def main(argv):
log.info('Beginning prediction')
funcs = pd.read_pickle(
os.path.join(FLAGS.resources,
'{}.pkl'.format(FLAGS.function)))['functions'].values
funcs = GODAG.initialize_idmap(funcs, FLAGS.function)
log.info('GO DAG initialized. Updated function list-{}'.format(len(funcs)))
FeatureExtractor.load(FLAGS.resources)
log.info('Loaded amino acid and ngram mapping data')
data = DataLoader(filename=FLAGS.inputfile)
if FLAGS.evaluate:
test_dataiter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.testsize,
dataloader=data,
functype=FLAGS.function,
featuretype='ngrams')
predict_evaluate(test_dataiter, 0.2, FLAGS.modelsdir)
else:
test_dataiter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.testsize,
dataloader=data,
functype=FLAGS.function,
featuretype='ngrams',
test=True)
predict(test_dataiter, 0.2, FLAGS.modelsdir, funcs)
def main(argv):
funcs = pd.read_pickle(os.path.join(FLAGS.resources, '{}.pkl'.format(FLAGS.function)))['functions'].values
funcs = GODAG.initialize_idmap(funcs, FLAGS.function)
log.info('GO DAG initialized. Updated function list-{}'.format(len(funcs)))
FeatureExtractor.load(FLAGS.resources)
log.info('Loaded amino acid and ngram mapping data')
data = DataLoader(filename=FLAGS.inputfile)
modelsavename = 'savedmodels_{}_{}'.format(__processor__, int(time.time()))
if FLAGS.predict != '':
modelsavename = FLAGS.predict
bestthres = 0.1
log.info('no training')
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.validationsize,
dataloader=data, functype=FLAGS.function, featuretype='onehot')
train_iter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen, dataloader=data,
numfiles=np.floor((FLAGS.trainsize * FLAGS.batchsize) / 250000),
functype=FLAGS.function, featuretype='onehot')
next(valid_dataiter)
next(train_iter)
else:
with tf.Session() as sess:
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.validationsize,
dataloader=data, functype=FLAGS.function, featuretype='onehot')
train_iter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen, dataloader=data,
numfiles=np.floor((FLAGS.trainsize * FLAGS.batchsize) / 250000),
functype=FLAGS.function, featuretype='onehot')
encoder = CHARCNNEncoder(vocab_size=len(FeatureExtractor.aminoacidmap) + 1,
inputsize=train_iter.expectedshape).build()
log.info('built encoder')
decoder = HierarchicalGODecoder(funcs, encoder.outputs, FLAGS.function).build(GODAG)
log.info('built decoder')
init = tf.global_variables_initializer()
init.run(session=sess)
chkpt = tf.train.Saver(max_to_keep=4)
train_writer = tf.summary.FileWriter(FLAGS.outputdir + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.outputdir + '/test')
step = 0
maxwait = 1
wait = 0
bestf1 = -1
metagraphFlag = True
log.info('starting epochs')
for epoch in range(FLAGS.num_epochs):
for x, y in train_iter:
if x.shape[0] != y.shape[0]:
raise Exception('invalid, x-{}, y-{}'.format(str(x.shape), str(y.shape)))
_, loss, summary = sess.run([decoder.train, decoder.loss, decoder.summary],
feed_dict={decoder.ys_: y, encoder.xs_: x,
decoder.threshold: [0.2]})
train_writer.add_summary(summary, step)
log.info('step-{}, loss-{}'.format(step, round(loss, 2)))
step += 1
if True:
log.info('beginning validation')
prec, recall, f1 = validate(valid_dataiter, sess, encoder, decoder, test_writer)
thres = np.argmax(np.round(f1, 2))
log.info('epoch: {} \n precision: {}, recall: {}, f1: {}'.format(epoch,
np.round(prec, 2)[thres],
np.round(recall, 2)[thres],
np.round(f1, 2)[thres]))
log.info('precision mat {}'.format(str(np.round(prec, 2))))
log.info('recall mat {}'.format(str(np.round(recall, 2))))
log.info('f1 mat {}'.format(str(np.round(f1, 2))))
log.info('selected threshold is {}'.format(thres/10 + 0.1))
if f1[thres] > (bestf1 + 1e-3):
bestf1 = f1[thres]
bestthres = THRESHOLD_RANGE[thres]
wait = 0
chkpt.save(sess, os.path.join(FLAGS.outputdir, modelsavename,
'model_{}_{}'.format(FLAGS.function, step)),
global_step=step, write_meta_graph=metagraphFlag)
