Remove redundant argument to range().

examples/conv_filter_visualization.py

@@ -59,7 +59,7 @@ def normalize(x):
 
 
 kept_filters = []
-for filter_index in range(0, 200):
+for filter_index in range(200):
     # we only scan through the first 200 filters,
     # but there are actually 512 of them
     print('Processing filter %d' % filter_index)

examples/image_ocr.py

@@ -260,7 +260,7 @@ def get_batch(self, index, size, train):
         input_length = np.zeros([size, 1])
         label_length = np.zeros([size, 1])
         source_str = []
-        for i in range(0, size):
+        for i in range(size):
             # Mix in some blank inputs.  This seems to be important for
             # achieving translational invariance
             if train and i > size - 4:
@@ -372,7 +372,7 @@ def show_edit_distance(self, num):
             word_batch = next(self.text_img_gen)[0]
             num_proc = min(word_batch['the_input'].shape[0], num_left)
             decoded_res = decode_batch(self.test_func, word_batch['the_input'][0:num_proc])
-            for j in range(0, num_proc):
+            for j in range(num_proc):
                 edit_dist = editdistance.eval(decoded_res[j], word_batch['source_str'][j])
                 mean_ed += float(edit_dist)
                 mean_norm_ed += float(edit_dist) / len(word_batch['source_str'][j])

keras/backend/cntk_backend.py

@@ -512,7 +512,7 @@ def dot(x, y):
         y_shape = int_shape(y)
         if len(y_shape) > 2:
             permutation = [len(y_shape) - 2]
-            permutation += list(range(0, len(y_shape) - 2))
+            permutation += list(range(len(y_shape) - 2))
             permutation += [len(y_shape) - 1]
             y = C.transpose(y, perm=permutation)
         return C.times(x, y, len(y_shape) - 1)

keras/backend/tensorflow_backend.py

@@ -3732,11 +3732,11 @@ def range_less_than(_, current_input):
                                      initializer=init, parallel_iterations=1)
     dense_mask = dense_mask[:, 0, :]
 
-    label_array = tf.reshape(tf.tile(tf.range(0, label_shape[1]), num_batches_tns),
+    label_array = tf.reshape(tf.tile(tf.range(label_shape[1]), num_batches_tns),
                              label_shape)
     label_ind = tf.boolean_mask(label_array, dense_mask)
 
-    batch_array = tf.transpose(tf.reshape(tf.tile(tf.range(0, label_shape[0]),
+    batch_array = tf.transpose(tf.reshape(tf.tile(tf.range(label_shape[0]),
                                                   max_num_labels_tns), reverse(label_shape, 0)))
     batch_ind = tf.boolean_mask(batch_array, dense_mask)
     indices = tf.transpose(tf.reshape(concatenate([batch_ind, label_ind], axis=0), [2, -1]))

keras/engine/training.py

@@ -373,7 +373,7 @@ def _make_batches(size, batch_size):
     """
     num_batches = int(np.ceil(size / float(batch_size)))
     return [(i * batch_size, min(size, (i + 1) * batch_size))
-            for i in range(0, num_batches)]
+            for i in range(num_batches)]
 
 
 def _slice_arrays(arrays, start=None, stop=None):

tests/keras/backend/backend_test.py

@@ -729,7 +729,7 @@ def test_in_top_k(self):
         targets = np.random.randint(num_classes, size=batch_size, dtype='int32')
 
         # (k == 0 or k > num_classes) does not raise an error but just return an unmeaningful tensor.
-        for k in range(0, num_classes + 1):
+        for k in range(num_classes + 1):
             z_list = [b.eval(b.in_top_k(b.variable(predictions, dtype='float32'),
                                         b.variable(targets, dtype='int32'), k))
                       for b in [KTH, KTF]]