def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
if 'background' in args.mode:
callback_logger = logging.info
sys.stdout, sys.stderr = setup_logging(
os.path.join(model_path, 'model.log'))
verbose = 0
else:
callback_logger = callable_print
verbose = 1
json_cfg = load_model_json(args)
json_cfg['model_path'] = model_path
json_cfg['stdout'] = sys.stdout
json_cfg['stderr'] = sys.stderr
json_cfg['logger'] = callback_logger
json_cfg['verbose'] = verbose
config = ModelConfig(**json_cfg)
if 'persistent' in args.mode:
save_model_info(config, model_path)
sys.path.append(args.model_dir)
import model
from model import fit
model.fit(config)
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
json_cfg = load_model_json(args, x_train=None, n_classes=None)
config = ModelConfig(**json_cfg)
sys.path.append(args.model_dir)
import model
from model import fit
model.fit(config)
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
rng = np.random.RandomState(args.seed)
json_cfg = load_model_json(args, x_train=None, n_classes=None)
model_cfg = ModelConfig(**json_cfg)
if args.verbose:
print("model_cfg " + str(model_cfg))
sys.path.append(args.model_dir)
import model
from model import build_model, fit_model, load_train, load_validation
train_data = load_train(args, model_cfg)
validation_data = load_validation(args, model_cfg)
if args.verbose:
print("loading model")
model = build_model(model_cfg, train_data, validation_data)
fit_model(model, train_data, validation_data, args)
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
sys.stdout, sys.stderr = setup_logging(args)
rng = np.random.RandomState(args.seed)
x_train, y_train = load_model_data(args.train_file,
args.data_name,
args.target_name,
n=args.n_train)
x_valid, y_valid = load_model_data(args.validation_file,
args.data_name,
args.target_name,
n=args.n_validation)
train_files = args.extra_train_file + [args.train_file]
train_files_iter = itertools.cycle(train_files)
n_classes = max(np.unique(y_train)) + 1
json_cfg = load_model_json(args, x_train, n_classes)
sys.path.append(args.model_dir)
from model import Model
model_cfg = ModelConfig(**json_cfg)
model = Model(model_cfg)
setattr(model, 'stop_training', False)
best_accuracy = 0.
best_epoch = 0
epoch = 1
iteration = 0
while True:
if not keep_training(epoch, best_epoch, model_cfg):
break
train_loss = train_one_epoch(model,
x_train,
y_train,
args,
model_cfg,
progress=args.progress)
val_loss, val_accuracy = validate(model,
x_valid,
y_valid,
args,
model_cfg,
progress=args.progress)
if val_accuracy > best_accuracy:
best_accuracy = val_accuracy
best_epoch = epoch
if model_path is not None:
model.save_weights(model_path + '.npz')
cPickle.dump(model, open(model_path + '.pkl', 'w'))
print(
'epoch={epoch:05d}, iteration={iteration:05d}, loss={loss:.04f}, val_loss={val_loss:.04f}, val_acc={val_acc:.04f} best=[accuracy={best_accuracy:.04f} epoch={best_epoch:05d}]'
.format(epoch=epoch,
iteration=iteration,
loss=train_loss,
val_loss=val_loss,
val_acc=val_accuracy,
best_accuracy=best_accuracy,
best_epoch=best_epoch))
iteration += 1
if iteration % len(train_files) == 0:
epoch += 1
x_train, y_train = load_model_data(next(train_files_iter),
args.data_name,
args.target_name,
n=args.n_train)
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
sys.stdout, sys.stderr = setup_logging(args, model_path)
x_train, y_train = load_model_data(args.train_file,
args.data_name, args.target_name)
x_validation, y_validation = load_model_data(
args.validation_file,
args.data_name, args.target_name)
rng = np.random.RandomState(args.seed)
if args.n_classes > -1:
n_classes = args.n_classes
else:
n_classes = max(y_train)+1
n_classes, target_names, class_weight = load_target_data(args, n_classes)
if class_weight is None and args.class_weight_auto:
n_samples = len(y_train)
weights = float(n_samples) / (n_classes * np.bincount(y_train))
if args.class_weight_exponent:
weights = weights**args.class_weight_exponent
class_weight = dict(zip(range(n_classes), weights))
if args.verbose:
logging.debug("n_classes {0} min {1} max {2}".format(
n_classes, min(y_train), max(y_train)))
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
y_validation_one_hot = np_utils.to_categorical(y_validation, n_classes)
if args.verbose:
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
min_vocab_index = np.min(x_train)
max_vocab_index = np.max(x_train)
if args.verbose:
logging.debug("min vocab index {0} max vocab index {1}".format(
min_vocab_index, max_vocab_index))
json_cfg = load_model_json(args, x_train, n_classes)
if args.verbose:
logging.debug("loading model")
sys.path.append(args.model_dir)
import model
from model import build_model
#######################################################################
# Subsetting
#######################################################################
if args.subsetting_function:
subsetter = getattr(M, args.subsetting_function)
else:
subsetter = None
def take_subset(subsetter, path, x, y, y_one_hot, n):
if subsetter is None:
return x[0:n], y[0:n], y_one_hot[0:n]
else:
mask = subsetter(path)
idx = np.where(mask)[0]
idx = idx[0:n]
return x[idx], y[idx], y_one_hot[idx]
x_train, y_train, y_train_one_hot = take_subset(
subsetter, args.train_file,
x_train, y_train, y_train_one_hot,
n=args.n_train)
x_validation, y_validation, y_validation_one_hot = take_subset(
subsetter, args.validation_file,
x_validation, y_validation, y_validation_one_hot,
n=args.n_validation)
#######################################################################
# Preprocessing
#######################################################################
if args.preprocessing_class:
preprocessor = getattr(M, args.preprocessing_class)(seed=args.seed)
else:
preprocessor = modeling.preprocess.NullPreprocessor()
if args.verbose:
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
model_cfg = ModelConfig(**json_cfg)
if args.verbose:
logging.info("model_cfg " + str(model_cfg))
net = build_model(model_cfg)
setattr(net, 'stop_training', False)
marshaller = None
if isinstance(net, keras.models.Graph):
marshaller = getattr(model, args.graph_marshalling_class)()
logging.info('model has {n_params} parameters'.format(
n_params=count_parameters(net)))
if len(args.extra_train_file) > 1:
callbacks = keras.callbacks.CallbackList()
else:
callbacks = []
save_model_info(args, model_path, model_cfg)
