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kleinschmidt/tasknet.ipynb

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a Flux.jl example with a toy neural network that learns and, or, and xor
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tasknet.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-task network in julia with Flux\n",
"\n",
"This is to illustrate how easy it is to build and train neural network models in Julia with [Flux.jl](https://github.com/FluxML/Flux.jl). Flux is a framework that gives you both a convenient, high-level API and the ability to mess with things at as low a level as you want.\n",
"\n",
"## Task and training data\n",
"\n",
"This network learns to do three different \"tasks\": binary and, or, and xor. The network gets two binary inputs plus a \"task\" signal, which is a one-hot encoding of the task variable. There's one hidden layer, and additionally the task input connect directly to the output as well."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12-element Array{Int64,1}:\n",
" 0\n",
" 0\n",
" 0\n",
" 1\n",
" 0\n",
" 1\n",
" 1\n",
" 1\n",
" 0\n",
" 1\n",
" 1\n",
" 0"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Flux\n",
"\n",
"# inputs for each task (columns are observations)\n",
"x1 = [0 0 1 1\n",
" 0 1 0 1]\n",
"\n",
"funcs = [&, |, xor]\n",
"\n",
"y = vcat((f.(x1[1,:], x1[2,:]) for f in funcs)...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here I'm using the julia broadcasting syntax `f.()` to apply each function `f` to the pairs of inputs (`x1[1,:]` is the first row of `x1`).\n",
"\n",
"We can use the `onehot` and `onehotbatch` functions to convert a vector of class labes (in this case, the task functions) into a boolean matrix using one-hot encoding:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3×12 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
" true true true true false … false false false false false\n",
" false false false false true true false false false false\n",
" false false false false false false true true true true"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_task = Flux.onehotbatch(repeat(funcs, inner=size(x1, 2)), funcs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll put the task inputs first so it'll be easy to pick them out later:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5×12 Array{Int64,2}:\n",
" 1 1 1 1 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 1 1 1 1 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 1 1 1 1\n",
" 0 0 1 1 0 0 1 1 0 0 1 1\n",
" 0 1 0 1 0 1 0 1 0 1 0 1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = vcat(x_task, repmat(x1, 1, 3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Specifying the network\n",
"\n",
"First, I'll show how to specify the network with as much as Flux's high-level API as possible. It's not just a bunch of stacked layers, so we have to do a little bit of manual fiddling (which goes to show how flexible this abstraction is!).\n",
"\n",
"In Flux, a network is trained by specifying a loss function, which is parametrized by Flux's special \"tracked\" arrays which support automatic differentiation and training. In practice, we can usually specify a bunch of Flux layers, which are just functions that take input and generate output but with Flux parameters. We can do it manually, too, which we'll see below.\n",
"\n",
