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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.
# ==============================================================================
"""Numpy-related utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.utils.to_categorical')
def to_categorical(y, num_classes=None):
"""Converts a class vector (integers) to binary class matrix.
E.g. for use with categorical_crossentropy.
Arguments:
y: class vector to be converted into a matrix
(integers from 0 to num_classes).
num_classes: total number of classes.
Returns:
A binary matrix representation of the input. The classes axis is placed
last.
"""
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=np.float32)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
@tf_export('keras.utils.normalize')
def normalize(x, axis=-1, order=2):
"""Normalizes a Numpy array.
Arguments:
x: Numpy array to normalize.
axis: axis along which to normalize.
order: Normalization order (e.g. 2 for L2 norm).
Returns:
A normalized copy of the array.
"""
l2 = np.atleast_1d(np.linalg.norm(x, order, axis))
l2[l2 == 0] = 1
return x / np.expand_dims(l2, axis)
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