From 5fc22e527f80980529fd27ee70be70a7ee70e6ee Mon Sep 17 00:00:00 2001
From: SubhadityMukherjee <msubhaditya@gmail.com>
Date: Tue, 11 Aug 2020 17:19:51 +0400
Subject: [PATCH 1/7] added VGG models as a first part of restructuring

---
 .../.ipynb_checkpoints/VGG16-checkpoint.ipynb | 439 ++++++++++++++++++
 .../.ipynb_checkpoints/VGG19-checkpoint.ipynb | 438 +++++++++++++++++
 vision/{cifar10 => VGG}/Manifest.toml         |   0
 vision/{cifar10 => VGG}/Project.toml          |   0
 vision/VGG/README.md                          |   9 +
 vision/VGG/README.md~                         |   3 +
 vision/VGG/VGG16.ipynb                        | 439 ++++++++++++++++++
 vision/VGG/VGG16.jl                           |  76 +++
 vision/VGG/VGG19.ipynb                        | 438 +++++++++++++++++
 vision/VGG/VGG19.jl                           |  75 +++
 vision/cifar10/cifar10.jl                     | 136 ------
 11 files changed, 1917 insertions(+), 136 deletions(-)
 create mode 100644 vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
 create mode 100644 vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb
 rename vision/{cifar10 => VGG}/Manifest.toml (100%)
 rename vision/{cifar10 => VGG}/Project.toml (100%)
 create mode 100644 vision/VGG/README.md
 create mode 100644 vision/VGG/README.md~
 create mode 100644 vision/VGG/VGG16.ipynb
 create mode 100644 vision/VGG/VGG16.jl
 create mode 100644 vision/VGG/VGG19.ipynb
 create mode 100644 vision/VGG/VGG19.jl
 delete mode 100644 vision/cifar10/cifar10.jl

diff --git a/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb b/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
new file mode 100644
index 000000000..65f8ab4ee
--- /dev/null
+++ b/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:29:16.357000+04:00",
+     "start_time": "2020-08-11T12:28:53.552Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flux, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated, partition\n",
+    "using Metalhead:trainimgs, CIFAR10\n",
+    "using Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:55:19.835000+04:00",
+     "start_time": "2020-08-11T12:55:18.394Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "┌ Info: Precompiling Flatten [4c728ea3-d9ee-5c9a-9642-b6f7d7dc04fa]\n",
+      "└ @ Base loading.jl:1278\n",
+      "WARNING: using Flatten.flatten in module Main conflicts with an existing identifier.\n"
+     ]
+    }
+   ],
+   "source": [
+    "using Flatten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:55:11.078000+04:00",
+     "start_time": "2020-08-11T12:55:11.076Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "getarray (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:07:39.471000+04:00",
+     "start_time": "2020-08-11T13:07:35.047Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "X = trainimgs(CIFAR10)\n",
+    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
+    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
+    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:29:25.375000+04:00",
+     "start_time": "2020-08-11T12:28:59.367Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
+       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
+       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valset = collect(49001:50000)\n",
+    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
+    "valY = labels[:, valset] |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:05.168000+04:00",
+     "start_time": "2020-08-11T13:05:05.159Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vgg16 (generic function with 2 methods)"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vgg16() = Chain(\n",
+    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  MaxPool((2,2)),\n",
+    "  x -> reshape(x, :, size(x, 4)),\n",
+    "  Dense(512, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 10),\n",
+    "  softmax) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:08.454000+04:00",
+     "start_time": "2020-08-11T13:05:08.040Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #36, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = vgg16()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:11.842000+04:00",
+     "start_time": "2020-08-11T13:05:11.839Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "loss (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss(x, y) = crossentropy(m(x), y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:12.481000+04:00",
+     "start_time": "2020-08-11T13:05:12.447Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "accuracy (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:13.807000+04:00",
+     "start_time": "2020-08-11T13:05:13.721Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:14.409000+04:00",
+     "start_time": "2020-08-11T13:05:14.407Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt = ADAM()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:07:16.455000+04:00",
+     "start_time": "2020-08-11T13:05:14.799Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy(valX, valY) = 0.112\n",
+      "accuracy(valX, valY) = 0.087\n",
+      "accuracy(valX, valY) = 0.087\n"
+     ]
+    },
+    {
+     "ename": "LoadError",
+     "evalue": "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
+     "output_type": "error",
+     "traceback": [
+      "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
+      "",
