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.' ]])