conv_split_awa.py [35:68]:
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NUM_RUNS = 5           # Number of experiments to average over
TRAIN_ITERS = 2000      # Number of training iterations per task
BATCH_SIZE = 16
LEARNING_RATE = 0.1    
RANDOM_SEED = 1234
VALID_OPTIMS = ['SGD', 'MOMENTUM', 'ADAM']
OPTIM = 'SGD'
OPT_MOMENTUM = 0.9
OPT_POWER = 0.9
VALID_ARCHS = ['CNN', 'VGG', 'RESNET-B']
ARCH = 'RESNET-B'
PRETRAIN = False

## Model options
#MODELS = ['VAN', 'PI', 'EWC', 'MAS', 'RWALK', 'M-EWC', 'GEM', 'A-GEM', 'S-GEM'] #List of valid models 
MODELS = ['VAN', 'PI', 'EWC', 'MAS', 'RWALK', 'A-GEM'] #List of valid models 
IMP_METHOD = 'VAN'
SYNAP_STGTH = 75000
FISHER_EMA_DECAY = 0.9      # Exponential moving average decay factor for Fisher computation (online Fisher)
FISHER_UPDATE_AFTER = 50    # Number of training iterations for which the F_{\theta}^t is computed (see Eq. 10 in RWalk paper) 
SAMPLES_PER_CLASS = 20   # Number of samples per task
IMG_HEIGHT = 224
IMG_WIDTH = 224
IMG_CHANNELS = 3
TOTAL_CLASSES = 50          # Total number of classes in the dataset 
MEASURE_CONVERGENCE_AFTER = 0.9
EPS_MEM_BATCH_SIZE = 128
DEBUG_EPISODIC_MEMORY = False
KEEP_EPISODIC_MEMORY_FULL = False
K_FOR_CROSS_VAL = 3
CLASSES_PER_TASK = 5

## Logging, saving and testing options
LOG_DIR = './split_awa_results'
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conv_split_awa_hybrid.py [35:67]:
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NUM_RUNS = 5           # Number of experiments to average over
TRAIN_ITERS = 2000      # Number of training iterations per task
BATCH_SIZE = 16
LEARNING_RATE = 0.1    
RANDOM_SEED = 1234
VALID_OPTIMS = ['SGD', 'MOMENTUM', 'ADAM']
OPTIM = 'SGD'
OPT_MOMENTUM = 0.9
OPT_POWER = 0.9
VALID_ARCHS = ['CNN', 'VGG', 'RESNET-B']
ARCH = 'RESNET-B'
PRETRAIN = False

## Model options
MODELS = ['VAN', 'PI', 'EWC', 'MAS', 'RWALK', 'A-GEM'] #List of valid models
IMP_METHOD = 'VAN'
SYNAP_STGTH = 75000
FISHER_EMA_DECAY = 0.9      # Exponential moving average decay factor for Fisher computation (online Fisher)
FISHER_UPDATE_AFTER = 50    # Number of training iterations for which the F_{\theta}^t is computed (see Eq. 10 in RWalk paper) 
SAMPLES_PER_CLASS = 20   # Number of samples per task
IMG_HEIGHT = 224
IMG_WIDTH = 224
IMG_CHANNELS = 3
TOTAL_CLASSES = 50          # Total number of classes in the dataset 
MEASURE_CONVERGENCE_AFTER = 0.9
EPS_MEM_BATCH_SIZE = 128
DEBUG_EPISODIC_MEMORY = False
KEEP_EPISODIC_MEMORY_FULL = False
K_FOR_CROSS_VAL = 3
CLASSES_PER_TASK = 5

## Logging, saving and testing options
LOG_DIR = './split_awa_results'
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