在当前的深度学习中,门控机制(Gate)和注意力机制(Attention)必须有一个,才能使得网络更深,因为无数的案例都说明,窄而深的网络,实践效果往往都比宽而浅的网络效果更好。当然不能无脑叠加线性层,事实证明是无效的。这两个机制比较典型的例子就是LSTM和Transformer。 此外,梯度传递过程中,残差结构(Residual Connection)...
# Initialize and train the LSTM model input_size = X_train.shape[2] hidden_size = 64 output_size = 1 model = LSTMNet(input_size, hidden_size, output_size) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=1e-4) epochs = 1000 for epoch in range...
through flatten operation and Equations (13)–(16).7: Use the predicted 𝑦𝑝𝑟𝑒,𝑡𝑟𝑎𝑖yipre,tra and the corresponding training label 𝑦𝑡𝑟𝑎𝑖yitra to calculate the loss function through Equations (17) and (18).8: Update the trainable parameters and ...
Each energy term 𝐸𝑖Ei has positive weights 𝑜𝑚𝑒𝑔𝑎𝑖omegai, and most terms involve non-linear parameters 𝑎𝑙𝑝ℎ𝑎𝑖alphai. The energy function incorporates various design principles, including alignment, balance, white space, scale, overlap, and boundaries. The ...