RuntimeError: mat1 and mat2 shapes cannot be multiplied (38400x1280 and 10x64)

这个错误是由于矩阵的维度不匹配导致的。在进行矩阵乘法时，两个矩阵的列数必须相等。例如，在这个错误中，一个矩阵的列数是1280，另一个矩阵的行数是10，这两个数字不相等，因此无法进行乘法运算。

您需要检查代码中涉及的矩阵的维度，确保它们满足矩阵乘法的要求。您可能需要调整矩阵的形状或重新设计算法来解决这个问题。

相关问题

RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x64 and 4096x4096)

This error occurs when attempting to perform matrix multiplication between two matrices with incompatible shapes. Specifically, the number of columns in the first matrix (mat1) must match the number of rows in the second matrix (mat2) in order for the multiplication to be valid.

In this case, mat1 has dimensions 64x64 and mat2 has dimensions 4096x4096. Since the number of columns in mat1 is not equal to the number of rows in mat2, it is not possible to perform matrix multiplication between these two matrices.

To resolve this error, either modify the dimensions of the matrices so that they are compatible for multiplication (i.e. the number of columns in mat1 matches the number of rows in mat2), or use a different operation that is compatible with the current dimensions of the matrices.

RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x1024 and 256x10)

这个错误表示矩阵mat1和mat2的形状不能相乘。具体而言，在这个例子中，mat1的形状是64x1024，mat2的形状是256x10。根据矩阵相乘的规则，两个矩阵可以相乘的条件是mat1的列数等于mat2的行数。但是在这个例子中，1024不等于256，所以无法相乘。

解决这个错误的方法包括：
1. 检查前面层的输出矩阵大小，确保与相应的全连接层的输入矩阵大小匹配。
2. 检查网络架构，确保在全连接层之前进行必要的维度变换，以保证输入矩阵的形状与全连接层的期望形状匹配。

例如，可以使用以下方法之一来解决这个问题：
- 方法一：使用`x.view(-1, 7 * 7 * 1024)`来进行维度变换，确保输入矩阵的形状与全连接层的期望形状匹配。
- 方法二：使用`x = torch.flatten(x, 1)`将输入矩阵拉成二维向量`[batch_size, size]`，以满足全连接层的输入要求。

请注意，具体使用哪种方法取决于你的网络架构和输入矩阵的形状。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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