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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """
- network config setting, will be used in train.py
- """
-
- from easydict import EasyDict as edict
-
- mnist_cfg = edict({
- 'num_classes': 10, # the number of classes of model's output
- 'lr': 0.01, # the learning rate of model's optimizer
- 'momentum': 0.9, # the momentum value of model's optimizer
- 'epoch_size': 10, # training epochs
- 'batch_size': 256, # batch size for training
- 'image_height': 32, # the height of training samples
- 'image_width': 32, # the width of training samples
- 'save_checkpoint_steps': 234, # the interval steps for saving checkpoint file of the model
- 'keep_checkpoint_max': 10, # the maximum number of checkpoint files would be saved
- 'device_target': 'Ascend', # device used
- 'data_path': '../../common/dataset/MNIST', # the path of training and testing data set
- 'dataset_sink_mode': False, # whether deliver all training data to device one time
- 'micro_batches': 32, # the number of small batches split from an original batch
- 'norm_bound': 1.0, # the clip bound of the gradients of model's training parameters
- 'initial_noise_multiplier': 0.05, # the initial multiplication coefficient of the noise added to training
- # parameters' gradients
- 'noise_mechanisms': 'Gaussian', # the method of adding noise in gradients while training
- 'clip_mechanisms': 'Gaussian', # the method of adaptive clipping gradients while training
- 'clip_decay_policy': 'Linear', # Decay policy of adaptive clipping, decay_policy must be in ['Linear', 'Geometric'].
- 'clip_learning_rate': 0.001, # Learning rate of update norm clip.
- 'target_unclipped_quantile': 0.9, # Target quantile of norm clip.
- 'fraction_stddev': 0.01, # The stddev of Gaussian normal which used in empirical_fraction.
- 'optimizer': 'Momentum' # the base optimizer used for Differential privacy training
- })
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