trainable_weights ) ) # Sample random points in the latent space. loss_fn ( labels, predictions ) grads = tape. discriminator ( combined_images ) d_loss = self. GradientTape () as tape : predictions = self. concat (, axis = 0 ) # Train the discriminator. concat (, axis = 0 ) # Assemble labels discriminating real from fake images. concat (, - 1 ) real_image_and_labels = tf. Note that we are concatenating the labels # with these images here.
Keras data generator example generator#
generator ( random_vector_labels ) # Combine them with real images. concat (, axis = 1 ) # Decode the noise (guided by labels) to fake images. shape ( real_images ) random_latent_vectors = tf.
reshape ( image_one_hot_labels, ( - 1, image_size, image_size, num_classes ) ) # Sample random points in the latent space and concatenate the labels. repeat ( image_one_hot_labels, repeats = ) image_one_hot_labels = tf. image_one_hot_labels = one_hot_labels image_one_hot_labels = tf. real_images, one_hot_labels = data # Add dummy dimensions to the labels so that they can be concatenated with # the images. loss_fn = loss_fn def train_step ( self, data ): # Unpack the data. Mean ( name = "discriminator_loss" ) def metrics ( self ): return def compile ( self, d_optimizer, g_optimizer, loss_fn ): super (). Model ): def _init_ ( self, discriminator, generator, latent_dim ): super (). Sequential (, name = "generator", )Ĭlass ConditionalGAN ( keras. Sequential (, name = "discriminator", ) # Create the generator. This example requires TensorFlow 2.5 or higher, as well as TensorFlow Docs, which can be If you need a refresher on GANs, you can refer to the "Generative adversarial networks" Lecture on Conditional Generation from Coursera.Conditional Generative Adversarial Nets.Its representations can also be used for other downstream tasks.įollowing are the references used for developing this example: Since the generator learns to associate the generated samples with the class labels,.It to generate novel images for the class that needs balancing. You could instead train a Conditional GAN and use
Such a model can have various useful applications:Īnd you'd like to gather more examples for the skewed class to balance the dataset.ĭata collection can be a costly process on its own. In this example, we'll build a Conditional GAN that can generate MNIST handwrittenĭigits conditioned on a given class. On a semantic input, such as the class of an image. To be able to control what we generate, we need to condition the GAN output With a GAN that generates MNIST handwritten digits, a simple DCGAN wouldn't let usĬhoose the class of digits we're generating. However, a simple DCGAN doesn't let us control It into something plausible (image, video, audio, etc.).
Keras data generator example series#
Typically, the random input is sampledįrom a normal distribution, before going through a series of transformations that turn Generative Adversarial Networks (GANs) let us generate novel image data, video data, Description: Training a GAN conditioned on class labels to generate handwritten digits.
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