WGAN model method based on depth convolution nerve network
A deep convolution and neural network technology, applied in the field of deep learning neural network, can solve the problems of slow speed, the discriminator cannot indicate the network training direction, and the generator cannot learn the characteristics of the data set, etc., to achieve a directional effect.
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[0025] This embodiment discloses a WGAN model method based on a deep convolutional neural network, which specifically includes the following steps:
[0026] Step S1, constructing a Wasserstein Generative Adversarial Network WGAN model, which includes a generator and a discriminator.
[0027] Step S2, constructing the discriminator into a deep convolutional neural network structure;
[0028] The discriminator is constructed in the form of a deep convolutional neural network. It is divided into several layers, and each layer has a corresponding convolution kernel, that is, a corresponding weight parameter.
[0029] Step S3, constructing the generator into a transposed convolutional neural network structure;
[0030] The number of convolutional network layers of the generator is the same as that of the discriminator, and the convolution kernel is the transpose of the convolution kernel of the discriminator.
[0031] Step S4, adopting the loss function of the Wasserstein distan...
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