Refactor training part of engine module. (keras-team#10029)

* Refactor topological part of Keras engine.

* Fix imports

* Fix merge mixup.

* Refactor training part of the Keras engine.

* Fix unit tests.

keras/utils/generic_utils.py

@@ -475,3 +475,44 @@ def is_all_none(iterable_or_element):
         if element is not None:
             return False
     return True
+
+
+def slice_arrays(arrays, start=None, stop=None):
+    """Slices an array or list of arrays.
+
+    This takes an array-like, or a list of
+    array-likes, and outputs:
+        - arrays[start:stop] if `arrays` is an array-like
+        - [x[start:stop] for x in arrays] if `arrays` is a list
+
+    Can also work on list/array of indices: `_slice_arrays(x, indices)`
+
+    # Arguments
+        arrays: Single array or list of arrays.
+        start: can be an integer index (start index)
+            or a list/array of indices
+        stop: integer (stop index); should be None if
+            `start` was a list.
+
+    # Returns
+        A slice of the array(s).
+    """
+    if arrays is None:
+        return [None]
+    elif isinstance(arrays, list):
+        if hasattr(start, '__len__'):
+            # hdf5 datasets only support list objects as indices
+            if hasattr(start, 'shape'):
+                start = start.tolist()
+            return [None if x is None else x[start] for x in arrays]
+        else:
+            return [None if x is None else x[start:stop] for x in arrays]
+    else:
+        if hasattr(start, '__len__'):
+            if hasattr(start, 'shape'):
+                start = start.tolist()
+            return arrays[start]
+        elif hasattr(start, '__getitem__'):
+            return arrays[start:stop]
+        else:
+            return [None]

tests/keras/test_sequential_model.py

@@ -13,7 +13,7 @@
 from keras.utils.test_utils import get_test_data, keras_test
 from keras.models import model_from_json, model_from_yaml
 from keras import losses
-from keras.engine.training import _make_batches
+from keras.engine.training_utils import make_batches
 
 
 input_dim = 16
@@ -112,7 +112,7 @@ def test_sequential(in_tmpdir):
     def data_generator(x, y, batch_size=50):
         index_array = np.arange(len(x))
         while 1:
-            batches = _make_batches(len(x_test), batch_size)
+            batches = make_batches(len(x_test), batch_size)
             for batch_index, (batch_start, batch_end) in enumerate(batches):
                 batch_ids = index_array[batch_start:batch_end]
                 x_batch = x[batch_ids]

tests/test_loss_masking.py

@@ -2,7 +2,7 @@
 import pytest
 
 from keras.models import Sequential
-from keras.engine.training import _weighted_masked_objective
+from keras.engine.training_utils import weighted_masked_objective
 from keras.layers import TimeDistributed, Masking, Dense
 from keras.utils.test_utils import keras_test
 from keras import losses
@@ -26,7 +26,7 @@ def test_masking():
 
 @keras_test
 def test_loss_masking():
-    weighted_loss = _weighted_masked_objective(losses.get('mae'))
+    weighted_loss = weighted_masked_objective(losses.get('mae'))
     shape = (3, 4, 2)
     x = np.arange(24).reshape(shape)
     y = 2 * x