from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import map_fn from tensorflow.python.ops import math_ops from tensorflow.python.ops import special_math_ops from tensorflow.python.util import dispatch from tensorflow.python.util.tf_export import tf_export # Linear algebra ops. band_part = array_ops.matrix_band_part cholesky = linalg_ops.cholesky cholesky_solve = linalg_ops.cholesky_solve det = linalg_ops.matrix_determinant slogdet = gen_linalg_ops.log_matrix_determinant tf_export('linalg.slogdet')(dispatch.add_dispatch_support(slogdet)) diag = array_ops.matrix_diag diag_part = array_ops.matrix_diag_part eigh = linalg_ops.self_adjoint_eig eigvalsh = linalg_ops.self_adjoint_eigvals einsum = special_math_ops.einsum eye = linalg_ops.eye inv = linalg_ops.matrix_inverse logm = gen_linalg_ops.matrix_logarithm lu = gen_linalg_ops.lu tf_export('linalg.logm')(dispatch.add_dispatch_support(logm)) lstsq = linalg_ops.matrix_solve_ls norm = linalg_ops.norm qr = linalg_ops.qr set_diag = array_ops.matrix_set_diag solve = linalg_ops.matrix_solve
from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import special_math_ops from tensorflow.python.util import dispatch from tensorflow.python.util.tf_export import tf_export # Linear algebra ops. band_part = array_ops.matrix_band_part cholesky = dispatch.add_dispatch_support(linalg_ops.cholesky) cholesky_solve = linalg_ops.cholesky_solve det = dispatch.add_dispatch_support(linalg_ops.matrix_determinant) slogdet = gen_linalg_ops.log_matrix_determinant tf_export('linalg.slogdet')(slogdet) diag = array_ops.matrix_diag diag_part = dispatch.add_dispatch_support(array_ops.matrix_diag_part) eigh = linalg_ops.self_adjoint_eig eigvalsh = linalg_ops.self_adjoint_eigvals einsum = special_math_ops.einsum eye = linalg_ops.eye inv = dispatch.add_dispatch_support(linalg_ops.matrix_inverse) logm = gen_linalg_ops.matrix_logarithm lu = gen_linalg_ops.lu tf_export('linalg.logm')(logm) lstsq = linalg_ops.matrix_solve_ls
_irfft.__doc__ = re.sub(" Treal.*?\n", "", ifft_fn.__doc__) return _irfft # FFT/IFFT 1/2/3D are exported via # third_party/tensorflow/core/api_def/python_api/ fft = gen_spectral_ops.fft ifft = gen_spectral_ops.ifft fft2d = gen_spectral_ops.fft2d ifft2d = gen_spectral_ops.ifft2d fft3d = gen_spectral_ops.fft3d ifft3d = gen_spectral_ops.ifft3d rfft = _rfft_wrapper(gen_spectral_ops.rfft, 1, "rfft") tf_export("signal.rfft", v1=["signal.rfft", "spectral.rfft"])(dispatch.add_dispatch_support(rfft)) irfft = _irfft_wrapper(gen_spectral_ops.irfft, 1, "irfft") tf_export("signal.irfft", v1=["signal.irfft", "spectral.irfft"])(dispatch.add_dispatch_support(irfft)) rfft2d = _rfft_wrapper(gen_spectral_ops.rfft2d, 2, "rfft2d") tf_export("signal.rfft2d", v1=["signal.rfft2d", "spectral.rfft2d"])(dispatch.add_dispatch_support(rfft2d)) irfft2d = _irfft_wrapper(gen_spectral_ops.irfft2d, 2, "irfft2d") tf_export("signal.irfft2d", v1=["signal.irfft2d", "spectral.irfft2d"])(dispatch.add_dispatch_support(irfft2d)) rfft3d = _rfft_wrapper(gen_spectral_ops.rfft3d, 3, "rfft3d") tf_export("signal.rfft3d", v1=["signal.rfft3d",
