def test_train_model_with_bn(self):
class SimpleTorchModel(nn.Module):
def __init__(self):
super(SimpleTorchModel, self).__init__()
self.dense1 = nn.Linear(2, 4)
self.bn1 = torch.nn.BatchNorm1d(4)
self.dense2 = nn.Linear(4, 1)
def forward(self, x):
x = self.dense1(x)
x = self.bn1(x)
x = torch.sigmoid(self.dense2(x))
return x
torch_model = SimpleTorchModel()
loss_fn = torch.nn.BCELoss()
az_model = TorchModel.from_pytorch(torch_model)
zoo_loss = TorchLoss.from_pytorch(loss_fn)
inputs = torch.Tensor([[1, 2], [1, 3], [3, 2], [5, 6], [8, 9], [1, 9]])
targets = torch.Tensor([[0], [0], [0], [1], [1], [1]])
train_loader = DataLoader(TensorDataset(inputs, targets), batch_size=2)
train_featureset = FeatureSet.pytorch_dataloader(train_loader)
val_loader = DataLoader(TensorDataset(inputs, targets), batch_size=2)
val_featureset = FeatureSet.pytorch_dataloader(val_loader)
zooOptimizer = Adam()
estimator = Estimator(az_model, optim_methods=zooOptimizer)
estimator.train_minibatch(train_featureset,
zoo_loss,
end_trigger=MaxEpoch(4),
checkpoint_trigger=EveryEpoch(),
validation_set=val_featureset,
validation_method=[Accuracy()])
trained_model = az_model.to_pytorch()
def _handle_data_loader(self, data, validation_data):
train_feature_set = FeatureSet.pytorch_dataloader(data, "", "")
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, DataLoader) or callable(data), \
"validation_data should be a pytorch DataLoader or a callable data_creator"
val_feature_set = FeatureSet.pytorch_dataloader(validation_data)
return train_feature_set, val_feature_set
def _handle_xshards(self, data, validation_data):
train_rdd = data.rdd.flatMap(xshard_to_sample)
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, SparkXShards), "validation_data should be a " \
"SparkXShards"
val_feature_set = FeatureSet.sample_rdd(
validation_data.rdd.flatMap(xshard_to_sample))
return train_feature_set, val_feature_set
def _handle_dataframe(self, data, validation_data, feature_cols,
label_cols):
schema = data.schema
train_rdd = data.rdd.map(
lambda row: row_to_sample(row, schema, feature_cols, label_cols))
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, DataFrame), "validation_data should also be a " \
"DataFrame"
val_feature_set = FeatureSet.sample_rdd(
validation_data.rdd.map(lambda row: row_to_sample(
row, schema, feature_cols, label_cols)))
return train_feature_set, val_feature_set
def test_estimator_train(self):
batch_size = 8
epoch_num = 5
images, labels = TestEstimator._generate_image_data(data_num=8,
img_shape=(3, 224,
224))
image_rdd = self.sc.parallelize(images)
labels = self.sc.parallelize(labels)
sample_rdd = image_rdd.zip(labels).map(
lambda img_label: Sample.from_ndarray(img_label[0], img_label[1]))
data_set = FeatureSet.sample_rdd(sample_rdd)
model = TestEstimator._create_cnn_model()
optim_method = SGD(learningrate=0.01)
estimator = Estimator(model, optim_method, "")
estimator.set_constant_gradient_clipping(0.1, 1.2)
estimator.train(train_set=data_set,
criterion=ClassNLLCriterion(),
end_trigger=MaxEpoch(epoch_num),
checkpoint_trigger=EveryEpoch(),
validation_set=data_set,
validation_method=[Top1Accuracy()],
batch_size=batch_size)
eval_result = estimator.evaluate(validation_set=data_set,
validation_method=[Top1Accuracy()])
assert isinstance(eval_result[0], EvaluatedResult)
assert len(eval_result) == 1
predict_result = model.predict(sample_rdd)
assert (predict_result.count(), 8)
def input_fn(mode):
if mode == tf.estimator.ModeKeys.TRAIN:
image_set = self.get_raw_image_set(with_label=True)
feature_set = FeatureSet.image_frame(
image_set.to_image_frame())
train_transformer = ChainedPreprocessing([
ImageBytesToMat(),
ImageResize(256, 256),
ImageRandomCrop(224, 224),
ImageRandomPreprocessing(ImageHFlip(), 0.5),
ImageChannelNormalize(0.485, 0.456, 0.406, 0.229, 0.224,
0.225),
ImageMatToTensor(to_RGB=True, format="NHWC"),
ImageSetToSample(input_keys=["imageTensor"],
target_keys=["label"])
])
feature_set = feature_set.transform(train_transformer)
feature_set = feature_set.transform(ImageFeatureToSample())
training_dataset = TFDataset.from_feature_set(
feature_set,
features=(tf.float32, [224, 224, 3]),
labels=(tf.int32, [1]),
batch_size=8)
return training_dataset
else:
raise NotImplementedError
def _get_training_data(self):
jvalue = callZooFunc("float", "createTFDataFeatureSet",
self.rdd.map(lambda x: x[0]), self.init_op_name,
self.table_init_op, self.output_names,
self.output_types, self.shard_index_op_name,
self.inter_threads, self.intra_threads)
return FeatureSet(jvalue=jvalue)
def evaluate(self,
data,
batch_size=32,
feature_cols="features",
label_cols="label"):
"""
Evaluate model.