+      "Stacktrace:",
+      " [1] wait at ./task.jl:267 [inlined]",
+      " [2] threading_run(::Function) at ./threadingconstructs.jl:34",
+      " [3] macro expansion at ./threadingconstructs.jl:93 [inlined]",
+      " [4] conv_im2col!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}; col::Array{Float32,3}, alpha::Float32, beta::Float32) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:49",
+      " [5] conv_im2col! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:30 [inlined]",
+      " [6] #conv!#41 at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53 [inlined]",
+      " [7] conv!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53",
+      " [8] conv!(::Array{Float32,4}, ::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70",
+      " [9] conv! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70 [inlined]",
+      " [10] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:116",
+      " [11] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:114",
+      " [12] (::Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/conv.jl:61",
+      " [13] applychain(::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 (repeats 13 times)",
+      " [14] (::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38",
+      " [15] accuracy(::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at ./In[180]:1",
+      " [16] macro expansion at ./show.jl:641 [inlined]",
+      " [17] (::var\"#39#40\")() at ./In[181]:1",
+      " [18] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:302 [inlined]",
+      " [19] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:298 [inlined]",
+      " [20] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:93 [inlined]",
+      " [21] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
+      " [22] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
+      " [23] top-level scope at In[183]:1",
+      " [24] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.5.0",
+   "language": "julia",
+   "name": "julia-1.5"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.5.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb b/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb
new file mode 100644
index 000000000..e39e6811d
--- /dev/null
+++ b/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb
@@ -0,0 +1,438 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:39.147000+04:00",
+     "start_time": "2020-08-11T13:13:16.198Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flux, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated, partition\n",
+    "using Metalhead:trainimgs, CIFAR10\n",
+    "using Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:39.377000+04:00",
+     "start_time": "2020-08-11T13:13:16.405Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flatten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:39.861000+04:00",
+     "start_time": "2020-08-11T13:13:16.558Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "getarray (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:45.622000+04:00",
+     "start_time": "2020-08-11T13:13:16.700Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "X = trainimgs(CIFAR10)\n",
+    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
+    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
+    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:46.662000+04:00",
+     "start_time": "2020-08-11T13:13:16.900Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
+       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
+       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valset = collect(49001:50000)\n",
+    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
+    "valY = labels[:, valset] |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:46.743000+04:00",
+     "start_time": "2020-08-11T13:13:17.068Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vgg19 (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vgg19() =  Chain(\n",
+    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  x -> reshape(x, :, size(x, 4)),\n",
+    "  Dense(512, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 10),\n",
+    "  softmax) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:46.553000+04:00",
+     "start_time": "2020-08-11T13:14:45.389Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #7, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = vgg19()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.788000+04:00",
+     "start_time": "2020-08-11T13:14:47.786Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "loss (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss(x, y) = crossentropy(m(x), y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.867000+04:00",
+     "start_time": "2020-08-11T13:14:47.866Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "accuracy (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.990000+04:00",
+     "start_time": "2020-08-11T13:14:47.906Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:48.860000+04:00",
+     "start_time": "2020-08-11T13:13:19.436Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt = ADAM()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:16:10.370000+04:00",
+     "start_time": "2020-08-11T13:14:58.340Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy(valX, valY) = 0.119\n"
+     ]
+    },
+    {
+     "ename": "LoadError",
