if __name__ == "__main__": # 测试函数 inp = torch.randn((584, 1, 9, 9)) model = CNN() out = model(inp) print(out.shape)
时间: 2024-05-18 21:12:27 浏览: 8
这段代码是一个测试函数,用于测试一个名为CNN的模型在输入数据inp上的输出结果out的形状shape。其中,torch.randn()是用于生成随机数的函数,inp的形状为(584, 1, 9, 9),表示有584个数据,每个数据有1个通道,每个通道大小为9x9。model(inp)是将inp数据输入到CNN模型中进行计算,得到输出结果out。最后,print(out.shape)是将out的形状输出到控制台上,以便我们查看。
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out = self.inp_prelu(self.inp_snorm(self.inp_conv(x)))
This code represents a neural network layer where an input tensor x is passed through a series of operations:
1. The first operation is inp_conv, which performs a convolution operation on the input tensor with some learnable filters.
2. The output of the convolution operation is then passed through inp_snorm, which performs a spatial normalization operation to normalize the output tensor across channels and spatial dimensions.
3. The normalized output is then passed through inp_prelu, which applies a parametric rectified linear unit (PReLU) activation function to introduce non-linearity.
4. Finally, the output of the PReLU activation function is returned as the output of the layer.
Overall, this layer can be used as a building block for a deeper neural network architecture to learn more complex representations of input data.
def __init__(self, inp, oup, reduction=32):
这是一个构造函数,用于初始化一个类的对象。该函数有三个参数:inp,oup和reduction。其中,inp表示输入数据的通道数,oup表示输出数据的通道数,reduction则用于计算中间特征图的通道数。
具体来说,reduction会被用来计算中间特征图的通道数,其计算方式是将inp除以reduction,然后再将结果乘以2。这个计算方式是为了在保持一定的模型复杂度的前提下降低模型的计算量。