rangemin, args.rangelen)
args.nrauxinputvars = 10 * args.useClassInputVars
trainarg = 0
dataloader_test = DataLoader( datadir, args, args.nrinputfiles_test, args.nrinputfiles_test, dataloader_train.seq_length, trainarg, file_label, print_input, args.rangemin, args.rangelen)
dataloader_test.createRandValues( )
args.nrtargetvars = 4*args.useStrokeOutputVars + args.nrClassOutputVars
if ( not args.incremental_seq_length) :
args.current_seq_length = dataloader_train.seq_length
if ( args.evaluate or args.predict or args.predictideal) :
configfile = os.path.join( 'save/'+args.explabel+'r'+str( args.runnr) , 'config.pkl')
if len( args.startingpoint ) > 0:
pos = args.startingpoint.find( 'model' )
savedfolder = args.startingpoint[ 0 : pos - 1 ]
print( 'savedfolder', savedfolder)
slashpos = savedfolder.find( '/') #find first slash
savedfolderlist = list( savedfolder)
savedfolderlist[ slashpos ] = 'x'
savedfolder = "".join( savedfolderlist)
slashpos = savedfolder.find( '/') #find second slash
savedfolder = args.startingpoint[ 0 : pos - 1 ]
savedfolderlist = list( savedfolder)
savedfolderlist = savedfolderlist[ :slashpos+1 ]
savedfolder = "".join( savedfolderlist) #get the path
configfile = os.path.join( savedfolder, 'config.pkl')
print( 'configfile', configfile)
fileexists = os.path.exists( configfile )
while not fileexists:
print ( "Waiting for config file", configfile)
time.sleep( 5)
fileexists = os.path.exists( configfile )
f = open( configfile, "rb")
saved_args = pickle.load( f)
saved_args.nrseq_per_batch = args.nrseq_per_batch
f.close( )
trainpredictmode = "Predict"
nrsequenceinputs = 4 #dx dy eos eod
nrinputvars_network = nrsequenceinputs + args.nrauxinputvars;
model_predict = Model( saved_args, trainpredictmode, True, nrinputvars_network, saved_args.nroutputvars_raw, args.nrtargetvars, args.nrClassOutputVars, maxdigitlength_nrpoints = saved_args.maxdigitlength_nrpoints)
if args.predict:
nrbatches = args.nrinputfiles_test / args.nrseq_per_batch
use_own_output_as_input = 1
with tf.Session( ) as sess:
performPrediction( sess, saved_args, args, model_predict, dataloader_test, nrbatches, use_own_output_as_input, outputdir, parser )
elif ( args.predictideal) :
nrbatches = args.nrinputfiles_test / args.nrseq_per_batch
use_own_output_as_input = 0
with tf.Session( ) as sess:
performPrediction( sess, saved_args, args, model_predict, dataloader_test, nrbatches, use_own_output_as_input, outputdir, parser )
else:
train( dataloader_train, dataloader_test, args, logdir, outputdir )
def savemodel( saver, sess, dataloader, args, batchnr ) :
checkpoint_path = os.path.join( 'save/'+args.explabel+'r'+str( args.runnr) , 'model.ckpt')
print( ( "saving model to {}".format( checkpoint_path) ) )
saver.save( sess, checkpoint_path, global_step = batchnr)
checkpoint_fullpath = checkpoint_path + "-" + str( batchnr )
print( ( 'saved checkpoint: '+checkpoint_fullpath) )
def restoreModel( sess, args, model_predict, dataloader, checkpoint_fullpath = "") :
