# -*- coding: utf-8 -*-
"""Multinomial logistic regression on Fashion-MNIST."""
import tensorflow as tf
from ..datasets.fmnist import fmnist
from ._logreg import _logreg
from .testproblem import TestProblem
[docs]class fmnist_logreg(TestProblem):
"""DeepOBS test problem class for multinomial logistic regression on \
Fasion-MNIST.
No regularization is used and the weights and biases are initialized to ``0.0``.
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.
accuracy: A scalar tf.Tensor containing the mini-batch mean accuracy.
"""
def __init__(self, batch_size, l2_reg=None):
"""Create a new logistic regression 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_logreg, 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 logistic regression test problem on Fashion-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
x, y = self.dataset.batch
linear_outputs = _logreg(x, num_outputs=10)
self.losses = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y, logits=linear_outputs
)
y_pred = tf.argmax(linear_outputs, 1)
y_correct = tf.argmax(y, 1)
correct_prediction = tf.equal(y_pred, y_correct)
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
self.regularizer = tf.losses.get_regularization_loss()