1 | #!/usr/bin/env python |
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2 | # -*- coding: utf-8 -*- |
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3 | |||
4 | import numpy as np |
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5 | |||
6 | from controllers import TrainingController |
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7 | from deepy.utils import FLOATX, shared_scalar |
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8 | |||
9 | import logging as loggers |
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10 | logging = loggers.getLogger(__name__) |
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11 | |||
12 | class LearningRateAnnealer(TrainingController): |
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13 | """ |
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14 | Learning rate annealer. |
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15 | """ |
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16 | |||
17 | def __init__(self, trainer, patience=3, anneal_times=4): |
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18 | """ |
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19 | :type trainer: deepy.trainers.base.NeuralTrainer |
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20 | """ |
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21 | super(LearningRateAnnealer, self).__init__(trainer) |
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22 | self._iter = -1 |
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23 | self._annealed_iter = -1 |
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24 | self._patience = patience |
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25 | self._anneal_times = anneal_times |
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26 | self._annealed_times = 0 |
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27 | self._learning_rate = self._trainer.config.learning_rate |
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28 | if type(self._learning_rate) == float: |
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29 | raise Exception("use shared_scalar to wrap the value in the config.") |
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30 | |||
31 | def invoke(self): |
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32 | """ |
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33 | Run it, return whether to end training. |
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34 | """ |
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35 | self._iter += 1 |
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36 | View Code Duplication | if self._iter - max(self._trainer.best_iter, self._annealed_iter) >= self._patience: |
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37 | if self._annealed_times >= self._anneal_times: |
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38 | logging.info("ending") |
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39 | return True |
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40 | else: |
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41 | self._trainer.set_params(*self._trainer.best_params) |
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42 | self._learning_rate.set_value(self._learning_rate.get_value() * 0.5) |
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43 | self._annealed_times += 1 |
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44 | self._annealed_iter = self._iter |
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45 | logging.info("annealed learning rate to %f" % self._learning_rate.get_value()) |
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46 | return False |
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47 | |||
48 | @staticmethod |
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49 | def learning_rate(value=0.01): |
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50 | """ |
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51 | Wrap learning rate. |
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52 | """ |
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53 | return shared_scalar(value, name="learning_rate") |
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54 | |||
55 | |||
56 | class ScheduledLearningRateAnnealer(TrainingController): |
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57 | """ |
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58 | Anneal learning rate according to pre-scripted schedule. |
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59 | """ |
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60 | |||
61 | def __init__(self, trainer, start_halving_at=5, end_at=10, halving_interval=1, rollback=False): |
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62 | super(ScheduledLearningRateAnnealer, self).__init__(trainer) |
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63 | logging.info("iteration to start halving learning rate: %d" % start_halving_at) |
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64 | self.iter_start_halving = start_halving_at |
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65 | self.end_at = end_at |
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66 | self._learning_rate = self._trainer.config.learning_rate |
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67 | self._iter = 0 |
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68 | self._halving_interval = halving_interval |
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69 | self._rollback = rollback |
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70 | self._last_halving_iter = 0 |
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71 | |||
72 | def invoke(self): |
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73 | self._iter += 1 |
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74 | View Code Duplication | if self._iter >= self.iter_start_halving and self._iter > self._last_halving_iter + self._halving_interval: |
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75 | if self._rollback: |
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76 | self._trainer.set_params(*self._trainer.best_params) |
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77 | self._learning_rate.set_value(self._learning_rate.get_value() * 0.5) |
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78 | logging.info("halving learning rate to %f" % self._learning_rate.get_value()) |
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79 | self._trainer.network.train_logger.record("set learning rate to %f" % self._learning_rate.get_value()) |
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80 | self._last_halving_iter = self._iter |
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81 | if self._iter >= self.end_at: |
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82 | logging.info("ending") |
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83 | return True |
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84 | return False |
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85 | |||
86 | |||
87 | class ExponentialLearningRateAnnealer(TrainingController): |
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88 | """ |
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89 | Exponentially decay learning rate after each update. |
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90 | """ |
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91 | |||
92 | def __init__(self, trainer, decay_factor=1.000004, min_lr=.000001, debug=False): |
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93 | super(ExponentialLearningRateAnnealer, self).__init__(trainer) |
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94 | logging.info("exponentially decay learning rate with decay factor = %f" % decay_factor) |
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95 | self.decay_factor = np.array(decay_factor, dtype=FLOATX) |
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96 | self.min_lr = np.array(min_lr, dtype=FLOATX) |
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97 | self.debug = debug |
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98 | self._learning_rate = self._trainer.config.learning_rate |
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99 | if type(self._learning_rate) == float: |
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100 | raise Exception("use shared_scalar to wrap the value in the config.") |
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101 | self._trainer.network.training_callbacks.append(self.update_callback) |
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102 | |||
103 | def update_callback(self): |
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104 | if self._learning_rate.get_value() > self.min_lr: |
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105 | self._learning_rate.set_value(self._learning_rate.get_value() / self.decay_factor) |
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106 | |||
107 | def invoke(self): |
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108 | if self.debug: |
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109 | logging.info("learning rate: %.8f" % self._learning_rate.get_value()) |
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110 | return False |
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111 | |||
112 | class SimpleScheduler(TrainingController): |
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113 | |||
114 | """ |
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115 | Simple scheduler with maximum patience. |
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116 | """ |
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117 | |||
118 | def __init__(self, trainer, patience=10): |
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119 | """ |
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120 | :type trainer: deepy.trainers.base.NeuralTrainer |
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121 | """ |
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122 | super(SimpleScheduler, self).__init__(trainer) |
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123 | self._iter = 0 |
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124 | self._patience = patience |
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125 | |||
126 | def invoke(self): |
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127 | """ |
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128 | Run it, return whether to end training. |
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129 | """ |
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130 | self._iter += 1 |
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131 | logging.info("{} epochs left to run".format(self._patience - self._iter)) |
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132 | if self._iter >= self._patience: |
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133 | return True |
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134 | else: |
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135 | return False |