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callback.py
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201 lines (144 loc) · 8.22 KB
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import poutyne as pt
from torch.utils.data import DataLoader, random_split
class activateGradient(pt.Callback):
def __init__(self, data_testeur, taille_testeur, reward_type = 'accuracy'):
super().__init__()
self.historique_accuracy_validation = list()
self.historique_perte_validation = list()
self.triggered = True
self.data = data_testeur
self.taille_data_test = taille_testeur
self.last_reward = None
self.reward_type = reward_type
def calculate_reward(self):
data_test, _ = random_split(self.data,[self.taille_data_test,len(self.data)-self.taille_data_test])
loss, precision = self.model.evaluate_dataset(data_test,verbose=False,batch_size=self.taille_data_test)
if self.reward_type == 'accuracy':
reward = precision
self.model.network.dropout.cumulated_rewards += self.model.network.dropout.last_played*reward
elif (self.reward_type == 'accuracy_increase'):
if self.last_reward != None:
reward = precision-self.last_reward
self.model.network.dropout.cumulated_rewards += self.model.network.dropout.last_played*reward
self.last_reward = precision
elif self.reward_type == 'loss':
reward = loss
self.model.network.dropout.cumulated_rewards += self.model.network.dropout.last_played*-reward
elif (self.reward_type == 'loss_increase'):
if self.last_reward != None:
reward = loss-self.last_reward
self.model.network.dropout.cumulated_rewards += self.model.network.dropout.last_played*-reward
self.last_reward = loss
def on_epoch_begin(self, epoch_number, logs):
if not self.model.network.dropout.batch_update:
self.model.network.dropout.get_dropout_rate_per_arm()
def on_train_batch_begin(self, epoch_number, logs):
if self.model.network.dropout.batch_update:
self.model.network.dropout.get_dropout_rate_per_arm()
def on_epoch_end(self, batch, logs):
## À chaque début d'epoch
self.historique_accuracy_validation.append(logs["val_acc"])
self.historique_perte_validation.append(logs["val_loss"])
if not self.model.network.dropout.batch_update:
self.calculate_reward()
def on_train_batch_end(self, batch, logs):
if self.model.network.dropout.batch_update:
self.calculate_reward()
class activateGradientBoltzmann(pt.Callback):
def __init__(self, data_testeur, taille_testeur, reward_type = 'accuracy'):
super().__init__()
self.historique_accuracy_validation = list()
self.historique_perte_validation = list()
self.triggered = True
self.data = data_testeur
self.taille_data_test = taille_testeur
self.last_reward = None
self.reward_type = reward_type
def calculate_reward(self):
data_test, _ = random_split(self.data,[self.taille_data_test,len(self.data)-self.taille_data_test])
loss, precision = self.model.evaluate_dataset(data_test,verbose=False,batch_size=self.taille_data_test)
if self.reward_type == 'accuracy':
reward = precision
self.model.network.dropout.cumulated_rewards += reward/100 * self.model.network.dropout.last_played
self.model.network.dropout.nb_played += self.model.network.dropout.last_played
self.model.network.dropout.choose_new_arms = True
elif (self.reward_type == 'accuracy_increase'):
if self.last_reward != None:
reward = precision-self.last_reward
self.model.network.dropout.cumulated_rewards += reward/100 * self.model.network.dropout.last_played
self.model.network.dropout.nb_played += self.model.network.dropout.last_played
self.model.network.dropout.choose_new_arms = True
self.last_reward = precision
elif self.reward_type == 'loss':
reward = loss
self.model.network.dropout.cumulated_rewards += -reward * self.model.network.dropout.last_played
self.model.network.dropout.nb_played += self.model.network.dropout.last_played
self.model.network.dropout.choose_new_arms = True
elif (self.reward_type == 'loss_increase'):
if self.last_reward != None:
reward = loss-self.last_reward
self.model.network.dropout.cumulated_rewards += -reward * self.model.network.dropout.last_played
self.model.network.dropout.nb_played += self.model.network.dropout.last_played
self.model.network.dropout.choose_new_arms = True
self.last_reward = loss
def on_epoch_begin(self, epoch_number, logs):
if not self.model.network.dropout.batch_update:
self.model.network.dropout.get_dropout_rate_per_arm()
def on_train_batch_begin(self, epoch_number, logs):
if self.model.network.dropout.batch_update:
self.model.network.dropout.get_dropout_rate_per_arm()
def on_epoch_end(self, batch, logs):
## À chaque début d'epoch
self.historique_accuracy_validation.append(logs["val_acc"])
self.historique_perte_validation.append(logs["val_loss"])
if not self.model.network.dropout.batch_update:
self.calculate_reward()
def on_train_batch_end(self, batch, logs):
if self.model.network.dropout.batch_update:
self.calculate_reward()
class activateGradientlinUCB(pt.Callback):
def __init__(self, data_testeur, taille_testeur, reward_type):
super().__init__()
self.historique_accuracy_validation = list()
self.historique_perte_validation = list()
self.triggered = False
self.data = data_testeur
self.taille_data_test = taille_testeur
self.last_loss = 0
self.last_reward = None
self.reward_type = reward_type
def on_epoch_begin(self, epoch_number, logs):
if not self.model.network.dropout.batch_update:
self.model.network.dropout.epoch_dropout_rate = self.model.network.dropout.choose_dropout_linucb()
def on_epoch_end(self, batch, logs):
## À chaque début d'epoch
self.historique_accuracy_validation.append(logs["val_acc"])
self.historique_perte_validation.append(logs["val_loss"])
if (len(self.historique_perte_validation)>1):
if (self.historique_perte_validation[-2] < self.historique_perte_validation[-1]):
self.triggered = True
self.model.network.dropout.triggered = True
if (self.model.network.dropout.triggered and not self.model.network.dropout.batch_update):
self.calculate_reward()
def on_train_batch_end(self, batch, logs):
if (self.model.network.dropout.triggered and self.model.network.dropout.batch_update):
self.calculate_reward()
def calculate_reward(self):
data_test, _ = random_split(self.data,[self.taille_data_test,len(self.data)-self.taille_data_test])
loss, precision = self.model.evaluate_dataset(data_test,verbose=False,batch_size=self.taille_data_test)
if self.reward_type == 'accuracy':
reward = precision
self.model.network.dropout.update_bandit(reward)
elif (self.reward_type == 'accuracy_increase'):
if self.last_reward != None:
reward = precision-self.last_reward
self.model.network.dropout.update_bandit(reward)
self.last_reward = precision
elif self.reward_type == 'loss':
reward = loss
self.model.network.dropout.update_bandit(-reward)
elif (self.reward_type == 'loss_increase'):
if self.last_reward != None:
reward = loss-self.last_reward
self.model.network.dropout.update_bandit(-reward)
self.last_reward = loss