"To start, we'll specify the two pathways. The pathway through the hidden layer has two layers of connections (which are all densely connected). There's an output nonlinearity after the first layer. (There will also be an output non-linearity as well but we need to add the output of the two pathways together before applying it.)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"loss (generic function with 1 method)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Flux: Dense\n",
"\n",
"n_hidden = 10\n",
"hidden = Chain(\n",
" Dense(size(x, 1), n_hidden, σ),\n",
" Dense(n_hidden, 1)\n",
")\n",
"\n",
"## direct pathway from task units to output\n",
"direct = Dense(size(x_task, 1), 1)\n",
"\n",
"## overall output: add hidden and direct output and apply sigmoid (all elementwise)\n",
"m(x::Matrix) = σ.(hidden(x) .+ direct(x[1:size(x_task,1),:]))\n",
"\n",
"# define the loss function we'll optimize\n",
"loss(x,y) = Flux.mse(m(x), y')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The initial loss before training, based on the randomly initialized weights:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Tracked 0-dimensional Array{Float64,0}:\n",
"0.249905"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that this is a special \"tracked\" array. This is how Flux supports automatic backprop.\n",
"\n",
"## Training the network\n",
"\n",
"Flux provides a number of conveniences for training models as well. _Optimizers_ create a special function that, when called, optimizes the parameters using backprop. (We can also do this manually, which we'll see below)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(::#58) (generic function with 1 method)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ps = vcat(params(hidden), params(direct))\n",
"η = 1\n",
"opt = SGD(ps, η)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`opt` is a function that will update the parameters we've extracted from the two layers. The `Flux.train!` function is a wrapper for the loop of calculating loss, backpropogation, and calling the optimizer (slightly annoying, the data needs to be a vector of x,y tuples, even if x is a matrix and y a vector of targets):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss(x, y) = param(0.246506)\n"
]
}
],
"source": [
"dataset = (x,y)\n",
"Flux.train!(loss, [dataset], opt, cb = () -> @show(loss(x,y)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`train!` runs through all the data we pass it once. We can put it in a loop to train until the tolerance reaches a certain threshold:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss(x, y) = param(0.243395)\n",
"loss(x, y) = param(1.61531e-5)\n",
" 15.496159 seconds (71.65 M allocations: 5.114 GiB, 4.17% gc time)\n"
]
}
],
"source": [
"using Base.Iterators: repeated\n",
"\n",
"tol = 1e-5\n",
"stop() = loss(x,y).data[] < tol\n",
"\n",
"# show the value of the less function every 10 seconds\n",
"callback = Flux.throttle(() -> @show(loss(x,y)), 10)\n",
"\n",
"dataset = repeated((x,y), 500)\n",
"\n",
"@time begin\n",
" while !stop()\n",
" ## currently there's a performance problem in the train! function that makes\n",
" ## gc time dominate for small models like this, so we'll run the train loop manually\n",
" #Flux.train!(loss, dataset, opt, cb = Flux.throttle(callback, 10))\n",
" for d in dataset\n",
" Flux.back!(loss(d...))\n",
" opt()\n",
" callback()\n",
" end\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Tracked 0-dimensional Array{Float64,0}:\n",
