def total_variation_loss(x): a = tf.square( x[:, : img_height -1, : img_width - 1, :] - x[:, 1:, : img_width -1, :] ) b = tf.square( x[:, : img_height -1, : img_width - 1, :] - x[:, : img_width -1, 1:, :] ) return tf.reduce_sum(tf.pow(a + b, 1.25))

这段代码定义了一个计算总变差损失的函数。总变差损失用于衡量图像的平滑程度。在该函数中，首先计算图像中每个像素与其相邻像素之间的差值的平方，并保存在变量a和b中。然后，通过对a和b进行加权求和，并使用一个指数值进行幂运算，得到总变差损失。

总变差损失可以促使生成的图像更加平滑，减少噪点和颗粒状的细节。它通过鼓励相邻像素之间的颜色一致性来实现这一点，从而使图像看起来更加自然和真实。

相关问题

PCX1 = 1.5482 $Shape factor Cfx for longitudinal force PDX1 = 1.1632 $Longitudinal friction Mux at Fznom PDX2 = -0.11154 $Variation of friction Mux with load PDX3 = 0.94173 $Variation of friction Mux with camber squared PEX1 = 0.27 $Longitudinal curvature Efx at Fznom PEX2 = 0.011693 $Variation of curvature Efx with load PEX3 = 0.053303 $Variation of curvature Efx with load squared PEX4 = 0.59223 $Factor in curvature Efx while driving PKX1 = 32.9102 $Longitudinal slip stiffness Kfx/Fz at Fznom PKX2 = 12.7911 $Variation of slip stiffness Kfx/Fz with load PKX3 = -0.11254 $Exponent in slip stiffness Kfx/Fz with load PHX1 = -0.0017527 $Horizontal shift Shx at Fznom PHX2 = 0.00068824 $Variation of shift Shx with load PVX1 = 0.068079 $Vertical shift Svx/Fz at Fznom PVX2 = 0.0023429 $Variation of shift Svx/Fz with load PPX1 = -0.8733 $linear influence of inflation pressure on longitudinal slip stiffness PPX2 = 0.7035 $quadratic influence of inflation pressure on longitudinal slip stiffness PPX3 = -0.0080216 $linear influence of inflation pressure on peak longitudinal friction PPX4 = -0.47776 $quadratic influence of inflation pressure on peak longitudinal friction RBX1 = 18.3369 $Slope factor for combined slip Fx reduction RBX2 = 18.2559 $Variation of slope Fx reduction with kappa RBX3 = 607.8133 $Influence of camber on stiffness for Fx combined RCX1 = 0.96372 $Shape factor for combined slip Fx reduction REX1 = -1.2699 $Curvature factor of combined Fx REX2 = -0.14323 $Curvature factor of combined Fx with load RHX1 = 0.0037359 $Shift factor for combined slip Fx reduction帮我做成表格

| 参数 | 值 |
| --- | --- |
| PCX1 | 1.5482 |
| PDX1 | 1.1632 |
| PDX2 | -0.11154 |
| PDX3 | 0.94173 |
| PEX1 | 0.27 |
| PEX2 | 0.011693 |
| PEX3 | 0.053303 |
| PEX4 | 0.59223 |
| PKX1 | 32.9102 |
| PKX2 | 12.7911 |
| PKX3 | -0.11254 |
| PHX1 | -0.0017527 |
| PHX2 | 0.00068824 |
| PVX1 | 0.068079 |
| PVX2 | 0.0023429 |
| PPX1 | -0.8733 |
| PPX2 | 0.7035 |
| PPX3 | -0.0080216 |
| PPX4 | -0.47776 |
| RBX1 | 18.3369 |
| RBX2 | 18.2559 |
| RBX3 | 607.8133 |
| RCX1 | 0.96372 |
| REX1 | -1.2699 |
| REX2 | -0.14323 |
| RHX1 | 0.0037359 |

SELECT a.[Sub-Region], a.[SBU], a.[CompanyCtry], a.[Inv_CM], b.[Inv_LM], ROW_NUMBER() OVER ( PARTITION BY a.[Sub-Region], a.[SBU] ORDER BY ABS(a.[Inv_CM] - b.[Inv_LM]) DESC ) as RowNo INTO [AP_Inventory].[dbo].[Temp_vs_CY_LM] FROM ( SELECT [Sub-Region], [SBU_2023] as [SBU], [CompanyCtry], SUM([TotalInv_EUR]) as [Inv_CM] FROM [AP_Inventory].[dbo].[Inv_Details_for_PowerBI] WHERE DATEDIFF(MONTH, DATEFROMPARTS([ReportYear], [ReportMonth], 1), GETDATE()) = 1 and [StockCategory] <> 'Vendor Consignment' and [CompanyCtry] is not null GROUP BY [Sub-Region], [SBU_2023], [CompanyCtry] ) as a, ( SELECT [Sub-Region], [SBU_2023] as [SBU], [CompanyCtry], SUM([TotalInv_EUR]) as [Inv_LM] FROM [AP_Inventory].[dbo].[Inv_Details_for_PowerBI] WHERE DATEDIFF(MONTH, DATEFROMPARTS([ReportYear], [ReportMonth], 1), GETDATE()) = 2 and [StockCategory] <> 'Vendor Consignment' and [CompanyCtry] is not null GROUP BY [Sub-Region], [SBU_2023], [CompanyCtry] ) as b WHERE a.[Sub-Region] = b.[Sub-Region] and a.[SBU] = b.[SBU] and a.[CompanyCtry] = b.[CompanyCtry]; GO UPDATE a SET a.[vs_CY_LM_Content] = b.[CompanyCtry], a.[vs_CY_LM_Content_Qty] = b.[Inv_CM], a.[vs_CY_LM_Delta_Qty] = b.[Inv_CM] - b.[Inv_LM] FROM [AP_Inventory].[dbo].[Calculate_Top_Variation_V4] as a, [AP_Inventory].[dbo].[Temp_vs_CY_LM] as b WHERE a.[Organization] = b.[Sub-Region] and a.[SBU] = b.[SBU] and a.[Dimension] = '[Country]' and a.[vs_CY_LM_Rank] = b.[RowNo] and DATEDIFF(MONTH, DATEFROMPARTS(a.[Report_Year], a.[Report_Month], 1), GETDATE()) = 1; GO DROP TABLE [AP_Inventory].[dbo].[Temp_vs_CY_LM]; GO

这段代码的作用是从表 [AP_Inventory].[dbo].[Inv_Details_for_PowerBI] 中获取当前月份（CM）和上个月（LM）的库存数据，并按照一定的排序规则计算出差值。然后将计算结果更新到目标表 [AP_Inventory].[dbo].[Calculate_Top_Variation_V4] 中的对应字段。

首先，通过两个子查询获取当前月份和上个月份的库存数据，并使用 SUM 函数计算每个组合的总库存量。然后，使用 ROW_NUMBER 函数为每个组合计算一个排序号（RowNo），排序规则是根据当前月份库存量与上个月份库存量之差的绝对值进行排序。

接下来，将计算结果插入到临时表 [AP_Inventory].[dbo].[Temp_vs_CY_LM] 中，用于后续的更新操作。

最后，通过 UPDATE 语句将临时表中的差值更新到目标表中的相应字段。更新的条件是目标表中的组织（Organization）、业务单位（SBU）、维度（Dimension）以及排名（Rank）与临时表中的对应字段匹配。

最后，使用 DROP TABLE 语句删除临时表。

整个代码段的目的是根据当前月份和上个月份的库存数据，计算出差值，并将差值更新到目标表中。通过使用临时表和动态计算，可以实现对差值的灵活计算和更新。