mnist_autoencoder_simple2.py

import matplotlib.pyplot as plt
import random
%matplotlib inline

# get MNIST images, clean and with noise
def get_mnist(noise_factor=0.5):
  (x_train, y_train), (x_test, y_test) = mnist.load_data()

  x_train = x_train.astype('float32') / 255.
  x_test = x_test.astype('float32') / 255.
  x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
  x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))

  x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) 
  x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) 

  x_train_noisy = np.clip(x_train_noisy, 0., 1.)
  x_test_noisy = np.clip(x_test_noisy, 0., 1.)
  
  return x_train, x_test, x_train_noisy, x_test_noisy, y_train, y_test

x_train, x_test, x_train_noisy, x_test_noisy, y_train, y_test = get_mnist()

# plot n random digits
# use labels to specify which digits to plot
def plot_mnist(x, y, n=10, randomly=False, labels=[]):
  plt.figure(figsize=(20, 2))
  if len(labels)>0:
    x = x[np.isin(y, labels)]
  for i in range(1,n,1):
      ax = plt.subplot(1, n, i)
      if randomly:
        j = random.randint(0,x.shape[0])
      else:
        j = i
      plt.imshow(x[j].reshape(28, 28))
      plt.gray()
      ax.get_xaxis().set_visible(False)
      ax.get_yaxis().set_visible(False)
  plt.show()
  
plot_mnist(x_test_noisy, y_test, randomly=True)