fix test dataset

keras/activations.py

@@ -31,7 +31,7 @@ def softmax(x, axis=-1):
         e = K.exp(x - K.max(x, axis=axis, keepdims=True))
         s = K.sum(e, axis=axis, keepdims=True)
         return e / s
-    elif K.backend()=='mxnet' and ndim == 0:
+    elif K.backend() == 'mxnet' and ndim == 0:
         # x dim is not inferred yet
         return K.softmax(x)
     else:

keras/backend/mxnet_backend.py

@@ -5680,8 +5680,8 @@ def _create_predict_module(self):
             if self._context and hasattr(self._context[0], 'device_type') and self._context[0].device_type == 'eia':
                 # Only Prediction is Supported with EI Context
                 self._predict_only_module = mx.mod.Module(self._pred_mxnet_symbol, data_names=self._data_names,
-                                             label_names=self._label_names, context=self._context[0],
-                                             fixed_param_names=self._fixed_weights)
+                                                          label_names=self._label_names, context=self._context[0],
+                                                          fixed_param_names=self._fixed_weights)
             else:
                 def sym_gen(phase):
                     return self._pred_mxnet_symbol, self._data_names, None

keras/datasets/boston_housing.py

@@ -26,7 +26,7 @@ def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
         path,
         origin='https://s3.amazonaws.com/keras-datasets/boston_housing.npz',
         file_hash='f553886a1f8d56431e820c5b82552d9d95cfcb96d1e678153f8839538947dff5')
-    with np.load(path) as f:
+    with np.load(path, allow_pickle=True) as f:
         x = f['x']
         y = f['y']
 

keras/datasets/imdb.py

@@ -55,7 +55,7 @@ def load_data(path='imdb.npz', num_words=None, skip_top=0,
     path = get_file(path,
                     origin='https://s3.amazonaws.com/text-datasets/imdb.npz',
                     file_hash='599dadb1135973df5b59232a0e9a887c')
-    with np.load(path) as f:
+    with np.load(path, allow_pickle=True) as f:
         x_train, labels_train = f['x_train'], f['y_train']
         x_test, labels_test = f['x_test'], f['y_test']
 

keras/datasets/reuters.py

@@ -53,7 +53,7 @@ def load_data(path='reuters.npz', num_words=None, skip_top=0,
     path = get_file(path,
                     origin='https://s3.amazonaws.com/text-datasets/reuters.npz',
                     file_hash='87aedbeb0cb229e378797a632c1997b6')
-    with np.load(path) as f:
+    with np.load(path, allow_pickle=True) as f:
         xs, labels = f['x'], f['y']
 
     np.random.seed(seed)