# -*- coding: utf-8 -*-
"""The all convolutional model All-CNN-C for CIFAR-100."""
import tensorflow as tf
from ..datasets.cifar100 import cifar100
from .testproblem import TestProblem
[docs]class cifar100_allcnnc(TestProblem):
"""DeepOBS test problem class for the All Convolutional Neural Network C
on Cifar-100.
Details about the architecture can be found in the `original paper`_.
The paper does not comment on initialization; here we use Xavier for conv
filters and constant 0.1 for biases.
A weight decay is used on the weights (but not the biases)
which defaults to ``5e-4``.
.. _original paper: https://arxiv.org/abs/1412.6806
The reference training parameters from the paper are ``batch size = 256``,
``num_epochs = 350`` using the Momentum optimizer with :math:`\\mu = 0.9` and
an initial learning rate of :math:`\\alpha = 0.05` and decrease by a factor of
``10`` after ``200``, ``250`` and ``300`` epochs.
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=0.0005):
"""Create a new All CNN C 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_allcnnc, self).__init__(batch_size, weight_decay)
[docs] def set_up(self):
"""Set up the All CNN C 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
def conv2d(inputs, filters, kernel_size=3, strides=(1, 1), padding="same"):
"""Convenience wrapper for conv layers."""
return tf.layers.conv2d(
inputs,
filters,
kernel_size,
strides,
padding,
activation=tf.nn.relu,
bias_initializer=tf.initializers.constant(0.1),
kernel_initializer=tf.keras.initializers.glorot_normal(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(self._weight_decay))
training = tf.equal(self.dataset.phase, "train")
x, y = self.dataset.batch
x = tf.layers.dropout(x, rate=0.2, training=training)
x = conv2d(x, 96, 3)
x = conv2d(x, 96, 3)
x = conv2d(x, 96, 3, strides=(2, 2))
x = tf.layers.dropout(x, rate=0.5, training=training)
x = conv2d(x, 192, 3)
x = conv2d(x, 192, 3)
x = conv2d(x, 192, 3, strides=(2, 2))
x = tf.layers.dropout(x, rate=0.5, training=training)
x = conv2d(x, 192, 3, padding="valid")
x = conv2d(x, 192, 1)
x = conv2d(x, 100, 1)
linear_outputs = tf.reduce_mean(x, axis=[1, 2])
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()