Get startedGet started for free

De-noising like an autoencoder

Okay, you have just built an autoencoder model. Let's see how it handles a more challenging task.

First, you will build a model that encodes images, and you will check how different digits are represented with show_encodings(). To build the encoder you will make use of your autoencoder, that has already being trained. You will just use the first half of the network, which contains the input and the bottleneck output. That way, you will obtain a 32 number output which represents the encoded version of the input image.

Then, you will apply your autoencoder to noisy images from MNIST, it should be able to clean the noisy artifacts.

X_test_noise is loaded in your workspace. The digits in this noisy dataset look like this:

Apply the power of the autoencoder!

This exercise is part of the course

Introduction to Deep Learning with Keras

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Build your encoder by using the first layer of your autoencoder
encoder = Sequential()
encoder.add(____.layers[____])

# Encode the noisy images and show the encodings for your favorite number [0-9]
encodings = ____.predict(____)
show_encodings(____, number = 1)
Edit and Run Code