callback_logger = logging.info if args.log else callable_print
#######################################################################
# Callbacks that need validation set predictions.
#######################################################################
pc = PredictionCallback(x_validation, callback_logger,
marshaller=marshaller, batch_size=model_cfg.batch_size)
callbacks.append(pc)
if args.classification_report:
cr = ClassificationReport(x_validation, y_validation,
callback_logger,
target_names=target_names)
pc.add(cr)
if args.confusion_matrix:
cm = ConfusionMatrix(x_validation, y_validation,
callback_logger)
pc.add(cm)
def get_mode(metric_name):
return {
'val_loss': 'min',
'val_acc': 'max',
'val_f1': 'max',
'val_f2': 'max',
'val_f0.5': 'max'
}[metric_name]
if args.early_stopping or args.early_stopping_metric is not None:
es = EarlyStopping(monitor=args.early_stopping_metric,
mode=get_mode(args.early_stopping_metric),
patience=model_cfg.patience,
verbose=1)
cb = DelegatingMetricCallback(
x_validation, y_validation, callback_logger,
delegate=es,
metric_name=args.early_stopping_metric,
marshaller=marshaller)
pc.add(cb)
if not args.no_save:
if args.save_all_checkpoints:
filepath = model_path + '/model-{epoch:04d}.h5'
else:
filepath = model_path + '/model.h5'
mc = ModelCheckpoint(
filepath=filepath,
mode=get_mode(args.checkpoint_metric),
verbose=1,
monitor=args.checkpoint_metric,
save_best_only=not args.save_every_epoch)
cb = DelegatingMetricCallback(
x_validation, y_validation, callback_logger,
delegate=mc,
metric_name=args.checkpoint_metric,
marshaller=marshaller)
pc.add(cb)
if model_cfg.optimizer == 'SGD':
callbacks.append(SingleStepLearningRateSchedule(patience=10))
if len(args.extra_train_file) > 1:
args.extra_train_file.append(args.train_file)
logging.info("Using the following files for training: " +
','.join(args.extra_train_file))
train_file_iter = itertools.cycle(args.extra_train_file)
current_train = args.train_file
callbacks._set_model(net)
callbacks.on_train_begin(logs={})
epoch = batch = 0
while True:
x_train, y_train_one_hot = preprocessor.fit_transform(
x_train, y_train_one_hot)
x_validation, y_validation_one_hot = preprocessor.transform(
x_validation, y_validation_one_hot)
iteration = batch % len(args.extra_train_file)
logging.info("epoch {epoch} iteration {iteration} - training with {train_file}".format(
epoch=epoch, iteration=iteration, train_file=current_train))
callbacks.on_epoch_begin(epoch, logs={})
n_train = x_train.shape[0]
callbacks.on_batch_begin(batch, logs={'size': n_train})
index_array = np.arange(n_train)
if args.shuffle:
rng.shuffle(index_array)
batches = keras.models.make_batches(n_train, model_cfg.batch_size)
logging.info("epoch {epoch} iteration {iteration} - starting {n_batches} batches".format(
epoch=epoch, iteration=iteration, n_batches=len(batches)))
avg_train_loss = avg_train_accuracy = 0.
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(net, keras.models.Graph):
train_data = marshaller.marshal(
x_train[batch_ids], y_train_one_hot[batch_ids])
train_loss = net.train_on_batch(
train_data, class_weight=class_weight)
# It looks like train_on_batch returns a different
# type for graph than sequential models.
train_loss = train_loss[0]
train_accuracy = 0.
else:
train_loss, train_accuracy = net.train_on_batch(
x_train[batch_ids], y_train_one_hot[batch_ids],
accuracy=True, class_weight=class_weight)
batch_end_logs = {'loss': train_loss, 'accuracy': train_accuracy}
avg_train_loss = (avg_train_loss * batch_index + train_loss)/(batch_index + 1)
avg_train_accuracy = (avg_train_accuracy * batch_index + train_accuracy)/(batch_index + 1)
callbacks.on_batch_end(batch,
logs={'loss': train_loss, 'accuracy': train_accuracy})
logging.info("epoch {epoch} iteration {iteration} - finished {n_batches} batches".format(
epoch=epoch, iteration=iteration, n_batches=len(batches)))
logging.info("epoch {epoch} iteration {iteration} - loss: {loss} - acc: {acc}".format(
epoch=epoch, iteration=iteration, loss=avg_train_loss, acc=avg_train_accuracy))
