Rolls back exposure of the optimizers_v2 in tf 1.x, until the keras model estimator backward incompatibility issues can be sorted out. (Namely, the use of global step in estimators & trying to assign to iterations not working in optimizers_v2

PiperOrigin-RevId: 225458875

tensorflow/python/keras/engine/saving_test.py

@@ -33,7 +33,6 @@ from tensorflow.python.framework import test_util
 from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.engine import saving
 from tensorflow.python.keras.engine import training
-from tensorflow.python.keras.optimizer_v2 import rmsprop
 from tensorflow.python.lib.io import file_io
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import random_ops
@@ -334,6 +333,7 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
 
 class TestWholeModelSaving(test.TestCase):
 
+  @test_util.run_v1_only('b/120994067')
   def test_sequential_model_saving(self):
     if h5py is None:
       self.skipTest('h5py required to run this test')
@@ -345,7 +345,7 @@ class TestWholeModelSaving(test.TestCase):
       model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
       model.compile(
           loss=keras.losses.MSE,
-          optimizer=rmsprop.RMSprop(lr=0.0001),
+          optimizer=keras.optimizers.RMSprop(lr=0.0001),
           metrics=[
               keras.metrics.categorical_accuracy,
               keras.metrics.CategoricalAccuracy()

tensorflow/python/keras/engine/topology_test.py

@@ -30,6 +30,7 @@ from tensorflow.python.keras import keras_parameterized
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.keras.engine import input_layer as input_layer_lib
 from tensorflow.python.keras.engine import network as network_lib
+from tensorflow.python.keras.optimizer_v2 import gradient_descent
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
@@ -859,7 +860,7 @@ class TopologyConstructionTest(keras_parameterized.TestCase):
     x = np.ones((100, 2))
     y = np.ones((100, 2))
     model.compile(
-        optimizer='sgd',
+        optimizer=gradient_descent.SGD(),
         loss='mse',
         run_eagerly=testing_utils.should_run_eagerly())
     loss = model.train_on_batch(x, y)
@@ -908,7 +909,7 @@ class TopologyConstructionTest(keras_parameterized.TestCase):
     model.add(keras.layers.Dense(3))
     model.compile(
         loss='mse',
-        optimizer='sgd',
+        optimizer=gradient_descent.SGD(),
         metrics=['acc'],
         run_eagerly=testing_utils.should_run_eagerly())
 

tensorflow/python/keras/models_test.py

@@ -31,7 +31,6 @@ from tensorflow.python.framework import test_util
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import metrics
 from tensorflow.python.keras import models
-from tensorflow.python.keras import optimizers
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import resource_variable_ops
@@ -482,7 +481,7 @@ class TestCloneAndBuildModel(test.TestCase):
     self.assert_optimizer_iterations_increases(adam.AdamOptimizer(0.01))
 
   def test_replace_keras_optimizer_iterations_variable(self):
-    self.assert_optimizer_iterations_increases(optimizers.Adam())
+    self.assert_optimizer_iterations_increases('adam')
 
   def test_clone_and_build_sequential_model_without_inputs_defined(self):
     with self.cached_session():

tensorflow/python/keras/optimizer_v2/adadelta.py

@@ -25,7 +25,7 @@ from tensorflow.python.training import training_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
-@tf_export('keras.optimizers.Adadelta')
+@tf_export('keras.optimizers.Adadelta', v1=[])
 class Adadelta(optimizer_v2.OptimizerV2):
   r"""Optimizer that implements the Adadelta algorithm.
 

tensorflow/python/keras/optimizer_v2/adagrad.py

@@ -30,7 +30,7 @@ from tensorflow.python.ops import state_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
-@tf_export('keras.optimizers.Adagrad')
+@tf_export('keras.optimizers.Adagrad', v1=[])
 class Adagrad(optimizer_v2.OptimizerV2):
   r"""Optimizer that implements the Adagrad algorithm.
 

tensorflow/python/keras/optimizer_v2/adam.py

@@ -27,7 +27,7 @@ from tensorflow.python.training import training_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
-@tf_export('keras.optimizers.Adam')
+@tf_export('keras.optimizers.Adam', v1=[])
 class Adam(optimizer_v2.OptimizerV2):
   """Optimizer that implements the Adam algorithm.
 