saver = tf.train.Saver( tf.trainable_variables( ) , max_to_keep = args.save_maxnrmodels_keep)
print( 'restoreModel: checkpoint_fullpath', checkpoint_fullpath)
if len( checkpoint_fullpath) >0: #model path provided
saver.restore( sess, checkpoint_fullpath)
else: #load most recent state of own run
modelfile = 'save/'+saved_args.explabel
if len( args.model_checkpointfile) > 1 :
modelfile = args.model_checkpointfile
ckpt = tf.train.get_checkpoint_state( modelfile)
print( 'restored checkpoint' )
print ( "loading model: ", ckpt.model_checkpoint_path)
saver.restore( sess, ckpt.model_checkpoint_path)
def performPrediction( sess, saved_args, args, model_predict, dataloader, nrbatches, use_own_output_as_input, outputdir, parser) :
print( 'performprediction')
saver = tf.train.Saver( tf.trainable_variables( ) , max_to_keep = args.save_maxnrmodels_keep)
restoreModel( sess, args, model_predict, dataloader, args.startingpoint)
print( 'restored model')
[ strokes, params ] = model_predict.sample( sess, dataloader, saved_args, nrbatches, use_own_output_as_input, outputdir)
print( 'Completed performPrediction' )
def writeOutputTarget( args, outputdir, batchnr, sequence_index, batch_seqnr, outputmat, outputmat_sampled, targetmat, stroketarget, lossvec, model, loss, mode, inputdata) :
fn = outputdir + "output-"+mode+"-batch-" + str( batchnr) + "-seqnr-"+str( batch_seqnr) +"-filenr-" + str( sequence_index[ batch_seqnr ] ) + ".txt"
np.savetxt( fn, outputmat, fmt = '%.3f')
fn = outputdir + "output-sampled-"+mode+"-batch-" + str( batchnr) + "-seqnr-"+str( batch_seqnr) +"-filenr-" + str( sequence_index[ batch_seqnr ] ) + ".txt"
np.savetxt( fn, outputmat_sampled, fmt = '%.3f')
fn = outputdir + "classtarget-" +mode+ "-batch-" + str( batchnr) + "-seqnr-" +str( batch_seqnr) +"-filenr-" + str( sequence_index[ batch_seqnr ] ) + ".txt"
np.savetxt( fn, targetmat[ :, 0:10 ], fmt = '%.3f')
fn = outputdir + "stroketarget-"+ mode + "-batch-" + str( batchnr) + "-seqnr-" +str( batch_seqnr) +"-filenr-" + str( sequence_index[ batch_seqnr ] ) + ".txt"
np.savetxt( fn, stroketarget, fmt = '%.3f')
fn = outputdir + "input-"+ mode + "-batch-" + str( batchnr) + "-seqnr-" +str( batch_seqnr) +"-filenr-" + str( sequence_index[ batch_seqnr ] ) + ".txt"
np.savetxt( fn, inputdata, fmt = '%.3f')
if batch_seqnr == 0:
if ( args.useStrokeOutputVars and args.useStrokeLoss) :
fn = outputdir + "lossvec-" +mode+ str( batchnr) + ".txt"
np.savetxt( fn, lossvec, fmt = '%.3f')
fn = outputdir + mode+"loss-" +mode+ str( batchnr) + ".txt"
file = open( fn, "w")
file.write( str( loss) + "\n" )
file.close( )
def writeMixture( args, outputdir, batchnr, sequence_index, batch_seqnr, mode, mixture, seq_pointnr) :
fn = outputdir + "mixture-"+mode+"-batch-" + str( batchnr) + "-seqnr-"+str( batch_seqnr) +"-filenr-" + str( sequence_index[ batch_seqnr ] ) + ".txt"
if seq_pointnr == 0:
f = open( fn, 'wb')
else:
f = open( fn, 'ab')
np.savetxt( f, mixture[ None ], fmt = '%.3f', delimiter = ", ")
f.close( )
def softmax( x) :
"""Compute softmax values for each sets of scores in x."""
