Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/tensorflow/python/keras/optimizer_v2/adadelta.py: 39%

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"""Adadelta optimizer implementation.""" 

16# pylint: disable=g-classes-have-attributes 

17 

18import numpy as np 

19from tensorflow.python.framework import tensor_conversion 

20from tensorflow.python.keras import backend_config 

21from tensorflow.python.keras.optimizer_v2 import optimizer_v2 

22from tensorflow.python.ops import array_ops 

23from tensorflow.python.training import gen_training_ops 

24from tensorflow.python.util.tf_export import keras_export 

25 

26 

27@keras_export('keras.optimizers.Adadelta') 

28class Adadelta(optimizer_v2.OptimizerV2): 

29 r"""Optimizer that implements the Adadelta algorithm. 

30 

31 Adadelta optimization is a stochastic gradient descent method that is based on 

32 adaptive learning rate per dimension to address two drawbacks: 

33 

34 - The continual decay of learning rates throughout training. 

35 - The need for a manually selected global learning rate. 

36 

37 Adadelta is a more robust extension of Adagrad that adapts learning rates 

38 based on a moving window of gradient updates, instead of accumulating all 

39 past gradients. This way, Adadelta continues learning even when many updates 

40 have been done. Compared to Adagrad, in the original version of Adadelta you 

41 don't have to set an initial learning rate. In this version, the initial 

42 learning rate can be set, as in most other Keras optimizers. 

43 

44 Args: 

45 learning_rate: Initial value for the learning rate: 

46 either a floating point value, 

47 or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance. 

48 Defaults to 0.001. 

49 Note that `Adadelta` tends to benefit from higher initial learning rate 

50 values compared to other optimizers. 

51 To match the exact form in the original paper, use 1.0. 

52 rho: A `Tensor` or a floating point value. The decay rate. 

53 epsilon: Small floating point value used to maintain numerical stability. 

54 name: Optional name prefix for the operations created when applying 

55 gradients. Defaults to `"Adadelta"`. 

56 **kwargs: Keyword arguments. Allowed to be one of 

57 `"clipnorm"` or `"clipvalue"`. 

58 `"clipnorm"` (float) clips gradients by norm and represents 

59 the maximum norm of each parameter; 

60 `"clipvalue"` (float) clips gradient by value and represents the 

61 maximum absolute value of each parameter. 

62 

63 Reference: 

64 - [Zeiler, 2012](http://arxiv.org/abs/1212.5701) 

65 """ 

66 

67 _HAS_AGGREGATE_GRAD = True 

68 

69 def __init__(self, 

70 learning_rate=0.001, 

71 rho=0.95, 

72 epsilon=1e-7, 

73 name='Adadelta', 

74 **kwargs): 

75 super(Adadelta, self).__init__(name, **kwargs) 

76 self._set_hyper('learning_rate', kwargs.get('lr', learning_rate)) 

77 self._set_hyper('decay', self._initial_decay) 

78 self._set_hyper('rho', rho) 

79 self.epsilon = epsilon or backend_config.epsilon() 

80 

81 def _create_slots(self, var_list): 

82 # Separate for-loops to respect the ordering of slot variables from v1. 

83 for v in var_list: 

84 self.add_slot(v, 'accum_grad') 

85 for v in var_list: 

86 self.add_slot(v, 'accum_var') 

87 

88 def _prepare_local(self, var_device, var_dtype, apply_state): 

89 super(Adadelta, self)._prepare_local(var_device, var_dtype, apply_state) 

90 apply_state[(var_device, var_dtype)].update( 

91 dict( 

92 epsilon=tensor_conversion.convert_to_tensor_v2_with_dispatch( 

93 self.epsilon, var_dtype 

94 ), 

95 rho=array_ops.identity(self._get_hyper('rho', var_dtype)), 

96 ) 

97 ) 

98 

99 def set_weights(self, weights): 

100 params = self.weights 

101 # Override set_weights for backward compatibility of Keras V1 optimizer 

102 # since it does not include iteration at head of the weight list. Set 

103 # iteration to 0. 

104 if len(params) == len(weights) + 1: 

105 weights = [np.array(0)] + weights 

106 super(Adadelta, self).set_weights(weights) 

107 

108 def _resource_apply_dense(self, grad, var, apply_state=None): 

109 var_device, var_dtype = var.device, var.dtype.base_dtype 

110 coefficients = ((apply_state or {}).get((var_device, var_dtype)) 

111 or self._fallback_apply_state(var_device, var_dtype)) 

112 

113 accum_grad = self.get_slot(var, 'accum_grad') 

114 accum_var = self.get_slot(var, 'accum_var') 

115 return gen_training_ops.ResourceApplyAdadelta( 

116 var=var.handle, 

117 accum=accum_grad.handle, 

118 accum_update=accum_var.handle, 

119 lr=coefficients['lr_t'], 

120 rho=coefficients['rho'], 

121 epsilon=coefficients['epsilon'], 

122 grad=grad, 

123 use_locking=self._use_locking) 

124 

125 def _resource_apply_sparse(self, grad, var, indices, apply_state=None): 

126 var_device, var_dtype = var.device, var.dtype.base_dtype 

127 coefficients = ((apply_state or {}).get((var_device, var_dtype)) 

128 or self._fallback_apply_state(var_device, var_dtype)) 

129 

130 accum_grad = self.get_slot(var, 'accum_grad') 

131 accum_var = self.get_slot(var, 'accum_var') 

132 return gen_training_ops.ResourceSparseApplyAdadelta( 

133 var=var.handle, 

134 accum=accum_grad.handle, 

135 accum_update=accum_var.handle, 

136 lr=coefficients['lr_t'], 

137 rho=coefficients['rho'], 

138 epsilon=coefficients['epsilon'], 

139 grad=grad, 

140 indices=indices, 

141 use_locking=self._use_locking) 

142 

143 def get_config(self): 

144 config = super(Adadelta, self).get_config() 

145 config.update({ 

146 'learning_rate': self._serialize_hyperparameter('learning_rate'), 

147 'decay': self._initial_decay, 

148 'rho': self._serialize_hyperparameter('rho'), 

149 'epsilon': self.epsilon, 

150 }) 

151 return config