"9.98587e-6"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12×2 Array{Float64,2}:\n",
" 0.000630202 0.0\n",
" 0.00138896 0.0\n",
" 0.00138876 0.0\n",
" 0.997339 1.0\n",
" 0.00297521 0.0\n",
" 0.998626 1.0\n",
" 0.998627 1.0\n",
" 0.997396 1.0\n",
" 0.00236595 0.0\n",
" 0.995386 1.0\n",
" 0.995389 1.0\n",
" 0.00639834 0.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hcat(m(x).data', y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Low-level interface\n",
"\n",
"We can also specify the network using a low-level interface and still benefit from Flux's abstractions (tracked arrays, automatic backprop)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Tracked 10-element Array{Float64,1}:\n",
" 2.44412 \n",
" 0.606339\n",
" -3.49427 \n",
" -0.467204\n",
" 1.7908 \n",
" -2.58228 \n",
" -0.329082\n",
" -2.60199 \n",
" 0.448517\n",
" -3.82918 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hidden[1].W\n",
"hidden[1].b"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Tracked 0-dimensional Array{Float64,0}:\n",
"3.15446"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Flux.Tracker: param, back!, data, grad\n",
"\n",
"W_hidden = param(randn(n_hidden, size(x,1)))\n",
"b_hidden = param(randn(n_hidden))\n",
"hidden2(x) = σ.(W_hidden*x .+ b_hidden)\n",
"\n",
"W_out = param(randn(1, n_hidden))\n",
"b_out = param(randn(1))\n",
"W_direct = param(randn(1, size(x_task, 1)))\n",
"predict(x::Matrix) = σ.(W_out*hidden2(x) .+ W_direct*x[1:3,:] .+ b_out)\n",
"\n",
"loss2(x,y) = sum((predict(x) .- y').^2)\n",
"loss2(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note how the value of `loss` is also `Tracked`. Calling `back!` on the tracked value will calculate the gradients of the individual parameters with respect to the loss:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Tracked 0-dimensional Array{Float64,0}:\n",
"3.15446"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = loss2(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10×5 Array{Float64,2}:\n",
" -0.00548446 -0.0199413 0.0175428 -0.0195804 -0.0199099 \n",
" 0.0214485 0.183165 -0.132873 0.122418 0.152889 \n",
" -0.00695408 -0.0751547 0.0498836 -0.0555422 -0.0568866 \n",
" 0.0316629 0.0468998 -0.0684927 0.0805919 0.116873 \n",
" -0.00050766 0.000505941 -0.00204762 0.00315389 0.00371962\n",
" 0.00411865 0.0195692 -0.0139061 0.0203315 0.0170381 \n",
" 0.00433668 0.0209703 -0.014808 0.0208767 0.0201817 \n",
" -0.00399932 0.0717888 -0.0267617 0.0879622 0.058855 \n",
" -0.0600394 -0.197018 0.0356492 -0.218219 -0.266648 \n",
" -0.00335029 -0.123585 0.119496 -0.0332793 -0.0536155 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"back!(loss2(x,y))\n",
"grad(W_hidden)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...and we can update `W_hidden` (using the `.data` field to access the underlying values in place) according to the gradient "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Tracked 0-dimensional Array{Float64,0}:\n",
"3.11955"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"W_hidden.data .-= 0.1*grad(W_hidden)\n",
"\n",
"loss2(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can define our own update function (which is essentially what `Flux.SGD` does) by looping over all the parameters and updating their `.data` based on the gradient of the parameters. Then, update until the loss is below tolerance (as above):"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss2(x, y) = param(2.82972)\n",
