# CIFAR-100 WideResNet 40-4¶

class deepobs.tensorflow.testproblems.cifar100_wrn404.cifar100_wrn404(batch_size, weight_decay=0.0005)[source]

DeepOBS test problem class for the Wide Residual Network 40-4 architecture for CIFAR-100.

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 = 200 using the Momentum optimizer with $$\mu = 0.9$$ and an initial learning rate of 0.1 with a decrease by 0.2 after 60, 120 and 160 epochs.

Parameters: 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.
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.

set_up()[source]

Set up the Wide ResNet 40-4 test problem on Cifar-100.