1# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Numpy-related utilities."""
16from __future__ import absolute_import
17from __future__ import division
18from __future__ import print_function
19
20import numpy as np
21from tensorflow.python.util.tf_export import keras_export
22
23
24@keras_export('keras.utils.to_categorical')
25def to_categorical(y, num_classes=None, dtype='float32'):
26  """Converts a class vector (integers) to binary class matrix.
27
28  E.g. for use with categorical_crossentropy.
29
30  Args:
31      y: class vector to be converted into a matrix
32          (integers from 0 to num_classes).
33      num_classes: total number of classes. If `None`, this would be inferred
34        as the (largest number in `y`) + 1.
35      dtype: The data type expected by the input. Default: `'float32'`.
36
37  Returns:
38      A binary matrix representation of the input. The classes axis is placed
39      last.
40
41  Example:
42
43  >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
44  >>> a = tf.constant(a, shape=[4, 4])
45  >>> print(a)
46  tf.Tensor(
47    [[1. 0. 0. 0.]
48     [0. 1. 0. 0.]
49     [0. 0. 1. 0.]
50     [0. 0. 0. 1.]], shape=(4, 4), dtype=float32)
51
52  >>> b = tf.constant([.9, .04, .03, .03,
53  ...                  .3, .45, .15, .13,
54  ...                  .04, .01, .94, .05,
55  ...                  .12, .21, .5, .17],
56  ...                 shape=[4, 4])
57  >>> loss = tf.keras.backend.categorical_crossentropy(a, b)
58  >>> print(np.around(loss, 5))
59  [0.10536 0.82807 0.1011  1.77196]
60
61  >>> loss = tf.keras.backend.categorical_crossentropy(a, a)
62  >>> print(np.around(loss, 5))
63  [0. 0. 0. 0.]
64
65  Raises:
66      Value Error: If input contains string value
67
68  """
69  y = np.array(y, dtype='int')
70  input_shape = y.shape
71  if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
72    input_shape = tuple(input_shape[:-1])
73  y = y.ravel()
74  if not num_classes:
75    num_classes = np.max(y) + 1
76  n = y.shape[0]
77  categorical = np.zeros((n, num_classes), dtype=dtype)
78  categorical[np.arange(n), y] = 1
79  output_shape = input_shape + (num_classes,)
80  categorical = np.reshape(categorical, output_shape)
81  return categorical
82
83
84@keras_export('keras.utils.normalize')
85def normalize(x, axis=-1, order=2):
86  """Normalizes a Numpy array.
87
88  Args:
89      x: Numpy array to normalize.
90      axis: axis along which to normalize.
91      order: Normalization order (e.g. `order=2` for L2 norm).
92
93  Returns:
94      A normalized copy of the array.
95  """
96  l2 = np.atleast_1d(np.linalg.norm(x, order, axis))
97  l2[l2 == 0] = 1
98  return x / np.expand_dims(l2, axis)
99