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diff --git a/data/mnist-valid.npz b/data/mnist-valid.npz
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diff --git a/data/mnist_test.pkl.gz b/data/mnist_test.pkl.gz
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diff --git a/data/mnist_train.pkl.gz b/data/mnist_train.pkl.gz
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diff --git a/data/mnist_valid.pkl.gz b/data/mnist_valid.pkl.gz
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diff --git a/mlp/data_providers.py b/mlp/data_providers.py
index d786c1c..279b178 100644
--- a/mlp/data_providers.py
+++ b/mlp/data_providers.py
@@ -5,10 +5,8 @@ This module provides classes for loading datasets and iterating over batches of
 data points.
 """
 
-import cPickle
-import gzip
-import numpy as np
 import os
+import numpy as np
 from mlp import DEFAULT_SEED
 
 
@@ -130,6 +128,10 @@ class DataProvider(object):
         self._curr_batch += 1
         return inputs_batch, targets_batch
 
+    # Python 3.x compatibility
+    def __next__(self):
+        return self.next()
+
 
 class OneOfKDataProvider(DataProvider):
     """1-of-K classification target data provider.
@@ -194,14 +196,14 @@ class MNISTDataProvider(OneOfKDataProvider):
         # separator for the current platform / OS is used
         # MLP_DATA_DIR environment variable should point to the data directory
         data_path = os.path.join(
-            os.environ['MLP_DATA_DIR'], 'mnist_{0}.pkl.gz'.format(which_set))
+            os.environ['MLP_DATA_DIR'], 'mnist-{0}.npz'.format(which_set))
         assert os.path.isfile(data_path), (
             'Data file does not exist at expected path: ' + data_path
         )
-        # use a context-manager to ensure the files are properly closed after
-        # we are finished with them
-        with gzip.open(data_path) as f:
-            inputs, targets = cPickle.load(f)
+        # load data from compressed numpy file
+        loaded = np.load(data_path)
+        inputs, targets = loaded['inputs'], loaded['targets']
+        inputs = inputs.astype(np.float32)
         # pass the loaded data to the parent class __init__
         super(MNISTDataProvider, self).__init__(
             inputs, targets, batch_size, max_num_batches, shuffle_order, rng)