Change Log

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[0.9.21]

Bugs Fixed

• (layers/convolutional 3 0 1 64 :parents [:split-1]) will work now.

[0.9.20]

Added

• Dropout is implemented in tensors (fewer cuda kernels!!)

[0.9.19]

• pooling layer is implemented in tensors.
• Non-overlapping nms algorithm for yolo for cases where you know things cannot overlap.

[0.9.17]

Added

• Metrics for rating object detection systems.
• Special NMS algorithm used for yolo is now in and unit tested.

##[0.9.14]

Added

• Yolo-style loss implemented with the tensor framework.
• Many optimizations and bugfixes around the tensor system.
• Lots of fast paths of the tensor system mapped to cudnn functions.
• Resnet50 optimizations - Memory significantly decreased (batch size of 32 possible in well under 1G video RAM).
• Resnet50 optimizations - Elide split when doing inference; simply reuse buffer without any copy operations.
• Resnet50 optimizations - GPU now pegged at 100% while training; batch upload happening during compute 100% of the time.

[0.9.11]

Bugs fixed

• Memory leak calling cuda kernels (!!)

[0.9.10]

Bugs fixed

• Small fix to ensure compilation in clojure-1.9 works properly
• Batch normalization could produce NAN in some cases.

[0.9.9]

Added

• "Censor" loss to prevent propagating gradients when labels are unknown
• model-upgrader project to upgrade models from older versions of cortex
• orthogonal weight initialization #178
• tensorboard view #172

Changed

• Loss functions are moved to their individual files to be consistent with optimizer layout

[0.9.8] - 2017-05-04

Bugs fixed

• Only save base java types in file. This avoids incompatibility issues over time and upgrades #163

[0.9.7] - 2017-05-03

Bugs fixed

• Dependencies updated to reduce and use latest version possible of most libraries.
• thread colorspace into experiment so the mnist framework can be used for color images #162.

[0.9.6] - 2017-04-28

Bugs fixed

• inferring and training were subtly broken.
• Bugfixes in the classifcation example.

[0.9.5] - 2017-04-26

Added

• CPU-only support. Cortex can now run on the CPU without CUDA drivers being installed.
• docker-example -- A simple example of how to run a cortex project in a docker container.
• multi-thread -- The execution context now supports specifying the device, allowing for more advanced asynchronous computations like pipeline parallelism and using multiple devices.

Bugs fixed

• Calling execute/run with a large dataset and a small batch size would throw stack-overflow exception. (https://github.com/thinktopic/think.resource/commit/0c435b2361cb5cf8f68410a9681625f7cfa7baff)