CIFAR-100 WideResNet 40-4¶
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
Training settings recommenden in the original paper:
batch size = 128,
num_epochs = 200using the Momentum optimizer with \(\mu = 0.9\) and an initial learning rate of
0.1with a decrease by
- 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.
The DeepOBS data set class for Cifar-100.
A tensorflow operation initializing the test problem for the training phase.
A tensorflow operation initializing the test problem for evaluating on training data.
A tensorflow operation initializing the test problem for evaluating on test data.
A tf.Tensor of shape (batch_size, ) containing the per-example loss values.
A scalar tf.Tensor containing a regularization term.
A scalar tf.Tensor containing the mini-batch mean accuracy.
Set up the Wide ResNet 40-4 test problem on Cifar-100.