pdfcoffee

Autoencoders
epoch_loss = 0
for step, batch_features in enumerate(dataset):
loss_values = train(loss, model, opt, batch_features)
epoch_loss += loss_values
model.loss.append(epoch_loss)
print('Epoch {}/{}. Loss: {}'.format(epoch + 1, epochs, epoch_
loss.numpy()))
Let us now train our autoencoder:
train_loop(autoencoder, opt, loss, training_dataset, epochs=max_
epochs)
The training graph is shown as follows. We can see that loss/cost is decreasing as
the network learns and after 50 epochs it is almost constant about a line. This means
further increasing the number of epochs will not be useful. If we want to improve
our training further, we should change the hyperparameters like learning rate and
batch_size:
plt.plot(range(max_epochs), autoencoder.loss)
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show()
In the following figure, you can see the original (top) and reconstructed (bottom)
images; they are slightly blurred, but accurate:
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)
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