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})