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config.py
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goal = "classification" #belief_match or classification
mode = "kd_at" #zero_shot or no_teacher or kd_at
dataset = "cifar10" #cifar10, svhn or fashion_mnist
seed = 0
save_path = "../PreTrainedModels"
model_type = "rnn" #rnn or efficient_net
test_mode = False
teacher_rnn = dict(
depth = 40,
widen_factor = 2,
dropRate = 0.0,
input_features = 3,
output_features = 16,
strides = [1, 2, 2]
)
student_rnn = dict(
depth = 16,
widen_factor = 1,
dropRate = 0.0,
input_features = 3,
output_features = 16,
strides = [1, 2, 2]
)
teacher_efficient_net = dict(
input_features = 3,
model = 'b7'
)
student_efficient_net = dict(
input_features = 3,
model = 'b2'
)
generator = dict(
input_dim= 100
)
downsample = dict(
action=False,
value= 10
)