pdfcoffee

Autoencoders
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
4. We use the same encoder, decoder, and autoencoder classes as defined in the
Vanilla autoencoders section:
# Encoder
class Encoder(K.layers.Layer):
def __init__(self, hidden_dim):
super(Encoder, self).__init__()
self.hidden_layer = K.layers.Dense(units=hidden_dim,
activation=tf.nn.relu)
def call(self, input_features):
activation = self.hidden_layer(input_features)
return activation
# Decoder
class Decoder(K.layers.Layer):
def __init__(self, hidden_dim, original_dim):
super(Decoder, self).__init__()
self.output_layer = K.layers.Dense(units=original_dim,
activation=tf.nn.relu)
def call(self, encoded):
activation = self.output_layer(encoded)
return activation
class Autoencoder(K.Model):
def __init__(self, hidden_dim, original_dim):
super(Autoencoder, self).__init__()
self.loss = []
self.encoder = Encoder(hidden_dim=hidden_dim)
self.decoder = Decoder(hidden_dim=hidden_dim, original_
dim=original_dim)
def call(self, input_features):
encoded = self.encoder(input_features)
reconstructed = self.decoder(encoded)
return reconstructed
5. Next we create the model and define the loss and optimizers to be used.
Notice that this time instead of writing the custom training loop we are
using the easier Keras inbuilt compile() and fit() methods:
model = Autoencoder(hidden_dim=hidden_dim, original_dim=original_
dim)
model.compile(loss='mse', optimizer='adam')
loss = model.fit(x_train_noisy,
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