# -*- coding: utf-8 -*-
"""VGG 16 architecture for CIFAR-100."""
import tensorflow as tf
from ..datasets.cifar100 import cifar100
from ._vgg import _vgg
from .testproblem import TestProblem
[docs]class cifar100_vgg16(TestProblem):
"""DeepOBS test problem class for the VGG 16 network on Cifar-100.
The CIFAR-100 images are resized to ``224`` by ``224`` to fit the input
dimension of the original VGG network, which was designed for ImageNet.
Details about the architecture can be found in the `original paper`_.
VGG 16 consists of 16 weight layers, of mostly convolutions. The model uses
cross-entroy loss. L2-Regularization is used on the weights (but not the biases)
which defaults to ``5e-4``.
.. _original paper: https://arxiv.org/abs/1409.1556
Args:
batch_size (int): Batch size to use.
l2_reg (float): L2-regularization factor. L2-Regularization (weight decay)
is used on the weights but not the biases.
Defaults to ``5e-4``.
Attributes:
dataset: The DeepOBS data set class for Cifar-100.
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.
accuracy: A scalar tf.Tensor containing the mini-batch mean accuracy.
"""
def __init__(self, batch_size, l2_reg=0.0005):
"""Create a new VGG 16 test problem instance on Cifar-100.
Args:
batch_size (int): Batch size to use.
l2_reg (float): L2-regularization factor. L2-Regularization (weight decay)
is used on the weights but not the biases.
Defaults to ``5e-4``.
"""
super(cifar100_vgg16, self).__init__(batch_size, l2_reg)
[docs] def set_up(self):
"""Set up the VGG 16 test problem on Cifar-100."""
self.dataset = cifar100(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, y = self.dataset.batch
linear_outputs = _vgg(
x, training, variant=16, num_outputs=100, l2_reg=self._l2_reg,
)
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()