Install Python tensorflow package

malmaud · Aug 8, 2016 · 43e19f4 · 43e19f4
1 parent 9647e1f
commit 43e19f4
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Showing 8 changed files with 55 additions and 5 deletions.
diff --git a/.gitignore b/.gitignore
@@ -1,3 +1,4 @@
 deps/bazel-out
 deps/downloads
+deps/miniconda2
 src/scratch.jl
diff --git a/REQUIRE b/REQUIRE
@@ -3,3 +3,5 @@ Requests
 BinDeps
 ProtoBuf
 PyCall
+Distributions
+Conda
diff --git a/deps/build.jl b/deps/build.jl
@@ -1,4 +1,5 @@
 import Requests
+import Conda
 
 base = dirname(@__FILE__)
 download_dir = joinpath(base, "downloads")
@@ -28,3 +29,6 @@ end
     mv("libtensorflow.so", "$base/bazel-out/local_linux-fastbuild/bin/tensorflow/libtensorflow.so", remove_destination=true)
     mv("libc_api.so", "$base/bazel-out/local_linux-fastbuild/bin/tensorflow/c/libc_api.so", remove_destination=true)
 end
+
+Conda.install("numpy")
+run(`$(Conda.PYTHONDIR)/pip install tensorflow-0.10.0rc0-py2-none-any.whl`)
diff --git a/examples/logistic.jl b/examples/logistic.jl
@@ -0,0 +1,25 @@
+using Distributions
+
+x = randn(100, 3)
+
+w = randn(3, 10)
+
+y = x*w
+
+sess = Session()
+
+X = placeholder(Float64)
+
+Y_obs = placeholder(Float64)
+
+W = Variable(randn(3, 10))
+
+Y = X*W
+
+Loss = reduce_sum((Y-Y_obs)^2)
+run(sess, initialize_all_variables())
+run(sess, Loss, Dict(X=>x, Y_obs=>y))
+
+grad = gradients(Loss, W)
+
+run(sess, grad, Dict(X=>x, Y_obs=>y))
diff --git a/src/core.jl b/src/core.jl
@@ -272,7 +272,13 @@ function Node(node_def::tensorflow.NodeDef)
                 if dtype == tensorflow._DataType.DT_FLOAT
                     desc["value"] = Tensor(attr.tensor.float_val[1])
                 elseif dtype == tensorflow._DataType.DT_INT32
-                    desc["value"] = Tensor(attr.tensor.int_val[1])
+                    if length(attr.tensor.int_val) == 0
+                        desc["value"] = Tensor(Int32[])
+                    else
+                        desc["value"] = Tensor(attr.tensor.int_val[1])
+                    end
+                elseif dtype == tensorflow._DataType.DT_DOUBLE
+                    desc["value"] = Tensor(attr.tensor.double_val[1])
                 end
             elseif attr_name == "keep_dims"
                 desc["keep_dims"] = attr.b
@@ -285,7 +291,7 @@ function Node(node_def::tensorflow.NodeDef)
     Node(desc)
 end
 
-node_name(node::Node) = ccall((:TF_NodeName), Cstring, (Ptr{Void},), node.ptr) |> unsafe_string
+node_name(node::AbstractNode) = ccall((:TF_NodeName), Cstring, (Ptr{Void},), Node(node).ptr) |> unsafe_string
 
 function get_attr_value_proto(node::Node, attr_name)
     buf = Buffer()
@@ -305,7 +311,16 @@ Base.getindex(node::Node, attr_name) = get_attr_value_proto(node, attr_name)
 
 function Base.eltype(node::AbstractNode)
     node = Node(node)
-    dtype = node["dtype"]._type
+    dtype = nothing
+    try
+        dtype = node["dtype"]._type
+    catch
+        try
+            dtype = node["T"]._type
+        catch
+            error("eltype called on node with no type information")
+        end
+    end
     dt = tensorflow._DataType
     type_map = Dict(dt.DT_FLOAT=>Float32, dt.DT_INT32=>Int32, dt.DT_DOUBLE=>Float64, dt.DT_INT64=>Int64)
     return type_map[dtype]

diff --git a/src/nn.jl b/src/nn.jl
@@ -1,4 +1,4 @@
-module NN
+module nn
 
 import ..TensorFlow: Node, NodeDescription, get_def_graph, capitalize, add_input, Port, get_name
 

diff --git a/src/ops.jl b/src/ops.jl
@@ -61,6 +61,7 @@ end
 
 *(x::Number, n::AbstractNode) = x.*n  # For supporting notation like `2x`
 ^(n::AbstractNode, x::Int) = invoke(^, (AbstractNode, Any), n, x)
+.^(n::AbstractNode, x::Number) = n^x
 
 for (jl_func_name, tf_func_name) in [
     (:log, "Log"),

diff --git a/src/py.jl b/src/py.jl
@@ -40,7 +40,7 @@ function extend_graph(graph, other::PyObject)
 end
 
 
-function gradients(y, x)
+function gradients(y, x::AbstractArray)
     py_graph = make_py_graph(get_def_graph())
     to_py_node = node->py_graph[:get_tensor_by_name](string(node_name(node), ":0"))
     py_x = [to_py_node(node) for node in x]
@@ -50,3 +50,5 @@ function gradients(y, x)
     extend_graph(get_def_graph(), py_graph_def)
     return [get_node_by_name(get_def_graph(), _[:name])|>get for _ in grad_node]
 end
+
+gradients(y, x) = gradients(y, [x])[1]