Add negative subscripts support to TimeseriesGenerator and Iterator.

keras_preprocessing/image/iterator.py

@@ -51,6 +51,8 @@ def __getitem__(self, idx):
                              'but the Sequence '
                              'has length {length}'.format(idx=idx,
                                                           length=len(self)))
+        if idx < 0:
+            idx = len(self) + idx
         if self.seed is not None:
             np.random.seed(self.seed + self.total_batches_seen)
         self.total_batches_seen += 1

keras_preprocessing/sequence.py

@@ -356,6 +356,8 @@ def __len__(self):
                 self.batch_size * self.stride) // (self.batch_size * self.stride)
 
     def __getitem__(self, index):
+        if index < 0:
+            index = len(self) + index
         if self.shuffle:
             rows = np.random.randint(
                 self.start_index, self.end_index + 1, size=self.batch_size)

tests/sequence_test.py

@@ -140,6 +140,26 @@ def test_TimeseriesGenerator_serde():
     assert (data_gen.targets == recovered_gen.targets).all()
 
 
+def test_TimeseriesGenerator_negative_subscript():
+    data = np.array([[i] for i in range(50)])
+    targets = np.array([[i] for i in range(50)])
+
+    data_gen = sequence.TimeseriesGenerator(data, targets,
+                                            length=10,
+                                            sampling_rate=2,
+                                            batch_size=2)
+    assert len(data_gen) == 20
+    assert (np.allclose(data_gen[19][0], data_gen[-1][0]))
+    assert (np.allclose(data_gen[19][1], data_gen[-1][1]))
+    assert (np.allclose(data_gen[18][0], data_gen[-2][0]))
+    assert (np.allclose(data_gen[18][1], data_gen[-2][1]))
+
+    size = len(data_gen)
+    for i in range(1, size + 1):
+        assert (np.allclose(data_gen[size - i][0], data_gen[-i][0]))
+        assert (np.allclose(data_gen[size - i][1], data_gen[-i][1]))
+
+
 def test_TimeseriesGenerator():
     data = np.array([[i] for i in range(50)])
     targets = np.array([[i] for i in range(50)])