batch += 1
# Validation frequency (this if-block) doesn't necessarily
# occur in the same iteration as beginning of an epoch
# (next if-block), so net.evaluate appears twice here.
kwargs = {
'batch_size': model_cfg.batch_size,
'verbose': 0 if args.log else 1
}
pargs = []
validation_data = {}
if isinstance(net, keras.models.Graph):
validation_data = marshaller.marshal(
x_validation, y_validation_one_hot)
pargs = [validation_data]
else:
pargs = [x_validation, y_validation_one_hot]
kwargs['show_accuracy'] = True
if (iteration + 1) % args.validation_freq == 0:
if isinstance(net, keras.models.Graph):
val_loss = net.evaluate(*pargs, **kwargs)
y_hat = net.predict(validation_data, batch_size=model_cfg.batch_size)
val_acc = accuracy_score(y_validation, np.argmax(y_hat['output'], axis=1))
else:
val_loss, val_acc = net.evaluate(
*pargs, **kwargs)
logging.info("epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}".format(
epoch=epoch, iteration=iteration, val_loss=val_loss, val_acc=val_acc))
epoch_end_logs = {'iteration': iteration, 'val_loss': val_loss, 'val_acc': val_acc}
callbacks.on_epoch_end(epoch, epoch_end_logs)
if batch % len(args.extra_train_file) == 0:
if isinstance(net, keras.models.Graph):
val_loss = net.evaluate(*pargs, **kwargs)
y_hat = net.predict(validation_data, batch_size=model_cfg.batch_size)
val_acc = accuracy_score(y_validation, np.argmax(y_hat['output'], axis=1))
else:
val_loss, val_acc = net.evaluate(
*pargs, **kwargs)
logging.info("epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}".format(
epoch=epoch, iteration=iteration, val_loss=val_loss, val_acc=val_acc))
epoch_end_logs = {'iteration': iteration, 'val_loss': val_loss, 'val_acc': val_acc}
epoch += 1
callbacks.on_epoch_end(epoch, epoch_end_logs)
if net.stop_training:
logging.info("epoch {epoch} iteration {iteration} - done training".format(
epoch=epoch, iteration=iteration))
break
current_train = next(train_file_iter)
x_train, y_train = load_model_data(current_train,
args.data_name, args.target_name)
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
if epoch > args.n_epochs:
break
callbacks.on_train_end(logs={})
else:
x_train, y_train_one_hot = preprocessor.fit_transform(
x_train, y_train_one_hot)
x_validation, y_validation_one_hot = preprocessor.transform(
x_validation, y_validation_one_hot)
if isinstance(net, keras.models.Graph):
train_data = marshaller.marshal(
x_train, y_train_one_hot)
validation_data = marshaller.marshal(
x_validation, y_validation_one_hot)
net.fit(train_data,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
validation_data=validation_data,
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)
else:
net.fit(x_train, y_train_one_hot,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
show_accuracy=True,
validation_data=(x_validation, y_validation_one_hot),
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
sys.stdout, sys.stderr = setup_logging(args)
rng = np.random.RandomState(args.seed)
x_train, y_train = load_model_data(args.train_file,
args.data_name, args.target_name,
n=args.n_train)
x_valid, y_valid = load_model_data(
args.validation_file,
args.data_name, args.target_name,
n=args.n_validation)
train_files = args.extra_train_file + [args.train_file]
train_files_iter = itertools.cycle(train_files)
n_classes = max(np.unique(y_train)) + 1
json_cfg = load_model_json(args, x_train, n_classes)
sys.path.append(args.model_dir)
from model import Model
model_cfg = ModelConfig(**json_cfg)
model = Model(model_cfg)
setattr(model, 'stop_training', False)
best_accuracy = 0.
best_epoch = 0
epoch = 1
iteration = 0
while True:
if not keep_training(epoch, best_epoch, model_cfg):
break
train_loss = train_one_epoch(model, x_train, y_train,
args, model_cfg, progress=args.progress)
val_loss, val_accuracy = validate(model, x_valid, y_valid,
args, model_cfg, progress=args.progress)
if val_accuracy > best_accuracy:
best_accuracy = val_accuracy
best_epoch = epoch
if model_path is not None:
model.save_weights(model_path + '.npz')
cPickle.dump(model, open(model_path + '.pkl', 'w'))
print('epoch={epoch:05d}, iteration={iteration:05d}, loss={loss:.04f}, val_loss={val_loss:.04f}, val_acc={val_acc:.04f} best=[accuracy={best_accuracy:.04f} epoch={best_epoch:05d}]'.format(
epoch=epoch, iteration=iteration,
loss=train_loss, val_loss=val_loss, val_acc=val_accuracy,
best_accuracy=best_accuracy, best_epoch=best_epoch))
iteration += 1
if iteration % len(train_files) == 0:
epoch += 1
x_train, y_train = load_model_data(
next(train_files_iter),
args.data_name, args.target_name,
n=args.n_train)
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
sys.stdout, sys.stderr = setup_logging(args, model_path)
x_train, y_train = load_model_data(args.train_file,
args.data_name, args.target_name)
x_validation, y_validation = load_model_data(
args.validation_file,
args.data_name, args.target_name)
rng = np.random.RandomState(args.seed)
if args.n_classes > -1:
n_classes = args.n_classes
else:
n_classes = max(y_train)+1
n_classes, target_names, class_weight = load_target_data(args, n_classes)
if len(class_weight) == 0:
n_samples = len(y_train)
print('n_samples', n_samples)
print('classes', range(n_classes))
print('weights', n_samples / (n_classes * np.bincount(y_train)))
class_weight = dict(zip(range(n_classes),
n_samples / (n_classes * np.bincount(y_train))))
print('class_weight', class_weight)
logging.debug("n_classes {0} min {1} max {2}".format(
n_classes, min(y_train), max(y_train)))
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
y_validation_one_hot = np_utils.to_categorical(y_validation, n_classes)
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
min_vocab_index = np.min(x_train)
max_vocab_index = np.max(x_train)
logging.debug("min vocab index {0} max vocab index {1}".format(
min_vocab_index, max_vocab_index))
json_cfg = load_model_json(args, x_train, n_classes)
logging.debug("loading model")
sys.path.append(args.model_dir)
import model
from model import build_model
#######################################################################
# Subsetting
#######################################################################
if args.subsetting_function:
subsetter = getattr(model, args.subsetting_function)
else:
subsetter = None
def take_subset(subsetter, path, x, y, y_one_hot, n):
if subsetter is None:
return x[0:n], y[0:n], y_one_hot[0:n]
else:
mask = subsetter(path)
idx = np.where(mask)[0]
idx = idx[0:n]
return x[idx], y[idx], y_one_hot[idx]
x_train, y_train, y_train_one_hot = take_subset(
subsetter, args.train_file,
x_train, y_train, y_train_one_hot,
n=args.n_train)
x_validation, y_validation, y_validation_one_hot = take_subset(
subsetter, args.validation_file,
x_validation, y_validation, y_validation_one_hot,
n=args.n_validation)
#######################################################################
# Preprocessing
#######################################################################
if args.preprocessing_class:
preprocessor = getattr(model, args.preprocessing_class)(seed=args.seed)
else:
preprocessor = modeling.preprocess.NullPreprocessor()
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
model_cfg = ModelConfig(**json_cfg)
logging.info("model_cfg " + str(model_cfg))
model = build_model(model_cfg)
setattr(model, 'stop_training', False)
logging.info('model has {n_params} parameters'.format(
n_params=count_parameters(model)))
if len(args.extra_train_file) > 1:
callbacks = keras.callbacks.CallbackList()
else:
callbacks = []
save_model_info(args, model_path, model_cfg)
if not args.no_save:
if args.save_all_checkpoints:
filepath = model_path + '/model-{epoch:04d}.h5'
else:
filepath = model_path + '/model.h5'
callbacks.append(ModelCheckpoint(
filepath=filepath,
verbose=1,
save_best_only=not args.save_every_epoch))
callback_logger = logging.info if args.log else callable_print
if args.n_epochs < sys.maxsize:
# Number of epochs overrides patience. If the number of epochs
# is specified on the command line, the model is trained for
# exactly that number; otherwise, the model is trained with
# early stopping using the patience specified in the model
# configuration.
callbacks.append(EarlyStopping(
monitor='val_loss', patience=model_cfg.patience, verbose=1))
if args.classification_report:
cr = ClassificationReport(x_validation, y_validation,
callback_logger,
target_names=target_names)
callbacks.append(cr)
if model_cfg.optimizer == 'SGD':
callbacks.append(SingleStepLearningRateSchedule(patience=10))
if len(args.extra_train_file) > 1:
args.extra_train_file.append(args.train_file)
logging.info("Using the following files for training: " +
','.join(args.extra_train_file))
train_file_iter = itertools.cycle(args.extra_train_file)
current_train = args.train_file
callbacks._set_model(model)
callbacks.on_train_begin(logs={})
epoch = batch = 0
while True:
x_train, y_train_one_hot = preprocessor.fit_transform(
x_train, y_train_one_hot)
x_validation, y_validation_one_hot = preprocessor.transform(
x_validation, y_validation_one_hot)
iteration = batch % len(args.extra_train_file)
logging.info("epoch {epoch} iteration {iteration} - training with {train_file}".format(
epoch=epoch, iteration=iteration, train_file=current_train))
callbacks.on_epoch_begin(epoch, logs={})
n_train = x_train.shape[0]
callbacks.on_batch_begin(batch, logs={'size': n_train})
index_array = np.arange(n_train)
if args.shuffle:
rng.shuffle(index_array)
batches = keras.models.make_batches(n_train, model_cfg.batch_size)
logging.info("epoch {epoch} iteration {iteration} - starting {n_batches} batches".format(
epoch=epoch, iteration=iteration, n_batches=len(batches)))
avg_train_loss = avg_train_accuracy = 0.
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(model, keras.models.Graph):
data = {
'input': x_train[batch_ids],
'output': y_train_one_hot[batch_ids]
}
train_loss = model.train_on_batch(data, class_weight=class_weight)
train_accuracy = 0.
else:
train_loss, train_accuracy = model.train_on_batch(
x_train[batch_ids], y_train_one_hot[batch_ids],
accuracy=True, class_weight=class_weight)
batch_end_logs = {'loss': train_loss, 'accuracy': train_accuracy}
avg_train_loss = (avg_train_loss * batch_index + train_loss)/(batch_index + 1)
avg_train_accuracy = (avg_train_accuracy * batch_index + train_accuracy)/(batch_index + 1)
callbacks.on_batch_end(batch,
logs={'loss': train_loss, 'accuracy': train_accuracy})
logging.info("epoch {epoch} iteration {iteration} - finished {n_batches} batches".format(
epoch=epoch, iteration=iteration, n_batches=len(batches)))
logging.info("epoch {epoch} iteration {iteration} - loss: {loss} - acc: {acc}".format(
epoch=epoch, iteration=iteration, loss=avg_train_loss, acc=avg_train_accuracy))