e_x = np.exp( x - np.max( x) )
return e_x / e_x.sum( )
def evaluate( sess, args, stats, stats_alldata, stats_inc, sequence_index, trainpredictmode, model, dataloader, outputdir, outputs, state, lossvec, train_loss, regularization_term, loss_plain, loss_total, weights, nrinputvars_network, targetdata, maxabsweight, avgweight, learningrate_value, train_on_output, epochnr, totbatchnr, totnrpoints_trained, writefiles, runtime, mode, printstate, batchsize_nrseq, x) :
nanfound = False
avgmu1 = 0
avgmu2 = 0
maxabscorr = 0
sse_stroke = 0
nrrowsused = 0
report_nrsequences = 10
with tf.variable_scope( trainpredictmode) :
if args.useStrokeOutputVars and args.useStrokeLoss:
[ o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_eos, o_eod, o_classvars, o_classpred ] = outputs
else:
[ o_classvars, o_classpred ] = outputs
batch_pointnr = 0
for batch_seqnr in range( 0, batchsize_nrseq ) : #for each sequence in the batch
targetmat = np.copy( targetdata[ batch_seqnr ])
absrowsum = np.absolute( targetmat) .sum( 1 )
mask = np.sign( absrowsum) #checked EdJ Sept 2
nzrows = np.nonzero( mask) #not binary; seems: this returns indices of nonzero rows
nzrows = nzrows[ 0 ]
if len( nzrows) >0:
last = len( nzrows) - 1
nrtargetrows = nzrows[ last ] + 1
else:
nrtargetrows = 0
if mode == "train":
evalseqlength = min( args.current_seq_length + 1, nrtargetrows + 1)
else:
evalseqlength = min( model.seq_length, nrtargetrows + 1)
outputmat = np.zeros( ( evalseqlength - 1, args.nroutputvars_final) , dtype = np.float32)
outputmat_sampled = np.zeros( ( evalseqlength - 1, args.nroutputvars_final) , dtype = np.float32)
mixture = 0
for p in range( evalseqlength - 1) :#process used points first
if args.useStrokeOutputVars:
if args.nrClassOutputVars > 0 and args.classweightfactor > 0:
outputmat[ p, :args.nrClassOutputVars ] = o_classpred[ batch_pointnr, ]
outputmat_sampled[ p, :args.nrClassOutputVars ] = o_classpred[ batch_pointnr, ]
if args.useStrokeLoss:
idx = get_pi_idx( dataloader.getRandValue( ) , o_pi[ batch_pointnr ])
next_x1, next_x2 = model.sample_gaussian_2d( o_mu1[ batch_pointnr, idx ], o_mu2[ batch_pointnr, idx ], o_sigma1[ batch_pointnr, idx ], o_sigma2[ batch_pointnr, idx ], o_corr[ batch_pointnr, idx ])
eos = 1 if dataloader.getRandValue( ) < o_eos[ batch_pointnr ] else 0
eod = 1 if dataloader.getRandValue( ) < o_eod[ batch_pointnr ] else 0
outputmat[ p, args.nrClassOutputVars:args.nrClassOutputVars+4 ] = [ o_mu1[ batch_pointnr, idx ], o_mu2[ batch_pointnr, idx ], o_sigma1[ batch_pointnr, idx ], o_sigma2[ batch_pointnr, idx ] ]
outputmat_sampled[ p, args.nrClassOutputVars:args.nrClassOutputVars+4 ] = [ next_x1, next_x2, eos, eod ]
if writefiles and args.reportmixture and ( sequence_index[ batch_seqnr ] < report_nrsequences) :
nrparams = args.num_mixture * 6
mixture = np.zeros( ( nrparams ) , dtype = np.float32 )
for m in range( args.num_mixture ) :
mixture[ m*6:( m+1) *6 ] = [ o_pi[ batch_pointnr, m ], o_mu1[ batch_pointnr, m ], o_mu2[ batch_pointnr, m ], o_sigma1[ batch_pointnr, m ], o_sigma2[ batch_pointnr, m ], o_corr[ batch_pointnr, m ] ]
writeMixture( args, outputdir, totbatchnr, sequence_index, batch_seqnr, mode, mixture, p)
else:
outputmat_sampled[ p, ] = o_classpred[ batch_pointnr, ]
batch_pointnr += 1
batch_pointnr += model.seq_length - evalseqlength #after cur seq len, skip to end of seq ( = seq_length - 1)
stroketarget = np.copy( targetmat[ :evalseqlength - 1, args.nrClassOutputVars:args.nrClassOutputVars + 4 ])
nrrowsused = nrtargetrows
stats_inc_rmse = 0
if args.useStrokeOutputVars and ( nrrowsused > 0) :
if args.useStrokeLoss:
outputmat_sampled[ :, args.nrClassOutputVars:args.nrClassOutputVars + 2 ] *= args.rangelen
