Keras 2 _*generator displaying warning always about semantic changes …

…from Keras 1 (keras-team#7001)

* Warn always about semantic changes if having keras1 args in *_generator calls.

* modified api upgrade warning message to be more detailed

* minor fix to pep8 syntax

keras/legacy/interfaces.py

@@ -577,14 +577,21 @@ def generator_methods_args_preprocessor(args, kwargs):
             if hasattr(generator, 'batch_size'):
                 kwargs['steps_per_epoch'] = samples_per_epoch // generator.batch_size
             else:
-                warnings.warn('The semantics of the Keras 2 argument '
-                              ' `steps_per_epoch` is not the same as the '
-                              'Keras 1 argument `samples_per_epoch`. '
-                              '`steps_per_epoch` is the number of batches '
-                              'to draw from the generator at each epoch. '
-                              'Update your method calls accordingly.', stacklevel=3)
                 kwargs['steps_per_epoch'] = samples_per_epoch
             converted.append(('samples_per_epoch', 'steps_per_epoch'))
+
+    keras1_args = {'samples_per_epoch', 'val_samples', 'nb_epoch', 'nb_val_samples', 'nb_worker'}
+    if keras1_args.intersection(kwargs.keys()):
+        warnings.warn('The semantics of the Keras 2 argument '
+                      '`steps_per_epoch` is not the same as the '
+                      'Keras 1 argument `samples_per_epoch`. '
+                      '`steps_per_epoch` is the number of batches '
+                      'to draw from the generator at each epoch. '
+                      'Basically steps_per_epoch = samples_per_epoch/batch_size. '
+                      'Similarly `nb_val_samples`->`validation_steps` and '
+                      '`val_samples`->`steps` arguments have changed. '
+                      'Update your method calls accordingly.', stacklevel=3)
+
     return args, kwargs, converted
 
 

tests/keras/legacy/interface_test.py

@@ -808,6 +808,11 @@ def pred_generator():
                         validation_data=val_generator(),
                         nb_val_samples=1,
                         nb_worker=1)
+    model.fit_generator(train_generator(),
+                        10,
+                        1,
+                        nb_val_samples=1,
+                        nb_worker=1)
     model.evaluate_generator(generator=train_generator(),
                              val_samples=2,
                              nb_worker=1)