tensorflow/python/keras/optimizer_v2/adamax.py

@@ -28,7 +28,7 @@ from tensorflow.python.training import training_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
-@tf_export('keras.optimizers.Adamax')
+@tf_export('keras.optimizers.Adamax', v1=[])
 class Adamax(adam.Adam):
   """Optimizer that implements the Adamax algorithm.
 

tensorflow/python/keras/optimizer_v2/ftrl.py

@@ -24,7 +24,7 @@ from tensorflow.python.training import training_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
-@tf_export('keras.optimizers.Ftrl')
+@tf_export('keras.optimizers.Ftrl', v1=[])
 class Ftrl(optimizer_v2.OptimizerV2):
   """Optimizer that implements the FTRL algorithm.
 

tensorflow/python/keras/optimizer_v2/gradient_descent.py

@@ -24,7 +24,7 @@ from tensorflow.python.training import training_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
-@tf_export("keras.optimizers.SGD")
+@tf_export("keras.optimizers.SGD", v1=[])
 class SGD(optimizer_v2.OptimizerV2):
   """Stochastic gradient descent and momentum optimizer.
 

tensorflow/python/keras/optimizer_v2/optimizer_v2.py

@@ -67,7 +67,7 @@ def _deduplicate_indexed_slices(values, indices):
 
 
 @six.add_metaclass(abc.ABCMeta)
-@tf_export("keras.optimizers.Optimizer")
+@tf_export("keras.optimizers.Optimizer", v1=[])
 class OptimizerV2(checkpointable.CheckpointableBase):
   """Updated base class for optimizers.
 

tensorflow/python/keras/optimizer_v2/rmsprop.py

@@ -23,7 +23,7 @@ from tensorflow.python.training import training_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
-@tf_export("keras.optimizers.RMSprop")
+@tf_export("keras.optimizers.RMSprop", v1=[])
 class RMSprop(optimizer_v2.OptimizerV2):
   r"""Optimizer that implements the RMSprop algorithm.
 

tensorflow/python/keras/optimizers.py

@@ -45,6 +45,7 @@ from tensorflow.python.training.checkpointable import base as checkpointable
 from tensorflow.python.util.tf_export import tf_export
 
 
+@tf_export(v1=['keras.optimizers.Optimizer'])
 class Optimizer(object):
   """Abstract optimizer base class.
 
@@ -158,6 +159,7 @@ class Optimizer(object):
     return cls(**config)
 
 
+@tf_export(v1=['keras.optimizers.SGD'])
 class SGD(Optimizer):
   """Stochastic gradient descent optimizer.
 
@@ -222,6 +224,7 @@ class SGD(Optimizer):
     return dict(list(base_config.items()) + list(config.items()))
 
 
+@tf_export(v1=['keras.optimizers.RMSprop'])
 class RMSprop(Optimizer):
   """RMSProp optimizer.
 
@@ -288,6 +291,7 @@ class RMSprop(Optimizer):
     return dict(list(base_config.items()) + list(config.items()))
 
 
+@tf_export(v1=['keras.optimizers.Adagrad'])
 class Adagrad(Optimizer):
   """Adagrad optimizer.
 
@@ -354,6 +358,7 @@ class Adagrad(Optimizer):
     return dict(list(base_config.items()) + list(config.items()))
 
 
+@tf_export(v1=['keras.optimizers.Adadelta'])
 class Adadelta(Optimizer):
   """Adadelta optimizer.
 
@@ -437,6 +442,7 @@ class Adadelta(Optimizer):
     return dict(list(base_config.items()) + list(config.items()))
 
 
+@tf_export(v1=['keras.optimizers.Adam'])
 class Adam(Optimizer):
   """Adam optimizer.
 
@@ -533,6 +539,7 @@ class Adam(Optimizer):
     return dict(list(base_config.items()) + list(config.items()))
 
 
+@tf_export(v1=['keras.optimizers.Adamax'])
 class Adamax(Optimizer):
   """Adamax optimizer from Adam paper's Section 7.
 
