Prefer generator expressions over list comprehensions

tensorflow/lite/python/lite.py

@@ -279,7 +279,7 @@ class QuantizationMode(object):
     })
 
     for node_def in self._graph_def.node:
-      if any([op in node_def.name for op in training_quant_ops]):
+      if any(op in node_def.name for op in training_quant_ops):
         return True
     return False
 

tensorflow/python/eager/function.py

@@ -726,7 +726,7 @@ class _DelayedRewriteGradientFunctions(object):
     # pylint: enable=protected-access
 
     capture_mapping = dict(
-        zip([ops.tensor_id(t) for t in self._func_graph.outputs], op.outputs))
+        zip((ops.tensor_id(t) for t in self._func_graph.outputs), op.outputs))
     remapped_captures = [
         capture_mapping.get(ops.tensor_id(capture), capture)
         for capture in backwards_function.captured_inputs

tensorflow/python/framework/subscribe.py

@@ -58,8 +58,7 @@ def _recursive_apply(tensors, apply_fn):
       return tuple(tensors)
     return tensors_type(*tensors)  # collections.namedtuple
   elif tensors_type is dict:
-    return dict([(k, _recursive_apply(v, apply_fn)) for k, v in tensors.items()
-                ])
+    return dict((k, _recursive_apply(v, apply_fn)) for k, v in tensors.items())
   else:
     raise TypeError('_recursive_apply argument %r has invalid type %r' %
                     (tensors, tensors_type))

tensorflow/python/framework/test_util.py

@@ -486,7 +486,7 @@ def skip_if_error(test_obj, error_type, messages=None):
   try:
     yield
   except error_type as e:
-    if not messages or any([message in str(e) for message in messages]):
+    if not messages or any(message in str(e) for message in messages):
       test_obj.skipTest("Skipping error: {}".format(str(e)))
     else:
       raise

tensorflow/python/keras/callbacks.py

@@ -2341,7 +2341,7 @@ class CSVLogger(Callback):
 
     if self.model.stop_training:
       # We set NA so that csv parsers do not fail for this last epoch.
-      logs = dict([(k, logs[k]) if k in logs else (k, 'NA') for k in self.keys])
+      logs = dict((k, logs[k]) if k in logs else (k, 'NA') for k in self.keys)
 
     if not self.writer:
 

tensorflow/python/keras/layers/preprocessing/category_crossing.py

@@ -140,9 +140,9 @@ class CategoryCrossing(Layer):
   def call(self, inputs):
     depth_tuple = self._depth_tuple if self.depth else (len(inputs),)
     ragged_out = sparse_out = False
-    if any([ragged_tensor.is_ragged(inp) for inp in inputs]):
+    if any(ragged_tensor.is_ragged(inp) for inp in inputs):
       ragged_out = True
-    elif any([isinstance(inp, sparse_tensor.SparseTensor) for inp in inputs]):
+    elif any(isinstance(inp, sparse_tensor.SparseTensor) for inp in inputs):
       sparse_out = True
 
     outputs = []

tensorflow/python/keras/layers/preprocessing/hashing.py

@@ -168,7 +168,7 @@ class Hashing(Layer):
   def _process_input_list(self, inputs):
     # TODO(momernick): support ragged_cross_hashed with corrected fingerprint
     # and siphash.
-    if any([isinstance(inp, ragged_tensor.RaggedTensor) for inp in inputs]):
+    if any(isinstance(inp, ragged_tensor.RaggedTensor) for inp in inputs):
       raise ValueError('Hashing with ragged input is not supported yet.')
     sparse_inputs = [
         inp for inp in inputs if isinstance(inp, sparse_tensor.SparseTensor)

tensorflow/python/keras/saving/hdf5_format.py

@@ -876,7 +876,7 @@ def _legacy_weights(layer):
       non_trainable_weights.
   """
   weights = layer.trainable_weights + layer.non_trainable_weights
-  if any([not isinstance(w, variables_module.Variable) for w in weights]):
+  if any(not isinstance(w, variables_module.Variable) for w in weights):
     raise NotImplementedError(
         'Save or restore weights that is not an instance of `tf.Variable` is '
         'not supported in h5, use `save_format=\'tf\'` instead. Got a model '

tensorflow/tools/dockerfiles/assembler.py

@@ -558,7 +558,7 @@ def main(argv):
       # Only build images for host architecture
       proc_arch = platform.processor()
       is_x86 = proc_arch.startswith('x86')
-      if (is_x86 and any([arch in tag for arch in ['ppc64le']]) or
+      if (is_x86 and any(arch in tag for arch in ['ppc64le']) or
           not is_x86 and proc_arch not in tag):
         continue
 

third_party/gpus/check_cuda_libs.py

@@ -62,7 +62,7 @@ def check_cuda_lib(path, check_soname=True):
     output = subprocess.check_output([objdump, "-p", path]).decode("utf-8")
     output = [line for line in output.splitlines() if "SONAME" in line]
     sonames = [line.strip().split(" ")[-1] for line in output]
-    if not any([soname == os.path.basename(path) for soname in sonames]):
+    if not any(soname == os.path.basename(path) for soname in sonames):
       raise ConfigError("None of the libraries match their SONAME: " + path)