make minst to run

demo/mnist/api_train_v2.py

@@ -50,11 +50,12 @@ def main():
                   parameters=parameters,
                   event_handler=event_handler,
                   batch_size=32,  # batch size should be refactor in Data reader
-                  data_types={  # data_types will be removed, It should be in
+                  data_types=[  # data_types will be removed, It should be in
                       # network topology
-                      'pixel': images.type,
-                      'label': label.type
-                  })
+                      ('pixel', images.type),
+                      ('label', label.type)],
+                  reader_dict={'pixel':0, 'label':1}
+                  )
 
 
 if __name__ == '__main__':

python/paddle/v2/__init__.py

@@ -18,6 +18,7 @@ import parameters
 import trainer
 import event
 import data_type
+import data_feeder
 import py_paddle.swig_paddle as api
 
 __all__ = [

python/paddle/v2/tests/test_data_feeder.py

@@ -36,7 +36,7 @@ class DataFeederTest(unittest.TestCase):
         def compare(input):
             feeder = DataFeeder([('image', data_type.dense_vector(784))],
                                 {'image': 0})
-            arg = feeder([input])
+            arg = feeder(input)
             output = arg.getSlotValue(0).copyToNumpyMat()
             input = np.array(input, dtype='float32')
             self.assertAlmostEqual(input.all(), output.all())
@@ -46,13 +46,17 @@ class DataFeederTest(unittest.TestCase):
         dim = 784
         data = []
         for i in xrange(batch_size):
-            data.append(self.dense_reader(784))
+            each_sample = []
+            each_sample.append(self.dense_reader(dim))
+            data.append(each_sample)
         compare(data)
 
         # test list
         data = []
         for i in xrange(batch_size):
-            data.append(self.dense_reader(784).tolist())
+            each_sample = []
+            each_sample.append(self.dense_reader(dim).tolist())
+            data.append(each_sample)
         compare(data)
 
     def test_sparse_binary(self):
@@ -60,7 +64,9 @@ class DataFeederTest(unittest.TestCase):
         batch_size = 32
         data = []
         for i in xrange(batch_size):
-            data.append([self.sparse_binary_reader(dim, 50)])
+            each_sample = []
+            each_sample.append(self.sparse_binary_reader(dim, 50))
+            data.append(each_sample)
         feeder = DataFeeder([('input', data_type.sparse_binary_vector(dim))],
                             {'input': 0})
         arg = feeder(data)
@@ -76,11 +82,13 @@ class DataFeederTest(unittest.TestCase):
         w = []
         data = []
         for dat in xrange(batch_size):
+            each_sample = []
             a = self.sparse_binary_reader(dim, 40, non_empty=True)
             b = self.dense_reader(len(a)).tolist()
             v.append(a)
             w.append(b[0])
-            data.append([zip(a, b)])
+            each_sample.append(zip(a, b))
+            data.append(each_sample)
 
         feeder = DataFeeder([('input', data_type.sparse_vector(dim))],
                             {'input': 0})
@@ -95,7 +103,9 @@ class DataFeederTest(unittest.TestCase):
         batch_size = 32
         index = []
         for i in xrange(batch_size):
-            index.append([np.random.randint(dim)])
+            each_sample = []
+            each_sample.append(np.random.randint(dim))
+            index.append(each_sample)
         feeder = DataFeeder([('input', data_type.integer_value(dim))],
                             {'input': 0})
         arg = feeder(index)

python/paddle/v2/trainer.py

@@ -69,7 +69,8 @@ class SGD(ITrainer):
               test_data_reader=None,
               event_handler=None,
               batch_size=32,
-              data_types=None):
+              data_types=None,
+              reader_dict=None):
         """
         Training method. Will train num_passes of input data.
 
@@ -103,13 +104,7 @@ class SGD(ITrainer):
         gm.start()
         out_args = api.Arguments.createArguments(0)
 
-        data_types_lists = []
-        for each in topology.input_layer_names:
-            if each not in data_types:
-                raise ValueError()
-            data_types_lists.append(data_types[each])
-
-        feeder = DataFeeder(input_types=data_types_lists)
+        feeder = DataFeeder(data_types, reader_dict)
 
         for pass_id in xrange(num_passes):
             updater.startPass()