def testBadIterableParametersError(self): fn = lambda x: [t + 1 for t in x] with self.assertRaisesRegex( TypeError, "iterable_parameters should be a list or tuple of string"): dispatch.add_dispatch_support(iterable_parameters="x")(fn)
# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= # pylint: disable=wildcard-import,unused-import """Protocol Buffer encoding and decoding from tensors.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import ops from tensorflow.python.ops.gen_decode_proto_ops import decode_proto_v2 as decode_proto from tensorflow.python.ops.gen_encode_proto_ops import encode_proto from tensorflow.python.util import dispatch from tensorflow.python.util.tf_export import tf_export tf_export("io.decode_proto")(dispatch.add_dispatch_support(decode_proto)) tf_export("io.encode_proto")(dispatch.add_dispatch_support(encode_proto)) ops.NotDifferentiable("DecodeProtoV2") ops.NotDifferentiable("EncodeProto")
from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import special_math_ops from tensorflow.python.util import dispatch from tensorflow.python.util.tf_export import tf_export # Linear algebra ops. band_part = array_ops.matrix_band_part cholesky = dispatch.add_dispatch_support(linalg_ops.cholesky) cholesky_solve = linalg_ops.cholesky_solve det = dispatch.add_dispatch_support(linalg_ops.matrix_determinant) slogdet = gen_linalg_ops.log_matrix_determinant tf_export('linalg.slogdet')(slogdet) diag = array_ops.matrix_diag diag_part = dispatch.add_dispatch_support(array_ops.matrix_diag_part) eigh = linalg_ops.self_adjoint_eig eigvalsh = linalg_ops.self_adjoint_eigvals einsum = special_math_ops.einsum eye = linalg_ops.eye inv = dispatch.add_dispatch_support(linalg_ops.matrix_inverse) logm = gen_linalg_ops.matrix_logarithm lu = gen_linalg_ops.lu tf_export('linalg.logm')(logm) lstsq = linalg_ops.matrix_solve_ls
return ifft_fn(input_tensor, fft_length, Treal=real_dtype, name=name) _irfft.__doc__ = re.sub(" Treal.*?\n", "", ifft_fn.__doc__) return _irfft # FFT/IFFT 1/2/3D are exported via # third_party/tensorflow/core/api_def/python_api/ fft = gen_spectral_ops.fft ifft = gen_spectral_ops.ifft fft2d = gen_spectral_ops.fft2d ifft2d = gen_spectral_ops.ifft2d fft3d = gen_spectral_ops.fft3d ifft3d = gen_spectral_ops.ifft3d rfft = _rfft_wrapper(gen_spectral_ops.rfft, 1, "rfft") tf_export("signal.rfft", v1=["signal.rfft", "spectral.rfft"])( dispatch.add_dispatch_support(rfft)) irfft = _irfft_wrapper(gen_spectral_ops.irfft, 1, "irfft") tf_export("signal.irfft", v1=["signal.irfft", "spectral.irfft"])( dispatch.add_dispatch_support(irfft)) rfft2d = _rfft_wrapper(gen_spectral_ops.rfft2d, 2, "rfft2d") tf_export("signal.rfft2d", v1=["signal.rfft2d", "spectral.rfft2d"])( dispatch.add_dispatch_support(rfft2d)) irfft2d = _irfft_wrapper(gen_spectral_ops.irfft2d, 2, "irfft2d") tf_export("signal.irfft2d", v1=["signal.irfft2d", "spectral.irfft2d"])( dispatch.add_dispatch_support(irfft2d)) rfft3d = _rfft_wrapper(gen_spectral_ops.rfft3d, 3, "rfft3d") tf_export("signal.rfft3d", v1=["signal.rfft3d", "spectral.rfft3d"])( dispatch.add_dispatch_support(rfft3d)) irfft3d = _irfft_wrapper(gen_spectral_ops.irfft3d, 3, "irfft3d") tf_export("signal.irfft3d", v1=["signal.irfft3d", "spectral.irfft3d"])( dispatch.add_dispatch_support(irfft3d))