Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
X_train=(X_train.astype(np.float32)-127.5)/127.5 halfSize= int (batchSize/2) dLossReal=[] dLossFake=[] gLossLogs=[] for e in range(epochs): index=np.random.randint(0,X_train.shape[0],halfSize) images=X_train[index] noise=np.random.normal(0,1,(halfSize,Noise_dim)) genImages=generator.predict(noise)