def populate_defaults(input_feature): set_default_values( input_feature, { 'tied_weights': None, 'preprocessing': {} } )
def populate_defaults(input_feature): set_default_values( input_feature, { 'tied_weights': None, 'encoder': 'parallel_cnn', 'level': 'word' } )
def update_hyperopt_params_with_defaults(hyperopt_params): set_default_value(hyperopt_params, STRATEGY, {}) set_default_value(hyperopt_params, EXECUTOR, {}) set_default_value(hyperopt_params, 'split', VALIDATION) set_default_value(hyperopt_params, 'output_feature', COMBINED) set_default_value(hyperopt_params, 'metric', LOSS) set_default_value(hyperopt_params, 'goal', MINIMIZE) set_default_values(hyperopt_params[STRATEGY], {"type": "random"}) strategy = get_from_registry(hyperopt_params[STRATEGY]["type"], strategy_registry) strategy_defaults = { k: v for k, v in strategy.__dict__.items() if k in get_class_attributes(strategy) } set_default_values( hyperopt_params[STRATEGY], strategy_defaults, ) set_default_values(hyperopt_params[EXECUTOR], {"type": "serial"}) executor = get_from_registry(hyperopt_params[EXECUTOR]["type"], executor_registry) executor_defaults = { k: v for k, v in executor.__dict__.items() if k in get_class_attributes(executor) } set_default_values( hyperopt_params[EXECUTOR], executor_defaults, )
def populate_defaults(output_feature): set_default_value(output_feature, LOSS, { 'type': 'mean_squared_error', 'weight': 1 }) set_default_value(output_feature[LOSS], 'type', 'mean_squared_error') set_default_value(output_feature[LOSS], 'weight', 1) set_default_values( output_feature, { 'clip': None, 'dependencies': [], 'reduce_input': SUM, 'reduce_dependencies': SUM })
def populate_defaults(output_feature): set_default_value( output_feature, LOSS, { 'robust_lambda': 0, 'confidence_penalty': 0, 'positive_class_weight': 1, 'weight': 1 }) set_default_values( output_feature, { 'threshold': 0.5, 'dependencies': [], 'reduce_input': SUM, 'reduce_dependencies': SUM })
def populate_defaults(output_feature): # If Loss is not defined, set an empty dictionary set_default_value(output_feature, LOSS, {}) set_default_values( output_feature[LOSS], { 'robust_lambda': 0, 'confidence_penalty': 0, 'positive_class_weight': 1, 'weight': 1 }) set_default_values( output_feature, { 'threshold': 0.5, 'dependencies': [], 'reduce_input': SUM, 'reduce_dependencies': SUM })
def populate_defaults(output_feature): set_default_value(output_feature, 'level', 'word') # If Loss is not defined, set an empty dictionary set_default_value(output_feature, LOSS, {}) # Populate the default values for LOSS if they aren't defined already set_default_values( output_feature[LOSS], { 'type': 'softmax_cross_entropy', 'labels_smoothing': 0, 'class_weights': 1, 'robust_lambda': 0, 'confidence_penalty': 0, 'class_similarities_temperature': 0, 'weight': 1 } ) if output_feature[LOSS]['type'] == 'sampled_softmax_cross_entropy': set_default_values( output_feature[LOSS], { 'sampler': 'log_uniform', 'negative_samples': 25, 'distortion': 0.75 } ) else: set_default_values( output_feature[LOSS], { 'sampler': None, 'negative_samples': 0, 'distortion': 1 } ) set_default_value(output_feature[LOSS], 'unique', False) set_default_value(output_feature, 'decoder', 'generator') if output_feature['decoder'] == 'tagger': set_default_value(output_feature, 'reduce_input', None) set_default_values( output_feature, { 'dependencies': [], 'reduce_input': SUM, 'reduce_dependencies': SUM } )