diff options
Diffstat (limited to 'tensorflow/python/training/ftrl_test.py')
| -rw-r--r-- | tensorflow/python/training/ftrl_test.py | 28 |
1 files changed, 14 insertions, 14 deletions
diff --git a/tensorflow/python/training/ftrl_test.py b/tensorflow/python/training/ftrl_test.py index 76ca5b45c9..09d6fe36d3 100644 --- a/tensorflow/python/training/ftrl_test.py +++ b/tensorflow/python/training/ftrl_test.py @@ -37,7 +37,7 @@ class FtrlOptimizerTest(test.TestCase): def doTestFtrlwithoutRegularization(self, use_resource=False): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session() as sess: + with self.cached_session() as sess: if use_resource: var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) @@ -76,7 +76,7 @@ class FtrlOptimizerTest(test.TestCase): def testFtrlwithoutRegularization2(self): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session() as sess: + with self.cached_session() as sess: var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -105,7 +105,7 @@ class FtrlOptimizerTest(test.TestCase): def testMinimizeSparseResourceVariable(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: - with self.test_session(): + with self.cached_session(): var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) x = constant_op.constant([[4.0], [5.0]], dtype=dtype) pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) @@ -121,7 +121,7 @@ class FtrlOptimizerTest(test.TestCase): def testFtrlWithL1(self): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session() as sess: + with self.cached_session() as sess: var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -150,7 +150,7 @@ class FtrlOptimizerTest(test.TestCase): def testFtrlWithL1_L2(self): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session() as sess: + with self.cached_session() as sess: var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -186,7 +186,7 @@ class FtrlOptimizerTest(test.TestCase): weights will tend to have smaller magnitudes with this parameter set. """ for dtype in [dtypes.half, dtypes.float32]: - with self.test_session() as sess: + with self.cached_session() as sess: var0 = variables.Variable([1.0, 2.0], dtype=dtype) var1 = variables.Variable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -335,7 +335,7 @@ class FtrlOptimizerTest(test.TestCase): # FTRL-Proximal performs same updates as Adagrad or GradientDescent. def testEquivAdagradwithoutRegularization(self): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(): + with self.cached_session(): val0, val1 = self.applyOptimizer( ftrl.FtrlOptimizer( 3.0, @@ -346,7 +346,7 @@ class FtrlOptimizerTest(test.TestCase): l2_regularization_strength=0.0), dtype) - with self.test_session(): + with self.cached_session(): val2, val3 = self.applyOptimizer( adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1), dtype) @@ -355,7 +355,7 @@ class FtrlOptimizerTest(test.TestCase): def testEquivSparseAdagradwithoutRegularization(self): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(): + with self.cached_session(): val0, val1 = self.applyOptimizer( ftrl.FtrlOptimizer( 3.0, @@ -367,7 +367,7 @@ class FtrlOptimizerTest(test.TestCase): dtype, is_sparse=True) - with self.test_session(): + with self.cached_session(): val2, val3 = self.applyOptimizer( adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1), dtype, @@ -378,7 +378,7 @@ class FtrlOptimizerTest(test.TestCase): def testEquivSparseGradientDescentwithoutRegularization(self): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(): + with self.cached_session(): val0, val1 = self.applyOptimizer( ftrl.FtrlOptimizer( 3.0, @@ -390,7 +390,7 @@ class FtrlOptimizerTest(test.TestCase): dtype, is_sparse=True) - with self.test_session(): + with self.cached_session(): val2, val3 = self.applyOptimizer( gradient_descent.GradientDescentOptimizer(3.0), dtype, @@ -401,7 +401,7 @@ class FtrlOptimizerTest(test.TestCase): def testEquivGradientDescentwithoutRegularization(self): for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(): + with self.cached_session(): val0, val1 = self.applyOptimizer( ftrl.FtrlOptimizer( 3.0, @@ -412,7 +412,7 @@ class FtrlOptimizerTest(test.TestCase): l2_regularization_strength=0.0), dtype) - with self.test_session(): + with self.cached_session(): val2, val3 = self.applyOptimizer( gradient_descent.GradientDescentOptimizer(3.0), dtype) |