# -*- coding: utf-8 -*-
"""A vanilla CNN architecture for Fashion MNIST."""
import warnings
from torch import nn
from ..datasets.fmnist import fmnist
from .testproblem import UnregularizedTestproblem
from .testproblems_modules import net_mnist_2c2d
[docs]class fmnist_2c2d(UnregularizedTestproblem):
"""DeepOBS test problem class for a two convolutional and two dense layered\
neural network on Fashion-MNIST.
The network has been adapted from the `TensorFlow tutorial\
<https://www.tensorflow.org/tutorials/estimators/cnn>`_ and consists of
- two conv layers with ReLUs, each followed by max-pooling
- one fully-connected layers with ReLUs
- 10-unit output layer with softmax
- cross-entropy loss
- No regularization
The weight matrices are initialized with truncated normal (standard deviation
of ``0.05``) and the biases are initialized to ``0.05``.
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:
data: The DeepOBS data set class for Fashion-MNIST.
loss_function: The loss function for this testproblem is torch.nn.CrossEntropyLoss().
net: The DeepOBS subclass of torch.nn.Module that is trained for this tesproblem (net_mnist_2c2d).
"""
def __init__(self, batch_size, l2_reg=None):
"""Create a new 2c2d 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_2c2d, self).__init__(batch_size, l2_reg)
if l2_reg is not None:
warnings.warn(
"L2-Regularization is non-zero but no L2-regularization is used for this model.",
RuntimeWarning,
)
[docs] def set_up(self):
"""Sets up the vanilla CNN test problem on Fashion-MNIST."""
self.data = fmnist(self._batch_size)
self.loss_function = nn.CrossEntropyLoss
self.net = net_mnist_2c2d(num_outputs=10)
self.net.to(self._device)
self.regularization_groups = self.get_regularization_groups()