"loss2(x, y) = param(0.000383281)\n",
"loss2(x, y) = param(0.000171005)\n",
"loss2(x, y) = param(0.000109063)\n"
]
}
],
"source": [
"function update!(ps, η)\n",
" back!(loss2(x,y))\n",
" for p in ps\n",
" ∇, dat = grad(p), data(p)\n",
" dat .-= η .* ∇\n",
" ∇ .= 0\n",
" end\n",
"end\n",
"\n",
"cb = Flux.throttle(() -> @show(loss2(x,y)), 5)\n",
"\n",
"ps = [W_hidden, b_hidden, W_out, b_out, W_direct]\n",
"tol = 1e-4\n",
"while loss2(x,y).data[] > tol\n",
" back!(loss2(x,y))\n",
" update!(ps, 0.1)\n",
" cb()\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"12×2 Array{Float64,2}:\n",
" 0.0 0.0\n",
" 0.0 0.0\n",
" 0.0 0.0\n",
" 1.0 1.0\n",
" 0.0 0.0\n",
" 1.0 1.0\n",
" 1.0 1.0\n",
" 1.0 1.0\n",
" 0.0 0.0\n",
" 1.0 1.0\n",
" 1.0 1.0\n",
" 0.01 0.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hcat(round.(predict(x).data', 2), y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performance\n",
"\n",
"To compare to the matlab implementation, we need to do a few extra things. That implementation shuffles the input data on every iteration and does one backprop step per observation (instead of in batch), and it saves the MSE throughout training. Also, there are no bias weights learned in the Matlab version, but that's not going to speed things up here."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"12-element Array{Tuple{Array{Float64,2},Float64},1}:\n",
" ([1.0; 0.0; … ; 0.0; 0.0], 0.0)\n",
" ([1.0; 0.0; … ; 0.0; 1.0], 0.0)\n",
" ([1.0; 0.0; … ; 1.0; 0.0], 0.0)\n",
" ([1.0; 0.0; … ; 1.0; 1.0], 1.0)\n",
" ([0.0; 1.0; … ; 0.0; 0.0], 0.0)\n",
" ([0.0; 1.0; … ; 0.0; 1.0], 1.0)\n",
" ([0.0; 1.0; … ; 1.0; 0.0], 1.0)\n",
" ([0.0; 1.0; … ; 1.0; 1.0], 1.0)\n",
" ([0.0; 0.0; … ; 0.0; 0.0], 0.0)\n",
" ([0.0; 0.0; … ; 0.0; 1.0], 1.0)\n",
" ([0.0; 0.0; … ; 1.0; 0.0], 1.0)\n",
" ([0.0; 0.0; … ; 1.0; 1.0], 0.0)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## make a vector single-observation x,y tuples (with x as a matrix so we don't need new methods)\n",
"xys = [(float(x[:,i:i]), float(y[i])) for i in 1:length(y)]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"12-element Array{TrackedArray{…,Array{Float64,0}},1}:\n",
" param(7.2204e-7) \n",
" param(1.89381e-6)\n",
" param(4.1702e-6) \n",
" param(1.28171e-5)\n",
" param(3.99636e-6)\n",
" param(2.52668e-9)\n",
" param(1.21483e-8)\n",
" param(6.62773e-6)\n",
" param(5.34491e-6)\n",
" param(1.47294e-5)\n",
" param(1.62699e-5)\n",
" param(3.34133e-5)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[loss2(xy...) for xy in xys]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss2(x, y) = param(0.000400447)\n",
" 3.513151 seconds (14.27 M allocations: 957.211 MiB, 3.84% gc time)\n"
]
},
{
"data": {
"text/plain": [
"2000-element Array{Float64,1}:\n",
" 2.06891 \n",
" 1.83597 \n",
" 1.97047 \n",
" 1.81257 \n",
" 1.34066 \n",
" 1.29541 \n",
" 1.11231 \n",
" 1.05008 \n",
" 0.954588 \n",
" 0.939797 \n",
" 0.897048 \n",
" 0.726046 \n",
" 0.671461 \n",
" ⋮ \n",
" 0.000231743\n",
" 0.000231601\n",
" 0.000231493\n",
" 0.000231337\n",
" 0.00023122 \n",
" 0.000231087\n",
" 0.000230968\n",
" 0.000230809\n",
" 0.000230671\n",
" 0.000230542\n",
" 0.000230429\n",
" 0.000230311"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reset!(x) = randn!(x.data)\n",
"reset!.(ps)\n",
"\n",
"n_training = 2000\n",
"η = 0.3\n",
"\n",
"function train!(ps, xys, η, n_training, cb)\n",
"\n",
" mses = zeros(n_training)\n",
"\n",
" for iter in 1:n_training\n",
" shuffle!(xys)\n",
