tf reorg
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Mike J Innes
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82d69757c7
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import Flow: Constant, postwalk, value, inputs, constant
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import TensorFlow: RawTensor
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cvalue(x) = x
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cvalue(c::Constant) = c.value
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cvalue(v::Vertex) = cvalue(value(v))
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graph(x::Tensor) = x
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graph(::typeof(*), args...) = *(args...)
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graph(::typeof(+), args...) = +(args...)
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graph(::typeof(softmax), x) = nn.softmax(x)
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graph(::typeof(relu), x) = nn.relu(x)
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graph(::typeof(tanh), x) = tanh(x)
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# reshape hack due to https://github.com/malmaud/TensorFlow.jl/issues/79
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batchsize(x::Tensor) = reduce_sum(slice(TensorFlow.shape(x), [0], [1]))
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graph(::typeof(flatten), x) = reshape(x, pack([batchsize(x), Int32(-1)]))
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graph(r::Reshape, x) = reshape(x, pack([batchsize(x), map(Int32, r.dims)...]))
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graph(::Input, x) = x
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graph(c::Conv2D, x) =
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nn.conv2d(x, graph(c.filter), [1,c.stride...,1], "VALID")
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graph(p::MaxPool, x) =
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nn.max_pool(x, [1, p.size..., 1], [1, p.stride..., 1], "VALID")
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type Op
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f
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shape
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end
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Op(f) = Op(f, (d...) -> nothing)
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graph(op::Op, xs...) = op.f(xs...)
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Flux.shape(op::Op, d...) = op.shape(d...)
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# TODO: detect variable reuse
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graph{T<:AArray}(p::Flux.Param{T}) = Variable(p.x)
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function graph(model::Model, args...)
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g = Flux.graph(model)
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g ≠ nothing || error("No graph for $model")
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g = spliceinputs(g, map(constant, args)...) |> detuple
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postwalk(g) do v
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vertex(graph(cvalue(v), cvalue.(inputs(v))...))
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end |> value
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end
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TensorFlow.Tensor(m::Flux.Model, args...) = graph(m, args...)
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RawTensor(data::Union{Batch,Seq}) = RawTensor(rawbatch(data))
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@ -0,0 +1,56 @@
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type Model
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session::Session
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inputs::Vector{Tensor}
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graph::Tensor
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grad::Tensor
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end
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function tf(model)
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sess = Session(Graph())
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input = placeholder(Float32)
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g = graph(model, input)
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run(sess, initialize_all_variables())
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Model(sess, [input], g, gradients(g, input))
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end
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batch(x) = Batch((x,))
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function (m::Model)(args::Batch...)
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@assert length(args) == length(m.inputs)
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run(m.session, m.graph, Dict(zip(m.inputs, args)))
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end
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(m::Model)(args...) = m(map(batch, args)...)
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function Flux.back!(m::Model, Δ, args...)
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@assert length(args) == length(m.inputs)
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# TODO: keyword arguments to `gradients`
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run(m.session, m.grad, Dict(zip(m.inputs, args)))
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end
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function Flux.update!(m::Model)
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error("update! is not yet supported on TensorFlow models")
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end
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import Juno: info
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function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
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loss = (y, y′) -> reduce_sum((y - y′).^2)/2,
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opt = TensorFlow.train.GradientDescentOptimizer(η))
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i = 0
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Y = placeholder(Float32)
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Loss = loss(m.graph, Y)
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minimize_op = TensorFlow.train.minimize(opt, Loss)
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for e in 1:epoch
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info("Epoch $e\n")
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@progress for (x, y) in train
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y, cur_loss, _ = run(m.session, vcat(m.graph, Loss, minimize_op),
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Dict(m.inputs[1]=>batch(x), Y=>batch(y)))
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if i % 5000 == 0
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@show y
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@show accuracy(m, test)
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end
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i += 1
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end
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end
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end
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@ -1,117 +1,11 @@
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module TF
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using ..Flux, Flow, TensorFlow, Juno
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import Flow: Constant, postwalk, value, inputs, constant
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import Flux: accuracy, spliceinputs, detuple
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import TensorFlow: RawTensor
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import Juno: info
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export tf
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cvalue(x) = x
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cvalue(c::Constant) = c.value
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cvalue(v::Vertex) = cvalue(value(v))
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graph(x::Tensor) = x
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# TODO: detect variable reuse
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graph{T<:AArray}(p::Flux.Param{T}) = Variable(p.x)
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function graph(model::Model, args...)
