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hpsearch_config_perm_ssge_pf.py
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hpsearch_config_perm_ssge_pf.py
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#!/usr/bin/env python3
# Copyright 2019 Christian Henning
#
# 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.
#
# title :probabilistic/prob_mnist/hpsearch_config_perm_ssge_pf.py
# author :ch
# contact :[email protected]
# created :02/03/2020
# version :1.0
# python_version :3.6.8
"""
Hyperparameter-search configuration for permMNIST using SSGE
------------------------------------------------------------
A configuration file for our custom hyperparameter search script. This
configuration is meant for hyperparameter searches of the simulation defined by
:mod:`probabilistic.prob_mnist.train_perm_ssge`.
"""
from probabilistic.prob_mnist import hpsearch_config_split_bbb as hpsplit
from probabilistic.prob_mnist import hpsearch_config_split_ssge as hpsplitssge
##########################################
### Please define all parameters below ###
##########################################
# Define a dictionary with parameter names as keys and a list of values for
# each parameter. For flag arguments, simply use the values [True, False].
# Note, the output directory is set by the hyperparameter search script.
#
# Example: {'option1': [12, 24], 'option2': [0.1, 0.5],
# 'option3': [True]}
# This disctionary would correspond to the following 4 configurations:
# python3 SCRIPT_NAME.py --option1=12 --option2=0.1 --option3
# python3 SCRIPT_NAME.py --option1=12 --option2=0.5 --option3
# python3 SCRIPT_NAME.py --option1=24 --option2=0.1 --option3
# python3 SCRIPT_NAME.py --option1=24 --option2=0.5 --option3
#
# If fields are commented out (missing), the default value is used.
# Note, that you can specify special conditions below.
grid = {
### Continual learning options ###
#'train_from_scratch' : [False],
#'cl_scenario' : [1], # 1, 2 or 3
#'split_head_cl3' : [False],
#'num_tasks' : [10],
### Training options ###
#'batch_size' : [128], # RELATED WORK - 128
#'n_iter' : [5000], # RELATED WORK - 5000
#'epochs' : [-1],
#'lr' : [0.0001], # RELATED WORK - 0.0001
#'momentum' : [0.],
#'weight_decay' : [0], # RELATED WORK - 0.
'use_adam' : [True], # RELATED WORK - True
#'adam_beta1' : [0.9], # RELATED WORK - 0.9
#'use_rmsprop' : [False],
#'use_adadelta' : [False],
#'use_adagrad' : [False],
#'clip_grad_value' : [-1],
#'clip_grad_norm' : [-1],
#'plateau_lr_scheduler': [False],
#'lambda_lr_scheduler': [False],
#'train_sample_size' : [1],
#'prior_variance' : [1.],
#'kl_scale' : [1.],
#'kl_schedule': [0],
#'num_kl_samples': [1],
#'training_set_size': [-1],
#'coreset_size': [-1],
#'per_task_coreset': [False],
#'coreset_reg': [1.],
#'coreset_batch_size': [-1],
#'past_and_future_coresets': [False],
### Main network options ###
#'mlp_arch' : ['"1000,1000"'], # RELATED WORK - '"1000,1000"'
#'net_act' : ['relu'], # RELATED WORK - 'relu'
