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Release v0.10.0

New Features

  • We release new python API.
  • Deep Learning 101 book in English and Chinese.
  • Support rectangle input for CNN.
  • Support stride pooling for seqlastin and seqfirstin.
  • Expose seq_concat_layer/seq_reshape_layer in trainer_config_helpers.
  • Add the cpu implementation of cmrnorm-projection.

Improvements

  • Support python virtualenv for paddle_trainer process.
  • Add pre-commit hooks, used for automatically format our code.
  • Use Protobuf 3.X as the default Paddle Protobuf version.
  • Add an option to check data type in python data provider.
  • Speedup the backward of average layer on GPU.
  • Reorganize the catalog of doc/ and refine several docs.
  • Add Travis-CI for checking dead links.
  • Add a example for explaining sparse_vector.
  • Add Relu in layer_math.py.
  • Add packages for automatically downloading public datasets.
  • Rename Argument::sumCost to Argument::sum since Argument does not have to have any relationship with cost.
    • Expose Argument::sum to Python
  • Add a new TensorExpression implementation for matrix-related expression evaluations.
  • Add Lazy Assignment for optimize the calculation of multiple expressions.
  • Add Function to reconstruct the computation function.
    • PadFunc and PadGradFunc.
    • ContextProjectionForwardFunc and ContextProjectionBackwardFunc.
    • CosSimBackward and CosSimBackwardFunc.
    • CrossMapNormalFunc and CrossMapNormalGradFunc.
    • MulFunc.
  • Add AutoCompare and FunctionCompare, which make it easier to write unittest for comparing gpu and cpu version of a function.
  • Add libpaddle_test_main.a and remove the main function inside the test file.
  • Support dense numpy vector in PyDataProvider2.
  • Clean code base, remove some copy & paste codes before.
    • Extract RowBuffer class for SparseRowMatrix.
    • Clean GradientMachine's interface.
    • Try use override keyword in layer.
    • Simplify Evaluator::create, use ClassRegister to create Evaluator.
  • Add md5 check when downloading demo's dataset.
  • Add paddle::Error which intentially replace LOG(FATAL) in Paddle.

Bug Fixes

  • Add layer check for recurrent_group.
  • Clang-format off on some cuda .cc files.
  • Fix LogActivation which is not defined.
  • Fix bug when run test_layerHelpers multiple times.
  • Fix protobuf size limit on seq2seq demo.
  • Fix unit test of paramRelu.
  • Fix some warning about CpuSparseMatrix.
  • Fix MultiGradientMachine error if trainer_count > batch_size.
  • Fix when async load data in PyDataProvider2.

Release v0.9.0

New Features:

  • New Layers
    • bilinear interpolation layer.
    • spatial pyramid-pool layer.
    • de-convolution layer.
    • maxout layer.
  • Support rectangle padding, stride, window and input for Pooling Operation.
  • Add —job=time in trainer, which can be used to print time info without compiler option -WITH_TIMER=ON.
  • Expose cost_weight/nce_layer in trainer_config_helpers
  • Add FAQ, concepts, h-rnn docs.
  • Add Bidi-LSTM and DB-LSTM to quick start demo @alvations
  • Add usage track scripts.

Improvements

  • Add Travis-CI for Mac OS X. Enable swig unittest in Travis-CI. Skip Travis-CI when only docs are changed.
  • Add code coverage tools.
  • Refine convolution layer to speedup and reduce GPU memory.
  • Speed up PyDataProvider2
  • Add ubuntu deb package build scripts.
  • Make Paddle use git-flow branching model.
  • PServer support no parameter blocks.

Bug Fixes

  • add zlib link to py_paddle
  • add input sparse data check for sparse layer at runtime
  • Bug fix for sparse matrix multiplication
  • Fix floating-point overflow problem of tanh
  • Fix some nvcc compile options
  • Fix a bug in yield dictionary in DataProvider
  • Fix SRL hang when exit.

Release v0.8.0beta.1

New features:

  • Mac OSX is supported by source code. #138

    • Both GPU and CPU versions of PaddlePaddle are supported.

  • Support CUDA 8.0

  • Enhance PyDataProvider2

    • Add dictionary yield format. PyDataProvider2 can yield a dictionary with key is data_layer's name, value is features.
    • Add min_pool_size to control memory pool in provider.

  • Add deb install package & docker image for no_avx machines.

    • Especially for cloud computing and virtual machines

  • Automatically disable avx instructions in cmake when machine's CPU don't support avx instructions.

  • Add Parallel NN api in trainer_config_helpers.

  • Add travis ci for Github

Bug fixes:

  • Several bugs in trainer_config_helpers. Also complete the unittest for trainer_config_helpers
  • Check if PaddlePaddle is installed when unittest.
  • Fix bugs in GTX series GPU
  • Fix bug in MultinomialSampler

Also more documentation was written since last release.

Release v0.8.0beta.0

PaddlePaddle v0.8.0beta.0 release. The install package is not stable yet and it's a pre-release version.