# -*- coding: utf-8 -*-
"""A vanilla CNN architecture for MNIST."""
import tensorflow as tf
from ._2c2d import _2c2d
from ..datasets.mnist import mnist
from .testproblem import TestProblem
[docs]class mnist_2c2d(TestProblem):
"""DeepOBS test problem class for a two convolutional and two dense layered\
neural network on MNIST.
The network has been adapted from the `TensorFlow tutorial\
<https://www.tensorflow.org/tutorials/estimators/cnn>`_ and consists of
- two conv layers with ReLUs, each followed by max-pooling
- one fully-connected layers with ReLUs
- 10-unit output layer with softmax
- cross-entropy loss
- No regularization
The weight matrices are initialized with truncated normal (standard deviation
of ``0.05``) and the biases are initialized to ``0.05``.
Args:
batch_size (int): Batch size to use.
weight_decay (float): No weight decay (L2-regularization) is used in this
test problem. Defaults to ``None`` and any input here is ignored.
Attributes:
dataset: The DeepOBS data set class for MNIST.
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.
Will always be ``0.0`` since no regularizer is used.
accuracy: A scalar tf.Tensor containing the mini-batch mean accuracy.
"""
def __init__(self, batch_size, weight_decay=None):
"""Create a new 2c2d test problem instance on MNIST.
Args:
batch_size (int): Batch size to use.
weight_decay (float): No weight decay (L2-regularization) is used in this
test problem. Defaults to ``None`` and any input here is ignored.
"""
super(mnist_2c2d, self).__init__(batch_size, weight_decay)
if weight_decay is not None:
print(
"WARNING: Weight decay is non-zero but no weight decay is used",
"for this model."
)
[docs] def set_up(self):
"""Sets up the vanilla CNN test problem on MNIST."""
self.dataset = mnist(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 = _2c2d(
x,
num_outputs=10)
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()