def initialize_params_for_evaluation_from_checkpoint():
params_initialization_for_evaluation = {}
params_initialization_for_evaluation[
'logs_dir'] = "/mnt/mnt/mounted_bucket/results_for_final_presentation/logs"
params_initialization_for_evaluation[
'summary_dir'] = "/mnt/mnt/mounted_bucket/results_for_final_presentation/summary"
params_initialization_for_evaluation['evaluate_model'] = True
params_initialization_for_evaluation['resume_training'] = False
params_initialization_for_evaluation['stage_of_development'] = "evaluation"
params_initialization_for_evaluation['type_of_evaluation'] = 'DEV'
params_initialization_for_evaluation[
'training_path'] = "/mnt/mnt/mounted_bucket/tfRecord_datasets_with_all_features"
params_initialization_for_evaluation['data_dir'] = 'data'
params_initialization_for_evaluation['max_steps'] = None
params_initialization_for_evaluation['num_epochs'] = 1
params_initialization_for_evaluation['batch_size'] = 32
params_initialization_for_evaluation['num_steps'] = 300
params_initialization_for_evaluation['pmRange'] = None
params_initialization_for_evaluation['year'] = None
params_initialization_for_evaluation['type_of_model'] = 'DehazeNet'
return AQP.initialize_params_tf_records(
experiment_directory_name,
experiment_directory_suffix,
"evaluation",
params_initialization_for_evaluation=
params_initialization_for_evaluation,
use_ranges=True)
def initialize_params_for_experiment():
params_initialization_for_training = {}
params_initialization_for_training[
'training_path'] = "/mnt/mnt/mounted_bucket/tfRecord_datasets_with_all_features"
params_initialization_for_training[
'logs_dir'] = "/mnt/mnt/mounted_bucket/results_for_final_presentation/logs"
params_initialization_for_training[
'summary_dir'] = "/mnt/mnt/mounted_bucket/results_for_final_presentation/summary"
params_initialization_for_training['webcamId'] = '17603'
params_initialization_for_training[
'dict_of_filePath_to_num_of_examples_in_tfrecord'] = dict_from_webcamId_to_filePath_to_num_of_examples_in_tfrecord[
params_initialization_for_training['webcamId']]
params_initialization_for_training[
'total_num_of_training_examples'] = dict_from_webcamId_to_total_num_of_training_examples[
params_initialization_for_training['webcamId']]
params_initialization_for_training['num_of_classes'] = 1
params_initialization_for_training['min_bins'] = None
params_initialization_for_training['max_bins'] = None
params_initialization_for_training['batch_size'] = 32
params_initialization_for_training['batch_size_per_tfrecord'] = None
params_initialization_for_training['stage_of_development'] = "training"
params_initialization_for_training['max_num_epochs'] = 4
params_initialization_for_training['type_of_model'] = 'ResNet'
params_initialization_for_training[
'model_path'] = "/mnt/mnt/mounted_bucket/pretrained_weights/ResNet/resnet_v2_50.ckpt"
params_initialization_for_training['type_of_optimizer'] = 'adam'
params_initialization_for_training['beta1'] = 0.9
params_initialization_for_training['beta2'] = 0.999
params_initialization_for_training['initial_num_steps'] = 50
params_initialization_for_training['initial_num_partial_steps'] = 50
params_initialization_for_training['initial_learning_rate'] = 0.0001
params_initialization_for_training[
'initial_partial_learning_rate'] = 0.0001
params_initialization_for_training['learning_rate_decay_factor'] = 0.99
params_initialization_for_training['num_steps'] = 1000
params_initialization_for_training['num_partial_steps'] = 0
return AQP.initialize_params_tf_records(
experiment_directory_name,
experiment_directory_suffix,
"training",
params_initialization_for_training=params_initialization_for_training,
training_with_eval=True)