![]() # Generate n real samples with class labels 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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