Automated rollback of commit 57527f7e47e3e67966b432065f510a601a4d8647

PiperOrigin-RevId: 204516578

2 files changed, 4 insertions, 100 deletions

diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py
index 5d66db232a..53d907a2cc 100644
--- a/tensorflow/python/keras/callbacks.py
+++ b/tensorflow/python/keras/callbacks.py
@@ -32,10 +32,8 @@ import numpy as np
 import six
 
 from tensorflow.python.keras import backend as K
-from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.utils.generic_utils import Progbar
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Variable
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary as tf_summary
 from tensorflow.python.util.tf_export import tf_export
@@ -644,35 +642,17 @@ class LearningRateScheduler(Callback):
     self.verbose = verbose
 
   def on_epoch_begin(self, epoch, logs=None):
-    # TODO(yashkatariya): Change the property checking when the learning
-    # rate attribute is unified across all TF Optimizers.
-    if isinstance(self.model.optimizer, optimizers.TFOptimizer):
-      if not hasattr(self.model.optimizer.optimizer, '_lr') and not hasattr(
-          self.model.optimizer.optimizer, '_learning_rate'):
-        raise ValueError(
-            'TF Optimizer must have a "_lr" or "_learning_rate" attribute.')
-      else:
-        opt = self.model.optimizer.optimizer
-        if hasattr(opt, '_lr'):
-          opt_lr = Variable(opt._lr)  # pylint: disable=protected-access
-        elif hasattr(opt, '_learning_rate'):
-          opt_lr = Variable(opt._learning_rate)  # pylint: disable=protected-access
-    else:
-      if not hasattr(self.model.optimizer, 'lr'):
-        raise ValueError('Optimizer must have a "lr" attribute.')
-      else:
-        opt = self.model.optimizer
-        opt_lr = opt.lr
-
+    if not hasattr(self.model.optimizer, 'lr'):
+      raise ValueError('Optimizer must have a "lr" attribute.')
     try:  # new API
-      lr = float(K.get_value(opt_lr))
+      lr = float(K.get_value(self.model.optimizer.lr))
       lr = self.schedule(epoch, lr)
     except TypeError:  # Support for old API for backward compatibility
       lr = self.schedule(epoch)
     if not isinstance(lr, (float, np.float32, np.float64)):
       raise ValueError('The output of the "schedule" function '
                        'should be float.')
-    K.set_value(opt_lr, lr)
+    K.set_value(self.model.optimizer.lr, lr)
     if self.verbose > 0:
       print('\nEpoch %05d: LearningRateScheduler reducing learning '
             'rate to %s.' % (epoch + 1, lr))
diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py
index 244d48591c..45598cafd3 100644
--- a/tensorflow/python/keras/callbacks_test.py
+++ b/tensorflow/python/keras/callbacks_test.py
@@ -29,16 +29,10 @@ import numpy as np
 
 from tensorflow.core.framework import summary_pb2
 from tensorflow.python import keras
-from tensorflow.python.eager import context
-from tensorflow.python.framework import test_util
 from tensorflow.python.keras import testing_utils
-from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Variable
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary.writer import writer_cache
-from tensorflow.python.training.adam import AdamOptimizer
-from tensorflow.python.training.gradient_descent import GradientDescentOptimizer
-
 
 try:
   import h5py  # pylint:disable=g-import-not-at-top
@@ -376,76 +370,6 @@ class KerasCallbacksTest(test.TestCase):
           float(keras.backend.get_value(
               model.optimizer.lr)) - 0.01 / 4) < keras.backend.epsilon()
 
-  @test_util.run_in_graph_and_eager_modes
-  def test_TF_LearningRateScheduler_Adam(self):
-    with self.test_session():
-      with context.eager_mode():
-        np.random.seed(1337)
-        (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
-            train_samples=TRAIN_SAMPLES,
-            test_samples=TEST_SAMPLES,
-            input_shape=(INPUT_DIM,),
-            num_classes=NUM_CLASSES)
-        y_test = keras.utils.to_categorical(y_test)
-        y_train = keras.utils.to_categorical(y_train)
-        model = keras.models.Sequential()
-        model.add(
-            keras.layers.Dense(
-                NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
-        model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
-        model.compile(
-            loss='categorical_crossentropy',
-            optimizer=AdamOptimizer(),
-            metrics=['accuracy'])
-        cbks = [keras.callbacks.LearningRateScheduler(lambda x: 1. / (1. + x))]
-        model.fit(
-            x_train,
-            y_train,
-            batch_size=BATCH_SIZE,
-            validation_data=(x_test, y_test),
-            callbacks=cbks,
-            epochs=5,
-            verbose=0)
-        opt_lr = model.optimizer.optimizer._lr
-        self.assertLess(
-            float(keras.backend.get_value(
-                Variable(opt_lr))) - 0.2, keras.backend.epsilon())
-
-  @test_util.run_in_graph_and_eager_modes
-  def test_TF_LearningRateScheduler_GradientDescent(self):
-    with self.test_session():
-      with context.eager_mode():
-        np.random.seed(1337)
-        (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
-            train_samples=TRAIN_SAMPLES,
-            test_samples=TEST_SAMPLES,
-            input_shape=(INPUT_DIM,),
-            num_classes=NUM_CLASSES)
-        y_test = keras.utils.to_categorical(y_test)
-        y_train = keras.utils.to_categorical(y_train)
-        model = keras.models.Sequential()
-        model.add(
-            keras.layers.Dense(
-                NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu'))
-        model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax'))
-        model.compile(
-            loss='categorical_crossentropy',
-            optimizer=GradientDescentOptimizer(1e-3),
-            metrics=['accuracy'])
-        cbks = [keras.callbacks.LearningRateScheduler(lambda x: 1. / (1. + x))]
-        model.fit(
-            x_train,
-            y_train,
-            batch_size=BATCH_SIZE,
-            validation_data=(x_test, y_test),
-            callbacks=cbks,
-            epochs=5,
-            verbose=0)
-        opt_lr = model.optimizer.optimizer._learning_rate
-        self.assertLess(
-            float(keras.backend.get_value(
-                Variable(opt_lr))) - 0.2, keras.backend.epsilon())
-
   def test_ReduceLROnPlateau(self):
     with self.test_session():
       np.random.seed(1337)