outputmat_sampled[ :, args.nrClassOutputVars:args.nrClassOutputVars + 2 ] += args.rangemin
outputmat[ :, args.nrClassOutputVars:args.nrClassOutputVars + 2 ] *= args.rangelen
outputmat[ :, args.nrClassOutputVars:args.nrClassOutputVars + 2 ] += args.rangemin
stroketarget[ :, 0:2 ] *= args.rangelen
stroketarget[ :, 0:2 ] += args.rangemin
err_stroke = outputmat_sampled[ :, args.nrClassOutputVars:args.nrClassOutputVars + 2 ]-stroketarget[ :, 0:2 ] #was: 1:2
sse_stroke = ( err_stroke ** 2) .sum( )
stats.stats_stroke.log_sse_sequential( sse_stroke, 2 * nrrowsused )
stats_alldata.stats_stroke.log_sse( sequence_index[ batch_seqnr ], sse_stroke, 2 * nrrowsused )
if mode == "train":
stats_inc.stats_stroke.log_sse_sequential( sse_stroke, 2 * nrrowsused ) #sequential counter; window of last n values
stats_inc_rmse = stats_inc.stats_stroke.rmse( )
if args.nrClassOutputVars > 0 and ( nrrowsused > 0) :
classindex_true = np.argmax( targetmat[ :evalseqlength - 1, :args.nrClassOutputVars ], 1)
classindex_pred = np.argmax( outputmat_sampled[ :, :args.nrClassOutputVars ], 1)
correct = np.equal( classindex_pred, classindex_true)
last_correct = correct[ nrrowsused - 1 ] #model.seq_length
if args.print_length_correct:
seqindex = sequence_index[ batch_seqnr ]
print( 'len-correct', mode, 'seq', seqindex, 'len', dataloader.seqlengthlist[ seqindex ], 'correct', 1*last_correct)
stats.stats_correct.log_value_sequential( last_correct, 1 )
stats.stats_correctfrac.log_value_sequential( correct.sum( ) , nrrowsused )
stats_alldata.stats_correct.log_value( sequence_index[ batch_seqnr ], last_correct, 1 )
stats_alldata.stats_correctfrac.log_value( sequence_index[ batch_seqnr ], correct.sum( ) , nrrowsused )
else:
avgcorrectfrac = 0
correctpreds = 0
if writefiles and ( sequence_index[ batch_seqnr ] < report_nrsequences) :
inputdata = np.copy( x[ batch_seqnr ] )
inputdata[ :, 10 * args.useClassInputVars : 10 * args.useClassInputVars + 2 ] *= args.rangelen
inputdata[ :, 10 * args.useClassInputVars : 10 * args.useClassInputVars + 2 ] += args.rangemin
writeOutputTarget( args, outputdir, totbatchnr, sequence_index, batch_seqnr, outputmat, outputmat_sampled, targetmat, stroketarget, lossvec, model, train_loss, mode, inputdata)
weights_o = sess.run( model.outputWeight) ;
bias = sess.run( model.outputBias) ;
avgbias = bias.mean( )
maxabsbias = np.absolute( bias) .max( )
avgstate = np.asarray( state) .mean( )
maxabsstate = np.absolute( state) .max( )
if args.useStrokeOutputVars and args.useStrokeLoss:
avgmu1 = outputmat[ :, 0 ].mean( )
avgmu2 = outputmat[ :, 1 ].mean( )
maxabscorr = np.absolute( o_corr) .max( )
avgw = avgweight
if ( len( avgweight) >1) :
avgw = avgweight.mean( )
maxabsw = maxabsweight
if ( len( maxabsweight) >1) :
maxabsw = maxabsweight.mean( )
print ( 'eval', mode, ': epoch', epochnr, 'totbatches', totbatchnr, 'totnrpoints_trained', totnrpoints_trained, 'nrtrainex', args.curnrtrainexamples, 'curseqlen', args.current_seq_length, 'curnrdigits', args.curnrdigits, 'rmse_stroke', stats.stats_stroke.rmse( ) , 'rmse_stroke_alldata', stats_alldata.stats_stroke.rmse( ) , 'rmse_stroke_inc', stats_inc_rmse, "correct", stats.stats_correct.average( ) , "correct_alldata", stats_alldata.stats_correct.average( ) , 'regularization', regularization_term[ 0 ], 'loss_total', loss_total, 'avgbias', avgbias, 'maxabsbias', maxabsbias, 'avgstate', avgstate, 'maxabsstate', maxabsstate, 'learningrate', learningrate_value, 'maxabscorr', maxabscorr, 'maxabsweight', maxabsw[ 0 ], 'avgweight', avgw[ 0 ], 'runtime', runtime )
#stats
if epochnr % 100 == 0:
graph = tf.get_default_graph( )
ops = graph.get_operations( )
print( ( 'mem nr ops: ', len( ops) ) )
print( 'mem usage:')
print( ( memusage( "eval") ) )
print( 'rand e', epochnr, dataloader.getRandValue( ) )
