Table 1 Network architecture of 1D-CNN...

Table 1

Network architecture of 1D-CNN

Network architecture
(features): Sequential(
(0): Conv1d(1, 16, kernel_size=(7,), stride=(1,))
(1): ReLU(inplace)
(2): MaxPool1d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv1d(16, 64, kernel_size=(5,), stride=(1,))
(4): ReLU(inplace)
(5): MaxPool1d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv1d(64, 256, kernel_size=(3,), stride=(1,))
(7): ReLU(inplace)
(8): MaxPool1d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Dropout(p=0.5)
(1): Linear(in_features=158976, out_features=1024, bias=True)
(2): ReLU(inplace)
(3): Dropout(p=0.5)
(4): Linear(in_features=1024, out_features=1024, bias=True)
(5): ReLU(inplace)
(6): Linear(in_features=1024, out_features=7, bias=True)
)

Network architecture
(features): Sequential(
(0): Conv1d(1, 16, kernel_size=(7,), stride=(1,))
(1): ReLU(inplace)
(2): MaxPool1d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv1d(16, 64, kernel_size=(5,), stride=(1,))
(4): ReLU(inplace)
(5): MaxPool1d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv1d(64, 256, kernel_size=(3,), stride=(1,))
(7): ReLU(inplace)
(8): MaxPool1d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(classifier): Sequential(
(0): Dropout(p=0.5)
(1): Linear(in_features=158976, out_features=1024, bias=True)
(2): ReLU(inplace)
(3): Dropout(p=0.5)
(4): Linear(in_features=1024, out_features=1024, bias=True)
(5): ReLU(inplace)
(6): Linear(in_features=1024, out_features=7, bias=True)
)

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