Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/normalization/unit_normalization.py: 46%

1# Copyright 2022 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"""Unit Normalization layer.""" 

16 

17 

18import tensorflow.compat.v2 as tf 

19 

20from keras.src.engine import base_layer 

21from keras.src.utils import tf_utils 

22 

23# isort: off 

24from tensorflow.python.util.tf_export import keras_export 

25 

26 

27@keras_export("keras.layers.UnitNormalization", v1=[]) 

28class UnitNormalization(base_layer.Layer): 

29 """Unit normalization layer. 

30 

31 Normalize a batch of inputs so that each input in the batch has a L2 norm 

32 equal to 1 (across the axes specified in `axis`). 

33 

34 Example: 

35 

36 >>> data = tf.constant(np.arange(6).reshape(2, 3), dtype=tf.float32) 

37 >>> normalized_data = tf.keras.layers.UnitNormalization()(data) 

38 >>> print(tf.reduce_sum(normalized_data[0, :] ** 2).numpy()) 

39 1.0 

40 

41 Args: 

42 axis: Integer or list/tuple. The axis or axes to normalize across. 

43 Typically this is the features axis or axes. The left-out axes are 

44 typically the batch axis or axes. Defaults to `-1`, the last dimension 

45 in the input. 

46 """ 

47 

48 def __init__(self, axis=-1, **kwargs): 

49 super().__init__(**kwargs) 

50 if isinstance(axis, (list, tuple)): 

51 self.axis = list(axis) 

52 elif isinstance(axis, int): 

53 self.axis = axis 

54 else: 

55 raise TypeError( 

56 "Invalid value for `axis` argument: " 

57 "expected an int or a list/tuple of ints. " 

58 f"Received: axis={axis}" 

59 ) 

60 self.supports_masking = True 

61 

62 def build(self, input_shape): 

63 self.axis = tf_utils.validate_axis(self.axis, input_shape) 

64 

65 def call(self, inputs): 

66 inputs = tf.cast(inputs, self.compute_dtype) 

67 return tf.linalg.l2_normalize(inputs, axis=self.axis) 

68 

69 def compute_output_shape(self, input_shape): 

70 return input_shape 

71 

72 def get_config(self): 

73 config = super().get_config() 

74 config.update({"axis": self.axis}) 

75 return config 

76