TensorFlow.jlA Julia wrapper for TensorFlow
TensorFlow
A wrapper around TensorFlow, a popular open source machine learning framework from Google.
|
---|
Tensorflow.jl is in minimal maintenance mode |
While it works, it is not receiving new features, and is bound to an old version, 1.13.1, of libtensorflow. |
The authors recommend Flux.jl for new users. |
Documentation
Other resources:
Why use TensorFlow.jl?
See a list of advantages over the Python API.
What's changed recently?
See NEWS.
Basic usage
using TensorFlow
using Test
sess = TensorFlow.Session()
x = TensorFlow.constant(Float64[1,2])
y = TensorFlow.Variable(Float64[3,4])
z = TensorFlow.placeholder(Float64)
w = exp(x + z + -y)
run(sess, TensorFlow.global_variables_initializer())
res = run(sess, w, Dict(z=>Float64[1,2]))
@test res[1] ≈ exp(-1)
Installation
Install via
Pkg.add("TensorFlow")
To enable support for GPU usage on Linux, set an environment variable TF_USE_GPU
to "1" and then rebuild the package. eg
ENV["TF_USE_GPU"] = "1"
Pkg.build("TensorFlow")
CUDA 8.0 and cudnn are required for GPU usage. If you need to use a different version of CUDA, or if you want GPU support on Mac OS X, you can compile libtensorflow from source.
Initial precompilation (eg, the first time you type using TensorFlow
) can take around five minutes, so please be patient. Subsequent load times will only be a few seconds.
Installation via Docker
Simply run docker run -it malmaud/julia:tf
to open a Julia REPL that already has TensorFlow installed:
julia> using TensorFlow
julia>
For a version of TensorFlow.jl that utilizes GPUs, use nvidia-docker run -it malmaud/julia:tf_gpu
. Download nvidia-docker if you don't already have it.
Logistic regression example
Realistic demonstration of using variable scopes and advanced optimizers
using TensorFlow
using Distributions
using Printf
# Generate some synthetic data
x = randn(100, 50)
w = randn(50, 10)
y_prob = exp.(x*w)
y_prob ./= sum(y_prob,dims=2)
function draw(probs)
y = zeros(size(probs))
for i in 1:size(probs, 1)
idx = rand(Categorical(probs[i, :]))
y[i, idx] = 1
end
return y
end
y = draw(y_prob)
# Build the model
sess = Session(Graph())
X = placeholder(Float64, shape=[-1, 50])
Y_obs = placeholder(Float64, shape=[-1, 10])
variable_scope("logisitic_model"; initializer=Normal(0, .001)) do
global W = get_variable("W", [50, 10], Float64)
global B = get_variable("B", [10], Float64)
end
Y=nn.softmax(X*W + B)
Loss = -reduce_sum(log(Y).*Y_obs)
optimizer = train.AdamOptimizer()
minimize_op = train.minimize(optimizer, Loss)
saver = train.Saver()
# Run training
run(sess, global_variables_initializer())
checkpoint_path = mktempdir()
@info("Checkpoint files saved in $checkpoint_path")
for epoch in 1:100
cur_loss, _ = run(sess, [Loss, minimize_op], Dict(X=>x, Y_obs=>y))
println(@sprintf("Current loss is %.2f.", cur_loss))
train.save(saver, sess, joinpath(checkpoint_path, "logistic"), global_step=epoch)
end
Troubleshooting
If you see issues from the ccall or python interop, try updating TensorFlow both in Julia and in the global python install:
julia> Pkg.build("TensorFlow")
$ pip install --upgrade tensorflow
Citing
If you use this software in your research, we would really appreciate if you cite us.
Malmaud, J. & White, L. (2018). TensorFlow.jl: An Idiomatic Julia Front End for TensorFlow. Journal of Open Source Software, 3(31), 1002, https://doi.org/10.21105/joss.01002
Optional: Using a custom TensorFlow binary
To build TensorFlow from source, or if you already have a TensorFlow binary that you wish to use, follow these instructions. This is recommended by Google for maximum performance, and is currently needed for Mac OS X GPU support.
For Linux users, a convenience script is included to use Docker to easily build the library. Just install docker and run julia build_libtensorflow.so
from the "deps" directory of the TensorFlow.jl package. Note that this method may not link to all libraries available on the target system such as Intel MKL.