f170df9d13
In addition, you can now fetch TensorData directly. This might be useful in
scenarios where you feed the result of a computation back in, like RNN.
Before:
benchmarking feedFetch/4 byte
time 83.31 μs (81.88 μs .. 84.75 μs)
0.997 R² (0.994 R² .. 0.998 R²)
mean 87.32 μs (86.06 μs .. 88.83 μs)
std dev 4.580 μs (3.698 μs .. 5.567 μs)
variance introduced by outliers: 55% (severely inflated)
benchmarking feedFetch/4 KiB
time 114.9 μs (111.5 μs .. 118.2 μs)
0.996 R² (0.994 R² .. 0.998 R²)
mean 117.3 μs (116.2 μs .. 118.6 μs)
std dev 3.877 μs (3.058 μs .. 5.565 μs)
variance introduced by outliers: 31% (moderately inflated)
benchmarking feedFetch/4 MiB
time 109.0 ms (107.9 ms .. 110.7 ms)
1.000 R² (0.999 R² .. 1.000 R²)
mean 108.6 ms (108.2 ms .. 109.2 ms)
std dev 740.2 μs (353.2 μs .. 1.186 ms)
After:
benchmarking feedFetch/4 byte
time 82.92 μs (80.55 μs .. 85.24 μs)
0.996 R² (0.993 R² .. 0.998 R²)
mean 83.58 μs (82.34 μs .. 84.89 μs)
std dev 4.327 μs (3.664 μs .. 5.375 μs)
variance introduced by outliers: 54% (severely inflated)
benchmarking feedFetch/4 KiB
time 85.69 μs (83.81 μs .. 87.30 μs)
0.997 R² (0.996 R² .. 0.999 R²)
mean 86.99 μs (86.11 μs .. 88.15 μs)
std dev 3.608 μs (2.854 μs .. 5.273 μs)
variance introduced by outliers: 43% (moderately inflated)
benchmarking feedFetch/4 MiB
time 1.582 ms (1.509 ms .. 1.677 ms)
0.970 R² (0.936 R² .. 0.993 R²)
mean 1.645 ms (1.554 ms .. 1.981 ms)
std dev 490.6 μs (138.9 μs .. 1.067 ms)
variance introduced by outliers: 97% (severely inflated)
|
||
|---|---|---|
| ci_build | ||
| docker | ||
| docs/haddock | ||
| google-shim | ||
| tensorflow | ||
| tensorflow-core-ops | ||
| tensorflow-mnist | ||
| tensorflow-mnist-input-data | ||
| tensorflow-nn | ||
| tensorflow-opgen | ||
| tensorflow-ops | ||
| tensorflow-proto | ||
| tensorflow-queue | ||
| tensorflow-test | ||
| third_party | ||
| tools | ||
| .gitignore | ||
| .gitmodules | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| README.md | ||
| stack.yaml | ||
README.md
The tensorflow-haskell package provides Haskell bindings to TensorFlow.
This is not an official Google product.
Instructions
Build with Docker on Linux
As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests.
git clone --recursive https://github.com/tensorflow/haskell.git tensorflow-haskell
cd tensorflow-haskell
IMAGE_NAME=tensorflow/haskell:v0
docker build -t $IMAGE_NAME docker
# TODO: move the setup step to the docker script.
stack --docker --docker-image=$IMAGE_NAME setup
stack --docker --docker-image=$IMAGE_NAME test
There is also a demo application:
cd tensorflow-mnist
stack --docker --docker-image=$IMAGE_NAME build --exec Main
Build on Mac OS X
The following instructions were verified with Mac OS X El Capitan.
-
Install dependencies via Homebrew:
brew install swig brew install bazel -
Build the TensorFlow library and install it on your machine:
cd third_party/tensorflow ./configure # Choose the defaults when prompted bazel build -c opt tensorflow:libtensorflow_c.so install bazel-bin/tensorflow/libtensorflow_c.so /usr/local/lib/libtensorflow_c.dylib install_name_tool -id libtensorflow_c.dylib /usr/local/lib/libtensorflow_c.dylib cd ../.. -
Run stack:
stack test
Note: you may need to upgrade your version of Clang if you get an error like the following:
tensorflow/core/ops/ctc_ops.cc:60:7: error: return type 'tensorflow::Status' must match previous return type 'const ::tensorflow::Status' when lambda expression has unspecified explicit return type
return Status::OK();
In that case you can just upgrade XCode and then run gcc --version to get the new version of the compiler.