First, let us plot the actual images, that is, input images:
n = 7
plt.figure(figsize=(20, 4))
for i in range(n):
ax = plt.subplot(1, n, i+1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
The plot of the actual images is as follows:
Plot the reconstructed image as follows:
n = 7
plt.figure(figsize=(20, 4))
for i in range(n):
ax = plt.subplot(2, n, i + n + 1)
plt.imshow(reconstructed_images[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
The following shows the reconstructed images:
As you can see, the autoencoder has learned better representations of the input images and reconstructed them.