# -*- coding: utf-8 -*-
"""A vanilla CNN architecture for CIFAR-10."""
import tensorflow as tf
from ._3c3d import _3c3d
from ..datasets.cifar10 import cifar10
from .testproblem import TestProblem
[docs]class cifar10_3c3d(TestProblem):
"""DeepOBS test problem class for a three convolutional and three dense \
layered neural network on Cifar-10.
The network consists of
- thre conv layers with ReLUs, each followed by max-pooling
- two fully-connected layers with ``512`` and ``256`` units and ReLU activation
- 10-unit output layer with softmax
- cross-entropy loss
- L2 regularization on the weights (but not the biases) with a default
factor of 0.002
The weight matrices are initialized using Xavier initialization and the biases
are initialized to ``0.0``.
A working training setting is ``batch size = 128``, ``num_epochs = 100`` and
SGD with learning rate of ``0.01``.
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 ``0.002``.
Attributes:
dataset: The DeepOBS data set class for Cifar-10.
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=0.002):
"""Create a new 3c3d test problem instance on Cifar-10.
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 ``0.002``.
"""
super(cifar10_3c3d, self).__init__(batch_size, weight_decay)
[docs] def set_up(self):
"""Set up the vanilla CNN test problem on Cifar-10."""
self.dataset = cifar10(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
x, y = self.dataset.batch
linear_outputs = _3c3d(
x,
num_outputs=10,
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()