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bb625337
编写于
2月 22, 2017
作者:
D
dangqingqing
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python/paddle/v2/data_feeder.py
python/paddle/v2/data_feeder.py
+41
-0
python/paddle/v2/trainer.py
python/paddle/v2/trainer.py
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未找到文件。
python/paddle/v2/data_feeder.py
浏览文件 @
bb625337
...
...
@@ -15,5 +15,46 @@
from
py_paddle
import
DataProviderConverter
__all__
=
[
'DataFeeder'
]
"""
DataFeeder converts the data returned by paddle.reader into a data structure
of Arguments which is defined in the API. The paddle.reader usually returns
a list of mini-batch data. Each item in the list is a tuple or list, which is
one sample with multiple features. DataFeeder converts this mini-batch data
into Arguments in order to feed it to C++ interface.
The example usage:
data_types = [paddle.data_type.dense_vector(784),
paddle.data_type.integer_value(10)]
feeder = DataFeeder(input_types=data_types)
minibatch_data = [
( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample
( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample
]
# or
# minibatch_data = [
# [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ], # first sample
# [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ] # second sample
# ]
arg = feeder(minibatch_data)
Args:
input_types: A list of input data types. It's length is equal to the length
of data returned by paddle.reader. Each item specifies the type
of each feature.
mintbatch_data: A list of mini-batch data. Each item is a list or tuple,
for example:
[
(feature_0, feature_1, feature_2, ...), # first sample
(feature_0, feature_1, feature_2, ...), # second sample
...
]
Returns:
An Arguments object contains this mini-batch data with multiple features.
The Arguments definition is in the API.
"""
DataFeeder
=
DataProviderConverter
python/paddle/v2/trainer.py
浏览文件 @
bb625337
...
...
@@ -89,7 +89,6 @@ class SGD(ITrainer):
event_handler
=
default_event_handler
topology
=
v2_layer
.
parse_network
(
topology
)
print
topology
__check_train_args__
(
**
locals
())
...
...
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