药学数据模型构建4--CNNLSTM

class CNNLSTM(nn.Module):

    def __init__(self, input_size, output_size):

        super(CNNLSTM, self).__init__()

        out_channels = 32

        hidden_size = 16

        self.relu = nn.ReLU(inplace=True)

        # (batch_size=30, seq_len=24, input_size=7) ---> permute(0, 2, 1)

        self.conv = nn.Sequential(

            nn.Conv1d(in_channels=input_size, out_channels=out_channels, kernel_size=3),

            nn.ReLU(),

            # nn.MaxPool1d(kernel_size=3, stride=1)

        )

        # (batch_size=30, out_channels=32, seq_len-4=20) ---> permute(0, 2, 1)

        # (30, 20, 32)

        self.lstm = nn.LSTM(input_size=out_channels, hidden_size=hidden_size,

                            batch_first=True)

        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x, option):

        # x(batch_size, seq_len, input_size)

        x = x.permute(0, 2, 1)

        if option == 1:

            # x(batch_size, input_size, seq_len)

            x = self.conv(x)

            x = x.permute(0, 2, 1)

            x, _ = self.lstm(x)

            x = self.fc(x[:, -1, :])

        else:

            raise ValueError("option 输入不合法")

        return x