You can follow along with the code in the Jupyter notebook ch-14a_SimpleGAN.
For building the GAN with TensorFlow, we build three networks, two discriminator models, and one generator model with the following steps:
- Start by adding the hyper-parameters for defining the network:
# graph hyperparametersg_learning_rate = 0.00001d_learning_rate = 0.01n_x = 784 # number of pixels in the MNIST image # number of hidden layers for generator and discriminatorg_n_layers = 3d_n_layers = 1# neurons in each hidden layerg_n_neurons = [256, 512, 1024]d_n_neurons = [256]# define parameter ditionaryd_params = {}g_params = {}activation = tf.nn.leaky_reluw_initializer = tf.glorot_uniform_initializerb_initializer = tf.zeros_initializer ...
