nnForge v2.3.0

I have added multi-GPU support to nnForge! Both training and inferene can be done on multiple GPUs now. Single node only is supported. Training is parallelized with data parallel approach, where mini-batch is split across multiple GPUs.
The framework moved to C++11 now, you will need gcc 4.7 or newer to build the lib, and MS VS 2013 for Windows.

nnForge v2.2.0

Hi, nnForge v2.2.0 is published!

• Convolutional layer
• strides added
• w/out bias option added
• check_gradient command added
• Imagenet: reproduced ResNet50 result (7.5% Top5 single crop)
• Average subsampling layer allows specifying output size instead of subsampling window sizes
• Added profiling to CUDA backend
• Max subsampling layer:
• round_up mode added
• Strides added
• Step learning rate decay policy added
• Added update_bn_weights action (but calculating mean and invsigma during training works well)
• Spatial Transformer:
• affine_grid_generator_layer added
• linear_sampler layer added
• Utilizing cudnnFindConvolution*AlgorithmEx functions to get maximum perf (cuDNN v5 is required for that)
• Added strides to sparse convolution layer

nnForge v2.1.0

2 months passed since the last release, this one is pretty big. A number of layers added, existing layers' functionality is extended. Here is the full list of changes in nnForge v2.1.0:

• New layers added: Concat, Reshape, CDFMax, PrefixSum, Upsampling, Add (element-wise), CDF2PDF, EntryConvolution
• Average and Max subsampling layers are now capable of subsampling in feature map and entry directions
• MSE Layer reworked into generic LError layer (L2 by default)
• Max subsampling can do MIN as well
• Optional scale parameter for AverageSubsampling layer added
• Detailed info on layers in the schema dumped
• Dumping graph with layer configs in debug mode
• Added dumping data in CSV format
• Runtime layer replacement with data layers
• Bug fixes