metagraphFlag = False
else:
wait += 1
if wait > maxwait:
log.info('f1 didnt improve for last {} validation steps, so stopping'.format(maxwait))
break
step += 1
train_iter.reset()
log.info('testing model')
test_dataiter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.testsize,
dataloader=data, functype=FLAGS.function, featuretype='onehot')
prec, recall, f1 = predict_evaluate(test_dataiter, [bestthres], os.path.join(FLAGS.outputdir, modelsavename))
log.info('test results')
log.info('precision: {}, recall: {}, F1: {}'.format(round(prec, 2), round(recall, 2), round(f1, 2)))
data.close()
def main(argv):
goids = GODAG.initialize_idmap(None, None)
labelembedding = load_labelembedding(os.path.join(FLAGS.resources, 'goEmbeddings.txt'), goids)
assert(labelembedding.shape[0] == (len(goids))) , 'label embeddings and known go ids differ'
## Add a row of zeros to refer to NOGO or STOPGO
labelembedding = np.vstack([np.zeros(labelembedding.shape[1]), labelembedding]).astype(np.float32)
labelembeddingsize = labelembedding.shape[1]
# shift all goids by 1, to allow STOPGO
GODAG.idmap = {key: (val + 1) for key, val in GODAG.idmap.items()}
log.info('min go index - {}'.format(min(list(GODAG.idmap.values()))))
GODAG.idmap['STOPGO'] = 0
GODAG.GOIDS.insert(0, 'STOPGO')
log.info('first from main-{}, from goids-{}, from idmap-{}, by reversemap-{}'.format(goids[0], GODAG.GOIDS[1], GODAG.id2node(1), GODAG.get_id(goids[0])))
FeatureExtractor.load(FLAGS.resources)
log.info('Loaded amino acid and ngram mapping data')
data = DataLoader(filename=FLAGS.inputfile)
modelsavename = FLAGS.predict
if FLAGS.predict == "":
modelsavename = 'savedmodels_{}'.format(int(time.time()))
with tf.Session() as sess:
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.validationsize,
dataloader=data, functype=FLAGS.function, featuretype='onehot',
onlyLeafNodes=True, numfuncs=FLAGS.maxnumfuncs)
train_iter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen, dataloader=data,
numfiles=np.floor((FLAGS.trainsize * FLAGS.batchsize) / 250000),
functype=FLAGS.function, featuretype='onehot', onlyLeafNodes=True, numfuncs=FLAGS.maxnumfuncs)
#encoder = CNNEncoder(vocab_size=len(FeatureExtractor.ngrammap) + 1, inputsize=train_iter.expectedshape).build()
encoder = MultiCharCNN(vocab_size=len(FeatureExtractor.aminoacidmap) + 1,
inputsize=train_iter.expectedshape, with_dilation=False, charfilter=32,
poolsize=80, poolstride=48).build()
log.info('built encoder')
decoder = GORNNDecoder(encoder.outputs, labelembedding, numfuncs=FLAGS.maxnumfuncs,
trainlabelEmbedding=FLAGS.trainlabel, distancefunc=FLAGS.distancefunc, godag=GODAG).build()
log.info('built decoder')
init = tf.global_variables_initializer()
init.run(session=sess)
chkpt = tf.train.Saver(max_to_keep=4)
train_writer = tf.summary.FileWriter(FLAGS.outputdir + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.outputdir + '/test')
step = 0
maxwait = 2
wait = 0
bestf1 = 0
bestthres = 0
metagraphFlag = True
log.info('starting epochs')
log.info('params - trainsize-{}, validsie-{}, rootfunc-{}, batchsize-{}'.format(FLAGS.trainsize, FLAGS.validationsize,
FLAGS.function, FLAGS.batchsize))
for epoch in range(FLAGS.num_epochs):
for x, y in train_iter:
if x.shape[0] != y.shape[0]:
raise Exception('invalid, x-{}, y-{}'.format(str(x.shape), str(y.shape)))
negatives = get_negatives(y, 10)
_, loss, summary = sess.run([decoder.train, decoder.loss, decoder.summary],
feed_dict={decoder.ys_: y[:, :FLAGS.maxnumfuncs], encoder.xs_: x,
decoder.negsamples: negatives, decoder.istraining: [True]})
train_writer.add_summary(summary, step)
log.info('step-{}, loss-{}'.format(step, round(loss, 2)))
step += 1
log.info('beginning validation')
prec, recall, f1 = validate(valid_dataiter, sess, encoder, decoder, test_writer)
log.info('epoch: {} \n precision: {}, recall: {}, f1: {}'.format(epoch,
np.round(prec, 2),
np.round(recall, 2),
np.round(f1, 2)))
if np.round(f1,2) >= (bestf1):