#'no_bias' : [False],
#'dropout_rate' : [-1],
#'batchnorm' : [False],
#'bn_no_running_stats': [False],
#'bn_no_stats_checkpointing': [False],
### Implicit-hypernet options ###
'imp_hnet_type' : ['hmlp'], # 'hmlp', 'chunked_hmlp', 'structured_hmlp',
# 'hdeconv', 'chunked_hdeconv'
#'imp_hmlp_arch' : ['"125,250,500"'],
#'imp_chmlp_chunk_size' : [1500],
#'imp_chunk_emb_size' : ['"32"'],
#'imp_hdeconv_shape' : ['"512,512,3"'],
#'imp_hdeconv_num_layers' : [5],
#'imp_hdeconv_filters' : ['"128,512,256,128"'],
#'imp_hdeconv_kernels': ['"5"'],
#'imp_hdeconv_attention_layers': ['"1,3"'],
#'imp_hnet_net_act': ['sigmoid'],
#'imp_hnet_no_bias': [False],
#'imp_hnet_dropout_rate': [-1],
#'imp_hnet_specnorm': [False],
#'full_support_perturbation': [-1],
### Network initialization options ###
#'normal_init' : [False],
#'std_normal_init' : [0.02],
#'std_normal_temb' : [1.],
#'std_normal_emb' : [1.],
#'hyper_fan_init' : [False],
### Evaluation options ###
#'val_iter' : [500],
#'val_batch_size' : [1000],
#'val_set_size' : [0],
#'full_test_interval' : [-1],
#'val_sample_size' : [100],
### Miscellaneous options ###
#'no_cuda' : [False],
#'deterministic_run': [True],
#'random_seed': [42],
#'mnet_only': [False],
#'store_final_model': [False],
#'during_acc_criterion': ['"-1"'],
### Implicit Distribution options ###
#'latent_dim' : [8],
#'latent_std' : [1.],
'full_support_perturbation' : [-1],
### SSGE options ###
#'heuristic_kernel' : [False],
#'rbf_kernel_width' : [1.],
#'num_ssge_eigenvals' : [-1],
#'thr_ssge_eigenvals' : [1.],
#'ssge_sample_size' : [10],
### Probabilistic CL Options ###
#'calibrate_temp': [False],
#'cal_temp_iter': [1000],
#'cal_sample_size': [-1],
### Permuted MNIST options ###
'padding': [2], # RELATED WORK - 2
#'trgt_padding': [0],
}
# Sometimes, not the whole grid should be searched. For instance, if an SGD
# optimizer has been chosen, then it doesn't make sense to search over multiple
# beta2 values of an Adam optimizer.
# Therefore, one can specify special conditions.
# NOTE, all conditions that are specified here will be enforced. Thus, they
# overwrite the grid options above.
#
# How to specify a condition? A condition is a key value tuple: whereas as the
# key as well as the value is a dictionary in the same format as in the grid
# above. If any configurations matches the values specified in the key dict,
# The values specified in the values dict will be searched instead.
#
# Note, if arguments are commented out above but appear in the conditions, the
# condition will be ignored.
conditions = [
# Note, we specify a particular set of base conditions below that should
# always be enforces: "_BASE_CONDITIONS".
### Add your conditions here ###
#({'clip_grad_value': [1.]}, {'clip_grad_norm': [-1]}),
#({'clip_grad_norm': [1.]}, {'clip_grad_value': [-1]}),
]
####################################
### DO NOT CHANGE THE CODE BELOW ###
####################################
conditions = conditions + hpsplit._BASE_CONDITIONS
# Name of the script that should be executed by the hyperparameter search.
# Note, the working directory is set seperately by the hyperparameter search
# script.
_SCRIPT_NAME = 'train_perm_ssge_pf.py'
# This file is expected to reside in the output folder of the simulation.
_SUMMARY_FILENAME = 'performance_overview.txt'
# These are the keywords that are supposed to be in the summary file.
# A summary file always has to include the keyword "finished"!.
_SUMMARY_KEYWORDS = hpsplitssge._SUMMARY_KEYWORDS
# The name of the command-line argument that determines the output folder
# of the simulation.
_OUT_ARG = 'out_dir'
# In case you need a more elaborate parser than the default one define by the
# function :func:`hpsearch.hpsearch._get_performance_summary`, you can pass a
# function handle to this attribute.
# Value `None` results in the usage of the default parser.
_SUMMARY_PARSER_HANDLE = None # Default parser is used.
#_SUMMARY_PARSER_HANDLE = _get_performance_summary # Custom parser is used.
# A function handle, that is used to evaluate the performance of a run.
_PERFORMANCE_EVAL_HANDLE = hpsplit._performance_criteria
# A key that must appear in the `_SUMMARY_KEYWORDS` list. If `None`, the first
# entry in this list will be selected.
# The CSV file will be sorted based on this keyword. See also attribute
# `_PERFORMANCE_SORT_ASC`.
_PERFORMANCE_KEY = 'acc_avg_final'
assert(_PERFORMANCE_KEY is None or _PERFORMANCE_KEY in _SUMMARY_KEYWORDS)
# Whether the CSV should be sorted ascending or descending based on the
# `_PERFORMANCE_KEY`.
_PERFORMANCE_SORT_ASC = False
# FIXME: This attribute will vanish in future releases.
# This attribute is only required by the `hpsearch_postprocessing` script.
# A function handle to the argument parser function used by the simulation
# script. The function handle should expect the list of command line options
# as only parameter.
# Example:
# >>> from probabilistic.prob_mnist import train_args as targs
# >>> f = lambda argv : targs.parse_cmd_arguments(mode='split_mnist_bbb',
# ... argv=argv)
# >>> _ARGPARSE_HANDLE = f
import probabilistic.prob_mnist.train_args as targs
_ARGPARSE_HANDLE = lambda argv : targs.parse_cmd_arguments( \
mode='perm_mnist_ssge_pf', argv=argv)
if __name__ == '__main__':
pass