![]() pile(loss='binary_crossentropy', optimizer=opt) pile(loss='binary_crossentropy', optimizer=opt, metrics=\)ĭef define_gan(generator, discriminator): Model.add(Dense(1, activation='sigmoid')) Model.add(Flatten(input_shape=input_shape)) Model.add(Dense(LENGTH_INPUT, activation='tanh')) Model.add(Dense(128, input_dim=latent_dim)) X1 = np.array(X1).reshape(n, LENGTH_INPUT)įrom keras.layers import Dense, Reshape, Flattenįrom keras.layers import ELU, PReLU, LeakyReLU # generate n real samples with class labels # train a generative adversarial network on a one-dimensional function ![]() Finally, we call the summarize_performance function to evaluate the performance of the generator and discriminator on a set of real and fake samples and generate a plot of the generated samples. We then call the train function to train the generator and discriminator on the real dataset, which will update the weights of the GAN model. We then define the generator and discriminator networks using the define_generator and define_discriminator functions, respectively, and connect them to create the GAN model using the define_gan function. In this example, we generate 1000 real samples using the generate_real_samples function.
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