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train.py
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train.py
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### This requires KERAS 1.0.7
import argparse
import os
import tensorflow as tf
import keras
from keras.models import load_model
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
import rme.models
from rme.utils import config_gpu, load_meta, parse_training_args, parse_kwparams
from rme import datasets
from rme.callbacks import MetaCheckpoint
from rme import schedules
from rme import preprocessing
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Train a model on the desired dataset.')
parser.add_argument('--architecture', type=str, required=True)
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument('--save_checkpoint', type=str, default='checkpoint.h5')
parser.add_argument('--load_checkpoint', type=str, default=None)
# Hyperparameters
parser.add_argument('--kwparams', type=str, nargs='+', default=None)
# Training args
parser.add_argument('--lr', type=float, default=None)
parser.add_argument('--batch_size', type=int, default=None)
parser.add_argument('--epochs', type=int, default=None)
parser.add_argument('--schedule', type=str, default=None)
parser.add_argument('--preprocessing', type=str, default=None)
parser.add_argument('--augmented', default=False, action='store_true')
# GPU args
parser.add_argument('--gpu', type=str, default='')
parser.add_argument('--allow_growth', default=False, action='store_true')
args = parser.parse_args()
config_gpu(args.gpu, args.allow_growth)
training_args = vars(args)
if args.load_checkpoint:
# Continue training
model = load_model(args.load_checkpoint)
meta = load_meta(args.load_checkpoint)
args.dataset = meta['training_args']['dataset']
arch = getattr(rme.models, meta['training_args']['architecture'])
training_args = meta['training_args']
chkpt_cbk = MetaCheckpoint(args.save_checkpoint, meta=meta)
initial_epoch = meta['epochs'][-1] + 1
else:
try:
arch = getattr(rme.models, args.architecture)
# arch = available_archs[args.architecture]
except KeyError as e:
raise ValueError('Architecture %s is not available.' %args.architecture)
parse_training_args(training_args, arch.default_args(args.dataset))
training_args['kwparams'] = parse_kwparams(args.kwparams)
chkpt_cbk = MetaCheckpoint(args.save_checkpoint, training_args=training_args)
model = arch.model(args.dataset, **training_args['kwparams'])
opt = SGD(lr=training_args['lr'], momentum=0.9, nesterov=True)
model.compile(optimizer=opt, loss='categorical_crossentropy',
metrics=['accuracy'])
initial_epoch = 0
# Load dataset
print('Loading dataset: %s' %training_args['dataset'])
if args.dataset == 'mnist':
train_set, valid_set, test_set = datasets.mnist.load('data/mnist')
elif args.dataset == 'cifar10':
train_set, valid_set, test_set = datasets.cifar10.load('data/cifar10')
elif args.dataset == 'cifar100':
train_set, valid_set, test_set = datasets.cifar100.load('data/cifar100')
elif args.dataset == 'svhn':
train_set, valid_set, test_set = datasets.svhn.load('data/svhn')
else:
raise NotImplementedError('Dataset %s is not available.' %training_args['dataset'])
# Preprocess it
print('Preprocessing dataset: %s' %training_args['dataset'])
if training_args['preprocessing']:
try:
preprocess_fun = getattr(rme.preprocessing, training_args['preprocessing'])
print('Using custom preprocessing: %s' %training_args['preprocessing'])
except AttributeError:
raise NotImplementedError('Preprocessing %s is not availabe' %training_args['preprocessing'])
else:
print('Using standard preprocessing for architecture %s' %training_args['architecture'])
preprocess_fun = arch.preprocess_data
(train_set['data'], valid_set['data'],
test_set['data']) = preprocess_fun(train_set['data'],
valid_set['data'],
test_set['data'], args.dataset)
callbacks = [chkpt_cbk]
if valid_set is None or valid_set['data'].size == 0:
print('No validation set, using test set as validation data.')
validation_data = (test_set['data'], test_set['labels'])
else:
chkpt_path, chkpt_name = os.path.split(training_args['save_checkpoint'])
best_model_name = os.path.join(chkpt_path, 'best_' + chkpt_name)
print('Saving model with best validation accuracy with name %s.'
%best_model_name)
best_cbk = MetaCheckpoint(best_model_name, save_best_only=True,
training_args=training_args)
validation_data = (valid_set['data'], valid_set['labels'])
# Append it to callbacks list
callbacks.append(best_cbk)
if training_args['schedule'] != 'none':
# Set learning rate schedule
if training_args['schedule'] is None:
# Use default
schedule_fun = arch.schedule
else:
try:
schedule_fun = getattr(rme.schedules, training_args['schedule'])
except AttributeError:
raise NotImplementedError('Schedule %s is not availabe' %training_args['schedule'])
# raise NotImplementedError('You should implement custom schedules.')
schedule = schedule_fun(training_args['dataset'], training_args['lr'])
callbacks.append(schedule)
else:
# Use fixed learning rate
print('No learning rate scheduling. Learning rate will be constant')
print('Training with:')
print('%s' %str(training_args))
if training_args['augmented']:
print('Training with data augmentation: crops and flips.')
data_gen = ImageDataGenerator(horizontal_flip=True,
width_shift_range=0.125,
height_shift_range=0.125,
fill_mode='constant')
data_iter = data_gen.flow(train_set['data'], train_set['labels'],
batch_size=training_args['batch_size'],
shuffle=True)
model.fit_generator(data_iter,
samples_per_epoch=train_set['data'].shape[0],
nb_epoch=training_args['epochs'],
validation_data=(test_set['data'],
test_set['labels']),
callbacks=callbacks, initial_epoch=initial_epoch)
else:
model.fit(train_set['data'], train_set['labels'],
batch_size=training_args['batch_size'],
nb_epoch=training_args['epochs'],
validation_data=validation_data,
callbacks=callbacks, initial_epoch=initial_epoch,
shuffle=True)
test_loss, test_acc = model.evaluate(test_set['data'], test_set['labels'],
verbose=2)
print('Test set loss = %g. Test set accuracy = %g' %(test_loss, test_acc))