:param data: validation data. It can be XShardsor or Spark DataFrame, each partition is
a dictionary of {'x': feature, 'y': label}, where feature(label) is a numpy array
or a list of numpy arrays.
:param batch_size: Batch size used for validation. Default: 32.
:param feature_cols: (Not supported yet) Feature column name(s) of data. Only used when
data is a Spark DataFrame. Default: None.
:param label_cols: (Not supported yet) Label column name(s) of data. Only used when data
is a Spark DataFrame. Default: None.
:return:
"""
assert data is not None, "validation data shouldn't be None"
assert self.metrics is not None, "metrics shouldn't be None, please specify the metrics" \
" argument when creating this estimator."
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, _, feature_cols = \
BigDLEstimator._combine_cols(data, [feature_cols], col_name="features")
if isinstance(label_cols, list):
data, _, label_cols = \
BigDLEstimator._combine_cols(data, label_cols, col_name="label")
self.nn_estimator._setNNBatchSize(batch_size)._setNNFeaturesCol(feature_cols) \
._setNNLabelCol(label_cols)
self.nn_estimator.setValidation(None, None, self.metrics,
batch_size)
if self.log_dir is not None and self.app_name is not None:
from bigdl.dllib.optim.optimizer import TrainSummary
from bigdl.dllib.optim.optimizer import ValidationSummary
val_summary = ValidationSummary(log_dir=self.log_dir,
app_name=self.app_name)
self.nn_estimator.setValidationSummary(val_summary)
result = self.nn_estimator._eval(data)
elif isinstance(data, SparkXShards):
from bigdl.orca.data.utils import xshard_to_sample
val_feature_set = FeatureSet.sample_rdd(
data.rdd.flatMap(xshard_to_sample))
result = self.estimator.evaluate(val_feature_set, self.metrics,
batch_size)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return bigdl_metric_results_to_dict(result)
def _get_validation_data(self):
if self.validation_dataset is not None:
jvalue = callZooFunc("float", "createTFDataFeatureSet",
self.val_rdd.map(lambda x: x[0]),
self.init_op_name, self.table_init_op,
self.output_names, self.output_types,
self.shard_index_op_name, self.inter_threads,
self.intra_threads)
return FeatureSet(jvalue=jvalue)
return None
def create_train_features_Set(self):
image_set = self.get_raw_image_set(with_label=True)
feature_set = FeatureSet.image_frame(image_set.to_image_frame())
train_transformer = ChainedPreprocessing([
ImageBytesToMat(),
ImageResize(256, 256),
ImageRandomCrop(224, 224),
ImageRandomPreprocessing(ImageHFlip(), 0.5),
ImageChannelNormalize(0.485, 0.456, 0.406, 0.229, 0.224, 0.225),
ImageMatToTensor(to_RGB=True, format="NHWC"),
ImageSetToSample(input_keys=["imageTensor"], target_keys=["label"])
])
feature_set = feature_set.transform(train_transformer)
feature_set = feature_set.transform(ImageFeatureToSample())
return feature_set
def test_estimator_train_imagefeature(self):
batch_size = 8
epoch_num = 5
images, labels = TestEstimator._generate_image_data(data_num=8,
img_shape=(200,
200, 3))
image_frame = DistributedImageFrame(self.sc.parallelize(images),
self.sc.parallelize(labels))
transformer = Pipeline([
BytesToMat(),
Resize(256, 256),
CenterCrop(224, 224),
ChannelNormalize(0.485, 0.456, 0.406, 0.229, 0.224, 0.225),
MatToTensor(),
ImageFrameToSample(target_keys=['label'])
])
data_set = FeatureSet.image_frame(image_frame).transform(transformer)
model = TestEstimator._create_cnn_model()
optim_method = SGD(learningrate=0.01)
estimator = Estimator(model, optim_method, "")
estimator.set_constant_gradient_clipping(0.1, 1.2)
estimator.train_imagefeature(train_set=data_set,
criterion=ClassNLLCriterion(),
end_trigger=MaxEpoch(epoch_num),
checkpoint_trigger=EveryEpoch(),
validation_set=data_set,
validation_method=[Top1Accuracy()],
batch_size=batch_size)
eval_result = estimator.evaluate_imagefeature(
validation_set=data_set, validation_method=[Top1Accuracy()])
assert isinstance(eval_result[0], EvaluatedResult)