batch += 1
# Validation frequency (this if-block) doesn't necessarily
# occur in the same iteration as beginning of an epoch
# (next if-block), so model.evaluate appears twice here.
kwargs = { 'verbose': 0 if args.log else 1 }
pargs = []
validation_data = {}
if isinstance(model, keras.models.Graph):
validation_data = {
'input': x_validation,
'output': y_validation_one_hot
}
pargs = [validation_data]
else:
pargs = [x_validation, y_validation_one_hot]
kwargs['show_accuracy'] = True
if (iteration + 1) % args.validation_freq == 0:
if isinstance(model, keras.models.Graph):
val_loss = model.evaluate(*pargs, **kwargs)
y_hat = model.predict(validation_data)
val_acc = accuracy_score(y_validation, np.argmax(y_hat['output'], axis=1))
else:
val_loss, val_acc = model.evaluate(
*pargs, **kwargs)
logging.info("epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}".format(
epoch=epoch, iteration=iteration, val_loss=val_loss, val_acc=val_acc))
epoch_end_logs = {'iteration': iteration, 'val_loss': val_loss, 'val_acc': val_acc}
callbacks.on_epoch_end(epoch, epoch_end_logs)
if batch % len(args.extra_train_file) == 0:
if isinstance(model, keras.models.Graph):
val_loss = model.evaluate(*pargs, **kwargs)
y_hat = model.predict(validation_data)
val_acc = accuracy_score(y_validation, np.argmax(y_hat['output'], axis=1))
else:
val_loss, val_acc = model.evaluate(
*pargs, **kwargs)
logging.info("epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}".format(
epoch=epoch, iteration=iteration, val_loss=val_loss, val_acc=val_acc))
epoch_end_logs = {'iteration': iteration, 'val_loss': val_loss, 'val_acc': val_acc}
epoch += 1
callbacks.on_epoch_end(epoch, epoch_end_logs)
if model.stop_training:
logging.info("epoch {epoch} iteration {iteration} - done training".format(
epoch=epoch, iteration=iteration))
break
current_train = next(train_file_iter)
x_train, y_train = load_model_data(current_train,
args.data_name, args.target_name)
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
if epoch > args.n_epochs:
break
callbacks.on_train_end(logs={})
else:
x_train, y_train_one_hot = preprocessor.fit_transform(
x_train, y_train_one_hot)
x_validation, y_validation_one_hot = preprocessor.transform(
x_validation, y_validation_one_hot)
if isinstance(model, keras.models.Graph):
data = {
'input': x_train,
'output': y_train_one_hot
}
validation_data = {
'input': x_validation,
'output': y_validation_one_hot
}
model.fit(data,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
validation_data=validation_data,
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)
y_hat = model.predict(validation_data)
print('val_acc %.04f' %
accuracy_score(y_validation, np.argmax(y_hat['output'], axis=1)))
else:
model.fit(x_train, y_train_one_hot,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
show_accuracy=True,
validation_data=(x_validation, y_validation_one_hot),
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)
def main(args):
if args.gpu >= 0:
cuda.check_cuda_available()
xp = cuda.cupy if args.gpu >= 0 else np
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
sys.stdout, sys.stderr = setup_logging(args)
x_train, y_train = load_model_data(args.train_file,
args.data_name, args.target_name,
n=args.n_train)
x_validation, y_validation = load_model_data(
args.validation_file,
args.data_name, args.target_name,
n=args.n_validation)
rng = np.random.RandomState(args.seed)
N = len(x_train)
N_validation = len(x_validation)
n_classes = max(np.unique(y_train)) + 1
json_cfg = load_model_json(args, x_train, n_classes)
print('args.model_dir', args.model_dir)
sys.path.append(args.model_dir)
from model import Model
model_cfg = ModelConfig(**json_cfg)
model = Model(model_cfg)
setattr(model, 'stop_training', False)
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model.to_gpu()
best_accuracy = 0.
best_epoch = 0
def keep_training(epoch, best_epoch):
if model_cfg.n_epochs is not None and epoch > model_cfg.n_epochs:
return False
if epoch > 1 and epoch - best_epoch > model_cfg.patience:
return False
return True
epoch = 1
while True:
if not keep_training(epoch, best_epoch):
break
if args.shuffle:
perm = np.random.permutation(N)
else:
perm = np.arange(N)
sum_accuracy = 0
sum_loss = 0
pbar = progressbar.ProgressBar(term_width=40,
widgets=[' ', progressbar.Percentage(),
' ', progressbar.ETA()],
maxval=N).start()
for j, i in enumerate(six.moves.range(0, N, model_cfg.batch_size)):
pbar.update(j+1)
x_batch = xp.asarray(x_train[perm[i:i + model_cfg.batch_size]].flatten())
y_batch = xp.asarray(y_train[perm[i:i + model_cfg.batch_size]])
pred, loss, acc = model.fit(x_batch, y_batch)
sum_loss += float(loss.data) * len(y_batch)
sum_accuracy += float(acc.data) * len(y_batch)
pbar.finish()
print('train epoch={}, mean loss={}, accuracy={}'.format(
epoch, sum_loss / N, sum_accuracy / N))
# Validation set evaluation
sum_accuracy = 0
sum_loss = 0
pbar = progressbar.ProgressBar(term_width=40,
widgets=[' ', progressbar.Percentage(),
' ', progressbar.ETA()],
maxval=N_validation).start()
for i in six.moves.range(0, N_validation, model_cfg.batch_size):
pbar.update(i+1)
x_batch = xp.asarray(x_validation[i:i + model_cfg.batch_size].flatten())
y_batch = xp.asarray(y_validation[i:i + model_cfg.batch_size])
pred, loss, acc = model.predict(x_batch, target=y_batch)
sum_loss += float(loss.data) * len(y_batch)
sum_accuracy += float(acc.data) * len(y_batch)
pbar.finish()
validation_accuracy = sum_accuracy / N_validation
validation_loss = sum_loss / N_validation
if validation_accuracy > best_accuracy:
best_accuracy = validation_accuracy
best_epoch = epoch
if model_path is not None:
if args.gpu >= 0:
model.to_cpu()
store = {
'args': args,
'model': model,
}
cPickle.dump(store, open(model_path + '.store', 'w'))
if args.gpu >= 0:
model.to_gpu()
print('validation epoch={}, mean loss={}, accuracy={} best=[accuracy={} epoch={}]'.format(
epoch, validation_loss, validation_accuracy,
best_accuracy,
best_epoch))
epoch += 1
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
