Image: load and preprocess your training data here images of ancient indian princesses

Style: Realistic Art, Size: 1024x640,
by orxcbeq5m0ss, at 2023-08-29 10:27:32 UTC

Import necessary libraries, import tensorflow as tf, from tensorflow.keras.models import Sequential, Model, from tensorflow.keras.layers import Dense, Reshape, Flatten, Input, Concatenate, from tensorflow.keras.layers import Conv2D, Conv2DTranspose, BatchNormalization, LeakyReLU, from tensorflow.keras.optimizers import Adam, import numpy as np, Define generator and discriminator models, def build generator():, Define the generator model architecture here, pass, def build discriminator():, Define the discriminator model architecture here, Build and compile the discriminator, discriminator build discriminator(), discriminator.compile(loss 'binary crossentropy', optimizer Adam(learning rate 0.0002, beta 1 0.5), metrics 'accuracy'), Build the generator, generator build generator(), Create the GAN model, z Input(shape (latent dim, image generator(z), discriminator.trainable False, validity discriminator(image), gan Model(z, validity), gan.compile(loss 'binary crossentropy', Load and preprocess your training data here (images of ancient Indian princesses), Training loop, for epoch in range(num epochs):, Select a random batch of images, Generate random noise, Generate images using the generator, Train the discriminator on real and generated images, Train the generator (via the GAN) to trick the discriminator, Print progress, Save generated images at certain intervals