def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.OPTIMIZER: self.optimizer,
consts.LEARNING_RATE: self.lr,
consts.BATCH_SIZE: self.batch_size,
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.BATCH_SIZE: 64,
consts.FILTERS: [8, 16, 32, 64, 128, 320, 2],
consts.KERNEL_SIDE_LENGTHS: [5, 5, 5, 5, 5, 5, 5],
consts.LEARNING_RATE: 0.001,
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.NUM_CHANNELS: 32,
consts.HARD_TRIPLET_MARGIN: 0.5,
consts.PREDICT_SIMILARITY_MARGIN: 3.0,
consts.DENSE_UNITS: [64],
consts.BATCH_SIZE: 64,
consts.OPTIMIZER: consts.ADAM_OPTIMIZER,
consts.LEARNING_RATE: 0.001,
consts.TRAIN_STEPS: 15 * 1000,
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.BATCH_SIZE: 300,
consts.TRAIN_STEPS: 5 * 1000,
consts.EVAL_STEPS_INTERVAL: 500,
consts.LEARNING_RATE: 0.0005,
consts.FILTERS: [32, 8],
consts.KERNEL_SIDE_LENGTHS: [5, 5],
consts.DENSE_UNITS: [],
consts.CONCAT_DENSE_UNITS: [4, 2],
consts.CONCAT_DROPOUT_RATES: [0.5, None],
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.NUM_CHANNELS: 32,
consts.FILTERS: [8, 16, 32, 64, 128, 320],
consts.KERNEL_SIDE_LENGTHS: [5, 5, 5, 5, 5, 5],
consts.HARD_TRIPLET_MARGIN: 0.5,
consts.PREDICT_SIMILARITY_MARGIN: 6.3,
consts.DENSE_UNITS: [80],
consts.BATCH_SIZE: 760,
consts.OPTIMIZER: consts.ADAM_OPTIMIZER,
consts.LEARNING_RATE: 0.001,
consts.TRAIN_STEPS: 465,
consts.SHUFFLE_BUFFER_SIZE: 10000,
consts.EVAL_STEPS_INTERVAL: 15,
consts.TRAIN_LOG_STEPS_INTERVAL: 15,
consts.GLOBAL_SUFFIX: "with_dense"
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(super().additional_model_params,
{consts.LEARNING_RATE: self.learning_rate})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(super().additional_model_params,
{consts.EXCLUDED_KEYS: self.excluded})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(super().additional_model_params,
{consts.DENSE_UNITS: [self.du]})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(super().additional_model_params,
{consts.PREDICT_SIMILARITY_MARGIN: self.pm})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.TRAIN_STEPS: 800 if self.im_size > 99 else 1000,
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(super().additional_model_params,
{consts.BATCH_SIZE: self.batch_size})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(super().additional_model_params,
{consts.NUM_CHANNELS: self.nc})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.FILTERS: self.filters,
consts.KERNEL_SIDE_LENGTHS: self.kernel_side_lengths,
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(
super().additional_model_params, {
consts.TRAIN_STEPS: 7 * 1000
})
def additional_model_params(self) -> Dict[str, Any]:
return merge_two_dicts(super().additional_model_params,
{consts.HARD_TRIPLET_MARGIN: self.htm})