" for (x,y) in xys\n",
" l = loss2(x,y)\n",
" mses[iter] += l.data[]\n",
" back!(l)\n",
" update!(ps, 0.3)\n",
" end\n",
" cb()\n",
" end\n",
"\n",
" return mses\n",
"end\n",
"\n",
"@time train!(ps, xys, 0.3, 2000, cb)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm getting 3.2 seconds for 2,000 training iterations, vs. about 2.6 for the matlab code. Matlab does surprisingly well here! The tradeoff is that this code is more concise and more expressive, without sacrificing detailed control. I'd have to dig in a little and see whether it can be optimized at all."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Profiling"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss2(x, y) = param(2.31568)\n",
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" 1 ...atmul.jl:519; _generic_matm...\n",
" 4 ...atmul.jl:522; _generic_matm...\n",
" 17 ...atmul.jl:523; _generic_matm...\n",
" 2 ...atmul.jl:525; _generic_matm...\n",
" 1 ...atmul.jl:581; _generic_matm...\n",
" 1 ...r/back.jl:21; back(::Tracked...\n",
" 1 ...adcast.jl:204; broadcast!\n",
" 1 ...dcast.jl:207; broadcast_c!\n",
" 1 ...array.jl:64; indices\n",
" 80 ...ker/lib.jl:106; unbroadcast(::Tr...\n",
" 49 ./array.jl:1819; filter(::Functi...\n",
" 2 ...tarray.jl:882; getindex\n",
" 2 ...sional.jl:438; _getindex\n",
" 2 ...sional.jl:442; macro expansion\n",
" 1 ...sional.jl:453; _unsafe_getind...\n",
" 1 ...ional.jl:458; macro expansion\n",
" 33 ...tarray.jl:1865; map(::Function...\n",
" 4 ./array.jl:455; _collect(::Unit...\n",
" 1 ./array.jl:0; reshape(::Array...\n",
" 1 ./array.jl:184; reshape(::Array...\n",
" 25 ...ucedim.jl:572; sum\n",
" 25 ...ucedim.jl:570; sum\n",
" 25 ...ucedim.jl:241; mapreducedim\n",
" 2 ...ucedim.jl:210; mapreducedim!\n",
" 2 ...ucedim.jl:202; _mapreducedim!...\n",
" 2 ...mdloop.jl:71; macro expansion\n",
" 23 ...ucedim.jl:73; reducedim_init...\n",
" 3 ...ucedim.jl:33; reduced_indice...\n",
" 1 ./array.jl:0; vect(::Base.On...\n",
" 1 ./array.jl:76; vect(::Base.On...\n",
" 16 ...ucedim.jl:43; reduced_indice...\n",
" 20 ...cker/lib.jl:116; back\n",
" 1 ...cker/lib.jl:0; (::Flux.Tracker.#...\n",
" 19 ...cker/lib.jl:116; (::Flux.Tracker.#...\n",
" 19 ./broadcast.jl:434; broadcast\n",
" 2 ...oadcast.jl:311; broadcast_c\n",
" 2 ./broadcast.jl:53; broadcast_indices\n",
" 1 ...oadcast.jl:48; broadcast_indices\n",
" 1 ...oadcast.jl:52; broadcast_indices\n",
" 1 ...tarray.jl:64; indices\n",
" 1 ...oadcast.jl:57; broadcast_shape\n",
" 1 ...oadcast.jl:57; broadcast_shape\n",
" 1 ...adcast.jl:63; _bcs\n",
" 1 ...adcast.jl:0; _bcs1(::Base.On...\n",
" 17 ...oadcast.jl:314; broadcast_c\n",
" 8 ...oadcast.jl:266; broadcast_t\n",
" 9 ...oadcast.jl:268; broadcast_t\n",
" 9 ...oadcast.jl:139; _broadcast!\n",
" 9 ...adcast.jl:147; macro expansion\n",
" 9 ...mdloop.jl:73; macro expansion\n",
" 8 ...adcast.jl:151; macro expansion\n",
" 1 ...adcast.jl:154; macro expansion\n",
" 1 ...ional.jl:247; setindex!\n",
" 47 ...racker/lib.jl:106; unbroadcast(::Tracked...\n",
" 23 ./array.jl:1819; filter(::Function, :...\n",
" 2 ...tractarray.jl:882; getindex\n",
" 2 ...imensional.jl:438; _getindex\n",
" 1 ...imensional.jl:441; macro expansion\n",
" 1 ...ractarray.jl:362; checkbounds\n",
" 1 ...imensional.jl:442; macro expansion\n",
" 1 ...mensional.jl:453; _unsafe_getindex(::...\n",
" 1 ...mensional.jl:458; macro expansion\n",
" 18 ...tractarray.jl:1865; map(::Function, ::Un...\n",
" 2 ./array.jl:455; _collect(::UnitRange...\n",
