def main():
train, test, _ = imdb.load_data(path='imdb.pkl',
n_words=10000,
valid_portion=0.1)
trainX, trainY = train
testX, testY = test
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
trainY = np.asarray(trainY)
testY = np.asarray(testY)
data_set = DataSet(trainX, trainY, testX, testY)
training_cnf = {
'classification': True,
'validation_scores': [('validation accuracy', util.accuracy_tf)],
'num_epochs': 50,
'input_size': (100, ),
'lr_policy': StepDecayPolicy(schedule={
0: 0.01,
30: 0.001,
})
}
util.init_logging('train.log',
file_log_level=logging.INFO,
console_log_level=logging.INFO)
learner = SupervisedLearner(model,
training_cnf,
classification=training_cnf['classification'],
is_summary=False)
learner.fit(data_set, weights_from=None, start_epoch=1)
def try_config(args, cnf):
"""For trying out configurations.
Args:
args: command line arguments regarding training
cnf: training configuration sampled from hyperband search space
Returns:
a dictionary containing final loss value and early stop flag
"""
model_def = util.load_module(args['model'])
model = model_def.model
if args['weights_from']:
weights_from = str(args['weights_from'])
else:
weights_from = args['weights_from']
data_set = DataSet(args['data_dir'],
model_def.image_size[0],
mode=cnf.get('mode'),
multilabel=cnf.get('multilabel', False))
standardizer = cnf.get('standardizer', NoOpStandardizer())
cutout = cnf.get('cutout', None)
training_iter, validation_iter = create_training_iters(
cnf,
data_set,
standardizer,
model_def.crop_size,
args['start_epoch'],
parallel=args['parallel'],
cutout=cutout,
data_balancing=cnf.get('data_balancing', False))
learner = SupervisedLearner(
model,
cnf,
training_iterator=training_iter,
validation_iterator=validation_iter,
resume_lr=args['resume_lr'],
classification=cnf['classification'],
gpu_memory_fraction=args['gpu_memory_fraction'],
num_classes=args['num_classes'],
is_summary=args['is_summary'],
loss_type=args['loss_type'],
weighted=args['weighted'],
log_file_name=args['log_file_name'],
verbosity=args['verbose'],
is_early_stop=cnf.get('is_early_stop', True))
_early_stop, _loss = learner.fit(data_set,
weights_from=weights_from,
start_epoch=args['start_epoch'],
weights_dir=args['weights_dir'],
summary_every=399)
return {'early_stop': _early_stop, 'loss': _loss}
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from,
weights_dir, resume_lr, gpu_memory_fraction, num_classes, is_summary,
loss_type, weighted, log_file_name):
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir,
model_def.image_size[0],
mode=cnf.get('mode'),
multilabel=cnf.get('multilabel', False))
standardizer = cnf.get('standardizer', NoOpStandardizer())
cutout = cnf.get('cutout', None)
training_iter, validation_iter = create_training_iters(
cnf,
data_set,
standardizer,
model_def.crop_size,
start_epoch,
parallel=parallel,
cutout=cutout,
data_balancing=cnf.get('data_balancing', False))
learner = SupervisedLearner(model,
cnf,
training_iterator=training_iter,
validation_iterator=validation_iter,
resume_lr=resume_lr,
classification=cnf['classification'],
gpu_memory_fraction=gpu_memory_fraction,
num_classes=num_classes,
is_summary=is_summary,
loss_type=loss_type,
weighted=weighted,
log_file_name=log_file_name)
learner.fit(data_set,
weights_from,
start_epoch=start_epoch,
weights_dir=weights_dir,
summary_every=399)
def train():
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
width = 28
height = 28
train_images = mnist[0].images.reshape(-1, height, width, 1)
train_labels = mnist[0].labels
validation_images = mnist[1].images.reshape(-1, height, width, 1)
validation_labels = mnist[1].labels
data_set = DataSet(train_images, train_labels, validation_images, validation_labels)
training_cnf = {
'classification':
True,
'validation_scores': [('accuracy', tf.metrics.accuracy),
('kappa', tf.contrib.metrics.cohen_kappa)],
'num_epochs':
50,
'batch_size_train':
32,
'batch_size_test':
32,
'input_size': (28, 28, 1),
'lr_policy':
StepDecayPolicy(schedule={
0: 0.01,
30: 0.001,
})
}
learner = SupervisedLearner(
model,
training_cnf,
classification=training_cnf['classification'],
is_summary=True,
num_classes=10)
learner.fit(data_set, weights_from=None, start_epoch=1, summary_every=10)
def try_config(args, cnf):
"""For trying out configurations.
Args:
args: command line arguments regarding training
cnf: training configuration sampled from hyperband search space
Returns:
a dictionary containing final loss value and early stop flag
"""
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
width = 28
height = 28
train_images = mnist[0].images
train_labels = mnist[0].labels
validation_images = mnist[1].images
validation_labels = mnist[1].labels
data_set = DataSet(train_images, train_labels, validation_images,
validation_labels)
model_def = util.load_module(args['model'])
model = model_def.model
learner = SupervisedLearner(model,
cnf,
classification=cnf['classification'],
is_summary=False,
num_classes=10,
verbosity=args['verbose'],
is_early_stop=cnf.get('is_early_stop', True))
_early_stop, _loss = learner.fit(data_set,
weights_from=None,
start_epoch=1)
return {'early_stop': _early_stop, 'loss': _loss}
def main(model, training_cnf, data_dir, parallel, start_epoch, weights_from,
weights_dir, resume_lr, gpu_memory_fraction, num_classes, is_summary,
loss_type):
model_def = util.load_module(model)
model = model_def.model
cnf = util.load_module(training_cnf).cnf
util.init_logging('train.log',
file_log_level=logging.INFO,
console_log_level=logging.INFO)
if weights_from:
weights_from = str(weights_from)
data_set = DataSet(data_dir, model_def.image_size[0])
standardizer = cnf.get('standardizer', NoOpStandardizer())
training_iter, validation_iter = create_training_iters(cnf,
data_set,
standardizer,
model_def.crop_size,
start_epoch,
parallel=parallel)
learner = SupervisedLearner(model,
cnf,
training_iterator=training_iter,
validation_iterator=validation_iter,
resume_lr=resume_lr,
classification=cnf['classification'],
gpu_memory_fraction=gpu_memory_fraction,
num_classes=num_classes,
is_summary=is_summary,
loss_type=loss_type)
learner.fit(data_set,
weights_from,
start_epoch=start_epoch,
weights_dir=weights_dir,
summary_every=399)
