本文整理汇总了Python中evaluator.Evaluator.get_result方法的典型用法代码示例。如果您正苦于以下问题：Python Evaluator.get_result方法的具体用法？Python Evaluator.get_result怎么用？Python Evaluator.get_result使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类evaluator.Evaluator的用法示例。
在下文中一共展示了Evaluator.get_result方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: run_cnn
# 需要导入模块: from evaluator import Evaluator [as 别名]
# 或者: from evaluator.Evaluator import get_result [as 别名]
def run_cnn(model_params, optimization_params, dataset_path, dataset_params, filename_params, visual_params, epochs, verbose=False):
print(filename_params)
if not os.path.exists(filename_params.results):
os.makedirs(filename_params.results)
is_config, config_values = interface.command.get_command("-config")
is_curriculum, curriculum_set = interface.command.get_command("-curriculum")
is_batch_run, batch_index = interface.command.get_command("-batch", default="0")
is_init_params, param_path = interface.command.get_command("-params")
if is_config:
#Assume config is speficially for running bootstrapping batches.
config_arr = eval(config_values)
if len(config_arr) == 2:
loss_function = config_arr[0]
label_noise = float(config_arr[1])
dataset_params.label_noise = label_noise
model_params.loss = loss_function
batch_index = loss_function + "-" + str(label_noise) + "-" + batch_index
print(batch_index)
if is_curriculum:
dataset_path = curriculum_set
weights = None
if is_init_params:
store = ParamStorage()
if not param_path:
param_path = "./results/params.pkl"
weights = store.load_params(path=param_path)['params']
dataset = DataLoader.create()
dataset.load(dataset_path, dataset_params, optimization_params.batch_size) #Input stage
model = ConvModel(model_params, verbose=True) #Create network stage
evaluator = Evaluator(model, dataset, optimization_params, dataset_path)
evaluator.run(epochs=epochs, verbose=verbose, init=weights)
report = evaluator.get_result()
network_store_path = filename_params.network_save_name
result_path = filename_params.results + "/results.json"
if is_batch_run:
network_store_path = filename_params.results + "/batch" + batch_index + ".pkl"
result_path =filename_params.results + "/batch" + batch_index + ".json"
storage = ParamStorage(path=network_store_path)
storage.store_params(model.params)
dataset.destroy()
if visual_params.gui_enabled:
interface.server.stop_job(report)
printing.print_section('Evaluation precision and recall')
prc = PrecisionRecallCurve(pr_path, model.params, model_params, dataset_params)
test_datapoints = prc.get_curves_datapoints(optimization_params.batch_size, set_name="test")
valid_datapoints = prc.get_curves_datapoints(optimization_params.batch_size, set_name="valid")
#Stores the model params. Model can later be restored.
printing.print_section('Storing model parameters')
if visual_params.gui_enabled:
interface.server.send_precision_recall_data(test_datapoints, valid_datapoints)
storage.store_result(result_path, evaluator.events, test_datapoints, valid_datapoints)