Source code for deepobs.tensorflow.testproblems.cifar100_vgg19

# -*- coding: utf-8 -*-
"""VGG 19 architecture for CIFAR-100."""

import tensorflow as tf

from ._vgg import _vgg
from ..datasets.cifar100 import cifar100
from .testproblem import TestProblem


[docs]class cifar100_vgg19(TestProblem): """DeepOBS test problem class for the VGG 19 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 19 consists of 19 weight layers, of mostly convolutions. The model uses cross-entroy loss. A weight decay 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. weight_decay (float): Weight decay factor. Weight decay (L2-regularization) 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, weight_decay=5e-4): """Create a new VGG 19 test problem instance on Cifar-100. Args: batch_size (int): Batch size to use. weight_decay (float): Weight decay factor. Weight decay (L2-regularization) is used on the weights but not the biases. Defaults to ``5e-4``. """ super(cifar100_vgg19, self).__init__(batch_size, weight_decay)
[docs] def set_up(self): """Set up the VGG 19 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.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=19, num_outputs=100, weight_decay=self._weight_decay) 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()