combiner = "sum"

这段代码指定了在进行Embedding层计算时，如何将同一电影的不同特征向量进行合并。在这里，`combiner`被设置为`"sum"`，表示将不同的特征向量进行求和操作，得到一个单一的特征向量，作为该电影的Embedding向量。

例如，假设一个电影有三个特征：导演、演员和类型。每个特征都被转换为一个特征向量，例如，导演可能转换为长度为256的向量，演员转换为长度为128的向量，类型转换为长度为32的向量。那么，使用`"sum"`作为`combiner`时，这三个特征向量将分别被加起来，得到一个长度为416的特征向量，作为该电影的Embedding向量。

相关问题

total_obj_num_distance_dict = {10: 0, 20: 0, 50: 0, 100: 0, 'nocare': 0} tolerance_num_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} tolerance_num_vanish_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} tolerance_num_via_box = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} tolerance_num_combine = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_vanish_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_via_box = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0} distance_error_sum_combine = {0.015: 0, 0.03: 0, 0.07: 0, 0.10: 0, 1000: 0}整理代码

可以使用字典的 `items()` 方法来遍历字典的键值对，然后按照需要的格式整理代码。以下是一个示例代码：

def format_dict_code(dictionary):
    code = ""
    for key, value in dictionary.items():
        code += f"{key}: {value}, "
    code = "{" + code.rstrip(", ") + "}"
    return code

total_obj_num_distance_dict_code = format_dict_code(total_obj_num_distance_dict)
tolerance_num_point_code = format_dict_code(tolerance_num_point)
tolerance_num_vanish_point_code = format_dict_code(tolerance_num_vanish_point)
tolerance_num_via_box_code = format_dict_code(tolerance_num_via_box)
tolerance_num_combine_code = format_dict_code(tolerance_num_combine)
distance_error_sum_point_code = format_dict_code(distance_error_sum_point)
distance_error_sum_vanish_point_code = format_dict_code(distance_error_sum_vanish_point)
distance_error_sum_via_box_code = format_dict_code(distance_error_sum_via_box)
distance_error_sum_combine_code = format_dict_code(distance_error_sum_combine)

print(f"total_obj_num_distance_dict = {total_obj_num_distance_dict_code}")
print(f"tolerance_num_point = {tolerance_num_point_code}")
print(f"tolerance_num_vanish_point = {tolerance_num_vanish_point_code}")
print(f"tolerance_num_via_box = {tolerance_num_via_box_code}")
print(f"tolerance_num_combine = {tolerance_num_combine_code}")
print(f"distance_error_sum_point = {distance_error_sum_point_code}")
print(f"distance_error_sum_vanish_point = {distance_error_sum_vanish_point_code}")
print(f"distance_error_sum_via_box = {distance_error_sum_via_box_code}")
print(f"distance_error_sum_combine = {distance_error_sum_combine_code}")

这段代码将会输出整理好的代码，你可以根据需要将其复制到你的程序中。

简化代码 total_obj_num_distance_dict = {10: 0, 20: 0, 50: 0, 100: 0, 'nocare': 0} tolerance_num_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0} tolerance_num_vanish_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0} tolerance_num_via_box = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0} tolerance_num_combine = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0} distance_error_sum_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0} distance_error_sum_vanish_point = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0} distance_error_sum_via_box = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0} distance_error_sum_combine = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0}

可以使用字典推导式来简化代码，将相似的字典合并为一个字典。具体代码如下所示：

total_obj_num_distance_dict = {10: 0, 20: 0, 50: 0, 100: 0, 'nocare': 0}
tolerance_dict = {0.015: 0, 0.03: 0, 0.07: 0, 0.15: 0, 1000: 0}

tolerance_num_point = tolerance_dict.copy()
tolerance_num_vanish_point = tolerance_dict.copy()
tolerance_num_via_box = tolerance_dict.copy()
tolerance_num_combine = tolerance_dict.copy()

distance_error_sum_point = tolerance_dict.copy()
distance_error_sum_vanish_point = tolerance_dict.copy()
distance_error_sum_via_box = tolerance_dict.copy()
distance_error_sum_combine = tolerance_dict.copy()

以上代码使用 `tolerance_dict.copy()` 来复制 `tolerance_dict` 字典中的键值对，以初始化其他字典。这样可以避免重复的定义和初始化步骤，并减少了代码的冗余。