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# 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."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
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
if isinstance(num_rows, ops.Tensor) or isinstance(
num_columns, ops.Tensor) or isinstance(batch_shape, ops.Tensor):
batch_shape = ops.convert_to_tensor(
batch_shape, name='shape', dtype=dtypes.int32)
diag_size = math_ops.minimum(num_rows, num_columns)
diag_shape = array_ops.concat((batch_shape, [diag_size]), 0)
if not is_square:
shape = array_ops.concat((batch_shape, [num_rows, num_columns]), 0)
else:
if not isinstance(num_rows, compat.integral_types) or not isinstance(
num_columns, compat.integral_types):
raise TypeError(
'num_rows and num_columns must be positive integer values.')
batch_shape = [dim for dim in batch_shape]
is_square = num_rows == num_columns
diag_shape = batch_shape + [np.minimum(num_rows, num_columns)]
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
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