return nanfound
def print_model( model ) :
print ( "model structure: " )
print ( "gradient vars: " )
for var in tf.get_collection( tf.GraphKeys.VARIABLES, scope = 'gradient') : # tf.variable_scope( "gradient") :
print ( "var: ", var.name )
print ( "all vars: " )
params = tf.all_variables( )
for var in params:
print ( "var: ", var.name )
def recordState( model, sess ) :
params = tf.all_variables( )
state = [ ]
varnames = [ ]
for var in params:
varnames.append( var.name )
value = sess.run( var) ;
state.append( value )
return state, varnames
def printState ( state, varnames, fn = '' ) :
i = 0
statefile = open( fn, "w" )
for var in state:
print( 'var: ', varnames[ i ], file = statefile )
print( var.sum( ) , file = statefile )
i += 1
statefile.close( )
def constructInputFromOutput( args, model, x, o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_eos, o_eod) :
xnrseq = np.shape( x) [ 0 ]
xnrpointsperseq = np.shape( x) [ 1 ]
getbatch = False
point = 0
for s in range( xnrseq) : #for each sequence in previous batch
outputmat = np.zeros( ( model.seq_length, 4) , dtype = np.float32)
batch_pointnr = 0
for p in range( xnrpointsperseq) :
idx = get_pi_idx( dataloader.getRandValue( ) , o_pi[ batch_pointnr ])
mu1out = o_mu1[ batch_pointnr, idx ] #these are regular numpy floats, not tensors
mu2out = o_mu2[ batch_pointnr, idx ]
sigma1out = o_sigma1[ batch_pointnr, idx ]
sigma2out = o_sigma2[ batch_pointnr, idx ]
corrout = o_corr[ batch_pointnr, idx ]
eosout = o_eos[ batch_pointnr ]
eodout = o_eod[ batch_pointnr ]
next_x1, next_x2 = model.sample_gaussian_2d( mu1out, mu2out, sigma1out, sigma2out, corrout)
eos = 1 if dataloader.getRandValue( ) < eosout else 0
eod = 1 if dataloader.getRandValue( ) < eodout else 0
if args.sample_from_output:
outputmat[ p, ] = [ next_x1, next_x2, eos, eod ]
else:
outputmat[ p, ] = [ mu1out, mu2out, eosout, eodout ]
batch_pointnr += 1
fromval = s*xnrpointsperseq
toval = ( s+1) *xnrpointsperseq-1 #last output: not used
xvalues = np.array( x[ s ])
xvalues[ 1:xnrpointsperseq, args.nrClassOutputVars:args.nrClassOutputVars+4 ] = outputmat[ 0:xnrpointsperseq-1, ]
x[ s ] = xvalues
return x
def printInputsTargets( args, x, y ) :
print ( "x")
xvalues = np.array( x)
xvalues[ :, args.nrClassOutputVars:args.nrClassOutputVars+2 ] *= args.rangelen
xvalues[ :, args.nrClassOutputVars:args.nrClassOutputVars+2 ] += args.rangemin
print( xvalues)
print( "y")
yvalues = np.array( y)
yvalues[ :, args.nrClassOutputVars:args.nrClassOutputVars+2 ] *= args.rangelen
yvalues[ :, args.nrClassOutputVars:args.nrClassOutputVars+2 ] += args.rangemin
print( yvalues)
def printWeightsGradients( sess ) :
allvars = tf.all_variables( )
for var in allvars:
isBias = var.name.find( "Bias") >= 0
if not isBias:
print( "var: ", var.name)
value = sess.run( var)
print( value )
def train( dataloader_train, dataloader_test, args, logdir, outputdir ) :
stats_train = Stats( args, args.stat_windowsize_nrsequences, 'stats_train' ) #stats over recent training data
stats_test = Stats( args, args.stat_windowsize_nrsequences, 'stats_test' ) #stats over recent test data
stats_train_alldata = Stats( args, args.nrinputfiles_train, 'stats_train' ) #stats over the most recent set of |trainingset| examples
stats_test_alldata = Stats( args, args.nrinputfiles_test, 'stats_test' ) #stats over the most recent set of |testset| examples
nrseq_inc = np.ceil( args.incremental_min_nrpoints / min( args.current_seq_length, dataloader_train.avgseqlength) )
stats_train_inc = Stats( args, nrseq_inc, 'stats_train_inc' ) #stats over most recent incremental_min_nrpoints, for incremental methods
random.seed( 100 * args.runnr )
np.random.seed( 100 * args.runnr )
tf.set_random_seed( 100 * args.runnr )
