INNER CODE UNIT · Python

forward

RichardS0268/CNN-for-Trading · model.py:38

    def forward(self, x): # input: [N, 32, 15]
        x = x.unsqueeze(1).to(torch.float32)   # output size: [N, 1, 32, 15]
        x = self.conv1(x) # output size: [N, 64, 16, 15]
        x = self.conv2(x) # output size: [N, 128, 8, 15]
        x = self.DropOut(x.view(x.shape[0], -1))
        x = self.FC(x) # output size: [N, 2]
        x = self.Softmax(x)
        
        return x
    
    
    
class CNN20d(nn.Module):
    # Input: [N, (1), 64, 60]; Output: [N, 2]
    # Three Convolution Blocks
    
    def init_weights(self, m):
        if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):

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