本文整理汇总了Python中official.utils.logs.logger._collect_run_params方法的典型用法代码示例。如果您正苦于以下问题：Python logger._collect_run_params方法的具体用法？Python logger._collect_run_params怎么用？Python logger._collect_run_params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official.utils.logs.logger的用法示例。
在下文中一共展示了logger._collect_run_params方法的2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: test_collect_run_params
# 需要导入模块: from official.utils.logs import logger [as 别名]
# 或者: from official.utils.logs.logger import _collect_run_params [as 别名]
def test_collect_run_params(self):
run_info = {}
run_parameters = {
"batch_size": 32,
"synthetic_data": True,
"train_epochs": 100.00,
"dtype": "fp16",
"resnet_size": 50,
"random_tensor": tf.constant(2.0)
}
logger._collect_run_params(run_info, run_parameters)
self.assertEqual(len(run_info["run_parameters"]), 6)
self.assertEqual(run_info["run_parameters"][0],
{"name": "batch_size", "long_value": 32})
self.assertEqual(run_info["run_parameters"][1],
{"name": "dtype", "string_value": "fp16"})
self.assertEqual(run_info["run_parameters"][2],
{"name": "random_tensor", "string_value":
"Tensor(\"Const:0\", shape=(), dtype=float32)"})
self.assertEqual(run_info["run_parameters"][3],
{"name": "resnet_size", "long_value": 50})
self.assertEqual(run_info["run_parameters"][4],
{"name": "synthetic_data", "bool_value": "True"})
self.assertEqual(run_info["run_parameters"][5],
{"name": "train_epochs", "float_value": 100.00}) 示例2: test_collect_run_params
# 需要导入模块: from official.utils.logs import logger [as 别名]
# 或者: from official.utils.logs.logger import _collect_run_params [as 别名]
def test_collect_run_params(self):
run_info = {}
run_parameters = {
"batch_size": 32,
"synthetic_data": True,
"train_epochs": 100.00,
"dtype": "fp16",
"resnet_size": 50,
"random_tensor": tf.constant(2.0)
}
logger._collect_run_params(run_info, run_parameters)
self.assertEqual(len(run_info["run_parameters"]), 6)
self.assertEqual(run_info["run_parameters"][0],
{"name": "batch_size", "long_value": 32})
self.assertEqual(run_info["run_parameters"][1],
{"name": "dtype", "string_value": "fp16"})
v1_tensor = {"name": "random_tensor", "string_value":
"Tensor(\"Const:0\", shape=(), dtype=float32)"}
v2_tensor = {"name": "random_tensor", "string_value":
"tf.Tensor(2.0, shape=(), dtype=float32)"}
self.assertIn(run_info["run_parameters"][2], [v1_tensor, v2_tensor])
self.assertEqual(run_info["run_parameters"][3],
{"name": "resnet_size", "long_value": 50})
self.assertEqual(run_info["run_parameters"][4],
{"name": "synthetic_data", "bool_value": "True"})
self.assertEqual(run_info["run_parameters"][5],
{"name": "train_epochs", "float_value": 100.00})