我正在为我的助手开发一个字灯模型。 我的模型结构包括一个微型、小型、小型和中型终端层,处理音频数据,然后是线性地层。 然而,Im正面临线电视界出乎意料的产出,造成混乱。
在将微额信贷额度的产出(星号:4,32,32)通过后,我预计产出将达到(4,32,1)。 然而,实际产出形态为(4、32、1)。
In my binary classification task, I aim to distinguish between two classes: 0 for "do not wake up" and 1 for "wake up the AI." My batch size is 32, and I anticipated the output to be in the shape (32, 1) to represent one prediction for each audio MFCC input.
能否就“线性”的正确配置或实现预期产出形态所需的任何处理步骤(32,1)向某人提供咨询意见? 任何见解或守则实例都会受到高度赞赏。
Not quite. You need to reshape your data to (32, 1) or (1, 32) in order for your linear layer to work. You can achieve this by adding a dimension with torch.unsqueeze() or even directly with torch.view(). If you use the unsqueeze function, the new shape should be (32, 1). If you use the view function, the new shape should be (1, 32).
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