pdfcoffee

Chapter 9
self.conv4 = Conv2D(1, 3, 1, activation='sigmoid',
padding='same')
self.upsample = UpSampling2D((2, 2))
def call(self, encoded):
x = self.conv1(encoded)
#print("dx1", x.shape)
x = self.upsample(x)
#print("dx2", x.shape)
x = self.conv2(x)
x = self.upsample(x)
x = self.conv3(x)
x = self.upsample(x)
return self.conv4(x)
6. We combine the encoder and decoder to make an autoencoder model.
This remains exactly the same as before:
class Autoencoder(K.Model):
def __init__(self, filters):
super(Autoencoder, self).__init__()
self.encoder = Encoder(filters)
self.decoder = Decoder(filters)
def call(self, input_features):
#print(input_features.shape)
encoded = self.encoder(input_features)
#print(encoded.shape)
reconstructed = self.decoder(encoded)
#print(reconstructed.shape)
return reconstructed
7. Now we instantiate our model, then specify the binary cross entropy as the
loss function and Adam as the optimizer in the compile() method. Then, fit
the model to the training dataset:
model = Autoencoder(filters)
model.compile(loss='binary_crossentropy', optimizer='adam')
loss = model.fit(x_train_noisy,
x_train,
validation_data=(x_test_noisy, x_test),
epochs=max_epochs,
batch_size=batch_size)
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