parser.add_argument('--train_path',
type=str,
default='data/cifar10_train.zip',
help='Path to train dataset')
parser.add_argument('--val_path',
type=str,
default='data/cifar10_val.zip',
help='Path to validation dataset')
parser.add_argument('--test_path',
type=str,
default='data/cifar10_test.zip',
help='Path to test dataset')
parser.add_argument(
'--query_path',
type=str,
default='examples/data/image_classification/cifar10_test_1.png',
help='Path(s) to query image(s), delimited by commas')
(args, _) = parser.parse_known_args()
queries = utils.dataset.load_images(args.query_path.split(',')).tolist()
test_model_class(model_file_path=__file__,
model_class='PyDenseNetBc',
task='IMAGE_CLASSIFICATION',
dependencies={
ModelDependency.TORCH: '1.0.1',
ModelDependency.TORCHVISION: '0.2.2'
},
train_dataset_path=args.train_path,
val_dataset_path=args.val_path,
test_dataset_path=args.test_path,
queries=queries)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--train_path',
type=str,
default='data/fashion_mnist_train.zip',
help='Path to train dataset')
parser.add_argument('--val_path',
type=str,
default='data/fashion_mnist_val.zip',
help='Path to validation dataset')
parser.add_argument('--test_path',
type=str,
default='data/fashion_mnist_test.zip',
help='Path to test dataset')
parser.add_argument(
'--query_path',
type=str,
default='examples/data/image_classification/fashion_mnist_test_1.png',
help='Path(s) to query image(s), delimited by commas')
(args, _) = parser.parse_known_args()
queries = utils.dataset.load_images(args.query_path.split(',')).tolist()
test_model_class(model_file_path=__file__,
model_class='TfFeedForward',
task='IMAGE_CLASSIFICATION',
dependencies={ModelDependency.TENSORFLOW: '1.12.0'},
train_dataset_path=args.train_path,
val_dataset_path=args.val_path,
test_dataset_path=args.test_path,
queries=queries)
test_model_class(model_file_path=__file__,
model_class='XgbReg',
task='TABULAR_REGRESSION',
dependencies={ModelDependency.XGBOOST: '0.90'},
train_dataset_path='data/bodyfat_train.csv',
val_dataset_path='data/bodyfat_val.csv',
train_args={
'features': [
'density', 'age', 'weight', 'height', 'neck',
'chest', 'abdomen', 'hip', 'thigh', 'knee',
'ankle', 'biceps', 'forearm', 'wrist'
],
'target':
'bodyfat'
},
queries=[{
'density': 1.0207,
'age': 65,
'weight': 224.5,
'height': 68.25,
'neck': 38.8,
'chest': 119.6,
'abdomen': 118.0,
'hip': 114.3,
'thigh': 61.3,
'knee': 42.1,
'ankle': 23.4,
'biceps': 34.9,
'forearm': 30.1,
'wrist': 19.4
}])
subsample=subsample,
colsample_bytree=colsample_bytree)
else:
clf = xgb.XGBClassifier(n_estimators=n_estimators,
min_child_weight=min_child_weight,
max_depth=max_depth,
gamma=gamma,
subsample=subsample,
colsample_bytree=colsample_bytree,
objective='multi:softmax',
num_class=num_class)
return clf
if __name__ == '__main__':
test_model_class(model_file_path=__file__,
model_class='XgbClf',
task='TABULAR_CLASSIFICATION',
dependencies={ModelDependency.XGBOOST: '0.90'},
train_dataset_path='data/titanic_train.csv',
val_dataset_path='data/titanic_val.csv',
train_args={
'features': ['Pclass', 'Sex', 'Age'],
'target': 'Survived'
},
queries=[{
'Pclass': 1,
'Sex': 'female',
'Age': 2.0
}])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--train_path',
type=str,
default='data/fashion_mnist_train.zip',
help='Path to train dataset')
parser.add_argument('--val_path',
type=str,
default='data/fashion_mnist_val.zip',
help='Path to validation dataset')
parser.add_argument('--test_path',
type=str,
default='data/fashion_mnist_test.zip',
help='Path to test dataset')
parser.add_argument(
'--query_path',
type=str,
default='examples/data/image_classification/fashion_mnist_test_1.png',
help='Path(s) to query image(s), delimited by commas')
(args, _) = parser.parse_known_args()
queries = utils.dataset.load_images(args.query_path.split(',')).tolist()
test_model_class(model_file_path=__file__,
model_class='SkSvm',
task='IMAGE_CLASSIFICATION',
dependencies={ModelDependency.SCIKIT_LEARN: '0.20.0'},
train_dataset_path=args.train_path,
val_dataset_path=args.val_path,
test_dataset_path=args.test_path,
queries=queries)
words_embed_tsr = self._word_embed(words_tsr.view(-1)).view(N, W, Ew)
# Apply dropout to word rep (N x W x Ew)
words_rep_tsr = self._word_dropout(words_embed_tsr)
# Apply bidirectional LSTM to word rep sequence (N x W x 2h)
(words_hidden_rep_tsr, _) = self._word_lstm(words_rep_tsr)
words_hidden_rep_tsr = words_hidden_rep_tsr.contiguous()
# Apply linear + softmax operation for sentence rep for all sentences (N x W x t)
word_probs_tsr = F.softmax(self._word_lin(
words_hidden_rep_tsr.view(N * W, self._h * 2)),
dim=1).view(N, W, t)
return word_probs_tsr
if __name__ == '__main__':
test_model_class(model_file_path=__file__,
model_class='PyBiLstm',
task='POS_TAGGING',
dependencies={ModelDependency.TORCH: '0.4.1'},
train_dataset_path='data/ptb_train.zip',
val_dataset_path='data/ptb_val.zip',
queries=[['Ms.', 'Haag', 'plays', 'Elianti', '18', '.'],
[
'The', 'luxury', 'auto', 'maker', 'last',
'year', 'sold', '1,214', 'cars', 'in', 'the',
'U.S.'
]])