pdfcoffee

Chapter 9
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D,
UpSampling2D
2. We specify our hyperparameters. If you look carefully, the list is slightly
different; as compared to earlier autoencoder implementations, instead of
learning rate and momentum, this time we are concerned with filters of the
convolutional layer:
np.random.seed(11)
tf.random.set_seed(11)
batch_size = 128
max_epochs = 50
filters = [32,32,16]
3. In the next step, we read in the data and preprocess it. Again, you may
observe slight variation from the previous code, especially in the way we
are adding noise and then limiting the range in between [0-1]. We are doing
so because in this case, instead of the mean square error loss, we will be
using binary cross entropy loss and the final output of the decoder will
pass through sigmoid activation, restricting it between [0-1]:
(x_train, _), (x_test, _) = K.datasets.mnist.load_data()
x_train = x_train / 255.
x_test = x_test / 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))
noise = 0.5
x_train_noisy = x_train + noise * np.random.normal(loc=0.0,
scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise * 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)
x_train_noisy = x_train_noisy.astype('float32')
x_test_noisy = x_test_noisy.astype('float32')
#print(x_test_noisy[1].dtype)
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