def run(self):
# load data
args_dict = self._default_configs()
args = dotdict(args_dict)
batchedData, maxTimeSteps, totalN = self.load_data(args,
mode=mode,
type=level)
model = model_fn(args, maxTimeSteps)
# count the num of params
num_params = count_params(model, mode='trainable')
all_num_params = count_params(model, mode='all')
model.config['trainable params'] = num_params
model.config['all params'] = all_num_params
print(model.config)
with tf.Session(graph=model.graph) as sess:
# restore from stored model
if keep == True:
ckpt = tf.train.get_checkpoint_state(savedir)
if ckpt and ckpt.model_checkpoint_path:
model.saver.restore(sess, ckpt.model_checkpoint_path)
print('Model restored from:' + savedir)
else:
print('Initializing')
sess.run(model.initial_op)
for epoch in range(num_epochs):
## training
start = time.time()
if mode == 'train':
print('Epoch', epoch + 1, '...')
batchErrors = np.zeros(len(batchedData))
batchRandIxs = np.random.permutation(len(batchedData))
for batch, batchOrigI in enumerate(batchRandIxs):
batchInputs, batchTargetSparse, batchSeqLengths = batchedData[
batchOrigI]
batchTargetIxs, batchTargetVals, batchTargetShape = batchTargetSparse
feedDict = {
model.inputX: batchInputs,
model.targetIxs: batchTargetIxs,
model.targetVals: batchTargetVals,
model.targetShape: batchTargetShape,
model.seqLengths: batchSeqLengths
}
if level == 'cha':
if mode == 'train':
_, l, pre, y, er = sess.run([
model.optimizer, model.loss, model.predictions,
model.targetY, model.errorRate
],
feed_dict=feedDict)
batchErrors[batch] = er
print(
'\n{} mode, total:{},batch:{}/{},epoch:{}/{},train loss={:.3f},mean train CER={:.3f}\n'
.format(level, totalN, batch + 1,
len(batchRandIxs), epoch + 1,
num_epochs, l, er / batch_size))
elif mode == 'test':
l, pre, y, er = sess.run([
model.loss, model.predictions, model.targetY,
model.errorRate
],
feed_dict=feedDict)
batchErrors[batch] = er
print(
'\n{} mode, total:{},batch:{}/{},test loss={:.3f},mean test CER={:.3f}\n'
.format(level, totalN, batch + 1,
len(batchRandIxs), l, er / batch_size))
elif level == 'phn':
if mode == 'train':
_, l, pre, y = sess.run([
model.optimizer, model.loss, model.predictions,
model.targetY
],
feed_dict=feedDict)
er = get_edit_distance([pre.values], [y.values],
True, level)
print(
'\n{} mode, total:{},batch:{}/{},epoch:{}/{},train loss={:.3f},mean train PER={:.3f}\n'
.format(level, totalN, batch + 1,
len(batchRandIxs), epoch + 1,
num_epochs, l, er))
batchErrors[batch] = er * len(batchSeqLengths)
elif mode == 'test':
l, pre, y = sess.run(
[model.loss, model.predictions, model.targetY],
feed_dict=feedDict)
er = get_edit_distance([pre.values], [y.values],
True, level)
print(
'\n{} mode, total:{},batch:{}/{},test loss={:.3f},mean test PER={:.3f}\n'
.format(level, totalN, batch + 1,
len(batchRandIxs), l, er))
batchErrors[batch] = er * len(batchSeqLengths)
# NOTE:
if er / batch_size == 1.0:
break
if batch % 30 == 0:
print('Truth:\n' + output_to_sequence(y, type=level))
print('Output:\n' +
output_to_sequence(pre, type=level))
if mode == 'train' and (
(epoch * len(batchRandIxs) + batch + 1) % 20 == 0 or
(epoch == num_epochs - 1
and batch == len(batchRandIxs) - 1)):
checkpoint_path = os.path.join(savedir, 'model.ckpt')
model.saver.save(sess,
checkpoint_path,
global_step=epoch)
print('Model has been saved in {}'.format(savedir))
end = time.time()
delta_time = end - start
print('Epoch ' + str(epoch + 1) + ' needs time:' +
