#import tensorflow as tf from keras.datasets import mnist from keras.layers import Dense, Dropout, Input from keras.models import Model,Sequential from keras.layers import LeakyReLU from keras.optimizers import Adam from tqdm import tqdm import nu...-Go服务器开发

There are several ways to improve the quality of generated images in GANs:

Increase the complexity of the generator: Add more layers or neurons to the generator model so that it can learn more complex features and generate more realistic images.
Use better activation functions: Replace the LeakyReLU activation function with other activation functions such as ReLU or ELU, which may lead to better convergence and higher quality generated images.
Train for longer: Increase the number of epochs or train for a longer period of time, allowing the model to learn more features and generate better images.
Use better optimization techniques: Try using different optimization techniques such as RMSprop or SGD instead of Adam, or adjust the learning rate and momentum values to see if they improve convergence and image quality.
Add noise to inputs: Adding random noise to input data can help prevent overfitting and generate more diverse images.
Use batch normalization: Batch normalization can help stabilize training by normalizing layer inputs across samples in each mini-batch, leading to faster convergence and higher quality generated images.
Experiment with hyperparameters: Experimenting with different hyperparameters such as batch size, latent dimension size, or regularization parameters may lead to better results.

#import tensorflow as tf from keras.datasets import mnist from keras.layers import Dense, Dropout, Input from keras.models import Model,Sequential from keras.layers import LeakyReLU from keras.optimizers import Adam from tqdm import tqdm import nu...