sys.stdout, sys.stderr = setup_logging(args, model_path)
x_train, y_train = load_model_data(args.train_file, args.data_name,
args.target_name)
x_validation, y_validation = load_model_data(args.validation_file,
args.data_name,
args.target_name)
rng = np.random.RandomState(args.seed)
if args.n_classes > -1:
n_classes = args.n_classes
else:
n_classes = max(y_train) + 1
n_classes, target_names, class_weight = load_target_data(args, n_classes)
if class_weight is None and args.class_weight_auto:
n_samples = len(y_train)
weights = float(n_samples) / (n_classes * np.bincount(y_train))
if args.class_weight_exponent:
weights = weights**args.class_weight_exponent
class_weight = dict(zip(range(n_classes), weights))
if args.verbose:
logging.debug("n_classes {0} min {1} max {2}".format(
n_classes, min(y_train), max(y_train)))
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
y_validation_one_hot = np_utils.to_categorical(y_validation, n_classes)
if args.verbose:
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
min_vocab_index = np.min(x_train)
max_vocab_index = np.max(x_train)
if args.verbose:
logging.debug("min vocab index {0} max vocab index {1}".format(
min_vocab_index, max_vocab_index))
json_cfg = load_model_json(args, x_train, n_classes)
if args.verbose:
logging.debug("loading model")
sys.path.append(args.model_dir)
import model
from model import build_model
#######################################################################
# Subsetting
#######################################################################
if args.subsetting_function:
subsetter = getattr(M, args.subsetting_function)
else:
subsetter = None
def take_subset(subsetter, path, x, y, y_one_hot, n):
if subsetter is None:
return x[0:n], y[0:n], y_one_hot[0:n]
else:
mask = subsetter(path)
idx = np.where(mask)[0]
idx = idx[0:n]
return x[idx], y[idx], y_one_hot[idx]
x_train, y_train, y_train_one_hot = take_subset(subsetter,
args.train_file,
x_train,
y_train,
y_train_one_hot,
n=args.n_train)
x_validation, y_validation, y_validation_one_hot = take_subset(
subsetter,
args.validation_file,
x_validation,
y_validation,
y_validation_one_hot,
n=args.n_validation)
#######################################################################
# Preprocessing
#######################################################################
if args.preprocessing_class:
preprocessor = getattr(M, args.preprocessing_class)(seed=args.seed)
else:
preprocessor = modeling.preprocess.NullPreprocessor()
if args.verbose:
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
model_cfg = ModelConfig(**json_cfg)
if args.verbose:
logging.info("model_cfg " + str(model_cfg))
net = build_model(model_cfg)
setattr(net, 'stop_training', False)
marshaller = None
if isinstance(net, keras.models.Graph):
marshaller = getattr(model, args.graph_marshalling_class)()
logging.info('model has {n_params} parameters'.format(
n_params=count_parameters(net)))
if len(args.extra_train_file) > 1:
callbacks = keras.callbacks.CallbackList()
else:
callbacks = []
save_model_info(args, model_path, model_cfg)
callback_logger = logging.info if args.log else callable_print
#######################################################################
# Callbacks that need validation set predictions.
#######################################################################
pc = PredictionCallback(x_validation,
callback_logger,
marshaller=marshaller,
batch_size=model_cfg.batch_size)
callbacks.append(pc)
if args.classification_report:
cr = ClassificationReport(x_validation,
y_validation,
callback_logger,
target_names=target_names)
pc.add(cr)
if args.confusion_matrix:
cm = ConfusionMatrix(x_validation, y_validation, callback_logger)
pc.add(cm)
def get_mode(metric_name):
return {
'val_loss': 'min',
'val_acc': 'max',
'val_f1': 'max',
'val_f2': 'max',
'val_f0.5': 'max'
}[metric_name]
if args.early_stopping or args.early_stopping_metric is not None:
es = EarlyStopping(monitor=args.early_stopping_metric,
mode=get_mode(args.early_stopping_metric),
patience=model_cfg.patience,
verbose=1)
cb = DelegatingMetricCallback(x_validation,
y_validation,
callback_logger,
delegate=es,
metric_name=args.early_stopping_metric,
marshaller=marshaller)
pc.add(cb)
if not args.no_save:
if args.save_all_checkpoints:
filepath = model_path + '/model-{epoch:04d}.h5'
else:
filepath = model_path + '/model.h5'
mc = ModelCheckpoint(filepath=filepath,
mode=get_mode(args.checkpoint_metric),
verbose=1,
monitor=args.checkpoint_metric,
save_best_only=not args.save_every_epoch)
cb = DelegatingMetricCallback(x_validation,
y_validation,
callback_logger,
delegate=mc,
metric_name=args.checkpoint_metric,
marshaller=marshaller)
pc.add(cb)
if model_cfg.optimizer == 'SGD':
callbacks.append(SingleStepLearningRateSchedule(patience=10))
if len(args.extra_train_file) > 1:
args.extra_train_file.append(args.train_file)
logging.info("Using the following files for training: " +
','.join(args.extra_train_file))
train_file_iter = itertools.cycle(args.extra_train_file)
current_train = args.train_file
callbacks._set_model(net)
callbacks.on_train_begin(logs={})
epoch = batch = 0
while True:
x_train, y_train_one_hot = preprocessor.fit_transform(
x_train, y_train_one_hot)
x_validation, y_validation_one_hot = preprocessor.transform(
x_validation, y_validation_one_hot)
iteration = batch % len(args.extra_train_file)
logging.info(
"epoch {epoch} iteration {iteration} - training with {train_file}"
.format(epoch=epoch,
iteration=iteration,
train_file=current_train))
callbacks.on_epoch_begin(epoch, logs={})
n_train = x_train.shape[0]
callbacks.on_batch_begin(batch, logs={'size': n_train})
index_array = np.arange(n_train)
if args.shuffle:
rng.shuffle(index_array)
batches = keras.models.make_batches(n_train, model_cfg.batch_size)
logging.info(
"epoch {epoch} iteration {iteration} - starting {n_batches} batches"
.format(epoch=epoch,
iteration=iteration,
n_batches=len(batches)))
avg_train_loss = avg_train_accuracy = 0.
