Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
= make_labels(eval_size) # create a GAN, a generator and a discriminator gan, g, d = DCGAN(sample_size) losses = [] for e in range(epochs): print('-'*15, 'Epoch %d' % (e+1), '-'*15) for i in tqdm_notebook(range(batchCount)): path_batch = train_path[i*batch_size:(i+1)*batch_size] image_batch =