Work in progress in migrating the TensorFlow.jl into the Julia 1.0.0

.travis.yml

@@ -6,7 +6,8 @@ os:
   - osx
   - linux
 julia:
-  - 0.6
+  - 0.7
+  - 1.0
   - nightly
 env:
     - CONDA_JL_VERSION="2" PYTHON=""

README.md

@@ -116,7 +116,7 @@ saver = train.Saver()
 # Run training
 run(sess, global_variables_initializer())
 checkpoint_path = mktempdir()
-info("Checkpoint files saved in $checkpoint_path")
+@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))

REQUIRE

@@ -1,14 +1,17 @@
-julia 0.6
+julia 0.7
 ProtoBuf 0.3.0
 PyCall 1.7.1
-TakingBroadcastSeriously 0.1.1
 Conda 0.6.0
 Distributions 0.10.2
 StatsFuns 0.3.0
+SpecialFunctions v0.7.0
 JLD2 0.0.6
 FileIO 0.1.2
 Juno 0.2.3
 Compat 0.18
 MacroTools 0.3.6
 AutoHashEquals 0.1.0
+Nullables 0.0.7
 MNIST 0.0.2
+Nullables 0.0.7
+SpecialFunctions 0.7.0

deps/.gitignore

@@ -0,0 +1 @@
+build.log

deps/build.jl

@@ -9,8 +9,8 @@ const cur_py_version = "1.8.0"
 # Error message for Windows
 ############################
 
-if is_windows()
-    error("TensorFlow.jl does not support Windows. Please see https://github.com/malmaud/TensorFlow.jl/issues/204")
+if Sys.iswindows()
+    @error("TensorFlow.jl does not support Windows. Please see https://github.com/malmaud/TensorFlow.jl/issues/204")
 end
 
 ############################
@@ -19,15 +19,15 @@ end
 
 use_gpu = "TF_USE_GPU" ∈ keys(ENV) && ENV["TF_USE_GPU"] == "1"
 
-if is_apple() && use_gpu
-    warn("No support for TF_USE_GPU on OS X - to enable the GPU, build TensorFlow from source. Falling back to CPU")
+if Sys.isapple() && use_gpu
+    @warn("No support for TF_USE_GPU on OS X - to enable the GPU, build TensorFlow from source. Falling back to CPU")
     use_gpu=false
 end
 
 if use_gpu
-    info("Building TensorFlow.jl for use on the GPU")
+    @info("Building TensorFlow.jl for use on the GPU")
 else
-    info("Building TensorFlow.jl for CPU use only. To enable the GPU, set the TF_USE_GPU environment variable to 1 and rebuild TensorFlow.jl")
+    @info("Building TensorFlow.jl for CPU use only. To enable the GPU, set the TF_USE_GPU environment variable to 1 and rebuild TensorFlow.jl")
 end
 
 
@@ -45,7 +45,7 @@ else
         # See if it works already
     catch ee
         typeof(ee) <: PyCall.PyError || rethrow(ee)
-        error("""
+        @error("""
 Python TensorFlow not installed
 Please either:
  - Rebuild PyCall to use Conda, by running in the julia REPL:
@@ -79,7 +79,7 @@ function download_and_unpack(url)
     run(`tar -xzf $tensorflow_zip_path -C downloads`)
 end
 
-@static if is_apple()
+@static if Sys.isapple()
     if use_gpu
         url = "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-darwin-x86_64-$cur_version.tar.gz"
     else
@@ -90,13 +90,13 @@ end
     mv("$lib_dir/libtensorflow_framework.so", "usr/bin/libtensorflow_framework.so", remove_destination=true)
 end
 
-@static if is_linux()
+@static if Sys.islinux()
     if use_gpu
         url = "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-$cur_version.tar.gz"
     else
         url = "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-$cur_version.tar.gz"
     end
     download_and_unpack(url)
-    mv("$lib_dir/libtensorflow.so", "usr/bin/libtensorflow.so", remove_destination=true)
-    mv("$lib_dir/libtensorflow_framework.so", "usr/bin/libtensorflow_framework.so", remove_destination=true)
+    mv("$lib_dir/libtensorflow.so", "usr/bin/libtensorflow.so", force=true)
+    mv("$lib_dir/libtensorflow_framework.so", "usr/bin/libtensorflow_framework.so", force=true)
 end

docs/src/logistic.md

@@ -39,7 +39,7 @@ saver = train.Saver()
 # Run training
 run(sess, global_variables_initializer())
 checkpoint_path = mktempdir()
-info("Checkpoint files saved in $checkpoint_path")
+@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))