assert len(eval_result) == 1
predict_result = model.predict_image(
image_frame.transform(transformer))
assert (predict_result.get_predict().count(), 8)
def evaluate(self,
data,
batch_size=None,
feature_cols=None,
label_cols=None,
validation_metrics=None):
"""
Evaluate model.
:param data: data: evaluation data. It can be an XShards, Spark Dataframe,
PyTorch DataLoader and PyTorch DataLoader creator function.
If data is an XShards, each partition can be a Pandas DataFrame or a dictionary of
{'x': feature, 'y': label}, where feature(label) is a numpy array or a list of
numpy arrays.
:param batch_size: Batch size used for evaluation. Only used when data is a SparkXShard.
:param feature_cols: Feature column name(s) of data. Only used when data
is a Spark DataFrame or an XShards of Pandas DataFrame. Default: None.
:param label_cols: Label column name(s) of data. Only used when data is
a Spark DataFrame or an XShards of Pandas DataFrame. Default: None.
:param validation_metrics: Orca validation metrics to be computed on validation_data.
:return: validation results.
"""
from bigdl.orca.data.utils import xshard_to_sample
assert data is not None, "validation data shouldn't be None"
assert self.metrics is not None, "metrics shouldn't be None, please specify the metrics" \
" argument when creating this estimator."
if isinstance(data, DataLoader):
assert batch_size is None and data.batch_size > 0, "When using PyTorch Dataloader as " \
"input, you need to specify the " \
"batch size in DataLoader and " \
"don't specify batch_size " \
"in the fit method."
else:
assert batch_size is not None and batch_size > 0, "batch_size should be greater than 0"
if isinstance(data, SparkXShards):
if data._get_class_name() == 'pandas.core.frame.DataFrame':
data = process_xshards_of_pandas_dataframe(
data, feature_cols, label_cols)
val_feature_set = FeatureSet.sample_rdd(
data.rdd.flatMap(xshard_to_sample))
result = self.estimator.evaluate(val_feature_set, self.metrics,
batch_size)
elif isinstance(data, DataFrame):
schema = data.schema
val_feature_set = FeatureSet.sample_rdd(
data.rdd.map(lambda row: row_to_sample(
row, schema, feature_cols, label_cols)))
result = self.estimator.evaluate(val_feature_set, self.metrics,
batch_size)
elif isinstance(data, DataLoader) or callable(data) or isinstance(
data, types.FunctionType):
if isinstance(data, types.FunctionType):
data = data(self.config, batch_size)
val_feature_set = FeatureSet.pytorch_dataloader(data)
result = self.estimator.evaluate_minibatch(val_feature_set,
self.metrics)
else:
raise ValueError(
"Data should be a SparkXShards, a DataLoader or a callable "
"data_creator, but get " + data.__class__.__name__)
return bigdl_metric_results_to_dict(result)
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--dir',
default='/tmp/data',
metavar='N',
help='the folder store mnist data')
parser.add_argument(
'--batch-size',
type=int,
default=256,
metavar='N',
help='input batch size for training per executor(default: 256)')
parser.add_argument(
'--test-batch-size',
type=int,
default=1000,
metavar='N',
help='input batch size for testing per executor(default: 1000)')
parser.add_argument('--epochs',
type=int,
default=2,
metavar='N',
help='number of epochs to train (default: 2)')
parser.add_argument('--lr',
type=float,
default=0.001,
metavar='LR',
help='learning rate (default: 0.001)')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument('--save-model',
action='store_true',
default=False,
help='For Saving the current Model')
parser.add_argument(
'--deploy-mode',
default="local",
help='supported deploy mode is local, yarn-client, yarn-cluster')
args = parser.parse_args()
torch.manual_seed(args.seed)
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
args.dir,
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
args.dir,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=args.test_batch_size,
shuffle=False)
