pdfcoffee

Chapter 10
(60000, 28, 28) (60000,)
(10000, 28, 28) (10000,)
We see some sample images:
number = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for index in range(number):
# display original
ax = plt.subplot(2, number, index + 1)
plt.imshow(x_train[index], cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
Figure 10: Sample images from the Fashion-MNIST dataset
Before we start, let us declare some hyperparameters like learning rate, dimensions
of the hidden layer and the latent space, batch size, epochs, and so on:
image_size = x_train.shape[1]*x_train.shape[2]
hidden_dim = 512
latent_dim = 10
num_epochs = 80
batch_size = 100
learning_rate = 0.001
We use the TensorFlow Keras Model API to build a VAE model. The __init__()
function defines all the layers that we will be using:
class VAE(tf.keras.Model):
def __init__(self,dim,**kwargs):
h_dim = dim[0]
z_dim = dim[1]
super(VAE, self).__init__(**kwargs)
self.fc1 = tf.keras.layers.Dense(h_dim)
self.fc2 = tf.keras.layers.Dense(z_dim)
self.fc3 = tf.keras.layers.Dense(z_dim)
self.fc4 = tf.keras.layers.Dense(h_dim)
self.fc5 = tf.keras.layers.Dense(image_size)
[ 401 ]