# Fashion-MNIST VAE¶

class deepobs.tensorflow.testproblems.fmnist_vae.fmnist_vae(batch_size, weight_decay=None)[source]

DeepOBS test problem class for a variational autoencoder (VAE) on Fashion-MNIST.

The network has been adapted from the here and consists of an encoder:

• With three convolutional layers with each 64 filters.
• Using a leaky ReLU activation function with $$\alpha = 0.3$$
• Dropout layers after each convolutional layer with a rate of 0.2.

and an decoder:

• With two dense layers with 24 and 49 units and leaky ReLU activation.
• With three deconvolutional layers with each 64 filters.
• Dropout layers after the first two deconvolutional layer with a rate of 0.2.
• A final dense layer with 28 x 28 units and sigmoid activation.

No regularization is used.

Parameters: batch_size (int) -- Batch size to use. weight_decay (float) -- No weight decay (L2-regularization) is used in this test problem. Defaults to None and any input here is ignored.
dataset

The DeepOBS data set class for Fashion-MNIST.

train_init_op

A tensorflow operation initializing the test problem for the training phase.

train_eval_init_op

A tensorflow operation initializing the test problem for evaluating on training data.

test_init_op

A tensorflow operation initializing the test problem for evaluating on test data.

losses

A tf.Tensor of shape (batch_size, ) containing the per-example loss values.

regularizer

A scalar tf.Tensor containing a regularization term. Will always be 0.0 since no regularizer is used.

set_up()[source]

Set up the VAE test problem on MNIST.