Minor optimisations (keras-team#12627)

* Avoid importing copy * Minor optimisation * Cleanup * PEP8 * Revert changes
franp9am · Apr 8, 2019 · b2771d1 · b2771d1
1 parent 30fe4ff
commit b2771d1
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Showing 2 changed files with 5 additions and 14 deletions.
diff --git a/keras/engine/base_layer.py b/keras/engine/base_layer.py
@@ -4,7 +4,6 @@
 from __future__ import absolute_import
 from __future__ import division
 
-import copy
 import re
 from six.moves import zip
 
@@ -139,10 +138,7 @@ def __init__(self, **kwargs):
             if 'batch_input_shape' in kwargs:
                 batch_input_shape = tuple(kwargs['batch_input_shape'])
             elif 'input_shape' in kwargs:
-                if 'batch_size' in kwargs:
-                    batch_size = kwargs['batch_size']
-                else:
-                    batch_size = None
+                batch_size = kwargs.get('batch_size')
                 batch_input_shape = (
                     batch_size,) + tuple(kwargs['input_shape'])
             self.batch_input_shape = batch_input_shape
@@ -155,10 +151,7 @@ def __init__(self, **kwargs):
                 dtype = K.floatx()
             self.dtype = dtype
 
-        if 'weights' in kwargs:
-            self._initial_weights = kwargs['weights']
-        else:
-            self._initial_weights = None
+        self._initial_weights = kwargs.get('weights')
 
     @staticmethod
     def _node_key(layer, node_index):
@@ -441,7 +434,7 @@ def __call__(self, inputs, **kwargs):
 
             # Handle mask propagation.
             previous_mask = _collect_previous_mask(inputs)
-            user_kwargs = copy.copy(kwargs)
+            user_kwargs = kwargs.copy()
             if not is_all_none(previous_mask):
                 # The previous layer generated a mask.
                 if has_arg(self.call, 'mask'):

diff --git a/keras/engine/training_arrays.py b/keras/engine/training_arrays.py
@@ -431,8 +431,7 @@ def test_loop(model, f, ins,
             batch_outs = f(ins)
             if isinstance(batch_outs, list):
                 if step == 0:
-                    for _ in enumerate(batch_outs):
-                        outs.append(0.)
+                    outs.extend([0.] * len(batch_outs))
                 for i, batch_out in enumerate(batch_outs):
                     if i in stateful_metric_indices:
                         outs[i] = float(batch_out)
@@ -471,8 +470,7 @@ def test_loop(model, f, ins,
             batch_outs = f(ins_batch)
             if isinstance(batch_outs, list):
                 if batch_index == 0:
-                    for batch_out in enumerate(batch_outs):
-                        outs.append(0.)
+                    outs.extend([0.] * len(batch_outs))
                 for i, batch_out in enumerate(batch_outs):
                     if i in stateful_metric_indices:
                         outs[i] = batch_out