Try to read data in mnist

demo/mnist/api_train.py

 import py_paddle.swig_paddle as api
+from py_paddle import DataProviderConverter
+import paddle.trainer.PyDataProvider2 as dp
 import paddle.trainer.config_parser
 import numpy as np
+from mnist_util import read_from_mnist
 
 
 def init_parameter(network):
@@ -13,6 +16,22 @@ def init_parameter(network):
             array[i] = np.random.uniform(-1.0, 1.0)
 
 
+def generator_to_batch(generator, batch_size):
+    ret_val = list()
+    for each_item in generator:
+        ret_val.append(each_item)
+        if len(ret_val) == batch_size:
+            yield ret_val
+            ret_val = list()
+    if len(ret_val) != 0:
+        yield ret_val
+
+
+def input_order_converter(generator):
+    for each_item in generator:
+        yield each_item['pixel'], each_item['label']
+
+
 def main():
     api.initPaddle("-use_gpu=false", "-trainer_count=4")  # use 4 cpu cores
     config = paddle.trainer.config_parser.parse_config(
@@ -30,10 +49,20 @@ def main():
     updater = api.ParameterUpdater.createLocalUpdater(opt_config)
     assert isinstance(updater, api.ParameterUpdater)
     updater.init(m)
+
+    converter = DataProviderConverter(
+        input_types=[dp.dense_vector(784), dp.integer_value(10)])
+
+    train_file = './data/raw_data/train'
+
     m.start()
 
     for _ in xrange(100):
         updater.startPass()
+        train_data_generator = input_order_converter(
+            read_from_mnist(train_file))
+        for data_batch in generator_to_batch(train_data_generator, 128):
+            inArgs = converter(data_batch)
 
         updater.finishPass()
 

demo/mnist/mnist_provider.py

 from paddle.trainer.PyDataProvider2 import *
-import numpy
+from mnist_util import read_from_mnist
 
 
 # Define a py data provider
@@ -8,27 +8,5 @@ import numpy
                  'label': integer_value(10)},
     cache=CacheType.CACHE_PASS_IN_MEM)
 def process(settings, filename):  # settings is not used currently.
-    imgf = filename + "-images-idx3-ubyte"
-    labelf = filename + "-labels-idx1-ubyte"
-    f = open(imgf, "rb")
-    l = open(labelf, "rb")
-
-    f.read(16)
-    l.read(8)
-
-    # Define number of samples for train/test
-    if "train" in filename:
-        n = 60000
-    else:
-        n = 10000
-
-    images = numpy.fromfile(
-        f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
-    images = images / 255.0 * 2.0 - 1.0
-    labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
-
-    for i in xrange(n):
-        yield {"pixel": images[i, :], 'label': labels[i]}
-
-    f.close()
-    l.close()
+    for each in read_from_mnist(filename):
+        yield each

demo/mnist/mnist_util.py

+import numpy
+
+__all__ = ['read_from_mnist']
+
+
+def read_from_mnist(filename):
+    imgf = filename + "-images-idx3-ubyte"
+    labelf = filename + "-labels-idx1-ubyte"
+    f = open(imgf, "rb")
+    l = open(labelf, "rb")
+
+    f.read(16)
+    l.read(8)
+
+    # Define number of samples for train/test
+    if "train" in filename:
+        n = 60000
+    else:
+        n = 10000
+
+    images = numpy.fromfile(
+        f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
+    images = images / 255.0 * 2.0 - 1.0
+    labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
+
+    for i in xrange(n):
+        yield {"pixel": images[i, :], 'label': labels[i]}
+
+    f.close()
+    l.close()