@@ -799,6 +806,7 @@ def deserialize(config, custom_objects=None):
   Returns:
       A Keras Optimizer instance.
   """
+  if tf2.enabled():
     all_classes = {
         'adadelta': adadelta_v2.Adadelta,
         'adagrad': adagrad_v2.Adagrad,
@@ -808,8 +816,17 @@ def deserialize(config, custom_objects=None):
         'rmsprop': rmsprop_v2.RMSprop,
         'sgd': gradient_descent_v2.SGD
     }
-  if not tf2.enabled():
+  else:
-    all_classes['nadam'] = Nadam
+    all_classes = {
+        'adadelta': Adadelta,
+        'adagrad': Adagrad,
+        'adam': Adam,
+        'adamax': Adamax,
+        'nadam': Nadam,
+        'rmsprop': RMSprop,
+        'sgd': SGD,
+        'tfoptimizer': TFOptimizer
+    }
 
   # Make deserialization case-insensitive for built-in optimizers.
   if config['class_name'].lower() in all_classes:

tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt

@@ -1,36 +1,15 @@
 path: "tensorflow.keras.optimizers.Adadelta"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.adadelta.Adadelta\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Adadelta\'>"
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.base.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
-  member {
-    name: "iterations"
-    mtype: "<type \'property\'>"
-  }
-  member {
-    name: "weights"
-    mtype: "<type \'property\'>"
-  }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'learning_rate\', \'rho\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.95\', \'1e-07\', \'Adadelta\'], "
+    argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'1.0\', \'0.95\', \'None\', \'0.0\'], "
-  }
-  member_method {
-    name: "add_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], "
-  }
-  member_method {
-    name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
-  }
-  member_method {
-    name: "apply_gradients"
-    argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "from_config"
-    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
     name: "get_config"
@@ -40,14 +19,6 @@ tf_class {
     name: "get_gradients"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_slot_names"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "get_updates"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
@@ -56,16 +27,8 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "minimize"
-    argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
-  }
   member_method {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "variables"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt

@@ -1,36 +1,15 @@
 path: "tensorflow.keras.optimizers.Adagrad"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.adagrad.Adagrad\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Adagrad\'>"
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.base.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
-  member {
-    name: "iterations"
-    mtype: "<type \'property\'>"
-  }
-  member {
-    name: "weights"
-    mtype: "<type \'property\'>"
-  }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'learning_rate\', \'initial_accumulator_value\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.1\', \'1e-07\', \'Adagrad\'], "
+    argspec: "args=[\'self\', \'lr\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'None\', \'0.0\'], "
-  }
-  member_method {
-    name: "add_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], "
-  }
-  member_method {
-    name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
-  }
-  member_method {
-    name: "apply_gradients"
-    argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "from_config"
-    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
     name: "get_config"
@@ -40,14 +19,6 @@ tf_class {
     name: "get_gradients"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_slot_names"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "get_updates"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
@@ -56,16 +27,8 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "minimize"
-    argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
-  }
   member_method {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "variables"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt

@@ -1,36 +1,15 @@
 path: "tensorflow.keras.optimizers.Adam"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.adam.Adam\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Adam\'>"
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.base.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
-  member {
-    name: "iterations"
-    mtype: "<type \'property\'>"
-  }
-  member {
-    name: "weights"
-    mtype: "<type \'property\'>"
-  }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'learning_rate\', \'beta_1\', \'beta_2\', \'epsilon\', \'amsgrad\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-07\', \'False\', \'Adam\'], "
+    argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\', \'amsgrad\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'None\', \'0.0\', \'False\'], "
-  }
-  member_method {
-    name: "add_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], "
-  }
-  member_method {
-    name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
-  }
-  member_method {
-    name: "apply_gradients"
-    argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "from_config"
-    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
     name: "get_config"
@@ -40,14 +19,6 @@ tf_class {
     name: "get_gradients"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_slot_names"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "get_updates"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
@@ -56,16 +27,8 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "minimize"
-    argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
-  }
   member_method {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "variables"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt

@@ -1,37 +1,15 @@
 path: "tensorflow.keras.optimizers.Adamax"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.adamax.Adamax\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Adamax\'>"
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.adam.Adam\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.base.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
-  member {
-    name: "iterations"
-    mtype: "<type \'property\'>"
-  }
-  member {
-    name: "weights"
-    mtype: "<type \'property\'>"
-  }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'learning_rate\', \'beta_1\', \'beta_2\', \'epsilon\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-07\', \'Adamax\'], "
+    argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.0\'], "
-  }
-  member_method {
-    name: "add_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], "
-  }
-  member_method {
-    name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
-  }
-  member_method {
-    name: "apply_gradients"
-    argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "from_config"
-    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
     name: "get_config"
@@ -41,14 +19,6 @@ tf_class {
     name: "get_gradients"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_slot_names"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "get_updates"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
@@ -57,16 +27,8 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "minimize"
-    argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
-  }
   member_method {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "variables"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt

@@ -1,35 +1,14 @@
 path: "tensorflow.keras.optimizers.Optimizer"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.base.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
-  member {
-    name: "iterations"
-    mtype: "<type \'property\'>"
-  }
-  member {
-    name: "weights"
-    mtype: "<type \'property\'>"
-  }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'name\'], varargs=None, keywords=kwargs, defaults=None"
+    argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
-  }
-  member_method {
-    name: "add_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], "
-  }
-  member_method {
-    name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
-  }
-  member_method {
-    name: "apply_gradients"
-    argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "from_config"
-    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
     name: "get_config"
@@ -39,14 +18,6 @@ tf_class {
     name: "get_gradients"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_slot_names"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "get_updates"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
@@ -55,16 +26,8 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "minimize"
-    argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
-  }
   member_method {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "variables"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt

@@ -1,36 +1,15 @@
 path: "tensorflow.keras.optimizers.RMSprop"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.rmsprop.RMSprop\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.RMSprop\'>"
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.base.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
-  member {
-    name: "iterations"
-    mtype: "<type \'property\'>"
-  }
-  member {
-    name: "weights"
-    mtype: "<type \'property\'>"
-  }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'learning_rate\', \'rho\', \'momentum\', \'epsilon\', \'centered\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.0\', \'1e-07\', \'False\', \'RMSprop\'], "
+    argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'None\', \'0.0\'], "
-  }
-  member_method {
-    name: "add_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], "
-  }
-  member_method {
-    name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
-  }
-  member_method {
-    name: "apply_gradients"
-    argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "from_config"
-    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
     name: "get_config"
@@ -40,14 +19,6 @@ tf_class {
     name: "get_gradients"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_slot_names"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "get_updates"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
@@ -56,16 +27,8 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "minimize"
-    argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
-  }
   member_method {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "variables"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt

@@ -1,36 +1,15 @@
 path: "tensorflow.keras.optimizers.SGD"
 tf_class {
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.gradient_descent.SGD\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.SGD\'>"
-  is_instance: "<class \'tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2\'>"
+  is_instance: "<class \'tensorflow.python.keras.optimizers.Optimizer\'>"
-  is_instance: "<class \'tensorflow.python.training.checkpointable.base.CheckpointableBase\'>"
   is_instance: "<type \'object\'>"
-  member {
-    name: "iterations"
-    mtype: "<type \'property\'>"
-  }
-  member {
-    name: "weights"
-    mtype: "<type \'property\'>"
-  }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'learning_rate\', \'momentum\', \'nesterov\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.0\', \'False\', \'SGD\'], "
+    argspec: "args=[\'self\', \'lr\', \'momentum\', \'decay\', \'nesterov\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'0.0\', \'0.0\', \'False\'], "
-  }
-  member_method {
-    name: "add_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\', \'initializer\'], varargs=None, keywords=None, defaults=[\'zeros\'], "
-  }
-  member_method {
-    name: "add_weight"
-    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'trainable\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'zeros\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
-  }
-  member_method {
-    name: "apply_gradients"
-    argspec: "args=[\'self\', \'grads_and_vars\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "from_config"
-    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
     name: "get_config"
@@ -40,14 +19,6 @@ tf_class {
     name: "get_gradients"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_slot"
-    argspec: "args=[\'self\', \'var\', \'slot_name\'], varargs=None, keywords=None, defaults=None"
-  }
-  member_method {
-    name: "get_slot_names"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
   member_method {
     name: "get_updates"
     argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None"
@@ -56,16 +27,8 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "minimize"
-    argspec: "args=[\'self\', \'loss\', \'var_list\', \'grad_loss\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
-  }
   member_method {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "variables"
-    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
-  }
 }