84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# 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.
|
||
|
|
# ==============================================================================
|
||
|
|
"""Operations for linear algebra."""
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from tensorflow.python.framework import dtypes
|
||
|
|
from tensorflow.python.framework import ops
|
||
|
|
from tensorflow.python.framework import tensor
|
||
|
|
from tensorflow.python.ops import array_ops
|
||
|
|
from tensorflow.python.ops import math_ops
|
||
|
|
from tensorflow.python.util import compat
|
||
|
|
|
||
|
|
# Names below are lower_case.
|
||
|
|
# pylint: disable=invalid-name
|
||
|
|
|
||
|
|
|
||
|
|
def eye(num_rows,
|
||
|
|
num_columns=None,
|
||
|
|
batch_shape=None,
|
||
|
|
dtype=dtypes.float32,
|
||
|
|
name=None):
|
||
|
|
"""Construct an identity matrix, or a batch of matrices.
|
||
|
|
|
||
|
|
See `linalg_ops.eye`.
|
||
|
|
"""
|
||
|
|
with ops.name_scope(
|
||
|
|
name, default_name='eye', values=[num_rows, num_columns, batch_shape]):
|
||
|
|
is_square = num_columns is None
|
||
|
|
batch_shape = [] if batch_shape is None else batch_shape
|
||
|
|
num_columns = num_rows if num_columns is None else num_columns
|
||
|
|
|
||
|
|
# We cannot statically infer what the diagonal size should be:
|
||
|
|
if (isinstance(num_rows, tensor.Tensor) or
|
||
|
|
isinstance(num_columns, tensor.Tensor)):
|
||
|
|
diag_size = math_ops.minimum(num_rows, num_columns)
|
||
|
|
else:
|
||
|
|
# We can statically infer the diagonal size, and whether it is square.
|
||
|
|
if not isinstance(num_rows, compat.integral_types) or not isinstance(
|
||
|
|
num_columns, compat.integral_types):
|
||
|
|
raise TypeError(
|
||
|
|
'Arguments `num_rows` and `num_columns` must be positive integer '
|
||
|
|
f'values. Received: num_rows={num_rows}, num_columns={num_columns}')
|
||
|
|
is_square = num_rows == num_columns
|
||
|
|
diag_size = np.minimum(num_rows, num_columns)
|
||
|
|
|
||
|
|
# We can not statically infer the shape of the tensor.
|
||
|
|
if isinstance(batch_shape, tensor.Tensor) or isinstance(
|
||
|
|
diag_size, tensor.Tensor
|
||
|
|
):
|
||
|
|
batch_shape = ops.convert_to_tensor(
|
||
|
|
batch_shape, name='shape', dtype=dtypes.int32
|
||
|
|
)
|
||
|
|
diag_shape = array_ops.concat((batch_shape, [diag_size]), axis=0)
|
||
|
|
if not is_square:
|
||
|
|
shape = array_ops.concat((batch_shape, [num_rows, num_columns]), axis=0)
|
||
|
|
# We can statically infer everything.
|
||
|
|
else:
|
||
|
|
batch_shape = list(batch_shape)
|
||
|
|
diag_shape = batch_shape + [diag_size]
|
||
|
|
if not is_square:
|
||
|
|
shape = batch_shape + [num_rows, num_columns]
|
||
|
|
|
||
|
|
diag_ones = array_ops.ones(diag_shape, dtype=dtype)
|
||
|
|
if is_square:
|
||
|
|
return array_ops.matrix_diag(diag_ones)
|
||
|
|
else:
|
||
|
|
zero_matrix = array_ops.zeros(shape, dtype=dtype)
|
||
|
|
return array_ops.matrix_set_diag(zero_matrix, diag_ones)
|
||
|
|
|
||
|
|
# pylint: enable=invalid-name,redefined-builtin
|