# init on yarn when HADOOP_CONF_DIR and ZOO_CONDA_NAME is provided.
if args.deploy_mode == "local":
sc = init_orca_context()
else:
sc = init_orca_context(cluster_mode=args.deploy_mode,
cores=2,
memory="2g",
num_nodes=4)
model = Net()
model.train()
criterion = nn.NLLLoss()
adam = torch.optim.Adam(model.parameters(), lr=args.lr)
zoo_model = TorchModel.from_pytorch(model)
zoo_criterion = TorchLoss.from_pytorch(criterion)
zoo_optim = TorchOptim.from_pytorch(adam)
zoo_estimator = Estimator(zoo_model, optim_methods=zoo_optim)
train_featureset = FeatureSet.pytorch_dataloader(train_loader)
test_featureset = FeatureSet.pytorch_dataloader(test_loader)
from bigdl.dllib.optim.optimizer import MaxEpoch, EveryEpoch
zoo_estimator.train_minibatch(train_featureset,
zoo_criterion,
end_trigger=MaxEpoch(args.epochs),
checkpoint_trigger=EveryEpoch(),
validation_set=test_featureset,
validation_method=[Accuracy()])
def fit(self,
data,
epochs,
batch_size=32,
feature_cols="features",
label_cols="label",
caching_sample=True,
validation_data=None,
validation_trigger=None,
checkpoint_trigger=None):
"""
Train this BigDL model with train data.
:param data: train data. It can be XShards or Spark DataFrame.
If data is XShards, each partition is a dictionary of {'x': feature,
'y': label}, where feature(label) is a numpy array or a list of numpy arrays.
:param epochs: Number of epochs to train the model.
:param batch_size: Batch size used for training. Default: 32.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame. Default: "features".
:param label_cols: Label column name(s) of data. Only used when data is a Spark DataFrame.
Default: "label".
:param caching_sample: whether to cache the Samples after preprocessing. Default: True
:param validation_data: Validation data. XShards and Spark DataFrame are supported.
If data is XShards, each partition is a dictionary of {'x': feature,
'y': label}, where feature(label) is a numpy array or a list of numpy arrays.
:param validation_trigger: Orca Trigger to trigger validation computation.
:param checkpoint_trigger: Orca Trigger to set a checkpoint.
:return:
"""
from bigdl.orca.learn.trigger import Trigger
assert batch_size > 0, "batch_size should be greater than 0"
if validation_data is not None:
assert self.metrics is not None, \
"You should provide metrics when creating this estimator if you provide " \
"validation_data."
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, validation_data, feature_cols = \
BigDLEstimator._combine_cols(data, feature_cols, col_name="features",
val_data=validation_data)
if isinstance(label_cols, list):
data, validation_data, label_cols = \
BigDLEstimator._combine_cols(data, label_cols, col_name="label",
val_data=validation_data)
self.nn_estimator.setBatchSize(batch_size).setMaxEpoch(epochs) \
.setCachingSample(caching_sample).setFeaturesCol(feature_cols) \
.setLabelCol(label_cols)
if validation_data is not None:
assert isinstance(validation_data, DataFrame), \
"validation_data should be a spark DataFrame."
assert validation_trigger is not None, \
"You should provide validation_trigger if you provide validation_data."
