default=1
)
args = parser.parse_args()
# Polyaxon
tracking.init()
logger.info('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=args.max_features,
skip_top=args.skip_top)
logger.info('train sequences %s', len(x_train))
logger.info('test sequences %s', len(x_test))
# Polyaxon
tracking.log_data_ref(content=x_train, name='x_train')
tracking.log_data_ref(content=y_train, name='y_train')
tracking.log_data_ref(content=x_test, name='x_test')
tracking.log_data_ref(content=y_test, name='y_test')
logger.info('Transforming data...')
x_train, y_train, x_test, y_test = transform_data(x_train,
y_train,
x_test,
y_test,
args.maxlen)
logger.info('Training...')
score, accuracy = train(max_features=args.max_features,
maxlen=args.maxlen,
epochs=args.epochs,
type=int,
default=1
)
args = parser.parse_args()
# Polyaxon
tracking.init()
logger.info('Downloading data ...')
mnist = mx.test_utils.get_mnist()
train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], args.batch_size,
shuffle=True)
val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], args.batch_size)
# Polyaxon
tracking.log_data_ref(content=mnist['train_data'], name='x_train')
tracking.log_data_ref(content=mnist['train_label'], name='y_train')
tracking.log_data_ref(content=mnist['test_data'], name='x_test')
tracking.log_data_ref(content=mnist['test_label'], name='y_test')
context = mx.gpu if os.environ.get('NVIDIA_VISIBLE_DEVICES') else mx.cpu
metrics = model(context=context,
train_iter=train_iter,
val_iter=val_iter,
conv1_kernel=args.conv1_kernel,
conv1_filters=args.conv1_filters,
conv1_activation=args.conv1_activation,
conv2_kernel=args.conv1_kernel,
conv2_filters=args.conv1_filters,
conv2_activation=args.conv1_activation,