@@ -35,15 +35,31 @@ class DataFeeder(DataProviderConverter):
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DataFeeder converts this mini-batch data entries into Arguments in order
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to feed it to C++ interface.
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- The example usage:
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+ The simple usage shows below
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+
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+ .. code-block:: python
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+
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+ data_types = [('image', paddle.data_type.dense_vector(784)),
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+ ('label', paddle.data_type.integer_value(10))]
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+ feeding = ['image', 'label']
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+ feeder = DataFeeder(data_types=data_types, feeding=feeding)
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+
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+ minibatch_data = [([1.0, 2.0, 3.0, ...], 5)]
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+
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+ arg = feeder(minibatch_data)
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+
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+
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+ If mini-batch data and data layers are not one to one mapping, we
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+ could pass a dictionary to feeding parameter to represent the mapping
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+ relationship.
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.. code-block:: python
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data_types = [('image', paddle.data_type.dense_vector(784)),
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('label', paddle.data_type.integer_value(10))]
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- reader_dict = {'image':0, 'label':1}
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- feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict )
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+ feeding = {'image':0, 'label':1}
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+ feeder = DataFeeder(data_types=data_types, feeding=feeding )
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minibatch_data = [
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( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample
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( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample
@@ -65,9 +81,9 @@ class DataFeeder(DataProviderConverter):
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a tuple of (data_name, data_type).
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:type data_types: list
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- :param reader_dict : A dictionary to specify the position of each data
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- in the input data.
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- :type feeding: dict
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+ :param feeding : A dictionary or a sequence to specify the position of each
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+ data in the input data.
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+ :type feeding: dict|collections.Sequence|None
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"""
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def __init__ (self , data_types , feeding = None ):
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