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malmaud/TensorFlow.jl

86

malmaud / TensorFlow.jl

Julia

A Julia wrapper for TensorFlow


READ ME

TensorFlow

Build Status codecov.io

A wrapper around TensorFlow, a popular open source machine learning framework from Google.

Documentation

Why use TensorFlow.jl?

See a list of advantages over the Python API.

What's changed recently?

See NEWS.

Basic usage

using TensorFlow

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]))
Base.Test.@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 Distributions

# Generate some synthetic data
x = randn(100, 50)
w = randn(50, 10)
y_prob = exp(x*w)
y_prob ./= sum(y_prob,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)
Y_obs = placeholder(Float64)

variable_scope("logistic_model", initializer=Normal(0, .001)) do
    global W = get_variable("weights", [50, 10], Float64)
    global B = get_variable("bias", [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

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 the instructions at https://malmaud.github.io/TensorFlow.jl/latest/build_from_source/. 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.