mean = torch.mean(a, 0) print(mean, mean.shape) 1. 2. 3. 4. 5. 例子2 如下对dim 1做mean N=3,即所选的dim,输出为剩下的维度(2,1) 第一个:(0+1+2)/3=1 第二个: (3+4+5)/3=4 a = torch.Tensor([0, 1, 2, 3, 4, 5]).view(2, 3, 1) print(a) mean = torch.mea...
y_0= torch.mean(x, dim=0)## 每列求均值y_1 = torch.mean(x, dim=1)### 每行求均值print(x)print(y_0)print(y_1) 输出: tensor([[1., 2., 3.], [4., 5., 6.]]) tensor([2.5000, 3.5000, 4.5000]) tensor([2., 5.]) 输入tensor的形状为(2, 3),其中2为第0维,3为第1维。
y = torch.mean(x, dim=1, keepdim=True) 三维张量求均值: import torch import numpy as np # ===初始化一个三维矩阵=== A = torch.ones((4,3,2)) # ===替换三维矩阵里面的值=== A[0] = torch.ones((3,2)) *1 A[1] = torch.ones((3,2)) *2 A[2] = torch.ones((3,2)) ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importmean[as 别名]defforward(self, x, y):means = torch.mean(x, dim=(2,3)) m = torch.mean(means, dim=-1, keepdim=True) v = torch.var(means, dim=-1, keepdim=True) means = (means - m) / (torch.sqrt(v +1e-5...
in networks.py 165 torch.mean(input, dim=[2, 3], keepdim=True) , why use list to dim
由于这几个np/torch的函数仅仅在形式参数上有差别,因此我们以np为例来讲。另外np.mean可以通过np.sum来求得,因此理解np.sum也就理解了所有。 np.sum底层调用的是np.add.reduceWhat is the difference between np.sum and np.add.reduce? def _sum(a, axis=None, dtype=None, out=None, keepdims=False):...
解析:torch.mean(x,[a,b],keepdim=True)中[a,b]的意思是,沿着将第a和第b维的维度变为1的方向做均值,其余维度不变。 直接上例子: import torch a = torch.tensor([ [[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]], ...
torch中的mean函数 torch.mean函数函数解释：返回了一个输入张量Tensor中所有元素的平均值，返回值同样是tensor类型。参数解释：●dim=0 按列求平均值，返回的形状是（1，列数）；●dim=1 按行求平均值，返回的形状是（行数，1）,●默认 返回的是所有元素的平均值。代码示例：x=x.float()x_mean=torch...
import numpy as np import torch X = np.load('X.npy') avg_np, _ = np.average(X, axis=1, returned=True) X_th = torch.tensor(X) avg_th = torch.mean(X_th, dim=1) assert (X == X_th.numpy()).all() # assert (avg_np == avg_th.numpy()).all() # this fails alread X...
torch.mean()和mean(dim=None, keepdim=False)的使用举例怎么分析,针对这个问题,这篇文章详细介绍了相对应的分析和解答,希望可以帮助更多想解决这个问题的小伙伴找到更简单易行的方法。 代码实验展示: Microsoft Windows [版本10.0.18363.1256](c)2019Microsoft Corporation。保留所有权利。