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schedule_cifar_reduced_marginloss.py
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schedule_cifar_reduced_marginloss.py
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from train import Train
if __name__ == '__main__':
path = "exp_cifar_reduced_marginloss"
train = Train()
experiments = [
# Reduced CIFAR10
{
"dataset": "cifar_reduced",
"loss": "marginloss",
"hash_size": 24,
"margin": 2.0,
"batch_size": 100,
"total_epoch_count": 120,
"number_of_epochs_per_decay": 100.0,
"weight_decay_factor": 0.001,
"learning_rate": 0.07 / 4.0,
"learning_rate_decay_factor": 2.0 / 3.0
},
{
"dataset": "cifar_reduced",
"loss": "marginloss",
"hash_size": 16,
"margin": 2.0,
"batch_size": 100,
"total_epoch_count": 120,
"number_of_epochs_per_decay": 100.0,
"weight_decay_factor": 0.001,
"learning_rate": 0.07 / 4.0,
"learning_rate_decay_factor": 2.0 / 3.0
},
{
"dataset": "cifar_reduced",
"loss": "marginloss",
"hash_size": 32,
"margin": 2.0,
"batch_size": 100,
"total_epoch_count": 120,
"number_of_epochs_per_decay": 100.0,
"weight_decay_factor": 0.001,
"learning_rate": 0.07 / 4.0,
"learning_rate_decay_factor": 2.0 / 3.0
},
{
"dataset": "cifar_reduced",
"loss": "marginloss",
"hash_size": 8,
"margin": 2.0,
"batch_size": 100,
"total_epoch_count": 120,
"number_of_epochs_per_decay": 100.0,
"weight_decay_factor": 0.001,
"learning_rate": 0.07 / 4.0,
"learning_rate_decay_factor": 2.0 / 3.0
},
{
"dataset": "cifar_reduced",
"loss": "marginloss",
"hash_size": 12,
"margin": 2.0,
"batch_size": 100,
"total_epoch_count": 120,
"number_of_epochs_per_decay": 100.0,
"weight_decay_factor": 0.001,
"learning_rate": 0.07 / 4.0,
"learning_rate_decay_factor": 2.0 / 3.0
},
{
"dataset": "cifar_reduced",
"loss": "marginloss",
"hash_size": 48,
"margin": 2.0,
"batch_size": 100,
"total_epoch_count": 120,
"number_of_epochs_per_decay": 100.0,
"weight_decay_factor": 0.001,
"learning_rate": 0.07 / 4.0,
"learning_rate_decay_factor": 2.0 / 3.0
}
]
for e in experiments:
train.run(path, e)