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
}
)
