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params.py
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params.py
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########################################
# parameters for image preprocessing
########################################
dataset_mean_value = 0.5
dataset_std_value = 0.5
dataset_mean = (dataset_mean_value, dataset_mean_value, dataset_mean_value)
dataset_std = (dataset_std_value, dataset_std_value, dataset_std_value)
########################################
# parameters for the model
########################################
hash_size = 16
########################################
# settings for training
########################################
# image-scale: 100 for office, 28 for mnist
image_scale = 28
shuffle_batch = True
train_data_path = {
"source": "F:/data/mnist/mini/training",
"target": "F:/data/mnist_m/mini/train-10-percent"
}
iterations = 100
batch_size = 50
learning_rate = 1e-4
num_classes = 10
# settings for discriminator
dcd_input_dims = 1000
dcd_hidden_dims = 500
dcd_output_dims = 4
# loss coefficients
gamma = 0.5
########################################
# settings for ml_test
########################################
# test data path
test_data_path = {
"query": "F:/data/mnist_m/query-db-split/query",
"db": "F:/data/mnist_m/query-db-split/db"
}
test_batch_size = 100
# in ml_test, retrieval precision within hamming radius `precision_radius` will be calculated
precision_radius = 2