parser.add_argument('--num_rounds', type=int, default=10) parser.add_argument('--min_child_weight', type=int, default=5) args = parser.parse_args() # Polyaxon experiment = Run(project='iris') experiment.create(tags=['examples', 'xgboost']) experiment.log_inputs(log_learning_rate=args.log_learning_rate, max_depth=args.max_depth, num_rounds=args.num_rounds, min_child_weight=args.min_child_weight) iris = load_iris() X = iris.data Y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2) # Polyaxon experiment.log_data_ref(content=X_train, name='x_train') experiment.log_data_ref(content=y_train, name='y_train') experiment.log_data_ref(content=X_test, name='X_test') experiment.log_data_ref(content=y_test, name='y_train') logger.info('Train model...') accuracy = model(log_learning_rate=args.log_learning_rate, max_depth=args.max_depth, num_rounds=args.num_rounds, min_child_weight=args.min_child_weight) experiment.log_outputs(accuracy=accuracy)
) args = parser.parse_args() # Polyaxon experiment = Run(project='sgd-classifier') experiment.create(tags=['examples', 'scikit-learn']) experiment.log_inputs(loss=args.loss, penalty=args.penalty, l1_ratio=args.l1_ratio, max_iter=args.max_iter, tol=args.tol) (X, y) = load_data() # Polyaxon experiment.log_data_ref(content=X, name='dataset_X') experiment.log_data_ref(content=y, name='dataset_y') accuracies = model(X=X, y=y, loss=args.loss, penalty=args.penalty, l1_ratio=args.l1_ratio, max_iter=args.max_iter, tol=args.tol) accuracy_mean, accuracy_std = (np.mean(accuracies), np.std(accuracies)) print('Accuracy: {} +/- {}'.format(accuracy_mean, accuracy_std)) # Polyaxon experiment.log_outputs(accuracy_mean=accuracy_mean, accuracy_std=accuracy_std)
# Polyaxon experiment.log_data_ref(content=x_train, name='x_train') experiment.log_data_ref(content=y_train, name='y_train') experiment.log_data_ref(content=x_test, name='x_test') experiment.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(experiment=experiment, max_features=args.max_features, maxlen=args.maxlen, epochs=args.epochs, embedding_size=args.embedding_size, pool_size=args.pool_size, kernel_size=args.kernel_size, filters=args.filters, lstm_output_size=args.lstm_output_size, batch_size=args.batch_size, optimizer=args.optimizer, log_learning_rate=args.log_learning_rate) # Polyaxon experiment.log_outputs(score=score, accuracy=accuracy) logger.info('Test score: %s', score) logger.info('Test accuracy: %s', accuracy)
mnist['train_label'], args.batch_size, shuffle=True) val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], args.batch_size) # Polyaxon experiment.log_data_ref(content=mnist['train_data'], name='x_train') experiment.log_data_ref(content=mnist['train_label'], name='y_train') experiment.log_data_ref(content=mnist['test_data'], name='x_test') experiment.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, 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, fc1_hidden=args.fc1_hidden, fc1_activation=args.fc1_activation, optimizer=args.optimizer, log_learning_rate=args.log_learning_rate, batch_size=args.batch_size, epochs=args.epochs) # Polyaxon experiment.log_outputs(accuracy=metrics)
parser.add_argument('--max_df', type=float, default=1.0, help='the maximum document frequency.') parser.add_argument( '--C', type=float, default=1.0, help='Inverse of regularization strength of LogisticRegression') args = parser.parse_args() # Polyaxon experiment = Run(project='newsgroup') experiment.create(tags=['examples', 'scikit-learn']) experiment.log_inputs(ngram_range=(args.ngram, args.ngram), max_features=args.max_features, max_df=args.max_df, C=args.C) # Train and eval the model with given parameters. # Polyaxon metrics = train_and_eval(ngram_range=(args.ngram, args.ngram), max_features=args.max_features, max_df=args.max_df, C=args.C) # Logging metrics print("Testing metrics: {}", metrics) # Polyaxon experiment.log_outputs(**metrics)