def __init__(self,
params,
tree_configs=None,
tree_stats=None,
device_assigner=None,
variables=None,
tree_variables_class=TreeVariables,
tree_graphs=None,
training=True):
self.params = params
self.device_assigner = (device_assigner
or framework_variables.VariableDeviceChooser())
logging.info('Constructing forest with params = ')
logging.info(self.params.__dict__)
self.variables = variables or ForestVariables(
self.params,
device_assigner=self.device_assigner,
training=training,
tree_variables_class=tree_variables_class,
tree_configs=tree_configs,
tree_stats=tree_stats)
tree_graph_class = tree_graphs or RandomTreeGraphs
self.trees = [
tree_graph_class(self.variables[i], self.params, i)
for i in range(self.params.num_trees)
]
def __init__(self,
params,
device_assigner=None,
optimizer_class=adagrad.AdagradOptimizer,
**kwargs):
self.device_assigner = (device_assigner
or framework_variables.VariableDeviceChooser())
self.params = params
self.optimizer = optimizer_class(self.params.learning_rate)
self.is_regression = params.regression
self.regularizer = None
if params.regularization == "l1":
self.regularizer = layers.l1_regularizer(
self.params.regularization_strength)
elif params.regularization == "l2":
self.regularizer = layers.l2_regularizer(
self.params.regularization_strength)
def __init__(self, params, layer_num, device_assigner, *args, **kwargs):
self.layer_num = layer_num
self.device_assigner = (
device_assigner or framework_variables.VariableDeviceChooser())
self.params = params
self._define_vars(params, **kwargs)
