TensorFlow library for Clojure
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As of July 6, 2018, I'm resigning from my work on Guildsman. While I have many things working, I never released a version. There are all kinds of reasons why I'm stopping this project. The most significant is that my career had been taking me into ML, but that is no longer the case.
Thanks to everyone who gave me support and encouragement!
https://bpiel.github.io/guildsman/posts/creeping-2018-03-01/
I spoke about, and demonstrated, Guildsman at Conj 2017 in Baltimore. You can watch here:
https://www.youtube.com/watch?v=8_HOB62rpvw
If you want to know more, please reach out. Any of these work: - file an issue - twitter: @bpiel - email: on my github profile page - slack -- https://clojurians.slack.com -
#tensorflow- bpiel
During this pre-release phase, I'll try to add to this README as it becomes clear through conversations what others are most interested in or confused by. Once this hits alpha, the project should be able to maintain the README itself, by learning from examples of other good READMEs. This is known as "self-documenting code".
A few people have expressed interest in helping out with Guildsman. The state of the project makes it impractical for anyone to contribute directly (ie no docs, no tests, highly unstable). BUT, you can contribute to TensorFlow in a way that has a VERY meaningful impact on what Guildsman is capable of -- by implementing gradients in TensorFlow's C++ layer.
NOTE: There's been confusion around this, so I want to be very clear. These c++ gradient implementations are going directly to TensorFlow's code base. You submit a PR to TensorFlow. At no point is this code in Guildsman.
The reasons why these gradients are so important are laid out (partially, at least) in the video (linked above, especially starting around the 18min mark).
More Important: - familiarity with Python - familiarity with C++ (My c++ is weak, but I've gotten by.)
Less Important:
The mathematical logic is already written out in Python. You only need to port it to C++. The difficulty of implementing a gradient varies wildly depending on its complexity. Most are not trivial. But, I don't think a deep understanding of the math makes the porting process easier.
If you do want to learn more about the math, this wikipedia article is one place you could start. It describes the context in which the individial gradient implementations are being used, what the higher goal is, and how it is acheived.
https://en.wikipedia.org/wiki/Automaticdifferentiation#Reverseaccumulation
Besides the actual coding, you'll need to determine which gradient to tackle, call dibs, and get your PR accepted. Each of those steps have their own unique set of challenges. If you have questions -- AFTER reading all of this :) -- please get in touch.
Here are instructions from TF related to contributing, both generally and gradients specifically. I wrote my own notes below, but please read these first. - https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md - https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc/gradients
Legal stuff!
Find a gradient implementation in the TF Python code that doesn't have a counterpart in c++.
(value to community)/(your time)
Implement the thing.
Implement a test.
Run the test.
Google has its own build tool, bazel, that TF uses. In addition to compilation (and who knows what else), you also use bazel to run tests. If there's a lot of compilation that needs to occurr before a test can be run (ex: the first time your run a test), you may be waiting for hours. Don't worry, subsequent runs will be fast (though, still not as fast as I'd like). Here's an example showing how I run the nn_grad tests:
sudo tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/cc:gradients_nn_grad_test
That would get called from the root dir of the TF repo.
The first PR of mine accepted into TensorFlow implemented the gradient for
BiasAdd.
BiasAddis just a special case of matrix addition that is optimized for neural networks, but that's not important for the purposes of this example. What is important is that this is a simple case. It's made especially simple by the fact that the gradient for
BiasAddis already implemented as its own operation,
BiasAddGrad. All I had to do was write some glue code and register it so that the auto differentiation logic could find it. This is not usually the case, but there are others like this.
My PR: https://github.com/tensorflow/tensorflow/pull/12448/files
Python Code (the code to be ported) https://github.com/tensorflow/tensorflow/blob/e5306d3dc75ea1b4338dc7b4518824a7698f0f92/tensorflow/python/ops/nn_grad.py#L237
@ops.RegisterGradient("BiasAdd") def _BiasAddGrad(op, received_grad): """Return the gradients for the 2 inputs of bias_op. The first input of unused_bias_op is the tensor t, and its gradient is just the gradient the unused_bias_op received. The second input of unused_bias_op is the bias vector which has one fewer dimension than "received_grad" (the batch dimension.) Its gradient is the received gradient Summed on the batch dimension, which is the first dimension. Args: op: The BiasOp for which we need to generate gradients. received_grad: Tensor. The gradients passed to the BiasOp. Returns: Two tensors, the first one for the "tensor" input of the BiasOp, the second one for the "bias" input of the BiasOp. """ try: data_format = op.get_attr("data_format") except ValueError: data_format = None return (received_grad, gen_nn_ops.bias_add_grad(out_backprop=received_grad, data_format=data_format))
The C++ code I wrote: https://github.com/tensorflow/tensorflow/blob/e5306d3dc75ea1b4338dc7b4518824a7698f0f92/tensorflow/cc/gradients/nn_grad.cc#L106
Status BiasAddGradHelper(const Scope& scope, const Operation& op, const std::vector& grad_inputs, std::vector* grad_outputs) { string data_format; BiasAddGrad::Attrs input_attrs; TF_RETURN_IF_ERROR( GetNodeAttr(op.output(0).node()->attrs(), "data_format", &data_format)); input_attrs.DataFormat(data_format); auto dx_1 = BiasAddGrad(scope, grad_inputs[0], input_attrs); grad_outputs->push_back(Identity(scope, grad_inputs[0])); grad_outputs->push_back(dx_1); return scope.status(); } REGISTER_GRADIENT_OP("BiasAdd", BiasAddGradHelper);
The test I wrote: https://github.com/tensorflow/tensorflow/blob/e5306d3dc75ea1b4338dc7b4518824a7698f0f92/tensorflow/cc/gradients/nngradtest.cc#L150
TEST_F(NNGradTest, BiasAddGradHelper) { TensorShape shape({4, 5}); TensorShape bias_shape({5}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); auto bias = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(bias_shape)); auto y = BiasAdd(scope_, x, bias); RunTest({x, bias}, {shape, bias_shape}, {y}, {shape}); }
Relevant Docs:
https://www.tensorflow.org/apidocs/cc/ https://www.tensorflow.org/apidocs/cc/class/tensorflow/ops/bias-add https://www.tensorflow.org/versions/master/apidocs/cc/class/tensorflow/ops/bias-add-grad https://www.tensorflow.org/apidocs/cc/struct/tensorflow/ops/bias-add-grad/attrs
https://www.tensorflow.org/apidocs/python/tf/nn/biasadd
https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/placeholder
I've (currently) had three other grads accepted in the following two PRs. I'll try to get to expanding those into nicer example write-ups like the one above.
