bsize = 20; blist = 1:bsize:(N-bsize+1);
时间: 2024-05-17 11:19:27 浏览: 13
这段代码定义了一个变量bsize,并将其赋值为20。接着定义了一个blist向量,它的元素是从1开始,以bsize为步长,一直加到(N-bsize+1)为止。这里的N可能是MNIST数据集中图片的数量。
这段代码的作用是将数据集分成若干个batch,每个batch包含bsize张图片。blist向量记录了每个batch的第一张图片在数据集中的索引位置。可以通过blist向量来遍历整个数据集中的所有batch。
相关问题
解释代码 def getData(dType="train",dataDir="",bSize=128,shuffle=True,imgH=224,imgW=224,channels=3):
这是一个函数定义,它的名称是getData,它接受5个参数:dType,dataDir,bSize,shuffle,imgH,imgW和channels。这个函数可以用来从文件夹中读取图像数据,这个文件夹的路径由dataDir参数指定,使用的数据类型由dType参数指定。bSize参数用于指定批次大小,shuffle参数用于指定是否将数据随机打乱,imgH和imgW参数用于指定图像的高度和宽度,channels参数用于指定图像的通道数。
bs_mean = bsize.view(-1, 3, 2).float().mean(dim=1) RuntimeError: shape '[-1, 3, 2]' is invalid for input of size 16
The error message suggests that the input tensor `bsize` has a size of 16, which is not compatible with the requested shape of `[-1, 3, 2]`.
The shape `[-1, 3, 2]` means that the first dimension can be of any size (indicated by `-1`), the second dimension should have size 3, and the third dimension should have size 2. This shape is often used to represent batches of 3D points, where each point has two coordinates.
To fix the error, you need to make sure that `bsize` has a compatible size. One way to do this is to reshape the tensor to have a total size of 16:
```
bsize = torch.randn(16)
bs_mean = bsize.view(8, 2).float().mean(dim=1)
```
This reshapes the tensor to have a shape of `[8, 2]`, which is compatible with the requested shape of `[-1, 3, 2]` when the first dimension is set to 5.
Alternatively, you could adjust the shape of `bsize` to match the requested shape:
```
bsize = torch.randn(5, 3, 2)
bs_mean = bsize.float().mean(dim=1)
```
This computes the mean along the second dimension, which has size 3, resulting in a tensor of shape `[5, 2]`.