Variational Autoencoder in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the encoder input layer for a Variational Autoencoder.

Computer Vision

inputs = Input(shape=[1])

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A(64, 64, 3)

B(32, 32, 3)

C(28, 28, 1)

D(100,)

Attempts:

3 left

💡 Hint

Common Mistakes

Using a shape with 3 channels for grayscale images.

Confusing the batch size with the input shape.

The input shape for the encoder in a VAE working on grayscale 28x28 images is (28, 28, 1).

2fill in blank

medium

Complete the code to sample latent variables z using the reparameterization trick.

Computer Vision

z = [1](mean, log_var)

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Areparametrize

Blatent_sample

Csample_latent

Dsampling

Attempts:

3 left

💡 Hint

Common Mistakes

Using a function name that is not defined in the code.

Confusing the sampling function with the encoder or decoder.

The function 'sampling' is commonly used to implement the reparameterization trick in VAEs.

3fill in blank

hard

Fix the error in the loss function by completing the KL divergence term.

Computer Vision

kl_loss = -0.5 * K.sum(1 + [1] - K.square(mean) - K.exp(log_var), axis=-1)

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Amean

Blog_var

Cz

Dinputs

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean instead of log variance in the KL divergence term.

Using the sampled latent variable z instead of log variance.

The KL divergence term uses the log variance (log_var) in the formula.

4fill in blank

hard

Fill both blanks to complete the decoder output layer and activation function.

Computer Vision

outputs = Dense([1], activation=[2])(decoder_hidden)

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A784

Bsigmoid

Crelu

D256

Attempts:

3 left

💡 Hint

Common Mistakes

Using relu activation in the output layer instead of sigmoid.

Setting output units to a hidden layer size instead of image size.

The decoder output layer should have 784 units (28*28) with sigmoid activation to output pixel probabilities.

5fill in blank

hard

Fill all three blanks to complete the VAE model compilation with optimizer, loss, and metrics.

Computer Vision

vae.compile(optimizer=[1], loss=[2], metrics=[[3]])

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A'adam'

Bvae_loss

C'accuracy'

D'sgd'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sgd' optimizer which may be slower to converge.

Using a standard loss instead of the custom VAE loss.

Not including any metrics or using incorrect metric names.

The VAE is commonly compiled with the 'adam' optimizer, a custom loss function 'vae_loss', and 'accuracy' as a metric.