Deep Learning, Autoencoder, Python Programming, Keras, Technology.
data noisy_samples = [] for i in range(0, len(x_val)): if i % 100 == 0: print(i) pure = np.array(y_val[i]) noise = np.random.normal(0, 1, pure.shape) signal = pure + noise_factor * noise noisy_samples.append([x_val[i], signal]) # Save data to file for re-use