bestf1 = np.round(f1,2)
wait = 0
log.info('saving meta graph')
#ipdb.set_trace()
chkpt.save(sess, os.path.join(FLAGS.outputdir, modelsavename,
'model_{}_{}'.format(FLAGS.function, step)),
global_step=step, write_meta_graph=metagraphFlag)
metagraphFlag = True
else:
wait += 1
if wait > maxwait:
log.info('f1 didnt improve for last {} validation steps, so stopping'.format(maxwait))
break
train_iter.reset()
prec, recall, f1 = validate(train_iter, sess, encoder, decoder, None)
log.info('training error,epoch-{}, precision: {}, recall: {}, f1: {}'.format(epoch,
np.round(prec, 2),
np.round(recall, 2),
np.round(f1, 2)))
train_iter.reset()
log.info('testing model')
test_dataiter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.testsize,
dataloader=data, functype=FLAGS.function, featuretype='onehot',
onlyLeafNodes=True, numfuncs=FLAGS.maxnumfuncs)
prec, recall, f1 = predict_evaluate(test_dataiter, os.path.join(FLAGS.outputdir, modelsavename))
log.info('test results')
log.info('precision: {}, recall: {}, F1: {}'.format(round(prec, 2), round(recall, 2), round(f1, 2)))
data.close()
def main(argv):
goids = GODAG.initialize_idmap(None, None)
# GO_MAT = GODAG.get_fullmat(goids)
# log.info('GO Matrix shape - {}'.format(GO_MAT.shape))
# GO_MAT = np.vstack([np.zeros(GO_MAT.shape[1]), GO_MAT])
labelembedding = load_labelembedding(os.path.join(FLAGS.data, 'goEmbeddings.txt'), goids)
assert(labelembedding.shape[0] == (len(goids) + 1)) , 'label embeddings and known go ids differ'
labelembeddingsize = labelembedding.shape[1]
FeatureExtractor.load(FLAGS.data)
log.info('Loaded amino acid and ngram mapping data')
data = DataLoader()
modelsavename = 'savedmodels_{}'.format(int(time.time()))
with tf.Session() as sess:
# sess = tf_debug.LocalCLIDebugWrapperSession(sess)
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.validationsize,
dataloader=data, functype=FLAGS.function, featuretype='ngrams',
onlyLeafNodes=True, limit=FLAGS.maxnumfuncs)
train_iter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen, dataloader=data,
numfiles=np.floor((FLAGS.trainsize * FLAGS.batchsize) / 250000),
functype=FLAGS.function, featuretype='ngrams', onlyLeafNodes=True, limit=FLAGS.maxnumfuncs)
encoder = CNNEncoder(vocab_size=len(FeatureExtractor.ngrammap) + 1, inputsize=train_iter.expectedshape).build()
log.info('built encoder')
decoder = GORNNDecoder(encoder.outputs, labelembedding, numfuncs=FLAGS.maxnumfuncs).build()
log.info('built decoder')
init = tf.global_variables_initializer()
init.run(session=sess)
chkpt = tf.train.Saver(max_to_keep=4)
train_writer = tf.summary.FileWriter(FLAGS.outputdir + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.outputdir + '/test')
step = 0
maxwait = 1
wait = 0
bestf1 = 0
bestthres = 0
metagraphFlag = True
log.info('starting epochs')
log.info('params - trainsize-{}, validsie-{}, rootfunc-{}, batchsize-{}'.format(FLAGS.trainsize, FLAGS.validationsize,
FLAGS.function, FLAGS.batchsize))
for epoch in range(FLAGS.num_epochs):
for x, y in train_iter:
if x.shape[0] != y.shape[0]:
raise Exception('invalid, x-{}, y-{}'.format(str(x.shape), str(y.shape)))
negatives = get_negatives(y, 10)
_, loss, summary = sess.run([decoder.train, decoder.loss, decoder.summary],
feed_dict={decoder.ys_: y, encoder.xs_: x,
decoder.negsamples: negatives})
train_writer.add_summary(summary, step)
log.info('step-{}, loss-{}'.format(step, round(loss, 2)))
step += 1
log.info('beginning validation')
prec, recall, f1 = validate(valid_dataiter, sess, encoder, decoder, test_writer)
log.info('epoch: {} \n precision: {}, recall: {}, f1: {}'.format(epoch,
np.round(prec, 2),
np.round(recall, 2),
np.round(f1, 2)))
if f1 > (bestf1 + 1e-3):
bestf1 = f1
wait = 0
chkpt.save(sess, os.path.join(FLAGS.outputdir, modelsavename,
'model_{}_{}'.format(FLAGS.function, step)),
global_step=step, write_meta_graph=metagraphFlag)
metagraphFlag = False
else:
wait += 1
if wait > maxwait:
