diff options
Diffstat (limited to 'tensorflow/contrib/layers/python/layers/layers_test.py')
| -rw-r--r-- | tensorflow/contrib/layers/python/layers/layers_test.py | 73 |
1 files changed, 5 insertions, 68 deletions
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index 5aa2253516..ff7f0e4462 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -1774,12 +1774,10 @@ class BatchNormTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'undefined'): _layers.batch_norm(inputs, data_format='NCHW') - def _testCreateOp(self, fused, dtype=None): - if dtype is None: - dtype = dtypes.float32 + def _testCreateOp(self, fused): height, width = 3, 3 with self.test_session(): - images = np.random.uniform(size=(5, height, width, 3)).astype(dtype.as_numpy_dtype) + images = np.random.uniform(size=(5, height, width, 3)).astype('f') output = _layers.batch_norm(images, fused=fused) expected_name = ('BatchNorm/FusedBatchNorm' if fused else 'BatchNorm/batchnorm') @@ -1794,9 +1792,6 @@ class BatchNormTest(test.TestCase): def testCreateOpFused(self): self._testCreateOp(True) - def testCreateOpFusedFloat16(self): - self._testCreateOp(True, dtypes.float16) - def _testCreateOpBetaRegularizer(self, fused=True): height, width = 3, 3 with self.test_session(): @@ -2664,68 +2659,10 @@ class BatchNormTest(test.TestCase): def testBatchNormBeta(self): # Test case for 11673 with self.test_session() as sess: - a_32 = array_ops.placeholder(dtypes.float32, shape=(10, 10, 10, 10)) - b_32 = _layers.batch_norm(a_32, center=False, data_format='NCHW', - zero_debias_moving_mean=True) - a_16 = array_ops.placeholder(dtypes.float16, shape=(10, 10, 10, 10)) - b_16 = _layers.batch_norm(a_16, center=False, data_format='NCHW', - zero_debias_moving_mean=True) - sess.run(variables_lib.global_variables_initializer()) - - def testVariablesAreFloat32(self): - height, width = 3, 3 - with self.test_session(): - images = random_ops.random_uniform((5, height, width, 3), - seed=1, dtype=dtypes.float16) - _layers.batch_norm(images, scale=True) - beta = variables.get_variables_by_name('beta')[0] - gamma = variables.get_variables_by_name('gamma')[0] - self.assertEqual(beta.dtype, dtypes.float32_ref) - self.assertEqual(gamma.dtype, dtypes.float32_ref) - moving_mean = variables.get_variables_by_name('moving_mean')[0] - moving_variance = variables.get_variables_by_name('moving_variance')[0] - self.assertEqual(moving_mean.dtype, dtypes.float32_ref) - self.assertEqual(moving_variance.dtype, dtypes.float32_ref) - - def _runFusedBatchNorm(self, shape, dtype): - channels = shape[1] - images = np.arange(np.product(shape), dtype=dtype).reshape(shape) - beta = init_ops.constant_initializer( - np.arange( - 2, channels + 2, dtype=np.float32)) - gamma = init_ops.constant_initializer( - np.arange( - 10, channels + 10, dtype=np.float32) * 2.0) - mean = init_ops.constant_initializer( - np.arange( - 3, channels + 3, dtype=np.float32) * 5.0) - variance = init_ops.constant_initializer( - np.arange( - 1, channels + 1, dtype=np.float32) * 4.0) - output = _layers.batch_norm( - images, - fused=True, - is_training=True, - scale=True, - epsilon=0.5, - param_initializers={ - 'beta': beta, - 'gamma': gamma, - 'moving_mean': mean, - 'moving_variance': variance, - }, - data_format='NCHW') - with self.test_session(use_gpu=True) as sess: + a = array_ops.placeholder(dtypes.float32, shape=(10, 10, 10, 10)) + b = _layers.batch_norm(a, center=False, data_format='NCHW', + zero_debias_moving_mean=True) sess.run(variables_lib.global_variables_initializer()) - return sess.run(output) - - def testFusedBatchNormFloat16MatchesFloat32(self): - if test.is_gpu_available(cuda_only=True): - shape = [5, 4, 2, 3] - res_32 = self._runFusedBatchNorm(shape, np.float32) - res_16 = self._runFusedBatchNorm(shape, np.float16) - self.assertAllClose(res_32, res_16, rtol=1e-3) - def testAdjustmentCreated(self): # Tests that the adjustment is appropriately passed to and used by the core |