Benchmarking on CIFAR-100:
The following mentioned model definition files are under the folder: models/cifar100/ .
DLBENCH_ROOT="path to the root directory of this benchmark"
Run TensorFlow with its default MNIST setting:
cd $DLBENCH_ROOT/models/cifar100/tensorflow/ python cifar100_train.py > train_log.txt 2>&1
After the completion of training, run the following command to test the tranined model:
python cifar100_eval.py > test_log.txt 2>&1
The Accuracy will appear after completion of cifar100_eval.py. And the Training Time and Testing Time can be extracted from the train_log.txt and test_log.txt.
Similarly, the NN network structure of Caffe is shown as follows:
Run Caffe with its default setting:
cd $DLBENCH_ROOT/models/cifar100/caffe ./train_quick.sh > log.txt 2>&1
The Training Time, Testing Time and Accuracy can be extracted from the log.txt file.
Run on CPU:
Note: The implementation for Theano on CIFAR-100 derived from Reslab Theano tutorial (10 February 2015)
cd $DLBENCH_ROOT/models/cifar100/theano THEANO_FLAGS=device=cpu python convolutional_mlp.py