+     "evalue": "InterruptException:",
+     "output_type": "error",
+     "traceback": [
+      "InterruptException:",
+      "",
+      "Stacktrace:",
+      " [1] BatchNorm at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/normalise.jl:199 [inlined]",
+      " [2] _pullback(::Zygote.Context, ::BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [3] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [4] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [5] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [6] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [7] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [8] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [9] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [10] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [11] Chain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38 [inlined]",
+      " [12] _pullback(::Zygote.Context, ::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [13] loss at ./In[17]:1 [inlined]",
+      " [14] _pullback(::Zygote.Context, ::typeof(loss), ::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [15] adjoint at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/lib/lib.jl:179 [inlined]",
+      " [16] _pullback at /home/subhaditya/.julia/packages/ZygoteRules/6nssF/src/adjoint.jl:47 [inlined]",
+      " [17] #17 at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:89 [inlined]",
+      " [18] _pullback(::Zygote.Context, ::Flux.Optimise.var\"#17#25\"{typeof(loss),Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [19] pullback(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:172",
+      " [20] gradient(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:53",
+      " [21] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:88 [inlined]",
+      " [22] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
+      " [23] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
+      " [24] top-level scope at In[21]:1",
+      " [25] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.5.0",
+   "language": "julia",
+   "name": "julia-1.5"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.5.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vision/cifar10/Manifest.toml b/vision/VGG/Manifest.toml
similarity index 100%
rename from vision/cifar10/Manifest.toml
rename to vision/VGG/Manifest.toml
diff --git a/vision/cifar10/Project.toml b/vision/VGG/Project.toml
similarity index 100%
rename from vision/cifar10/Project.toml
rename to vision/VGG/Project.toml
diff --git a/vision/VGG/README.md b/vision/VGG/README.md
new file mode 100644
index 000000000..9ea3c1e21
--- /dev/null
+++ b/vision/VGG/README.md
@@ -0,0 +1,9 @@
+# VGG Models
+
+- This folder contains the Julia implementations of the VGG models 
+- The notebooks contain a running implementation you can open in Jupyter lab/notebook/colab etc
+- The .jl files can be run directly using julia <filename.jl>
+
+
+- Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
+ [Paper](https://arxiv.org/pdf/1409.1556.pdf)
diff --git a/vision/VGG/README.md~ b/vision/VGG/README.md~
new file mode 100644
index 000000000..48e6390f9
--- /dev/null
+++ b/vision/VGG/README.md~
@@ -0,0 +1,3 @@
+# VGG Models
+
+This folder contains the Julia implementations of the VGG models 
diff --git a/vision/VGG/VGG16.ipynb b/vision/VGG/VGG16.ipynb
new file mode 100644
index 000000000..65f8ab4ee
--- /dev/null
+++ b/vision/VGG/VGG16.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:29:16.357000+04:00",
+     "start_time": "2020-08-11T12:28:53.552Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flux, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated, partition\n",
+    "using Metalhead:trainimgs, CIFAR10\n",
+    "using Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:55:19.835000+04:00",
+     "start_time": "2020-08-11T12:55:18.394Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "┌ Info: Precompiling Flatten [4c728ea3-d9ee-5c9a-9642-b6f7d7dc04fa]\n",
+      "└ @ Base loading.jl:1278\n",
+      "WARNING: using Flatten.flatten in module Main conflicts with an existing identifier.\n"
+     ]
+    }
+   ],
+   "source": [
+    "using Flatten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:55:11.078000+04:00",
+     "start_time": "2020-08-11T12:55:11.076Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "getarray (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:07:39.471000+04:00",
+     "start_time": "2020-08-11T13:07:35.047Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "X = trainimgs(CIFAR10)\n",
+    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
+    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
+    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:29:25.375000+04:00",
+     "start_time": "2020-08-11T12:28:59.367Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
+       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
+       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valset = collect(49001:50000)\n",
+    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
+    "valY = labels[:, valset] |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:05.168000+04:00",
+     "start_time": "2020-08-11T13:05:05.159Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vgg16 (generic function with 2 methods)"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vgg16() = Chain(\n",
