CNTK_1_7_1_Release_Notes

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CNTK v.1.7.1 Release Notes

This is a summary on what's new in CNTK 1.7.1 Binary Release.

The are two breaking changes in this release. Please, read this section carefully:

Layers library default initialization was changed from heNormal to glorotNormal. Pass init=”heNormal” to get 1.7 behaviour.
fsAdagrad had a bug. Learning rates must be retuned. To somewhat approximate the old behaviour, scale by sqrt(number of parameter tensors)/400.

We have the following improvements in BrainScript.

BrainScript now allows relational operators inline, and scalar constants get automatically casted to Constant(). Example:

HammingLoss (y, p) = ReduceSum (y != (p > 0.5))

compare this to the previous syntax

HammingLoss (y, p) = ReduceSum (NotEqual (y, (Greater (p, Constant(0.5)))))

The following changes and improvements are introduced in V.1.7.1:

Model evaluation support for Azure Applications. The Evaluate a model in an Azure WebApi section in the Wiki provides detailed steps.
The Extended Eval interface adds support for evaluation of RNN model.

Adding forceDeterministicAlgorithms=true to the configuration will force use of deterministic algorithms if possible. This flag will force use of only a single thread for MKL and OMP operations.
IMPORTANT! The determinism changes require a new version of the CNTK Custom MKL (v2). For binary downloads this is included in the package. If you build CNTK from sources please follow the installation instructions described in the Wiki for Windows or Linux.

We have made the following performance related improvements:

GPU prefetch with pinned memory is implemented for the new readers (HTKDeserializers, CNTKTextFormat and Image reader)
Type optimizations in the image reader that decrease memory pressure

You will find the following fixes in this release:

Fix for packed sequences
Correctness for Sequence2Sequence
Automated minibatch scaling
Optimized lstm from cudnn5.1
Automatic minibatch-sizing no longer affects accuracy
Improved performance for certain kinds of recurrent networks (PastValue())
fanout in glorot initialization is now correct
Fix for dimension error in cudnn RNN wrapper
fsAdagrad denominator is now aggregated correctly
LSTMBlock{} and StabilizerLayer{} no longer create parameters from inside apply()
Improved default for ```BatchNormalizationLayer{}`` time constant

Starting from v.1.5 you can use the preview of Python API. See CNTK v.1.5 Release Notes for further instructions.