  The training of neural networks using different deep learning frameworks may
lead to drastically differing accuracy levels despite the use of the same
neural network architecture and identical training hyperparameters such as
learning rate and choice of optimization algorithms. Currently, our ability to
build standardized deep learning models is limited by the availability of a
suite of neural network and corresponding training hyperparameter benchmarks
that expose differences between existing deep learning frameworks. In this
paper, we present a living dataset of models and hyperparameters, called
CrossedWires, that exposes semantic differences between two popular deep
learning frameworks: PyTorch and Tensorflow. The CrossedWires dataset currently
consists of models trained on CIFAR10 images using three different computer
vision architectures: VGG16, ResNet50 and DenseNet121 across a large
hyperparameter space. Using hyperparameter optimization, each of the three
models was trained on 400 sets of hyperparameters suggested by the HyperSpace
search algorithm. The CrossedWires dataset includes PyTorch and Tensforflow
models with test accuracies as different as 0.681 on syntactically equivalent
models and identical hyperparameter choices. The 340 GB dataset and benchmarks
presented here include the performance statistics, training curves, and model
weights for all 1200 hyperparameter choices, resulting in 2400 total models.
The CrossedWires dataset provides an opportunity to study semantic differences
between syntactically equivalent models across popular deep learning
frameworks. Further, the insights obtained from this study can enable the
development of algorithms and tools that improve reliability and
reproducibility of deep learning frameworks. The dataset is freely available at
https://github.com/maxzvyagin/crossedwires through a Python API and direct
download link.
