248 Star 846 Fork 47

MindSpore / mindarmour

Create your Gitee Account
Explore and code with more than 6 million developers,Free private repositories !:)
Sign up
Clone or download
lenet5_config.py 2.48 KB
Copy Edit Web IDE Raw Blame History
# 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
})
Python
1
https://gitee.com/mindspore/mindarmour.git
git@gitee.com:mindspore/mindarmour.git
mindspore
mindarmour
mindarmour
r1.2

Search