CLN: minor fix

Author: facaiy

tensorflow_addons/optimizers/BUILD

@@ -3,7 +3,7 @@ licenses(["notice"])  # Apache 2.0
 package(default_visibility = ["//visibility:public"])
 
 py_library(
-    name = "opt_py",
+    name = "optimizers_py",
     srcs = [
         "__init__.py",
         "python/__init__.py",
@@ -20,7 +20,7 @@ py_test(
     ],
     main = "python/lazy_adam_optimizer_test.py",
     deps = [
-        ":opt_py",
+        ":optimizers_py",
     ],
     srcs_version = "PY2AND3",
 )

tensorflow_addons/optimizers/python/lazy_adam_optimizer.py

@@ -29,12 +29,10 @@
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
-from tensorflow.python.ops import state_ops
 
 
 class LazyAdamOptimizer(adam.Adam):
-    """Variant of the Adam optimizer that handles sparse updates more
-    efficiently.
+    """Variant of the Adam optimizer that handles sparse updates more efficiently.
 
     The original Adam algorithm maintains two moving-average accumulators for
     each trainable variable; the accumulators are updated at every step.

tensorflow_addons/optimizers/python/lazy_adam_optimizer_test.py

@@ -60,7 +60,9 @@ def get_beta_accumulators(opt, dtype):
     return (beta_1_power, beta_2_power)
 
 
-class AdamOptimizerTest(test.TestCase):
+class LazyAdamOptimizerTest(test.TestCase):
+
+    # TODO: remove v1 tests (keep pace with adam_test.py in keras).
     @test_util.run_deprecated_v1
     def testSparse(self):
         for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
@@ -138,9 +140,11 @@ def testSparseRepeatedIndices(self):
                 aggregated_update_var = variables.Variable([[1.0], [2.0]],
                                                            dtype=dtype)
                 grad_repeated_index = ops.IndexedSlices(
-                    constant_op.constant([0.1, 0.1], shape=[2, 1],
+                    constant_op.constant([0.1, 0.1],
+                                         shape=[2, 1],
                                          dtype=dtype),
-                    constant_op.constant([1, 1]), constant_op.constant([2, 1]))
+                    constant_op.constant([1, 1]),
+                    constant_op.constant([2, 1]))
                 grad_aggregated = ops.IndexedSlices(
                     constant_op.constant([0.2], shape=[1, 1], dtype=dtype),
                     constant_op.constant([1]), constant_op.constant([2, 1]))