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noise = np.random.normal(loc=0.5, scale=0.5, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.5, scale=0.5, size=x_test.shape)
x_test_noisy = x_test + noise
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
Chapter 9
Clearing images using a Denoising
autoencoder
Let us use the Denoising autoencoder to clear the handwritten MNIST digits.
1. We start with importing the required modules:
import numpy as np
import tensorflow as tf
import tensorflow.keras as K
import matplotlib.pyplot as plt
2. Next we define the hyperparameters for our model:
np.random.seed(11)
tf.random.set_seed(11)
batch_size = 256
max_epochs = 50
learning_rate = 1e-3
momentum = 8e-1
hidden_dim = 128
original_dim = 784
3. We read in the MNIST dataset, normalize it, and introduce noise in it:
(x_train, _), (x_test, _) = K.datasets.mnist.load_data()
x_train = x_train / 255.
x_test = x_test / 255.
x_train = x_train.astype(np.float32)
x_test = x_test.astype(np.float32)
x_train = np.reshape(x_train, (x_train.shape[0], 784))
x_test = np.reshape(x_test, (x_test.shape[0], 784))
# Generate corrupted MNIST images by adding noise with normal dist
# centered at 0.5 and std=0.5
noise = np.random.normal(loc=0.5, scale=0.5, size=x_train.shape)
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