log.info('f1 didnt improve for last {} validation steps, so stopping'.format(maxwait))
break
train_iter.reset()
log.info('testing model')
test_dataiter = DataIterator(batchsize=FLAGS.batchsize, size=FLAGS.testsize,
dataloader=data, functype=FLAGS.function, featuretype='ngrams',
onlyLeafNodes=True, limit=FLAGS.maxnumfuncs)
prec, recall, f1 = predict_evaluate(test_dataiter, [bestthres], os.path.join(FLAGS.outputdir, modelsavename))
log.info('test results')
log.info('precision: {}, recall: {}, F1: {}'.format(round(prec, 2), round(recall, 2), round(f1, 2)))
data.close()
def main(argv):
funcs = pd.read_pickle(
os.path.join(FLAGS.resources,
'{}.pkl'.format(FLAGS.function)))['functions'].values
funcs = GODAG.initialize_idmap(funcs, FLAGS.function)
log.info('GO DAG initialized. Updated function list-{}'.format(len(funcs)))
FeatureExtractor.load(FLAGS.resources)
log.info('Loaded amino acid and ngram mapping data')
pretrained = None
featuretype = 'onehot'
if FLAGS.pretrained != '':
log.info('loading pretrained embedding')
pretrained, ngrammap = load_pretrained_embedding(FLAGS.pretrained)
FeatureExtractor.ngrammap = ngrammap
featuretype = 'ngrams'
with tf.Session() as sess:
data = DataLoader(filename=FLAGS.inputfile)
log.info('initializing validation data')
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.validationsize,
dataloader=data,
functype=FLAGS.function,
featuretype='ngrams',
numfuncs=len(funcs),
all_labels=False,
autoreset=True)
log.info('initializing train data')
train_iter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen,
dataloader=data,
numfiles=4,
numfuncs=len(funcs),
functype=FLAGS.function,
featuretype='ngrams',
all_labels=False,
autoreset=True)
vocabsize = ((len(FeatureExtractor.ngrammap) +
1) if featuretype == 'ngrams' else
(len(FeatureExtractor.aminoacidmap) + 1))
model = KerasDeepGO(funcs,
FLAGS.function,
GODAG,
train_iter.expectedshape,
vocabsize,
pretrained_embedding=pretrained).build()
log.info('built encoder')
log.info('built decoder')
keras.backend.set_session(sess)
log.info('starting epochs')
model_path = FLAGS.outputdir + 'models/model_seq_' + FLAGS.function + '.h5'
checkpointer = keras.callbacks.ModelCheckpoint(filepath=model_path,
verbose=1,
save_best_only=True,
save_weights_only=True)
earlystopper = keras.callbacks.EarlyStopping(monitor='val_loss',
patience=10,
verbose=1)
model_jsonpath = FLAGS.outputdir + 'models/model_{}.json'.format(
FLAGS.function)
f = open(model_jsonpath, 'w')
f.write(model.to_json())
f.close()
model.fit_generator(train_iter,
steps_per_epoch=FLAGS.trainsize,
epochs=5,
validation_data=valid_dataiter,
validation_steps=FLAGS.validationsize,
max_queue_size=128,
callbacks=[checkpointer, earlystopper])
valid_dataiter.close()
train_iter.close()
log.info('initializing test data')
test_dataiter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.testsize,
seqlen=FLAGS.maxseqlen,
dataloader=data,
numfiles=4,
numfuncs=len(funcs),
functype=FLAGS.function,
featuretype='ngrams',
all_labels=True)
prec, recall, f1 = predict_evaluate(test_dataiter, model_jsonpath,
model_path)
log.info('testing error, prec-{}, recall-{}, f1-{}'.format(
np.round(prec, 3), np.round(recall, 3), np.round(f1, 3)))
data.close()
def main(argv):
funcs = GODAG.initialize_idmap(None, None)
log.info('GO DAG initialized. Updated function list-{}'.format(len(funcs)))
FeatureExtractor.load(FLAGS.resources)
log.info('Loaded amino acid and ngram mapping data')
data = DataLoader(filename=FLAGS.inputfile)
modelsavename = 'savedmodels_{}_{}'.format(__processor__, int(time.time()))
pretrained = None
featuretype = FLAGS.featuretype
if FLAGS.pretrained != '':
log.info('loading pretrained embedding')
pretrained, ngrammap = load_pretrained_embedding(FLAGS.pretrained)
FeatureExtractor.ngrammap = ngrammap
featuretype = 'ngrams'
log.info('using feature type - {}'.format(featuretype))
with tf.Session() as sess:
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.validationsize,
