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
and49
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.
- With three convolutional layers with each