str(delta_time) + ' s')
if mode == 'train':
if (epoch + 1) % 1 == 0:
checkpoint_path = os.path.join(savedir, 'model.ckpt')
model.saver.save(sess,
checkpoint_path,
global_step=epoch)
print('Model has been saved in {}'.format(savedir))
epochER = batchErrors.sum() / totalN
print('Epoch', epoch + 1, 'mean train error rate:',
epochER)
logging(model,
logfile,
epochER,
epoch,
delta_time,
mode='config')
logging(model,
logfile,
epochER,
epoch,
delta_time,
mode=mode)
if mode == 'test':
with open(os.path.join(resultdir, level + '_result.txt'),
'a') as result:
result.write(output_to_sequence(y, type=level) + '\n')
result.write(
output_to_sequence(pre, type=level) + '\n')
result.write('\n')
epochER = batchErrors.sum() / totalN
print(' test error rate:', epochER)
logging(model, logfile, epochER, mode=mode)
def test(self):
# load data
args = self.args
batchedData, maxTimeSteps, totalN = self.load_data(args,
mode='test',
type=args.level)
if args.model == 'ResNet':
model = ResNet(args, maxTimeSteps)
elif args.model == 'BiRNN':
model = BiRNN(args, maxTimeSteps)
elif args.model == 'DBiRNN':
model = DBiRNN(args, maxTimeSteps)
num_params = count_params(model, mode='trainable')
all_num_params = count_params(model, mode='all')
model.config['trainable params'] = num_params
model.config['all params'] = all_num_params
with tf.Session(graph=model.graph) as sess:
ckpt = tf.train.get_checkpoint_state(args.save_dir)
if ckpt and ckpt.model_checkpoint_path:
model.saver.restore(sess, ckpt.model_checkpoint_path)
print('Model restored from:' + args.save_dir)
batchErrors = np.zeros(len(batchedData))
batchRandIxs = np.random.permutation(len(batchedData))
for batch, batchOrigI in enumerate(batchRandIxs):
batchInputs, batchTargetSparse, batchSeqLengths = batchedData[
batchOrigI]
batchTargetIxs, batchTargetVals, batchTargetShape = batchTargetSparse
feedDict = {
model.inputX: batchInputs,
model.targetIxs: batchTargetIxs,
model.targetVals: batchTargetVals,
model.targetShape: batchTargetShape,
model.seqLengths: batchSeqLengths
}
if args.level == 'cha':
l, pre, y, er = sess.run([
model.loss, model.predictions, model.targetY,
model.errorRate
],
feed_dict=feedDict)
batchErrors[batch] = er
print(
'\ntotal:{},batch:{}/{},loss={:.3f},mean CER={:.3f}\n'.
format(totalN, batch + 1, len(batchRandIxs), l,
er / args.batch_size))
elif args.level == 'phn':
l, pre, y = sess.run(
[model.loss, model.predictions, model.targetY],
feed_dict=feedDict)
er = get_edit_distance([pre.values], [y.values], True,
'test', args.level)
print(
'\ntotal:{},batch:{}/{},loss={:.3f},mean PER={:.3f}\n'.
format(totalN, batch + 1, len(batchRandIxs), l,
er / args.batch_size))
batchErrors[batch] = er * len(batchSeqLengths)
print('Truth:\n' + output_to_sequence(y, type=args.level))
print('Output:\n' + output_to_sequence(pre, type=args.level))
'''
l, pre, y = sess.run([ model.loss,
model.predictions,
model.targetY],
feed_dict=feedDict)
er = get_edit_distance([pre.values], [y.values], True, 'test', args.level)
print(output_to_sequence(y,type=args.level))
print(output_to_sequence(pre,type=args.level))
'''
with open(args.task + '_result.txt', 'a') as result:
result.write(output_to_sequence(y, type=args.level) + '\n')
result.write(
output_to_sequence(pre, type=args.level) + '\n')
result.write('\n')
epochER = batchErrors.sum() / totalN
print(args.task + ' test error rate:', epochER)
logging(model, self.logfile, epochER, mode='test')
