Neural network acceleration and compression method based on trace norm constraints
A technology of neural network and compression method, which is applied in the direction of neural learning method, biological neural network model, etc., which can solve the problems of considering the compression ability of the model, reducing the loss of precision, and limited application range, so that the accuracy of the model is not affected and the processing is improved. The effect that the speed and accuracy are not affected
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[0018] The present invention will be further described in detail below with reference to the drawings and specific embodiments.
[0019] figure 1 The following shows a method for accelerating and compressing neural networks based on trace norm constraints. The specific steps are:
[0020] Step 1. Perform forward propagation of neural network:
[0021] The specific process of forward propagation is to pass the input data through each layer of the network layer by layer, and calculate the parameters of each layer until the output value of the last layer is obtained. By judging whether the error between the network output value and the actual value is less than the specified threshold, judge whether the neural network training has converged. If it does not, proceed to step two, otherwise skip to step five;
[0022] Step 2: Separate the loss function and trace norm constraints related to the task, and perform the loss function backward propagation:
[0023] Obtain the parameters of each l...
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