dataloader=data,
functype='',
featuretype=featuretype)
train_iter = DataIterator(
batchsize=FLAGS.batchsize,
size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen,
dataloader=data,
numfiles=np.floor((FLAGS.trainsize * FLAGS.batchsize) / 250000),
functype='',
featuretype=featuretype)
vocabsize = ((len(FeatureExtractor.ngrammap)) if featuretype
== 'ngrams' else (len(FeatureExtractor.aminoacidmap)))
encoder = ConvAutoEncoder(vocab_size=vocabsize,
maxlen=train_iter.expectedshape).build()
init = tf.global_variables_initializer()
init.run(session=sess)
chkpt = tf.train.Saver(max_to_keep=4)
train_writer = tf.summary.FileWriter(FLAGS.outputdir + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.outputdir + '/test')
step = 0
maxwait = 1
wait = 0
metagraphFlag = True
log.info('starting epochs')
for epoch in range(FLAGS.num_epochs):
for x, y in train_iter:
if x.shape[0] != y.shape[0]:
raise Exception('invalid, x-{}, y-{}'.format(
str(x.shape), str(y.shape)))
_, loss = sess.run([encoder.train, encoder.loss],
feed_dict={encoder.xs_: x})
# train_writer.add_summary(summary, step)
#ipdb.set_trace()
log.info('step-{}, loss-{}'.format(step, round(loss, 2)))
step += 1
chkpt.save(sess,
os.path.join(FLAGS.outputdir, modelsavename,
'model_epoch{}'.format(epoch)),
global_step=step,
write_meta_graph=metagraphFlag)
train_iter.reset()
data.close()
def main(argv):
funcs = GODAG.initialize_idmap(None, None)
log.info('GO DAG initialized. Updated function list-{}'.format(len(funcs)))
FeatureExtractor.load(FLAGS.resources)
log.info('Loaded amino acid and ngram mapping data')
data = DataLoader(filename=FLAGS.inputfile)
modelsavename = 'savedmodels_{}_{}'.format(__processor__, int(time.time()))
pretrained = None
featuretype = FLAGS.featuretype
if FLAGS.pretrained != '':
log.info('loading pretrained embedding')
pretrained, ngrammap = load_pretrained_embedding(FLAGS.pretrained)
FeatureExtractor.ngrammap = ngrammap
featuretype = 'ngrams'
log.info('using feature type - {}'.format(featuretype))
with tf.Session() as sess:
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.validationsize,
seqlen=FLAGS.maxseqlen,
dataloader=data,
functype='',
featuretype=featuretype)
train_iter = DataIterator(
batchsize=FLAGS.batchsize,
size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen,
dataloader=data,
numfiles=np.floor((FLAGS.trainsize * FLAGS.batchsize) / 250000),
functype='',
featuretype=featuretype)
vocabsize = ((len(FeatureExtractor.ngrammap)) if featuretype
== 'ngrams' else (len(FeatureExtractor.aminoacidmap)))
cae_model_obj = ConvAutoEncoder(vocab_size=vocabsize,
maxlen=train_iter.expectedshape,
batch_size=FLAGS.batchsize,
embedding_dim=256)
cae_model_obj.build()
init = tf.global_variables_initializer()
init.run(session=sess)
chkpt = tf.train.Saver(max_to_keep=4)
train_writer = tf.summary.FileWriter(FLAGS.outputdir + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.outputdir + '/test')
step = 0
maxwait = 1
wait = 0
metagraphFlag = True
log.info('starting epochs')
# -------------------------- #
# Start training the autoencoder
# -------------------------- #
bestloss = np.infty
maxwait = 10
wait = 0
slack = 1e-5
earlystop = False
for epoch in range(FLAGS.num_epochs):
for x, y in train_iter:
if x.shape[0] != y.shape[0]:
raise Exception('invalid, x-{}, y-{}'.format(
str(x.shape), str(y.shape)))
_, sp_ss_loss, spmloss, smloss = sess.run(
[
cae_model_obj.train, cae_model_obj.loss,
cae_model_obj.loss1, cae_model_obj.loss2
],
feed_dict={cae_model_obj.x_input: x})
log.info('step :: {}, sploss :: {}, spm:{}, sm:{}'.format(
step, round(sp_ss_loss, 3), round(spmloss, 3),
round(smloss, 3)))
step += 1
if step % 100 == 0:
#ipdb.set_trace()
x, y = next(valid_dataiter)
valid_loss, tp = sess.run(
[cae_model_obj.loss, cae_model_obj.max_out],
feed_dict={cae_model_obj.x_input: x})
# pdb.set_trace()
log.info(
'validation loss at step: {} is {}, precision is: {}'.