print( 'runnr', args.runnr, 'after seed, rand:', random.random( ) , 'np rand', np.random.rand( ) )
print( 'starting time: ', strftime( "%Y-%m-%d %H:%M:%S") )
nrsequenceinputs = 4 #dx dy eos eod
nrinputvars_network = nrsequenceinputs + args.nrauxinputvars;
args.nroutputvars_raw = ( 2 + args.num_mixture * 6) * args.useStrokeOutputVars + args.nrClassOutputVars
args.nroutputvars_final = ( 2 + 2) * args.useStrokeOutputVars + args.nrClassOutputVars
print( "nrinputvars_network", nrinputvars_network)
print( "nrauxinputvars", args.nrauxinputvars)
print( "args.nroutputvars_final", args.nroutputvars_final)
trainpredictmode = "Predict"
model = Model( args, trainpredictmode, False, nrinputvars_network, args.nroutputvars_raw, args.nrtargetvars, args.nrClassOutputVars, dataloader_train.rangemin, dataloader_train.rangelen, args.maxdigitlength_nrpoints )
#store info from model in args so it's saved:
args.seq_length = model.seq_length
print( 'about to save config in', os.path.join( 'save/'+args.explabel+'r'+str( args.runnr) , 'config.pkl') )
with open( os.path.join( 'save/'+args.explabel+'r'+str( args.runnr) , 'config.pkl') , 'wb') as f:
pickle.dump( args, f)
print_model( model )
checkpoint_fullpath = ""
nanfound = False
nrnanbatches = 0
train_on_output = 0
printstate = args.reportstate
with tf.Session( ) as sess:
random.seed( 100 * args.runnr )
np.random.seed( 100 * args.runnr )
randop = tf.random_normal( [ 1 ], seed = random.random( ) ) #, seed = 1234
print( 'runnr', args.runnr, 'after seed, rand:', random.random( ) , 'np rand', np.random.rand( ) , 'tf rand', sess.run( randop) )
dataloader_train.createRandValues( )
dataloader_test.createRandValues( )
tf.initialize_all_variables( ) .run( )
saver = tf.train.Saver( tf.trainable_variables( ) , max_to_keep = args.save_maxnrmodels_keep)
if args.useInitializers:
init_op_weights = sess.run( model.init_op_weights)
if ( len( args.startingpoint) >0) :
print( "Starting from saved model: ", args.startingpoint)
restoreModel( sess, args, model, dataloader_train, args.startingpoint)
if args.useInitializers:
init_op_weights = sess.run( model.init_op_weights)
totnrbatches = 0
totnrpoints = 0
totnrpoints_trained = 0
tstart = time.time( )
nrbatches_per_epoch = dataloader_train.nrbatches_per_epoch
epochnr = 0
cont = True
while cont: #batch loop
if ( totnrbatches % nrbatches_per_epoch == 0 ) :
learningrate_value = args.learning_rate * ( args.decay_rate ** epochnr)
learningrate = sess.run( model.learningrateop, feed_dict = {model.learningrate_ph: learningrate_value})
epochnr += 1
modes = [ "train" ]
if totnrbatches > 0 and ( totnrbatches % args.test_every_nrbatches == 0 ) :
modes = [ "train", "test" ]
for mode in modes:
if mode == "train":
dataloader = dataloader_train
runseqlength = args.current_seq_length
else:
dataloader = dataloader_test
runseqlength = model.seq_length
if mode == "train":
totnrbatches += 1
stats = stats_train
stats_alldata = stats_train_alldata
stats_inc = stats_train_inc
else:
stats = stats_test
stats_alldata = stats_test_alldata
stats_inc = 0
start_batch = time.time( )
tstartdata = time.time( )
getbatch = True
if epochnr > 0:
if args.train_on_own_output_method == 1 and mode == "train":
train_on_output = dataloader.getRandValue( ) < 1.0 / ( 2. + mean( model.batch_rmse_stroke, model.batch_rmse_class) ) #training --> rand ok
if train_on_output:
x = constructInputFromOutput( args, model, x, o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_eos, o_eod)
getbatch = False
if getbatch: #not training on own output
x, y, sequence_index = dataloader.next_batch( args, runseqlength ) #can contain multiple sequence
tgetdata = ( time.time( ) - tstartdata) / 60
start_train = time.time( )
batchsize_nrseq = len( x )
