change data_type to type

demo/mnist/api_train.py

@@ -9,7 +9,6 @@ The user api could be simpler and carefully designed.
 import random
 
 import numpy as np
-import paddle.trainer.PyDataProvider2 as dp
 import paddle.v2 as paddle_v2
 import py_paddle.swig_paddle as api
 from paddle.trainer_config_helpers import *
@@ -71,8 +70,10 @@ def main():
     assert isinstance(updater, api.ParameterUpdater)
 
     # define network
-    images = paddle_v2.layer.data(name='pixel', data_type=dp.dense_vector(784))
-    label = paddle_v2.layer.data(name='label', data_type=dp.integer_value(10))
+    images = paddle_v2.layer.data(
+        name='pixel', type=paddle_v2.data.dense_vector(784))
+    label = paddle_v2.layer.data(
+        name='label', type=paddle_v2.data.integer_value(10))
     hidden1 = paddle_v2.layer.fc(input=images, size=200)
     hidden2 = paddle_v2.layer.fc(input=hidden1, size=200)
     inference = paddle_v2.layer.fc(input=hidden2,
@@ -98,8 +99,7 @@ def main():
 
     # DataProvider Converter is a utility convert Python Object to Paddle C++
     # Input. The input format is as same as Paddle's DataProvider.
-    converter = DataProviderConverter(
-        input_types=[images.data_type, label.data_type])
+    converter = DataProviderConverter(input_types=[images.type, label.type])
 
     train_file = './data/raw_data/train'
     test_file = './data/raw_data/t10k'

demo/mnist/api_train_v2.py

 import numpy
 import paddle.v2 as paddle
-from paddle.trainer.PyDataProvider2 import dense_vector, integer_value
 
 import mnist_util
 
@@ -16,8 +15,8 @@ def main():
     paddle.init(use_gpu=False, trainer_count=1)
 
     # define network topology
-    images = paddle.layer.data(name='pixel', data_type=dense_vector(784))
-    label = paddle.layer.data(name='label', data_type=integer_value(10))
+    images = paddle.layer.data(name='pixel', type=paddle.data.dense_vector(784))
+    label = paddle.layer.data(name='label', type=paddle.data.integer_value(10))
     hidden1 = paddle.layer.fc(input=images, size=200)
     hidden2 = paddle.layer.fc(input=hidden1, size=200)
     inference = paddle.layer.fc(input=hidden2,
@@ -51,8 +50,8 @@ def main():
                   batch_size=32,  # batch size should be refactor in Data reader
                   data_types={  # data_types will be removed, It should be in
                       # network topology
-                      'pixel': images.data_type,
-                      'label': label.data_type
+                      'pixel': images.type,
+                      'label': label.type
                   })
 
 

python/paddle/v2/__init__.py

@@ -17,10 +17,12 @@ import activation
 import parameters
 import trainer
 import event
+import data
 import py_paddle.swig_paddle as api
 
 __all__ = [
-    'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event'
+    'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
+    'event', 'data'
 ]
 
 

python/paddle/v2/data.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer.PyDataProvider2 import \
+    InputType, dense_vector, sparse_binary_vector,\
+    sparse_vector, integer_value
+
+__all__ = [
+    'InputType', 'dense_vector', 'sparse_binary_vector', 'sparse_vector',
+    'integer_value'
+]

python/paddle/v2/layer.py

@@ -67,7 +67,7 @@ paddle.v2.parameters.create, no longer exposed to users.
 """
 
 import paddle.trainer_config_helpers as conf_helps
-import paddle.trainer.PyDataProvider2 as dp
+from . import data as v2_data
 from paddle.trainer_config_helpers.config_parser_utils import \
     parse_network_config as __parse__
 from paddle.trainer_config_helpers.default_decorators import wrap_name_default
@@ -165,10 +165,10 @@ So we also need to implement some special LayerV2.
 
 
 class DataLayerV2(Layer):
-    def __init__(self, name, data_type, **kwargs):
-        assert isinstance(data_type, dp.InputType)
+    def __init__(self, name, type, **kwargs):
+        assert isinstance(type, v2_data.InputType)
 
-        self.data_type = data_type
+        self.type = type
         self.__method_name__ = 'data_layer'
         self.__kwargs__ = kwargs
 
@@ -176,7 +176,7 @@ class DataLayerV2(Layer):
 
     def to_proto_impl(self, **kwargs):
         args = dict()
-        args['size'] = self.data_type.dim
+        args['size'] = self.type.dim
         for each in kwargs:
             args[each] = kwargs[each]
         for each in self.__kwargs__:
@@ -198,8 +198,8 @@ cross_entropy_cost = __convert_to_v2__(
     parent_names=['input', 'label'])
 
 if __name__ == '__main__':
-    pixel = data(name='pixel', data_type=dp.dense_vector(784))
-    label = data(name='label', data_type=dp.integer_value(10))
+    pixel = data(name='pixel', type=v2_data.dense_vector(784))
+    label = data(name='label', type=v2_data.integer_value(10))
     hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation())
     inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation())
     maxid = max_id(input=inference)