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Application of weight initialization in fully convolutional neural network
Application of weight initialization in fully convolutional neural network
In a fully convolutional neural network (FCN), basically for each layer, there is a random weight initialization. And there are two points to note:
Fully convolutional neural network (FCN) will not use 0 as a weight during backpropagation. This is because when calculating the gradient dL/dX of the intermediate layer, if the weight is set to 0, the gradient will become 0, causing the network to fail to update. Therefore, FCN usually uses non-zero weights to ensure efficient calculation and update of gradients.
In order to avoid using a single constant to initialize all weights of a fully convolutional neural network (FCN), we can use some more complex methods. A common approach is to use random initialization, which initializes the weights to random decimal values. In this way, each neuron will have a different initial value during the training process, giving the network weights a richer structure. Another method is to use pre-trained weights, that is, using weights that have been trained on other tasks as initial values. This can leverage previous knowledge to speed up the training process of the network. Using a combination of these methods, we are able to better understand the complex distribution of input data and improve network performance.
There is also a loss function, taking tanh as an example. If we use tanh as the activation function, we need to pay attention to the initialization of the weights. If the weights are initialized too large, the output of each layer of the network will gradually approach 1 or -1. However, if the weights are initialized too small, the output of each layer will gradually approach 0. Both situations may lead to vanishing gradient problems. Therefore, we need a suitable weight initialization method to solve this problem.
In order to solve this problem, we hope to have a way to initialize the weights of each layer to maintain changes. Simply put, we want to ensure that changes in the input of each layer are consistent with changes in the output.
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