Given groups=1, weight of size [1, 1, 3, 3], expected input[1, 10, 28, 28] to have 1 channels, but got 10 channels instead
时间: 2024-01-22 19:57:06 浏览: 23
这个错误通常是由于使用了错误的权重尺寸导致的。权重的尺寸应该是 [out_channels, in_channels, kernel_size, kernel_size],所以如果你的输入是 [1, 10, 28, 28],那么权重的尺寸应该是 [1, 10, kernel_size, kernel_size]。
在这个错误中,权重的尺寸是 [1, 1, 3, 3],这意味着该卷积层只有一个输入通道和一个输出通道。但是你的输入具有 10 个通道,所以它们的尺寸不匹配,会导致该错误。
要解决这个问题,你需要确保卷积层的权重尺寸与输入的通道数匹配。你可以尝试改变权重的尺寸,以匹配你的输入尺寸。具体来说,你可以将权重的尺寸改为 [10, 1, kernel_size, kernel_size],以匹配你的输入通道数。
相关问题
Given groups=1, weight of size [16, 3, 5, 5], expected input[265, 1, 28, 28] to have 3 channels, but got 1 channels instead
根据你提供的信息,给定的输入张量的形状为[265, 1, 28, 28],其中1表示通道数。然而,期望的输入应该有3个通道。这个错误通常发生在卷积神经网络中,当输入通道数与期望的通道数不匹配时会出现。
可能的原因是在定义网络结构时,输入层的通道数没有正确设置为3。你可以检查一下网络定义的地方,确保输入层的通道数与期望的通道数一致。
如果你使用的是某个预训练模型,可能需要对输入进行相应的预处理,以使其与模型期望的输入匹配。例如,可以使用torchvision库中的transforms来对输入进行预处理。
Given groups=1, weight of size [24, 24, 3], expected input[256, 96, 10] to have 24 channels, but got 96 channels instead
.This error message suggests that there is a discrepancy between the expected and actual number of input channels in a convolutional neural network (CNN). In this case, the network was designed with groups=1 and a weight size of [24, 24, 3]. This means that the network expects to process inputs with 24 channels. However, when the input of size [256, 96, 10] was provided, it had 96 channels instead of the expected 24 channels.
This error could occur for several reasons. One possible cause is that the input data was not properly preprocessed or formatted before being passed to the CNN. Another possibility is that there is a bug in the implementation of the CNN architecture, causing the expected number of channels to be different from the actual number of channels in the input.
To resolve this error, the input data should be carefully checked and preprocessed to ensure that it has the correct number of channels. Additionally, the CNN architecture should be carefully examined to identify any bugs or errors that may be causing the discrepancy between the expected and actual number of input channels.
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