# TensorFlow

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 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.