What exactly does error mean in the residual module?

The residual module is a commonly used technique in deep learning, used to solve the problems of gradient disappearance and gradient explosion, and improve the accuracy and stability of the model. Its core is residual connection, which adds input data and output data to form a cross-layer connection, making it easier for the model to learn residual information. Error refers to the error at the residual junction. In the following, this concept will be explained in detail.

In deep learning, error usually refers to the difference between the predicted value of the training data and the true value, also known as the loss. In the residual module, the error calculation method is different from that of the ordinary neural network model, including the following two aspects:

1. Residual calculation error

The residual connection in the residual module implements cross-layer connections by adding input data and output data. At the residual join, we need to calculate the residual, which is the difference between the input data and the output data. In order to measure the error in residual calculation, indicators such as square error or mean square error are generally used. The squared error is the square of the difference between the predicted value and the true value, while the mean squared error is the average of the squared errors. By reducing the residual calculation error, we can know that the smaller the difference at the residual connection, the better the fitting effect of the model.

2. Residual propagation error

In the residual module, the residual connection not only adds the input data to the output data, Also propagates errors back to previous levels. Residual propagation error therefore refers to the error involved in propagating errors from the output layer back to previous layers. In traditional neural networks, errors can only be propagated forward from the output layer, while in the residual module, errors can be propagated forward and backward from the residual connection. This propagation method can make it easier for the model to learn the residual difference information, thereby improving the accuracy and stability of the model.

Therefore, during the training process, it is necessary to minimize the error at the residual connection while ensuring that the error can be effectively propagated back to the previous layers. In order to achieve this goal, the back propagation algorithm can be used to calculate the error gradient, and the model parameters can be updated through the optimization algorithm, so that the error is gradually reduced and the accuracy of the model is gradually improved.

It should be noted that the error under the residual module is relative to the ordinary neural network. It emphasizes the difference between input and output, while the ordinary neural network emphasizes The difference between input and prediction. Therefore, when designing and optimizing the residual module, it is necessary to consider how to effectively utilize the residual information to improve the expression and generalization capabilities of the model, thereby achieving better performance.

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