https://github.com/tensorflow/tensorflow/pull/12665 https://github.com/tensorflow/tensorflow/pull/12391
as of Oct 18, 2017
These seem to be more important. Ordered by priority:
SoftmaxCrossEntropyWithLogits
Floor
Cast
GatherV2
Pow
Sub
Prod
ConcatV2
Slice
Tile
TopKV2
Atan2
AvgPool
AvgPool3D
AvgPool3DGrad
AvgPoolGrad
BadGrad
BatchNormWithGlobalNormalization
Betainc
BiasAddGrad
BiasAddV1
Cast
Ceil
Cholesky
ComplexAbs
Concat
ConcatV2
Conv2DBackpropFilter
Conv2DBackpropInput
Conv3D
Conv3DBackpropFilterV2
Conv3DBackpropInputV2
CopyOp
copy_override
CropAndResize
Cross
CTCLoss
Cumprod
Cumsum
CustomSquare
DebugGradientIdentity
DepthwiseConv2dNative
Digamma
Dilation2D
EluGrad
Enter
Erfc
Exit
ExtractImagePatches
FakeQuantWithMinMaxArgs
FakeQuantWithMinMaxVars
FakeQuantWithMinMaxVarsPerChannel
FFT
FFT2D
FFT3D
Fill
Floor
FloorDiv
FloorMod
FractionalAvgPool
FractionalMaxPool
FusedBatchNorm
FusedBatchNormGrad
FusedBatchNormGradV2
FusedBatchNormV2
Gather
GatherV2
IdentityN
IFFT
IFFT2D
IFFT3D
Igamma
Igammac
InvGrad
IRFFT
IRFFT2D
LoopCond
LRN
MatrixDeterminant
MatrixDiagPart
MatrixInverse
MatrixSetDiag
MatrixSolve
MatrixSolveLs
MatrixTriangularSolve
MaxPool3D
MaxPool3DGrad
MaxPool3DGradGrad
MaxPoolGrad
MaxPoolGradGrad
MaxPoolGradV2
MaxPoolWithArgmax
Merge
NaNGrad
NextIteration
NthElement
PlaceholderWithDefault
Polygamma
Pow
PreventGradient
Prod
ReadVariableOp
ReciprocalGrad
RefEnter
RefExit
RefMerge
RefNextIteration
RefSwitch
ReluGrad
ResizeBicubic
ResizeBilinear
ResizeNearestNeighbor
ResourceGather
Reverse
RFFT
RFFT2D
Rint
Round
RsqrtGrad
SegmentMax
SegmentMean
SegmentMin
SegmentSum
Select
SelfAdjointEigV2
SeluGrad
SigmoidGrad
Slice
SoftmaxCrossEntropyWithLogits
Softplus
SoftplusGrad
Softsign
SparseAdd
SparseDenseCwiseAdd
SparseDenseCwiseDiv
SparseDenseCwiseMul
SparseFillEmptyRows
SparseMatMul
SparseReduceSum
SparseReorder
SparseSegmentMean
SparseSegmentSqrtN
SparseSegmentSum
SparseSoftmax
SparseSoftmaxCrossEntropyWithLogits
SparseSparseMaximum
SparseSparseMinimum
SparseTensorDenseAdd
SparseTensorDenseMatMul
SplitV
SqrtGrad
StridedSlice
StridedSliceGrad
Sub
Svd
Switch
TanhGrad
TensorArrayConcat
TensorArrayConcatV2
TensorArrayConcatV3
TensorArrayGather
TensorArrayGatherV2
TensorArrayGatherV3
TensorArrayRead
TensorArrayReadV2
TensorArrayReadV3
TensorArrayScatter
TensorArrayScatterV2
TensorArrayScatterV3
TensorArraySplit
TensorArraySplitV2
TensorArraySplitV3
TensorArrayWrite
TensorArrayWriteV2
TensorArrayWriteV3
TestStringOutput
Tile
TopK
TopKV2
TruncateDiv
UnsortedSegmentMax
UnsortedSegmentSum
Zeta