Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/optimizers/legacy/adadelta.py: 36%

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 

17import numpy as np 

18import tensorflow.compat.v2 as tf 

19 

20from keras.src import backend_config 

21from keras.src.optimizers.legacy import optimizer_v2 

22 

23# isort: off 

24from tensorflow.python.util.tf_export import keras_export 

25 

26 

27@keras_export( 

28 "keras.optimizers.legacy.Adadelta", 

29 v1=["keras.optimizers.Adadelta", "keras.optimizers.legacy.Adadelta"], 

30) 

31class Adadelta(optimizer_v2.OptimizerV2): 

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

33 

34 Adadelta optimization is a stochastic gradient descent method that is based 

35 on adaptive learning rate per dimension to address two drawbacks: 

36 

37 - The continual decay of learning rates throughout training. 

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

39 

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

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

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

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

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

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

46 

47 Args: 

48 learning_rate: Initial value for the learning rate: 

49 either a floating point value, 

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

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

52 values compared to other optimizers. 

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

54 Defaults to `0.001`. 

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

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

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

58 gradients. Defaults to `"Adadelta"`. 

59 **kwargs: keyword arguments. Allowed arguments are `clipvalue`, 

60 `clipnorm`, `global_clipnorm`. 

61 If `clipvalue` (float) is set, the gradient of each weight 

62 is clipped to be no higher than this value. 

63 If `clipnorm` (float) is set, the gradient of each weight 

64 is individually clipped so that its norm is no higher than this value. 

65 If `global_clipnorm` (float) is set the gradient of all weights is 

66 clipped so that their global norm is no higher than this value. 

67 

68 Reference: 

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

70 """ 

71 

72 _HAS_AGGREGATE_GRAD = True 

73 

74 def __init__( 

75 self, 

76 learning_rate=0.001, 

77 rho=0.95, 

78 epsilon=1e-7, 

79 name="Adadelta", 

80 **kwargs 

81 ): 

82 super().__init__(name, **kwargs) 

83 self._set_hyper("learning_rate", kwargs.get("lr", learning_rate)) 

84 self._set_hyper("decay", self._initial_decay) 

85 self._set_hyper("rho", rho) 

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

87 

88 def _create_slots(self, var_list): 

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

90 for v in var_list: 

91 self.add_slot(v, "accum_grad") 

92 for v in var_list: 

93 self.add_slot(v, "accum_var") 

94 

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

96 super()._prepare_local(var_device, var_dtype, apply_state) 

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

98 dict( 

99 epsilon=tf.convert_to_tensor(self.epsilon, var_dtype), 

100 rho=tf.identity(self._get_hyper("rho", var_dtype)), 

101 ) 

102 ) 

103 

104 def set_weights(self, weights): 

105 params = self.weights 

106 # Override set_weights for backward compatibility of Keras V1 optimizer 

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

108 # iteration to 0. 

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

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

111 super().set_weights(weights) 

112 

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

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

115 coefficients = (apply_state or {}).get( 

116 (var_device, var_dtype) 

117 ) or self._fallback_apply_state(var_device, var_dtype) 

118 

119 accum_grad = self.get_slot(var, "accum_grad") 

120 accum_var = self.get_slot(var, "accum_var") 

121 return tf.raw_ops.ResourceApplyAdadelta( 

122 var=var.handle, 

123 accum=accum_grad.handle, 

124 accum_update=accum_var.handle, 

125 lr=coefficients["lr_t"], 

126 rho=coefficients["rho"], 

127 epsilon=coefficients["epsilon"], 

128 grad=grad, 

129 use_locking=self._use_locking, 

130 ) 

131 

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

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

134 coefficients = (apply_state or {}).get( 

135 (var_device, var_dtype) 

136 ) or self._fallback_apply_state(var_device, var_dtype) 

137 

138 accum_grad = self.get_slot(var, "accum_grad") 

139 accum_var = self.get_slot(var, "accum_var") 

140 return tf.raw_ops.ResourceSparseApplyAdadelta( 

141 var=var.handle, 

142 accum=accum_grad.handle, 

143 accum_update=accum_var.handle, 

144 lr=coefficients["lr_t"], 

145 rho=coefficients["rho"], 

146 epsilon=coefficients["epsilon"], 

147 grad=grad, 

148 indices=indices, 

149 use_locking=self._use_locking, 

150 ) 

151 

152 def get_config(self): 

153 config = super().get_config() 

154 config.update( 

155 { 

156 "learning_rate": self._serialize_hyperparameter( 

157 "learning_rate" 

158 ), 

159 "decay": self._initial_decay, 

160 "rho": self._serialize_hyperparameter("rho"), 

161 "epsilon": self.epsilon, 

162 } 

163 ) 

164 return config 

165