Keras-1D-ACGAN-Data-Augmentation

Keras-1D-ACGAN-Data-Augmentation

What is the ACGAN(Auxiliary Classifier GANs) ?

Related Paper : [Abstract : Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.]

The following shows the structure of ACGAN. Discriminator(D) consists of two classifiers. One is to determine the same real/fake as the original GAN. The other is to determine the class of data.

About this Code

This code is based on the code from the referenced site.

Reference

This code would be useful to whom are going to use (1) an 1-D dataset classification based on the GAN model or (2) 1-D data Augmentation based on the GAN.

If running it, you can see a screen like below one. As model is continuosly saved, you could stop as you are satisfied with the results.

How to Generate the Sample (Data-augmentation) using this Code

you can get the generated data sinutaneously, as you are running this code.

The generated data is saved to the .csv format.

This is worked by this code in the file.

generated_fake_data = np.append(X_fake_temp, labels_fake_temp, axis=1)
np.savetxt('generated_data/generated_fake_data %s th.csv' % (i + 1), generated_fake_data, delimiter=",")

The outputs are saved like below.