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opt_config.yml
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General_settings:
num_cpus: 12
num_gpus: 2
num_gpus_per_trial: 1
num_cpus_per_trial: 6
num_parallel_trials: 2
optimization_type: 'tree_parzen_estimators'
mode: 'max'
working_directory: None
ctlearn_config: myconfig.yml
n_initial_points: 20
num_max_evals: 40
random_state: 288
remove_training_folders: True
reload_trials: False
reload_optimization_results: False
predict: False
data_set_to_optimize : 'validation'
metrics_val_to_log: ['auc', 'acc', 'acc_gamma', 'acc_proton', 'loss']
metrics_pred_to_log: ['auc', 'acc', 'bacc', 'f1', 'prec', 'rec', 'log_loss']
metric_to_optimize : 'auc'
user_defined_metric_val:
label: 'user_defined_val'
expression: '(auc + accuracy_gamma)*0.5'
user_defined_metric_pred:
label: 'user_defined_pred'
expression: '(auc + f1 + sklearn.metrics.balanced_accuracy_score(labels, predicted_class))*0.5'
Optimizer_settings:
tree_parzen_estimators_config:
gamma: 0.25
gaussian_processes_config:
base_estimator: 'GP'
acq_function: 'gp_hedge'
acq_optimizer: 'auto'
xi: 0.01
kappa: 1.96
genetic_algorithm_config:
max_generation: 5
population_size: 10
population_decay: 0.95
keep_top_ratio: 0.2
selection_bound: 0.4
crossover_bound: 0.4
CTLearn_settings:
seed: 1234
num_training_steps_per_validation: 2500
num_validations: 15
example_type: 'single_tel'
model: single_tel
sorting: null
min_num_tels: 1
selected_tel_types: ['MST:NectarCam']
training_file_list: 'data_train.txt'
prediction_file_list: 'data_predict.txt'
batch_size : 64
model_directory: '/home/jredondo/ctlearn/ctlearn/default_models'
validation_split: 0.1
Hyperparameters_settings:
hyperparameters_to_log: [number_of_layers, layer1_filters,layer2_filters,layer3_filters,
layer4_filters,layer1_kernel, layer2_kernel, layer3_kernel, layer4_kernel]
config:
pool_size: ['Model', 'Model Parameters', 'basic', 'conv_block','max_pool','size']
pool_strides: ['Model', 'Model Parameters', 'basic', 'conv_block','max_pool','strides']
optimizer_type: ['Training', 'Hyperparameters', 'optimizer']
base_learning_rate: ['Training', 'Hyperparameters', 'base_learning_rate']
adam_epsilon: ['Training', 'Hyperparameters', 'adam_epsilon']
cnn_rnn_dropout: ['Model', 'Model Parameters', 'cnn_rnn', 'dropout_rate']
layer2_filters: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 1, 'filters']
layer3_filters: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 2, 'filters']
layer4_filters: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 3, 'filters']
layer1_filters: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 0, 'filters']
layer1_kernel: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 0, 'kernel_size']
layer2_kernel: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 1, 'kernel_size']
layer3_kernel: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 2, 'kernel_size']
layer4_kernel: ['Model', 'Model Parameters', 'basic', 'conv_block', 'layers', 3, 'kernel_size']
fixed_hyperparameters:
pool_size: 2
pool_strides: 2
optimizer_type: 'Adam'
base_learning_rate: 5.0e-05
adam_epsilon: 1.0e-08
cnn_rnn_dropout: 0.5
dependent_hyperparameters:
layer2_filters: '2 * layer1_filters'
layer3_filters: '4 * layer1_filters'
layer4_filters: '8 * layer1_filters'
hyperparameters_to_optimize:
base_learning_rate:
type: loguniform
range: [-5, 0]
layer1_filters:
type: 'quniform'
range: [16, 64]
step: 1
layer1_kernel:
type: 'quniform'
range: [2, 10]
step: 1
layer2_kernel:
type: 'quniform'
range: [2, 10]
step: 1
layer3_kernel:
type: 'quniform'
range: [2, 10]
step: 1
layer4_kernel:
type: 'quniform'
range: [2, 10]
step: 1
optimizer_type:
type: 'choice'
range: ['Adadelta', 'Adam', 'RMSProp', 'SGD']
cnn_rnn_dropout:
type: 'uniform'
range: [0,1]
number_of_layers:
type: 'conditional'
range:
- value: 1
cond_params:
layer1_kernel:
type: 'quniform'
range: [2, 10]
step: 1
layer1_filters:
type: 'quniform'
range: [16, 64]
step: 1
- value: 2
cond_params:
layer1_kernel:
type: 'quniform'
range: [2, 10]
step: 1
layer1_filters:
type: 'quniform'
range: [16, 64]
step: 1
layer2_kernel:
type: 'quniform'
range: [2, 10]
step: 1
layer2_filters:
type: 'quniform'
range: [16, 128]
step: 1