zero_initial_state = sess.run( model.initial_state, feed_dict = {model.batch_size_ph: batchsize_nrseq, model.seq_length_ph: runseqlength}) #Get zero state given current batchsz
if ( mode == "test") or ( totnrbatches == 1) or ( not args.usePreviousEndState) :
state = zero_initial_state
else:
state = last_train_state
feed = {model.input_data: x, model.target_data: y, model.initial_state: state, model.batch_size_ph: batchsize_nrseq, model.seq_length_ph: args.current_seq_length} #model.seq_length
if args.useStrokeOutputVars and args.useStrokeLoss:
if mode == "train":
train_loss, last_train_state, lossvec, o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_eos, o_eod, o_classvars, o_classpred, regularization_term, loss_plain, lossnrpoints, maxabsweight, avgweight, _ = sess.run( [ model.loss_total, model.final_state, model.lossvector, model.pi, model.mu1, model.mu2, model.sigma1, model.sigma2, model.corr, model.eos, model.eod, model.classvars, model.classpred, model.regularization_term, model.loss_plain, model.lossnrpoints, model.maxabsweight, model.avgweight, model.train_op ], feed)
state_report = last_train_state
else: #test --> omit train op, and don't replace state
train_loss, state_report, lossvec, o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_eos, o_eod, o_classvars, o_classpred, regularization_term, loss_plain, lossnrpoints, maxabsweight, avgweight = sess.run( [ model.loss_total, model.final_state, model.lossvector, model.pi, model.mu1, model.mu2, model.sigma1, model.sigma2, model.corr, model.eos, model.eod, model.classvars, model.classpred, model.regularization_term, model.loss_plain, model.lossnrpoints, model.maxabsweight, model.avgweight ], feed)
outputs = [ o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_eos, o_eod, o_classvars, o_classpred ]
else: #no stroke loss, only learn classes
z = np.zeros( ( 1) , dtype = np.float32 )
[ o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_eos, o_eod ] = [ z, z, z, z, z, z, z, z ]
if mode == "train":
train_loss, last_train_state, output, lossvec, o_classvars, o_classpred, regularization_term, loss_plain, result4, result, result_before_mask, lossnrpoints, mask, classpred, targetdata_classvars, crossentropy, maxabsweight, avgweight, _ = sess.run( [ model.loss_total, model.final_state, model.output, model.lossvector, model.classvars, model.classpred, model.regularization_term, model.loss_plain, model.result4, model.result, model.result_before_mask, model.lossnrpoints, model.mask, model.classpred, model.targetdata_classvars, model.crossentropy, model.maxabsweight, model.avgweight, model.train_op ], feed)
state_report = last_train_state
else:
train_loss, state_report, output, lossvec, o_classvars, o_classpred, regularization_term, loss_plain, result4, result, mask, maxabsweight, avgweight, classpred, targetdata_classvars = sess.run( [ model.loss_total, model.final_state, model.output, model.lossvector, model.classvars, model.classpred, model.regularization_term, model.loss_plain, model.result4, model.result, model.mask, model.maxabsweight, model.avgweight, model.classpred, model.targetdata_classvars ], feed)
outputs = [ o_classvars, o_classpred ]
if mode == "train":
totnrpoints_trained += lossnrpoints
weights_o = sess.run( model.outputWeight) ;
nanfound = math.isnan( train_loss)
if nanfound:
print( ( "NAN encountered --> stopping.") )
sys.exit( ) ;
end_train = time.time( )
train_loss = train_loss.mean( )
start_eval = time.time( )
#evaluation
if ( epochnr % args.eval_every == 0 ) :
writefiles = totnrbatches % args.report_every == 0 and ( totnrbatches > 0)
weights = 0
runtime = ( time.time( ) - tstart) / 60
