# -*- coding: utf-8 -*-
"""Wide ResNet 16-4 architecture for SVHN."""
import tensorflow as tf
from ._wrn import _wrn
from ..datasets.svhn import svhn
from .testproblem import TestProblem
[docs]class svhn_wrn164(TestProblem):
"""DeepOBS test problem class for the Wide Residual Network 16-4 architecture\
for SVHN.
Details about the architecture can be found in the `original paper`_.
A weight decay is used on the weights (but not the biases)
which defaults to ``5e-4``.
Training settings recommenden in the `original paper`_:
``batch size = 128``, ``num_epochs = 160`` using the Momentum optimizer
with :math:`\\mu = 0.9` and an initial learning rate of ``0.01`` with a decrease by
``0.1`` after ``80`` and ``120`` epochs.
.. _original paper: https://arxiv.org/abs/1605.07146
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 SVHN.
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 WRN 16-4 test problem instance on SVHN.
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(svhn_wrn164, self).__init__(batch_size, weight_decay)
[docs] def set_up(self):
"""Set up the Wide ResNet 16-4 test problem on SVHN."""
self.dataset = svhn(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 = _wrn(
x,
training,
num_residual_units=2,
widening_factor=4,
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()