validation_trigger = Trigger.convert_trigger(
validation_trigger)
self.nn_estimator.setValidation(validation_trigger,
validation_data, self.metrics,
batch_size)
if self.log_dir is not None and self.app_name is not None:
from bigdl.dllib.optim.optimizer import TrainSummary
from bigdl.dllib.optim.optimizer import ValidationSummary
train_summary = TrainSummary(log_dir=self.log_dir,
app_name=self.app_name)
self.nn_estimator.setTrainSummary(train_summary)
val_summary = ValidationSummary(log_dir=self.log_dir,
app_name=self.app_name)
self.nn_estimator.setValidationSummary(val_summary)
if self.model_dir is not None and checkpoint_trigger is not None:
checkpoint_trigger = Trigger.convert_trigger(
checkpoint_trigger)
self.nn_estimator.setCheckpoint(self.model_dir,
checkpoint_trigger)
self.nn_model = self.nn_estimator.fit(data)
self.is_nnframe_fit = True
elif isinstance(data, SparkXShards):
from bigdl.orca.data.utils import xshard_to_sample
end_trigger = MaxEpoch(epochs)
checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
if isinstance(data, SparkXShards):
train_rdd = data.rdd.flatMap(xshard_to_sample)
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, SparkXShards), \
"validation_data should be a XShards"
val_feature_set = FeatureSet.sample_rdd(
validation_data.rdd.flatMap(xshard_to_sample))
if self.log_dir is not None and self.app_name is not None:
self.estimator.set_tensorboard(self.log_dir, self.app_name)
self.estimator.train(train_feature_set, self.loss, end_trigger,
checkpoint_trigger, val_feature_set,
self.metrics, batch_size)
self.is_nnframe_fit = False
else:
raise ValueError(
"Data and validation data should be XShards, but get " +
data.__class__.__name__)
else:
raise ValueError(
"Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return self
def main():
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--max_epochs', default=90, type=int, metavar='N',
help='number of max epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)',
dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
help='use pre-trained model')
parser.add_argument('--world-size', default=-1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
help='node rank for distributed training')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--cores', default=4, type=int,
help='num of CPUs to use.')
parser.add_argument('--nodes', default=1, type=int,
help='num of nodes to use.')
parser.add_argument('--executor_memory', default='20g', type=str,
help='size of executor memory.')
parser.add_argument('--driver_memory', default='20g', type=str,
help='size of driver memory.')
parser.add_argument('--driver_cores', default=1, type=int,
help='num of driver cores to use.')
args = parser.parse_args()
if os.environ.get('HADOOP_CONF_DIR') is None:
sc = init_spark_on_local(cores=args.cores, conf={"spark.driver.memory": "20g"})
else:
hadoop_conf_dir = os.environ.get('HADOOP_CONF_DIR')
num_executors = args.nodes
executor_memory = args.executor_memory
driver_memory = args.driver_memory
driver_cores = args.driver_cores
num_cores_per_executor = args.cores
os.environ['ZOO_MKL_NUMTHREADS'] = str(num_cores_per_executor)
os.environ['OMP_NUM_THREADS'] = str(num_cores_per_executor)
sc = init_spark_on_yarn(
hadoop_conf=hadoop_conf_dir,
conda_name=detect_conda_env_name(), # auto detect current conda env name
num_executors=num_executors,
executor_cores=num_cores_per_executor,
executor_memory=executor_memory,
driver_memory=driver_memory,
driver_cores=driver_cores,
conf={"spark.rpc.message.maxSize": "1024",
"spark.task.maxFailures": "1",
"spark.driver.extraJavaOptions": "-Dbigdl.failure.retryTimes=1"})
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True)
model = torchvision.models.resnet50()
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False)
iterationPerEpoch = int(math.ceil(float(1281167) / args.batch_size))
step = Step(iterationPerEpoch * 30, 0.1)
zooOptimizer = SGD(args.lr, momentum=args.momentum, dampening=0.0,
leaningrate_schedule=step, weightdecay=args.weight_decay)
zooModel = TorchModel.from_pytorch(model)
criterion = torch.nn.CrossEntropyLoss()
zooCriterion = TorchLoss.from_pytorch(criterion)
estimator = Estimator(zooModel, optim_methods=zooOptimizer)
train_featureSet = FeatureSet.pytorch_dataloader(train_loader)
test_featureSet = FeatureSet.pytorch_dataloader(val_loader)
estimator.train_minibatch(train_featureSet, zooCriterion, end_trigger=MaxEpoch(args.max_epochs),
checkpoint_trigger=EveryEpoch(), validation_set=test_featureSet,
validation_method=[Accuracy(), Top5Accuracy()])