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__init__(self,
flow,
verbose=False,
**kwargs)
Initialize the internal variables. |
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setup_parallel_training(self,
data_iterables,
checkpoints,
train_callable_class=<class 'mdp.parallel.parallelflows.FlowTrainCallable'>,
**kwargs)
Checkpoint version of parallel training. |
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train(self,
data_iterables,
checkpoints,
scheduler=None,
train_callable_class=<class 'mdp.parallel.parallelflows.FlowTrainCallable'>,
overwrite_result_container=True,
**kwargs)
Train all trainable nodes in the flow. |
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use_results(self,
results)
Checkpoint version of use_results. |
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Inherited from object :
__delattr__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__setattr__
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execute(self,
iterable,
nodenr=None,
scheduler=None,
execute_callable_class=None,
overwrite_result_container=True)
Train all trainable nodes in the flow. |
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get_task(self)
Return a task either for either training or execution. |
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setup_parallel_execution(self,
iterable,
nodenr=None,
execute_callable_class=<class 'mdp.parallel.parallelflows.FlowExecuteCallable'>)
Prepare the flow for handing out tasks to do the execution. |
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__call__(self,
iterable,
nodenr=None)
Calling an instance is equivalent to call its 'execute' method. |
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_train_node(self,
data_iterable,
nodenr)
Train a single node in the flow. |
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append(flow,
node)
append node to flow end |
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copy(self,
protocol=-1)
Return a deep copy of the flow. |
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extend(flow,
iterable)
extend flow by appending... |
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insert(flow,
index,
node)
insert node before index |
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inverse(self,
iterable)
Process the data through all nodes in the flow backwards
(starting from the last node up to the first node) by calling the
inverse function of each node. |
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node
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pop(flow,
index=...)
remove and return node at index... |
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save(self,
filename,
protocol=-1)
Save a pickled serialization of the flow to 'filename'. |
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