" 1 ./array.jl:184; reshape(::Array{Floa...\n",
" 22 ./reducedim.jl:572; sum\n",
" 22 ./reducedim.jl:570; sum\n",
" 22 ./reducedim.jl:241; mapreducedim\n",
" 2 ./reducedim.jl:210; mapreducedim!\n",
" 2 ./reducedim.jl:194; _mapreducedim!(::Ba...\n",
" 2 ./simdloop.jl:73; macro expansion\n",
" 2 ./reducedim.jl:195; macro expansion\n",
" 20 ./reducedim.jl:73; reducedim_initarray...\n",
" 1 ./reducedim.jl:0; reduced_indices(::T...\n",
" 1 ./reducedim.jl:33; reduced_indices(::T...\n",
" 1 ./array.jl:76; vect(::Base.OneTo{I...\n",
" 17 ./reducedim.jl:43; reduced_indices(::T...\n",
" 12 ...tracker/lib.jl:116; back\n",
" 11 .../tracker/lib.jl:116; (::Flux.Tracker.##16#18...\n",
" 11 ./broadcast.jl:434; broadcast\n",
" 1 ./broadcast.jl:311; broadcast_c\n",
" 1 ./broadcast.jl:53; broadcast_indices\n",
" 1 ./tuple.jl:159; map\n",
" 1 ./broadcast.jl:48; broadcast_indices\n",
" 1 ./broadcast.jl:52; broadcast_indices\n",
" 1 ...tractarray.jl:64; indices\n",
" 10 ./broadcast.jl:314; broadcast_c\n",
" 8 ./broadcast.jl:266; broadcast_t\n",
" 2 ./broadcast.jl:268; broadcast_t\n",
" 2 ./broadcast.jl:139; _broadcast!\n",
" 2 ./broadcast.jl:147; macro expansion\n",
" 2 ./simdloop.jl:73; macro expansion\n",
" 2 ./broadcast.jl:153; macro expansion\n",
" 1 .../tracker/lib.jl:106; unbroadcast(::TrackedArr...\n",
" 21 ...c/tracker/lib.jl:116; back\n",
" 1 ...rc/tracker/lib.jl:0; (::Flux.Tracker.##16#18{Fl...\n",
" 19 ...rc/tracker/lib.jl:116; (::Flux.Tracker.##16#18{Fl...\n",
" 19 ./broadcast.jl:434; broadcast\n",
" 19 ./broadcast.jl:314; broadcast_c\n",
" 17 ./broadcast.jl:266; broadcast_t\n",
" 2 ./broadcast.jl:268; broadcast_t\n",
" 2 ./broadcast.jl:139; _broadcast!\n",
" 2 ./broadcast.jl:147; macro expansion\n",
" 2 ./simdloop.jl:73; macro expansion\n",
" 2 ./broadcast.jl:153; macro expansion\n",
" 7 ...src/tracker/lib.jl:55; back\n",
" 1 ...src/tracker/lib.jl:52; sum(::TrackedArray{…,Array...\n",
" 1 ...x/src/tracker/lib.jl:4; toarray\n",
" 2 ./In[16]:3; update!(::Array{Flux.Tracker.T...\n",
" 6 ./In[16]:4; update!(::Array{Flux.Tracker.T...\n",
" 57 ./In[16]:5; update!(::Array{Flux.Tracker.T...\n",
" 29 ./broadcast.jl:204; broadcast!(::Function, ::Arra...\n",
" 1 ./broadcast.jl:208; broadcast_c!\n",
" 1 ./broadcast.jl:89; check_broadcast_indices\n",
" 1 ./broadcast.jl:84; check_broadcast_shape(::Tuple...\n",
" 1 ./broadcast.jl:0; check_broadcast_shape(::Tuple...\n",
" 26 ./broadcast.jl:211; broadcast_c!\n",
" 6 ./broadcast.jl:139; _broadcast!(::##15#16, ::Arra...\n",
" 2 ./broadcast.jl:144; macro expansion\n",
" 4 ./broadcast.jl:147; macro expansion\n",
" 1 ./simdloop.jl:66; macro expansion\n",
" 2 ./simdloop.jl:72; macro expansion\n",
" 1 ./simdloop.jl:73; macro expansion\n",
" 1 ./broadcast.jl:154; macro expansion\n",
" 1 ...ltidimensional.jl:247; setindex!\n",
" 8 ./In[16]:6; update!(::Array{Flux.Tracker.T...\n",
" 2 ./broadcast.jl:22; broadcast!(::Base.#identity, :...\n",
" 1 ./array.jl:227; fill!(::Array{Float64,2}, ::I...\n",
" 1 ./array.jl:228; fill!(::Array{Float64,1}, ::I...\n",
" 5 ./In[20]:19; train!(::Array{Flux.Tracker.Tr...\n",
" 4 ....6/Flux/src/utils.jl:37; (::Flux.##throttled#4#9{Bool,Bo...\n"
]
}
],
"source": [
"reset!.(ps)\n",
"@profile train!(ps, xys, 0.3, 2000, cb)\n",
"Profile.print()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.6.0",
"language": "julia",
"name": "julia-0.6"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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