Merge pull request #1438 from reyoung/feature/mnist_reader

MNIST dataset reader implementation

python/paddle/v2/dataset/config.py

+import os
+
+__all__ = ['DATA_HOME']
+
+DATA_HOME = os.path.expanduser('~/.cache/paddle_data_set')
+
+if not os.path.exists(DATA_HOME):
+    os.makedirs(DATA_HOME)

python/paddle/v2/dataset/mnist.py

+import sklearn.datasets.mldata
+import sklearn.model_selection
+import numpy
+from config import DATA_HOME
+
+__all__ = ['train_creator', 'test_creator']
+
+
+def __mnist_reader_creator__(data, target):
+    def reader():
+        n_samples = data.shape[0]
+        for i in xrange(n_samples):
+            yield (data[i] / 255.0).astype(numpy.float32), int(target[i])
+
+    return reader
+
+
+TEST_SIZE = 10000
+
+data = sklearn.datasets.mldata.fetch_mldata(
+    "MNIST original", data_home=DATA_HOME)
+X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
+    data.data, data.target, test_size=TEST_SIZE, random_state=0)
+
+
+def train_creator():
+    return __mnist_reader_creator__(X_train, y_train)
+
+
+def test_creator():
+    return __mnist_reader_creator__(X_test, y_test)
+
+
+def unittest():
+    assert len(list(test_creator()())) == TEST_SIZE
+
+
+if __name__ == '__main__':
+    unittest()