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(net, keras.models.Graph):
train_data = marshaller.marshal(x_train[batch_ids],
y_train_one_hot[batch_ids])
train_loss = net.train_on_batch(train_data,
class_weight=class_weight)
# It looks like train_on_batch returns a different
# type for graph than sequential models.
train_loss = train_loss[0]
train_accuracy = 0.
else:
train_loss, train_accuracy = net.train_on_batch(
x_train[batch_ids],
y_train_one_hot[batch_ids],
accuracy=True,
class_weight=class_weight)
batch_end_logs = {
'loss': train_loss,
'accuracy': train_accuracy
}
avg_train_loss = (avg_train_loss * batch_index +
train_loss) / (batch_index + 1)
avg_train_accuracy = (avg_train_accuracy * batch_index +
train_accuracy) / (batch_index + 1)
callbacks.on_batch_end(batch,
logs={
'loss': train_loss,
'accuracy': train_accuracy
})
logging.info(
"epoch {epoch} iteration {iteration} - finished {n_batches} batches"
.format(epoch=epoch,
iteration=iteration,
n_batches=len(batches)))
logging.info(
"epoch {epoch} iteration {iteration} - loss: {loss} - acc: {acc}"
.format(epoch=epoch,
iteration=iteration,
loss=avg_train_loss,
acc=avg_train_accuracy))
batch += 1
# Validation frequency (this if-block) doesn't necessarily
# occur in the same iteration as beginning of an epoch
# (next if-block), so net.evaluate appears twice here.
kwargs = {
'batch_size': model_cfg.batch_size,
'verbose': 0 if args.log else 1
}
pargs = []
validation_data = {}
if isinstance(net, keras.models.Graph):
validation_data = marshaller.marshal(x_validation,
y_validation_one_hot)
pargs = [validation_data]
else:
pargs = [x_validation, y_validation_one_hot]
kwargs['show_accuracy'] = True
if (iteration + 1) % args.validation_freq == 0:
if isinstance(net, keras.models.Graph):
val_loss = net.evaluate(*pargs, **kwargs)
y_hat = net.predict(validation_data,
batch_size=model_cfg.batch_size)
val_acc = accuracy_score(
y_validation, np.argmax(y_hat['output'], axis=1))
else:
val_loss, val_acc = net.evaluate(*pargs, **kwargs)
logging.info(
"epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}"
.format(epoch=epoch,
iteration=iteration,
val_loss=val_loss,
val_acc=val_acc))
epoch_end_logs = {
'iteration': iteration,
'val_loss': val_loss,
'val_acc': val_acc
}
callbacks.on_epoch_end(epoch, epoch_end_logs)
if batch % len(args.extra_train_file) == 0:
if isinstance(net, keras.models.Graph):
val_loss = net.evaluate(*pargs, **kwargs)
y_hat = net.predict(validation_data,
batch_size=model_cfg.batch_size)
val_acc = accuracy_score(
y_validation, np.argmax(y_hat['output'], axis=1))
else:
val_loss, val_acc = net.evaluate(*pargs, **kwargs)
logging.info(
"epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}"
.format(epoch=epoch,
iteration=iteration,
val_loss=val_loss,
val_acc=val_acc))
epoch_end_logs = {
'iteration': iteration,
'val_loss': val_loss,
'val_acc': val_acc
}
epoch += 1
callbacks.on_epoch_end(epoch, epoch_end_logs)
if net.stop_training:
logging.info(
"epoch {epoch} iteration {iteration} - done training".
format(epoch=epoch, iteration=iteration))
break
current_train = next(train_file_iter)
x_train, y_train = load_model_data(current_train, args.data_name,
args.target_name)
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
if epoch > args.n_epochs:
break
callbacks.on_train_end(logs={})
else:
x_train, y_train_one_hot = preprocessor.fit_transform(
x_train, y_train_one_hot)
x_validation, y_validation_one_hot = preprocessor.transform(
x_validation, y_validation_one_hot)
if isinstance(net, keras.models.Graph):
train_data = marshaller.marshal(x_train, y_train_one_hot)
validation_data = marshaller.marshal(x_validation,
y_validation_one_hot)
net.fit(train_data,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
validation_data=validation_data,
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)
else:
net.fit(x_train,
y_train_one_hot,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
show_accuracy=True,
validation_data=(x_validation, y_validation_one_hot),
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)
def main(args):
model_id = build_model_id(args)
model_path = build_model_path(args, model_id)
setup_model_dir(args, model_path)
sys.stdout, sys.stderr = setup_logging(args, model_path)
x_train, y_train = load_model_data(args.train_file, args.data_name,
args.target_name)
x_validation, y_validation = load_model_data(args.validation_file,
args.data_name,
args.target_name)
rng = np.random.RandomState(args.seed)
if args.n_classes > -1:
n_classes = args.n_classes
else:
n_classes = max(y_train) + 1
n_classes, target_names, class_weight = load_target_data(args, n_classes)
logging.debug("n_classes {0} min {1} max {2}".format(
n_classes, min(y_train), max(y_train)))
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
y_validation_one_hot = np_utils.to_categorical(y_validation, n_classes)
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
min_vocab_index = np.min(x_train)
max_vocab_index = np.max(x_train)
logging.debug("min vocab index {0} max vocab index {1}".format(
min_vocab_index, max_vocab_index))
json_cfg = load_model_json(args, x_train, n_classes)
logging.debug("loading model")
sys.path.append(args.model_dir)
import model
from model import build_model
if args.subsetting_function:
subsetter = getattr(model, args.subsetting_function)
else:
subsetter = None
def take_subset(subsetter, path, x, y, y_one_hot, n):
if subsetter is None:
return x[0:n], y[0:n], y_one_hot[0:n]
else:
mask = subsetter(path)
idx = np.where(mask)[0]
idx = idx[0:n]
return x[idx], y[idx], y_one_hot[idx]
x_train, y_train, y_train_one_hot = take_subset(subsetter,
args.train_file,
x_train,
y_train,
y_train_one_hot,
n=args.n_train)
x_validation, y_validation, y_validation_one_hot = take_subset(
subsetter,
args.validation_file,
x_validation,
y_validation,
y_validation_one_hot,
n=args.n_validation)
logging.debug("y_train_one_hot " + str(y_train_one_hot.shape))
logging.debug("x_train " + str(x_train.shape))
model_cfg = ModelConfig(**json_cfg)
logging.info("model_cfg " + str(model_cfg))
model = build_model(model_cfg)
setattr(model, 'stop_training', False)
logging.info('model has {n_params} parameters'.format(
n_params=count_parameters(model)))
if len(args.extra_train_file) > 1:
callbacks = keras.callbacks.CallbackList()
else:
callbacks = []
save_model_info(args, model_path, model_cfg)
if not args.no_save:
callbacks.append(
ModelCheckpoint(filepath=model_path + '/model-{epoch:04d}.h5',
verbose=1,
save_best_only=True))
callback_logger = logging.info if args.log else callable_print
if args.n_epochs < sys.maxsize:
# Number of epochs overrides patience. If the number of epochs
# is specified on the command line, the model is trained for
# exactly that number; otherwise, the model is trained with
# early stopping using the patience specified in the model
# configuration.
callbacks.append(
EarlyStopping(monitor='val_loss',
patience=model_cfg.patience,
verbose=1))
if args.classification_report:
cr = ClassificationReport(x_validation,
y_validation,
callback_logger,
target_names=target_names)
callbacks.append(cr)
if model_cfg.optimizer == 'SGD':
callbacks.append(SingleStepLearningRateSchedule(patience=10))
if len(args.extra_train_file) > 1:
args.extra_train_file.append(args.train_file)
logging.info("Using the following files for training: " +
','.join(args.extra_train_file))
train_file_iter = itertools.cycle(args.extra_train_file)
current_train = args.train_file
callbacks._set_model(model)
callbacks.on_train_begin(logs={})
epoch = batch = 0
while True:
iteration = batch % len(args.extra_train_file)
logging.info(
"epoch {epoch} iteration {iteration} - training with {train_file}"
.format(epoch=epoch,
iteration=iteration,
train_file=current_train))
callbacks.on_epoch_begin(epoch, logs={})
n_train = x_train.shape[0]
callbacks.on_batch_begin(batch, logs={'size': n_train})
index_array = np.arange(n_train)
if args.shuffle:
rng.shuffle(index_array)
batches = keras.models.make_batches(n_train, model_cfg.batch_size)
logging.info(
"epoch {epoch} iteration {iteration} - starting {n_batches} batches"
.format(epoch=epoch,
iteration=iteration,
n_batches=len(batches)))
avg_train_loss = avg_train_accuracy = 0.
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(model, keras.models.Graph):
data = {
'input': x_train[batch_ids],
'output': y_train_one_hot[batch_ids]
}
train_loss = model.train_on_batch(
data, class_weight=class_weight)
train_accuracy = 0.
else:
train_loss, train_accuracy = model.train_on_batch(
x_train[batch_ids],
y_train_one_hot[batch_ids],
accuracy=True,
class_weight=class_weight)
batch_end_logs = {
'loss': train_loss,
'accuracy': train_accuracy
}
avg_train_loss = (avg_train_loss * batch_index +
train_loss) / (batch_index + 1)
avg_train_accuracy = (avg_train_accuracy * batch_index +
train_accuracy) / (batch_index + 1)
callbacks.on_batch_end(batch,
logs={
'loss': train_loss,
'accuracy': train_accuracy
})
logging.info(
"epoch {epoch} iteration {iteration} - finished {n_batches} batches"
.format(epoch=epoch,
iteration=iteration,
n_batches=len(batches)))
logging.info(
"epoch {epoch} iteration {iteration} - loss: {loss} - acc: {acc}"
.format(epoch=epoch,
iteration=iteration,
loss=avg_train_loss,
acc=avg_train_accuracy))
batch += 1
# Validation frequency (this if-block) doesn't necessarily
# occur in the same iteration as beginning of an epoch
# (next if-block), so model.evaluate appears twice here.
if (iteration + 1) % args.validation_freq == 0:
val_loss, val_acc = model.evaluate(
x_validation,
y_validation_one_hot,
show_accuracy=True,
verbose=0 if args.log else 1)
logging.info(
"epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}"
.format(epoch=epoch,
iteration=iteration,
val_loss=val_loss,
val_acc=val_acc))
epoch_end_logs = {
'iteration': iteration,
'val_loss': val_loss,
'val_acc': val_acc
}
callbacks.on_epoch_end(epoch, epoch_end_logs)
if batch % len(args.extra_train_file) == 0:
val_loss, val_acc = model.evaluate(
x_validation,
y_validation_one_hot,
show_accuracy=True,
verbose=0 if args.log else 1)
logging.info(
"epoch {epoch} iteration {iteration} - val_loss: {val_loss} - val_acc: {val_acc}"
.format(epoch=epoch,
iteration=iteration,
val_loss=val_loss,
val_acc=val_acc))
epoch_end_logs = {
'iteration': iteration,
'val_loss': val_loss,
'val_acc': val_acc
}
epoch += 1
callbacks.on_epoch_end(epoch, epoch_end_logs)
if model.stop_training:
logging.info(
"epoch {epoch} iteration {iteration} - done training".
format(epoch=epoch, iteration=iteration))
break
current_train = next(train_file_iter)
x_train, y_train = load_model_data(current_train, args.data_name,
args.target_name)
y_train_one_hot = np_utils.to_categorical(y_train, n_classes)
if epoch > args.n_epochs:
break
callbacks.on_train_end(logs={})
else:
print('args.n_epochs', args.n_epochs)
if isinstance(model, keras.models.Graph):
data = {'input': x_train, 'output': y_train_one_hot}
validation_data = {
'input': x_validation,
'output': y_validation_one_hot
}
model.fit(
data,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
#show_accuracy=True,
validation_data=validation_data,
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)
y_hat = model.predict_classes(data)
print('val_acc %.04f' % accuracy_score(y_validate, y_hat))
else:
model.fit(x_train,
y_train_one_hot,
shuffle=args.shuffle,
nb_epoch=args.n_epochs,
batch_size=model_cfg.batch_size,
show_accuracy=True,
validation_data=(x_validation, y_validation_one_hot),
callbacks=callbacks,
class_weight=class_weight,
verbose=2 if args.log else 1)