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)