format(step, round(valid_loss, 3),
sum(sum(tp == x)) / (x.size)))
if (valid_loss <=
(bestloss + slack)) or (valid_loss + slack <=
bestloss):
wait = 0
bestloss = valid_loss
chkpt.save(sess,
os.path.join(FLAGS.outputdir, modelsavename,
'model_epoch{}'.format(epoch)),
global_step=step,
write_meta_graph=metagraphFlag)
else:
wait += 1
if wait > maxwait:
earlystop = True
break
chkpt.save(sess,
os.path.join(FLAGS.outputdir, modelsavename,
'model_epoch{}'.format(epoch)),
global_step=step,
write_meta_graph=metagraphFlag)
train_iter.reset()
valid_dataiter.reset()
if earlystop:
log.info(
'stopping early at epoch: {}, step:{}, loss:{}'.format(
epoch, step, bestloss))
break
data.close()
def main(argv):
funcs = pd.read_pickle(
os.path.join(FLAGS.resources,
'{}.pkl'.format(FLAGS.function)))['functions'].values
funcs = GODAG.initialize_idmap(funcs, FLAGS.function)
log.info('GO DAG initialized. Updated function list-{}'.format(len(funcs)))
FeatureExtractor.load(FLAGS.resources)
log.info('Loaded amino acid and ngram mapping data')
with tf.Session() as sess:
data = DataLoader(filename=FLAGS.inputfile)
log.info('initializing validation data')
valid_dataiter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.validationsize,
dataloader=data,
functype=FLAGS.function,
featuretype='onehot',
numfuncs=len(funcs),
all_labels=False,
autoreset=True)
log.info('initializing train data')
train_iter = DataIterator(batchsize=FLAGS.batchsize,
size=FLAGS.trainsize,
seqlen=FLAGS.maxseqlen,
dataloader=data,
numfiles=4,
numfuncs=len(funcs),
functype=FLAGS.function,
featuretype='onehot',
all_labels=False,
autoreset=True)
global original_dim
original_dim = train_iter.expectedshape
model = get_AE(train_iter.expectedshape,
len(FeatureExtractor.aminoacidmap),
train_iter.expectedshape)
keras.backend.set_session(sess)
log.info('starting epochs')
model_path = FLAGS.outputdir + 'models/model_seq_' + FLAGS.function + '.h5'
checkpointer = keras.callbacks.ModelCheckpoint(filepath=model_path,
verbose=1,
save_best_only=True,
save_weights_only=True)
earlystopper = keras.callbacks.EarlyStopping(monitor='val_loss',
patience=10,
verbose=1)
model_jsonpath = FLAGS.outputdir + 'models/model_{}.json'.format(
FLAGS.function)
f = open(model_jsonpath, 'w')
f.write(model.to_json())
f.close()
model.fit_generator(datawrapper(train_iter),
steps_per_epoch=FLAGS.trainsize,
epochs=5,
validation_data=datawrapper(valid_dataiter),
validation_steps=FLAGS.validationsize,
callbacks=[checkpointer, earlystopper])
valid_dataiter.close()
train_iter.close()
data.close()