# -*- coding: utf-8 -*-
"""A Variational Autoencoder architecture for Fashion-MNIST."""
import tensorflow as tf
from ..datasets.fmnist import fmnist
from ._vae import _vae
from .testproblem import TestProblem
[docs]class fmnist_vae(TestProblem):
"""DeepOBS test problem class for a variational autoencoder (VAE) on \
Fashion-MNIST.
The network has been adapted from the `here\
<https://towardsdatascience.com/teaching-a-variational-autoencoder-vae-to-draw-mnist-characters-978675c95776>`_
and consists of an encoder:
- With three convolutional layers with each ``64`` filters.
- Using a leaky ReLU activation function with :math:`\\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.
Args:
batch_size (int): Batch size to use.
l2_reg (float): No L2-Regularization (weight decay) is used in this
test problem. Defaults to ``None`` and any input here is ignored.
Attributes:
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.
"""
def __init__(self, batch_size, l2_reg=None):
"""Create a new VAE test problem instance on Fashion-MNIST.
Args:
batch_size (int): Batch size to use.
l2_reg (float): No L2-Regularization (weight decay) is used in this
test problem. Defaults to ``None`` and any input here is ignored.
"""
super(fmnist_vae, self).__init__(batch_size, l2_reg)
if l2_reg is not None:
print(
"WARNING: L2-Regularization is non-zero but no L2-regularization is used",
"for this model.",
)
[docs] def set_up(self):
"""Set up the VAE test problem on MNIST."""
self.dataset = fmnist(self._batch_size)
self.train_init_op = self.dataset.train_init_op
self.train_eval_init_op = self.dataset.train_eval_init_op
self.valid_init_op = self.dataset.valid_init_op
self.test_init_op = self.dataset.test_init_op
training = tf.equal(self.dataset.phase, "train")
x, _ = self.dataset.batch
img, mean, std_dev = _vae(x, training, n_latent=8)
# Define Loss
flatten_img = tf.reshape(img, [-1, 28 * 28])
x_flat = tf.reshape(x, shape=[-1, 28 * 28])
img_loss = tf.reduce_sum(tf.squared_difference(flatten_img, x_flat), 1)
latent_loss = -0.5 * tf.reduce_sum(
1.0 + 2.0 * std_dev - tf.square(mean) - tf.exp(2.0 * std_dev), 1
)
self.losses = img_loss + latent_loss
self.regularizer = tf.losses.get_regularization_loss()