nanfound = nanfound or evaluate( sess, args, stats, stats_alldata, stats_inc, sequence_index, trainpredictmode, model, dataloader, outputdir, outputs, state_report, lossvec, train_loss, regularization_term, loss_plain, train_loss, weights, nrinputvars_network, y, maxabsweight, avgweight, learningrate_value, train_on_output, epochnr, totnrbatches, totnrpoints_trained, writefiles, runtime, mode, printstate, batchsize_nrseq, x)
stats.reset( )
end_eval = time.time( )
tot_time = end_train-start_train + end_eval-start_eval
#saving:
if ( not nanfound ) and ( mode == "train") :
if totnrbatches % args.save_every_nrbatches == 0 and ( totnrbatches > 0) :
savemodel( saver, sess, dataloader, args, totnrbatches)
if nanfound and mode == "train":
print( ( "NAN encountered --> stopping.") )
sys.exit( ) ;
end_batch = time.time( )
print ( "End of batch: time_train", end_train-start_train, "time ev", end_eval-start_eval, "tdata", tgetdata, "tot", tot_time, 'batch time', end_batch-start_batch, "sequences/sec", dataloader.nrseq_per_batch/tot_time)
if mode == "train":
if stats_train_inc.stats_stroke.totnrpoints >= args.incremental_min_nrpoints:
reached_threshold = False
if args.incremental_seq_length:
print( 'inc seq len: rmse', stats_train_inc.stats_stroke.rmse( ) , 'thr', args.threshold_rmse_stroke)
if ( stats_train_inc.stats_stroke.rmse( ) < args.threshold_rmse_stroke) and ( args.current_seq_length < model.seq_length) :
args.current_seq_length = min( model.seq_length, args.current_seq_length * 2 )
reached_threshold = True
print( "REACHED THRESHOLD! --> increasing cur_seq_length to ", args.current_seq_length, ' max ', model.seq_length)
if args.incremental_nr_trainexamples:
print( 'inc nrtrainex: rmse stroke ', stats_train_inc.stats_stroke.rmse( ) , 'thr', args.threshold_rmse_stroke)
if ( stats_train_inc.stats_stroke.rmse( ) < args.threshold_rmse_stroke) and ( args.curnrtrainexamples < args.nrinputfiles_train ) :
args.curnrtrainexamples = min( 2 * args.curnrtrainexamples, args.nrinputfiles_train )
dataloader_train.curnrexamples = args.curnrtrainexamples
reached_threshold = True
print( "REACHED THRESHOLD! --> increasing curnrtrainexamples to ", args.curnrtrainexamples)
dataloader.nrbatches_per_epoch = max( 1, int( args.curnrtrainexamples / dataloader.nrseq_per_batch) )
dataloader.reset_batch_pointer( args )
args.incremental_min_nrpoints = 50 * args.curnrtrainexamples
print ( "setting new nrbatches_per_epoch to: ", dataloader.nrbatches_per_epoch)
if args.incremental_nr_digits:
print( 'inc nr digits: rmse ', stats_train_inc.stats_stroke.rmse( ) , ' thr', args.threshold_rmse_stroke)
if ( stats_train_inc.stats_stroke.rmse( ) < args.threshold_rmse_stroke) and ( args.curnrdigits < 10 ) :
args.curnrdigits = min( 2 * args.curnrdigits, 10 )
reached_threshold = True
print( "REACHED THRESHOLD! --> increasing curnrdigits to ", args.curnrdigits)
dataloader.findAvailableExamples( args )
dataloader.nrbatches_per_epoch = max( 1, int( args.curnrtrainexamples / dataloader.nrseq_per_batch) )
dataloader.reset_batch_pointer( args )
print ( "setting new nrbatches_per_epoch to: ", dataloader.nrbatches_per_epoch)
if reached_threshold: #reset rmse counters used for incremental learning
nrseq_inc = np.ceil( args.incremental_min_nrpoints / min( args.current_seq_length, dataloader_train.avgseqlength) )
stats_train_inc = Stats( args, nrseq_inc, 'stats_train_inc' ) #stats over most recent incremental_min_nrpoints, for incremental methods
#end of while loop ( batch) :
cont = totnrpoints_trained <= args.maxnrpoints
if ( stats_train.stats_stroke.rmse( ) < args.stopcrit_threshold_stroke_rmse_train ) :
cont = False
#end of run:
print( 'End of run --> saving model' )
savemodel( saver, sess, dataloader_train, args, totnrbatches)
print( 'done' )
if __name__ == '__main__':
main( )