lab_1_pickle_bug_fix

mlp/data_providers.py

@@ -5,7 +5,7 @@ This module provides classes for loading datasets and iterating over batches of
 data points.
 """
 
-import cPickle
+import pickle
 import gzip
 import numpy as np
 import os
@@ -121,22 +121,25 @@ class MNISTDataProvider(DataProvider):
         # 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
+        # load data from compressed numpy file
-        # we are finished with them
+        loaded = np.load(data_path)
-        with gzip.open(data_path) as f:
+        inputs, targets = loaded['inputs'], loaded['targets']
-            inputs, targets = cPickle.load(f)
+        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)
 
-    #def next(self):
+    def next(self):
-    #    """Returns next data batch or raises `StopIteration` if at end."""
+       """Returns next data batch or raises `StopIteration` if at end."""
-    #    inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
+       inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
-    #    return inputs_batch, self.to_one_of_k(targets_batch)
+       return inputs_batch, self.to_one_of_k(targets_batch)
+
+    def __next__(self):
+        return self.next()
 
     def to_one_of_k(self, int_targets):
         """Converts integer coded class target to 1 of K coded targets.