diff options
| -rw-r--r-- | tensorflow/core/api_def/base_api/api_def_MatrixExponential.pbtxt | 31 | ||||
| -rw-r--r-- | tensorflow/core/kernels/matrix_exponential_op.cc | 1 | ||||
| -rw-r--r-- | tensorflow/core/ops/linalg_ops.cc | 2 | ||||
| -rw-r--r-- | tensorflow/core/public/version.h | 4 | ||||
| -rw-r--r-- | tensorflow/python/kernel_tests/BUILD | 2 | ||||
| -rw-r--r-- | tensorflow/python/kernel_tests/matrix_logarithm_op_test.py | 30 |
6 files changed, 29 insertions, 41 deletions
diff --git a/tensorflow/core/api_def/base_api/api_def_MatrixExponential.pbtxt b/tensorflow/core/api_def/base_api/api_def_MatrixExponential.pbtxt index d7b56aec87..46da1de1c3 100644 --- a/tensorflow/core/api_def/base_api/api_def_MatrixExponential.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_MatrixExponential.pbtxt @@ -1,32 +1,5 @@ op { graph_op_name: "MatrixExponential" - in_arg { - name: "input" - description: <<END -Shape is `[..., M, M]`. -END - } - out_arg { - name: "output" - description: <<END -Shape is `[..., M, M]`. - -@compatibility(scipy) -Equivalent to scipy.linalg.expm -@end_compatibility -END - } - summary: "Computes the matrix exponential of one or more square matrices:" - description: <<END -\\(exp(A) = \sum_{n=0}^\infty A^n/n!\\) - -The exponential is computed using a combination of the scaling and squaring -method and the Pade approximation. Details can be founds in: -Nicholas J. Higham, "The scaling and squaring method for the matrix exponential -revisited," SIAM J. Matrix Anal. Applic., 26:1179-1193, 2005. - -The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions -form square matrices. The output is a tensor of the same shape as the input -containing the exponential for all input submatrices `[..., :, :]`. -END + visibility: SKIP + summary: "Deprecated, use python implementation tf.linalg.matrix_exponential." } diff --git a/tensorflow/core/kernels/matrix_exponential_op.cc b/tensorflow/core/kernels/matrix_exponential_op.cc index 99db898301..01d4894438 100644 --- a/tensorflow/core/kernels/matrix_exponential_op.cc +++ b/tensorflow/core/kernels/matrix_exponential_op.cc @@ -49,6 +49,7 @@ class MatrixExponentialOp : public LinearAlgebraOp<Scalar> { TF_DISALLOW_COPY_AND_ASSIGN(MatrixExponentialOp); }; +// Deprecated kernels (2018/08/21). REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp<float>), float); REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp<double>), double); REGISTER_LINALG_OP("MatrixExponential", (MatrixExponentialOp<complex64>), diff --git a/tensorflow/core/ops/linalg_ops.cc b/tensorflow/core/ops/linalg_ops.cc index f37f79ddbf..1d4d51a25d 100644 --- a/tensorflow/core/ops/linalg_ops.cc +++ b/tensorflow/core/ops/linalg_ops.cc @@ -235,6 +235,8 @@ REGISTER_OP("MatrixInverse") .SetShapeFn(BatchUnchangedSquareShapeFn); REGISTER_OP("MatrixExponential") + .Deprecated( + 27, "Use Python implementation tf.linalg.matrix_exponential instead.") .Input("input: T") .Output("output: T") .Attr("T: {double, float, complex64, complex128}") diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 563564119f..4129c93af5 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -96,10 +96,12 @@ limitations under the License. // GraphDef. (7dec2017) // 27. Deprecate TensorArray ops v2 in favor of v3 and deprecated io_ops // deprecated in favor of V2 ops. (2018/01/23) +// 28. Deprecate MatrixExponential op in favor of Python implementation. +// (2018/08/21). #define TF_GRAPH_DEF_VERSION_MIN_PRODUCER 0 #define TF_GRAPH_DEF_VERSION_MIN_CONSUMER 0 -#define TF_GRAPH_DEF_VERSION 26 +#define TF_GRAPH_DEF_VERSION 27 // Checkpoint compatibility versions (the versions field in SavedSliceMeta). // diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index b9c5f26cb7..a9982a7ae0 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -601,7 +601,7 @@ tf_py_test( tf_py_test( name = "matrix_logarithm_op_test", - size = "small", + size = "medium", srcs = ["matrix_logarithm_op_test.py"], additional_deps = [ "//third_party/py/numpy", diff --git a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py index 24edc4f59f..723a15fbd1 100644 --- a/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py +++ b/tensorflow/python/kernel_tests/matrix_logarithm_op_test.py @@ -30,6 +30,7 @@ from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables +from tensorflow.python.ops.linalg import linalg_impl from tensorflow.python.platform import test @@ -39,7 +40,7 @@ class LogarithmOpTest(test.TestCase): inp = x.astype(np_type) with self.test_session(use_gpu=True): # Verify that expm(logm(A)) == A. - tf_ans = gen_linalg_ops.matrix_exponential( + tf_ans = linalg_impl.matrix_exponential( gen_linalg_ops.matrix_logarithm(inp)) out = tf_ans.eval() self.assertAllClose(inp, out, rtol=1e-4, atol=1e-3) @@ -98,16 +99,25 @@ class LogarithmOpTest(test.TestCase): self._verifyLogarithmComplex(np.empty([0, 2, 2], dtype=np.complex64)) self._verifyLogarithmComplex(np.empty([2, 0, 0], dtype=np.complex64)) - def testRandomSmallAndLarge(self): + def testRandomSmallAndLargeComplex64(self): np.random.seed(42) - for dtype in np.complex64, np.complex128: - for batch_dims in [(), (1,), (3,), (2, 2)]: - for size in 8, 31, 32: - shape = batch_dims + (size, size) - matrix = np.random.uniform( - low=-1.0, high=1.0, - size=np.prod(shape)).reshape(shape).astype(dtype) - self._verifyLogarithmComplex(matrix) + for batch_dims in [(), (1,), (3,), (2, 2)]: + for size in 8, 31, 32: + shape = batch_dims + (size, size) + matrix = np.random.uniform( + low=-1.0, high=1.0, + size=np.prod(shape)).reshape(shape).astype(np.complex64) + self._verifyLogarithmComplex(matrix) + + def testRandomSmallAndLargeComplex128(self): + np.random.seed(42) + for batch_dims in [(), (1,), (3,), (2, 2)]: + for size in 8, 31, 32: + shape = batch_dims + (size, size) + matrix = np.random.uniform( + low=-1.0, high=1.0, + size=np.prod(shape)).reshape(shape).astype(np.complex128) + self._verifyLogarithmComplex(matrix) def testConcurrentExecutesWithoutError(self): with self.test_session(use_gpu=True) as sess: |