80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Operations for linear algebra."""
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import numpy as np
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util import compat
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# Names below are lower_case.
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# pylint: disable=invalid-name
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def eye(num_rows,
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num_columns=None,
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batch_shape=None,
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dtype=dtypes.float32,
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name=None):
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"""Construct an identity matrix, or a batch of matrices.
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See `linalg_ops.eye`.
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"""
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with ops.name_scope(
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name, default_name='eye', values=[num_rows, num_columns, batch_shape]):
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is_square = num_columns is None
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batch_shape = [] if batch_shape is None else batch_shape
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num_columns = num_rows if num_columns is None else num_columns
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# We cannot statically infer what the diagonal size should be:
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if (isinstance(num_rows, ops.Tensor) or
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isinstance(num_columns, ops.Tensor)):
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diag_size = math_ops.minimum(num_rows, num_columns)
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else:
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# We can statically infer the diagonal size, and whether it is square.
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if not isinstance(num_rows, compat.integral_types) or not isinstance(
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num_columns, compat.integral_types):
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raise TypeError(
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'Arguments `num_rows` and `num_columns` must be positive integer '
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f'values. Received: num_rows={num_rows}, num_columns={num_columns}')
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is_square = num_rows == num_columns
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diag_size = np.minimum(num_rows, num_columns)
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# We can not statically infer the shape of the tensor.
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if isinstance(batch_shape, ops.Tensor) or isinstance(diag_size, ops.Tensor):
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batch_shape = ops.convert_to_tensor(
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batch_shape, name='shape', dtype=dtypes.int32)
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diag_shape = array_ops.concat((batch_shape, [diag_size]), axis=0)
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if not is_square:
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shape = array_ops.concat((batch_shape, [num_rows, num_columns]), axis=0)
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# We can statically infer everything.
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else:
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batch_shape = list(batch_shape)
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diag_shape = batch_shape + [diag_size]
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if not is_square:
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shape = batch_shape + [num_rows, num_columns]
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diag_ones = array_ops.ones(diag_shape, dtype=dtype)
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if is_square:
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return array_ops.matrix_diag(diag_ones)
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else:
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zero_matrix = array_ops.zeros(shape, dtype=dtype)
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return array_ops.matrix_set_diag(zero_matrix, diag_ones)
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# pylint: enable=invalid-name,redefined-builtin
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