pdfcoffee

Chapter 9
x_train,
validation_data=(x_test_noisy, x_test),
epochs=max_epochs,
batch_size=batch_size)
6. Now let's plot the training loss:
plt.plot(range(max_epochs), loss.history['loss'])
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show()
7. And finally, let's see our model in action. The top row shows the input noisy
image and the bottom row shows cleaned images produced from our trained
Denoising autoencoder:
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_test_noisy[index].reshape(28, 28), cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, number, index + 1 + number)
plt.imshow(model(x_test_noisy)[index].numpy().reshape(28, 28),
cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
[ 359 ]