def create_experiment(self,
name=None,
framework=None,
tags=None,
description=None,
config=None):
experiment = Experiment(project=self.project,
group_id=self.group_id,
client=self.client,
track_logs=self.track_logs,
track_code=self.track_code,
track_env=self.track_env,
outputs_store=self.outputs_store)
experiment.create(name=name,
framework=framework,
tags=tags,
description=description,
config=config,
base_outputs_path=self.base_outputs_path)
return experiment
)
parser.add_argument(
'--batch_size',
type=int,
default=100
)
parser.add_argument(
'--epochs',
type=int,
default=1
)
args = parser.parse_args()
# Polyaxon
experiment = Experiment('mnist')
experiment.create(framework='tensorflow', tags=['examples'])
experiment.log_params(
conv1_size=args.conv1_size,
conv1_out=args.conv1_out,
conv1_activation=args.conv1_activation,
pool1_size=args.pool1_size,
conv2_size=args.conv2_size,
conv2_out=args.conv2_out,
conv2_activation=args.conv2_activation,
pool2_size=args.pool2_size,
fc1_activation=args.fc1_activation,
fc1_size=args.fc1_size,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
batch_size=args.batch_size,
dropout=args.dropout,
type=int,
default=30,
help='Top occurring words to skip')
parser.add_argument('--maxlen', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--num_nodes', type=int, default=8)
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--dropout', type=float, default=0.8)
parser.add_argument('--epochs', type=int, default=1)
parser.add_argument('--seed', type=int, default=234)
args = parser.parse_args()
# Polyaxon
experiment = Experiment('bidirectional-lstm')
experiment.create(framework='keras', tags=['examples'])
experiment.log_params(max_features=args.max_features,
skip_top=args.skip_top,
maxlen=args.maxlen,
batch_size=args.batch_size,
num_nodes=args.num_nodes,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
dropout=args.dropout,
epochs=args.epochs,
seed=args.seed)
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,
h = .02 # step size in the mesh
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
for weights in ['uniform', 'distance']:
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X, y)
y_model = clf.predict(X)
model_accuracy = accuracy_score(y, y_model)
experiment = Experiment()
experiment.create()
experiment.log_metrics(model_accuracy=model_accuracy)
experiment.log_params(weights=weights, n_neighbors=n_neighbors)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
max_features=max_features,
min_samples_leaf=min_samples_leaf,
)
return cross_val_score(classifier, X, y, cv=5)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--n_estimators', type=int, default=3)
parser.add_argument('--max_features', type=int, default=3)
parser.add_argument('--min_samples_leaf', type=int, default=80)
args = parser.parse_args()
# Polyaxon
experiment = Experiment(project='random-forest')
experiment.create(framework='scikit-learn', tags=['examples'])
experiment.log_params(n_estimators=args.n_estimators,
max_features=args.max_features,
min_samples_leaf=args.min_samples_leaf)
(X, y) = load_data()
# Polyaxon
experiment.log_data_ref(data=X, data_name='dataset_X')
experiment.log_data_ref(data=y, data_name='dataset_y')
accuracies = model(X=X,
y=y,
n_estimators=args.n_estimators,
max_features=args.max_features,
min_samples_leaf=args.min_samples_leaf)
)
parser.add_argument(
'--log_learning_rate',
type=int,
default=-3
)
parser.add_argument(
'--epochs',
type=int,
default=1
)
args = parser.parse_args()
# Polyaxon
experiment = Experiment(project='mnist')
experiment.create(tags=['keras'])
experiment.log_params(conv1_size=args.conv1_size,
conv2_size=args.conv2_size,
dropout=args.dropout,
hidden1_size=args.hidden1_size,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
epochs=args.epochs)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Polyaxon
experiment.log_data_ref(data=x_train, data_name='x_train')
experiment.log_data_ref(data=y_train, data_name='y_train')
experiment.log_data_ref(data=x_test, data_name='x_test')
experiment.log_data_ref(data=y_test, data_name='y_test')
parser.add_argument('--conv1_kernel', type=int, default=5)
parser.add_argument('--conv1_filters', type=int, default=10)
parser.add_argument('--conv1_activation', type=str, default='relu')
parser.add_argument('--conv2_kernel', type=int, default=5)
parser.add_argument('--conv2_filters', type=int, default=10)
parser.add_argument('--conv2_activation', type=str, default='relu')
parser.add_argument('--fc1_hidden', type=int, default=10)
parser.add_argument('--fc1_activation', type=str, default='relu')
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--epochs', type=int, default=1)
args = parser.parse_args()
experiment = Experiment('mnist')
experiment.create(tags=['mxnet'])
experiment.log_params(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,
epochs=args.epochs)
logger.info('Downloading data ...')
mnist = mx.test_utils.get_mnist()
train_iter = mx.io.NDArrayIter(mnist['train_data'],
parser.add_argument('--conv1_kernel', type=int, default=5)
parser.add_argument('--conv1_filters', type=int, default=10)
parser.add_argument('--conv1_activation', type=str, default='relu')
parser.add_argument('--conv2_kernel', type=int, default=5)
parser.add_argument('--conv2_filters', type=int, default=10)
parser.add_argument('--conv2_activation', type=str, default='relu')
parser.add_argument('--fc1_hidden', type=int, default=10)
parser.add_argument('--fc1_activation', type=str, default='relu')
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--epochs', type=int, default=1)
args = parser.parse_args()
experiment = Experiment('mnist')
experiment.create(framework='mxnet', tags=['examples'])
experiment.log_params(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,
epochs=args.epochs)
logger.info('Downloading data ...')
mnist = mx.test_utils.get_mnist()
train_iter = mx.io.NDArrayIter(mnist['train_data'],
parser.add_argument('--conv2_size', type=int, default=5)
parser.add_argument('--conv2_out', type=int, default=64)
parser.add_argument('--conv2_activation', type=str, default='relu')
parser.add_argument('--pool2_size', type=int, default=2)
parser.add_argument('--dropout', type=float, default=0.2)
parser.add_argument('--fc1_size', type=int, default=1024)
parser.add_argument('--fc1_activation', type=str, default='sigmoid')
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--epochs', type=int, default=1)
args = parser.parse_args()
# Polyaxon
experiment = Experiment('mnist')
experiment.create(tags=['tensorflow'])
experiment.log_params(conv1_size=args.conv1_size,
conv1_out=args.conv1_out,
conv1_activation=args.conv1_activation,
pool1_size=args.pool1_size,
conv2_size=args.conv2_size,
conv2_out=args.conv2_out,
conv2_activation=args.conv2_activation,
pool2_size=args.pool2_size,
fc1_activation=args.fc1_activation,
fc1_size=args.fc1_size,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
batch_size=args.batch_size,
dropout=args.dropout,
epochs=args.epochs)
model.fit(X_train, y_train)
pred = model.predict(X_test)
return accuracy_score(pred, y_test)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--max_depth', type=int, default=3)
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 = Experiment('iris')
experiment.create(framework='xgboost', tags=['examples'])
experiment.log_params(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(data=X_train, data_name='x_train')
experiment.log_data_ref(data=y_train, data_name='y_train')
experiment.log_data_ref(data=X_test, data_name='X_test')
)
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 = Experiment('iris')
experiment.create(tags=['xgboost'])
experiment.log_params(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(data=X_train, data_name='x_train')
experiment.log_data_ref(data=y_train, data_name='y_train')
experiment.log_data_ref(data=X_test, data_name='X_test')