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g = Flux.graph(model)
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g ≠ nothing || error("No graph for $model")
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g = spliceinputs(g, map(constant, args)...) |> detuple
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postwalk(g) do v
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vertex(graph(cvalue(v), cvalue.(inputs(v))...))
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end |> value
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end
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graph(::typeof(*), args...) = *(args...)
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graph(::typeof(+), args...) = +(args...)
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graph(::typeof(softmax), x) = nn.softmax(x)
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graph(::typeof(relu), x) = nn.relu(x)
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graph(::typeof(tanh), x) = tanh(x)
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# reshape hack due to https://github.com/malmaud/TensorFlow.jl/issues/79
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batchsize(x::Tensor) = reduce_sum(slice(TensorFlow.shape(x), [0], [1]))
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graph(::typeof(flatten), x) = reshape(x, pack([batchsize(x), Int32(-1)]))
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graph(r::Reshape, x) = reshape(x, pack([batchsize(x), map(Int32, r.dims)...]))
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graph(::Input, x) = x
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graph(c::Conv2D, x) =
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nn.conv2d(x, graph(c.filter), [1,c.stride...,1], "VALID")
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graph(p::MaxPool, x) =
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nn.max_pool(x, [1, p.size..., 1], [1, p.stride..., 1], "VALID")
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TensorFlow.Tensor(m::Flux.Model, args...) = graph(m, args...)
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type Model
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session::Session
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inputs::Vector{Tensor}
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graph::Tensor
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grad::Tensor
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end
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function tf(model)
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sess = Session(Graph())
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input = placeholder(Float32)
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g = graph(model, input)
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run(sess, initialize_all_variables())
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Model(sess, [input], g, gradients(g, input))
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end
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batch(x) = Batch((x,))
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RawTensor(data::Batch) = RawTensor(rawbatch(data))
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function (m::Model)(args::Batch...)
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@assert length(args) == length(m.inputs)
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run(m.session, m.graph, Dict(zip(m.inputs, args)))
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end
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(m::Model)(args...) = m(map(batch, args)...)
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function Flux.back!(m::Model, Δ, args...)
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@assert length(args) == length(m.inputs)
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# TODO: keyword arguments to `gradients`
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run(m.session, m.grad, Dict(zip(m.inputs, args)))
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end
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function Flux.update!(m::Model)
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error("update! is not yet supported on TensorFlow models")
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end
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function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
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loss = (y, y′) -> reduce_sum((y - y′).^2)/2,
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opt = TensorFlow.train.GradientDescentOptimizer(η))
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i = 0
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Y = placeholder(Float32)
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Loss = loss(m.graph, Y)
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minimize_op = TensorFlow.train.minimize(opt, Loss)
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for e in 1:epoch
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info("Epoch $e\n")
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@progress for (x, y) in train
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y, cur_loss, _ = run(m.session, vcat(m.graph, Loss, minimize_op),
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Dict(m.inputs[1]=>batch(x), Y=>batch(y)))
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if i % 5000 == 0
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@show y
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@show accuracy(m, test)
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end
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i += 1
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end
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end
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end
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type Op
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f
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shape
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end
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Op(f) = Op(f, (d...) -> nothing)
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graph(op::Op, xs...) = op.f(xs...)
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Flux.shape(op::Op, d...) = op.shape(d...)
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include("graph.jl")
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include("model.jl")
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end
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