def clone(self):
"""Generates a clone of the GraphVertexData object. Results in a
splitting in the DAG.
"""
cloned_layers = []
for i,node in enumerate(self.layers):
temp = lbann.Split(node)
layers[i] = lbann.Identity(temp)
cloned_layers.append(lbann.Identity(temp))
return GraphVertexData(cloned_layers, self.num_features)
help='Keep error signals (default: False)')
parser.add_argument('--batch-job',
action='store_true',
help='Run as a batch job (default: false)')
lbann.contrib.args.add_optimizer_arguments(
parser,
default_optimizer="adam",
default_learning_rate=0.001,
)
args = parser.parse_args()
# Construct layer graph
input = lbann.Input(io_buffer='partitioned', target_mode='regression')
universes = lbann.Split(input)
secrets = lbann.Split(input)
statistics_group_size = 1 if args.local_batchnorm else -1
preds = CosmoFlow(
input_width=args.input_width,
output_size=args.num_secrets,
use_bn=args.use_batchnorm,
bn_statistics_group_size=statistics_group_size)(universes)
mse = lbann.MeanSquaredError([preds, secrets])
obj = lbann.ObjectiveFunction([mse])
layers = list(lbann.traverse_layer_graph(input))
# Set parallel_strategy
parallel_strategy = get_parallel_strategy_args(
sample_groups=args.mini_batch_size, depth_groups=args.depth_groups)
pooling_id = 0
parser.add_argument(
'--data-reader',
action='store',
default='default',
type=str,
help=
'Data reader options: \"numpy_npz_int16\", or \"mnist\" (default: data_reader_mnist.prototext)'
)
lbann.contrib.args.add_optimizer_arguments(parser, default_learning_rate=0.1)
args = parser.parse_args()
# Start of layers
# Construct layer graph
input_ = lbann.Input(name='data')
image = lbann.Split(input_, name='image')
dummy = lbann.Dummy(input_, name='dummy')
# Encoder
encode1 = lbann.FullyConnected(image,
name="encode1",
num_neurons=1000,
has_bias=True)
encode1neuron = lbann.Relu(encode1, name="encode1neuron")
encode2 = lbann.FullyConnected(encode1neuron,
name="encode2",
num_neurons=500,
has_bias=True)
parser.add_argument(
'--data-reader',
action='store',
default='./data_readers/data_reader_candle_pilot1.prototext',
type=str,
help=
'scheduler job name (default: data_readers/data_reader_candle_pilot1.prototext)'
)
lbann.contrib.args.add_optimizer_arguments(parser, default_learning_rate=0.1)
args = parser.parse_args()
# Start of layers
# Construct layer graph
input_ = lbann.Input(name='input', target_mode="reconstruction")
data = lbann.Split(input_, name='data')
dummy = lbann.Dummy(input_, name='dummy')
# Encoder
encode1 = lbann.FullyConnected(data,
name="encode1",
data_layout="model_parallel",
num_neurons=2000,
has_bias=True)
relu1 = lbann.Relu(encode1, name="relu1", data_layout="model_parallel")
encode2 = lbann.FullyConnected(relu1,
name="encode2",
data_layout="model_parallel",
num_neurons=1000,
parser.add_argument(
'--data-reader',
action='store',
default='data_readers/data_reader_candle_pilot1.prototext',
type=str,
help=
'scheduler job name (default: data_readers/data_reader_candle_pilot1.prototext)'
)
lbann.contrib.args.add_optimizer_arguments(parser, default_learning_rate=0.1)
args = parser.parse_args()
# Start of layers
# Construct layer graph
input_ = lbann.Input(name='data')
finetunedata = lbann.Split(input_, name='finetunedata')
label = lbann.Split(input_, name='label')
# Encoder
encode1 = lbann.FullyConnected(finetunedata,
name="encode1",
data_layout="model_parallel",
num_neurons=2000,
has_bias=True)
relu1 = lbann.Relu(encode1, name="relu1", data_layout="model_parallel")
encode2 = lbann.FullyConnected(relu1,
name="encode2",
data_layout="model_parallel",
num_neurons=1000,
help='Run as a batch job (default: false)')
lbann.contrib.args.add_optimizer_arguments(
parser,
default_optimizer="adam",
default_learning_rate=0.001,
)
args = parser.parse_args()
parallel_strategy = get_parallel_strategy_args(
sample_groups=args.mini_batch_size, depth_groups=args.depth_groups)
# Construct layer graph
input = lbann.Input(io_buffer='partitioned',
target_mode='label_reconstruction')
volume = lbann.Split(input)
output = UNet3D()(volume)
segmentation = lbann.Split(input)
ce = lbann.CrossEntropy([output, segmentation], use_labels=True)
obj = lbann.ObjectiveFunction([ce])
layers = list(lbann.traverse_layer_graph(input))
for l in layers:
l.parallel_strategy = parallel_strategy
# Setup model
metrics = [lbann.Metric(ce, name='CE', unit='')]
callbacks = [
lbann.CallbackPrint(),
lbann.CallbackTimer(),
lbann.CallbackGPUMemoryUsage(),
lbann.CallbackProfiler(skip_init=True),