docs/src/tutorial.md

@@ -64,7 +64,7 @@ end
 ### Evaluate the model
 
 ```julia
-correct_prediction = indmax(y, 2) .== indmax(y_, 2)
+correct_prediction = argmax(y, 2) .== argmax(y_, 2)
 accuracy=reduce_mean(cast(correct_prediction, Float32))
 testx, testy = load_test_set()
 
@@ -137,7 +137,7 @@ cross_entropy = reduce_mean(-reduce_sum(y_.*log(y_conv), axis=[2]))
 
 train_step = train.minimize(train.AdamOptimizer(1e-4), cross_entropy)
 
-correct_prediction = indmax(y_conv, 2) .== indmax(y_, 2)
+correct_prediction = argmax(y_conv, 2) .== argmax(y_, 2)
 
 accuracy = reduce_mean(cast(correct_prediction, Float32))
 
@@ -147,12 +147,12 @@ for i in 1:1000
     batch = next_batch(loader, 50)
     if i%100 == 1
         train_accuracy = run(session, accuracy, Dict(x=>batch[1], y_=>batch[2], keep_prob=>1.0))
-        info("step $i, training accuracy $train_accuracy")
+        @info("step $i, training accuracy $train_accuracy")
     end
     run(session, train_step, Dict(x=>batch[1], y_=>batch[2], keep_prob=>.5))
 end
 
 testx, testy = load_test_set()
 test_accuracy = run(session, accuracy, Dict(x=>testx, y_=>testy, keep_prob=>1.0))
-info("test accuracy $test_accuracy")
+@info("test accuracy $test_accuracy")
 ```

examples/ae.jl

@@ -77,15 +77,15 @@ history = MVHistory()
             push!(history,:loss_val, epoch, val_loss)
             plot(history, reuse=true)
             scatter3d(center[:,1],center[:,2],center[:,3], zcolor = testy, legend=false, title="Latent space", reuse=true)
-            info("step $epoch, training loss $train_loss, time taken: $(printtime(t0))")
+            @info("step $epoch, training loss $train_loss, time taken: $(printtime(t0))")
             train.save(saver, session, joinpath(checkpoint_path, "ae_mnist"), global_step=epoch)
         end
         run(session, train_step, Dict(x=>batch))
     end
 end
 
 test_loss, center, reconstruction = run(session, [loss_MSE, l_z, l_out], Dict(x=>testx))
-info("test accuracy $test_loss")
+@info("test accuracy $test_loss")
 
 # Plot som example reconstructions
 offset = 0

examples/logistic.jl

@@ -5,7 +5,7 @@ using Distributions
 x = randn(100, 50)
 w = randn(50, 10)
 y_prob = exp.(x*w)
-y_prob ./= sum(y_prob,2)
+y_prob ./= sum(y_prob,dims=2)
 
 function draw(probs)
     y = zeros(size(probs))
@@ -39,7 +39,7 @@ saver = train.Saver()
 # Run training
 run(sess, global_variables_initializer())
 checkpoint_path = mktempdir()
-info("Checkpoint files saved in $checkpoint_path")
+@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))

examples/mnist_full.jl

@@ -62,7 +62,7 @@ end
 
 train_step = train.minimize(train.AdamOptimizer(1e-4), cross_entropy)
 
-correct_prediction = indmax(y_conv, 2) .== indmax(y_, 2)
+correct_prediction = argmax(y_conv, 2) .== argmax(y_, 2)
 
 accuracy = reduce_mean(cast(correct_prediction, Float32))
 
@@ -72,13 +72,13 @@ for i in 1:200
     batch = next_batch(loader, 50)
     if i%100 == 1
         train_accuracy = run(session, accuracy, Dict(x=>batch[1], y_=>batch[2], keep_prob=>1.0))
-        info("step $i, training accuracy $train_accuracy")
+        @info("step $i, training accuracy $train_accuracy")
     end
     run(session, train_step, Dict(x=>batch[1], y_=>batch[2], keep_prob=>.5))
 end
 
 testx, testy = load_test_set()
 test_accuracy = run(session, accuracy, Dict(x=>testx, y_=>testy, keep_prob=>1.0))
-info("test accuracy $test_accuracy")
+@info("test accuracy $test_accuracy")
 
 visualize()

examples/mnist_simple.jl

@@ -18,7 +18,7 @@ y = nn.softmax(x*W + b)
 cross_entropy = reduce_mean(-reduce_sum(y_ .* log(y), axis=[2]))
 train_step = train.minimize(train.GradientDescentOptimizer(.00001), cross_entropy)
 
-correct_prediction = indmax(y, 2) .== indmax(y_, 2)
+correct_prediction = argmax(y, 2) .== argmax(y_, 2)
 accuracy=reduce_mean(cast(correct_prediction, Float32))
 
 for i in 1:1000

src/TensorFlow.jl

@@ -1,7 +1,6 @@
-__precompile__(true)
 module TensorFlow
 
-warn("Loading a new version of TensorFlow.jl for the first time. This initial load can take around 5 minutes as code is precompiled; subsequent usage will only take a few seconds.")
+@warn("Loading a new version of TensorFlow.jl for the first time. This initial load can take around 5 minutes as code is precompiled; subsequent usage will only take a few seconds.")
 
 export
 Graph,
@@ -129,8 +128,12 @@ tf_versioninfo
 
 const pyproc = Ref(0)
 
+function deallocator(data, len, arg)
+
+end
+
 function __init__()
-    c_deallocator[] = cfunction(deallocator, Void, (Ptr{Void}, Csize_t, Ptr{Void}))
+    c_deallocator[] = @cfunction(deallocator, Cvoid, (Ptr{Cvoid}, Csize_t, Ptr{Cvoid}))
 end
 
 function load_python_process(;force_reload=false)