+    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  MaxPool((2,2)),\n",
+    "  x -> reshape(x, :, size(x, 4)),\n",
+    "  Dense(512, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 10),\n",
+    "  softmax) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:08.454000+04:00",
+     "start_time": "2020-08-11T13:05:08.040Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #36, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = vgg16()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:11.842000+04:00",
+     "start_time": "2020-08-11T13:05:11.839Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "loss (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss(x, y) = crossentropy(m(x), y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:12.481000+04:00",
+     "start_time": "2020-08-11T13:05:12.447Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "accuracy (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:13.807000+04:00",
+     "start_time": "2020-08-11T13:05:13.721Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:14.409000+04:00",
+     "start_time": "2020-08-11T13:05:14.407Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt = ADAM()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:07:16.455000+04:00",
+     "start_time": "2020-08-11T13:05:14.799Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy(valX, valY) = 0.112\n",
+      "accuracy(valX, valY) = 0.087\n",
+      "accuracy(valX, valY) = 0.087\n"
+     ]
+    },
+    {
+     "ename": "LoadError",
+     "evalue": "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
+     "output_type": "error",
+     "traceback": [
+      "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
+      "",
+      "Stacktrace:",
+      " [1] wait at ./task.jl:267 [inlined]",
+      " [2] threading_run(::Function) at ./threadingconstructs.jl:34",
+      " [3] macro expansion at ./threadingconstructs.jl:93 [inlined]",
+      " [4] conv_im2col!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}; col::Array{Float32,3}, alpha::Float32, beta::Float32) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:49",
+      " [5] conv_im2col! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:30 [inlined]",
+      " [6] #conv!#41 at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53 [inlined]",
+      " [7] conv!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53",
+      " [8] conv!(::Array{Float32,4}, ::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70",
+      " [9] conv! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70 [inlined]",
+      " [10] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:116",
+      " [11] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:114",
+      " [12] (::Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/conv.jl:61",
+      " [13] applychain(::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 (repeats 13 times)",
+      " [14] (::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38",
+      " [15] accuracy(::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at ./In[180]:1",
+      " [16] macro expansion at ./show.jl:641 [inlined]",
+      " [17] (::var\"#39#40\")() at ./In[181]:1",
+      " [18] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:302 [inlined]",
+      " [19] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:298 [inlined]",
+      " [20] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:93 [inlined]",
+      " [21] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
+      " [22] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
+      " [23] top-level scope at In[183]:1",
+      " [24] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.5.0",
+   "language": "julia",
+   "name": "julia-1.5"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.5.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vision/VGG/VGG16.jl b/vision/VGG/VGG16.jl
new file mode 100644
index 000000000..07a98449f
--- /dev/null
+++ b/vision/VGG/VGG16.jl
@@ -0,0 +1,76 @@
+using Flux, Statistics
+using Flux: onehotbatch, onecold, crossentropy, throttle
+using Base.Iterators: repeated, partition
+using Metalhead:trainimgs, CIFAR10
+using Images
+
+using Flatten
+
+getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))
+
+X = trainimgs(CIFAR10)
+imgs = [getarray(X[i].img) for i in 1:50000];
+labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);
+train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);
+
+valset = collect(49001:50000)
+valX = cat(imgs[valset]..., dims = 4) |> gpu
+valY = labels[:, valset] |> gpu
+
+vgg16() = Chain(
+  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(64),
+  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(64),
+  MaxPool((2,2)),
+  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(128),
+  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(128),
+  MaxPool((2,2)),
+  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(256),
+  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(256),
+  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(256),
+  MaxPool((2,2)),
+  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  MaxPool((2,2)),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  MaxPool((2,2)),
+  x -> reshape(x, :, size(x, 4)),
+  Dense(512, 4096, relu),
+  Dropout(0.5),
+  Dense(4096, 4096, relu),
+  Dropout(0.5),
+  Dense(4096, 10),
+  softmax) |> gpu
+
+m = vgg16()
+
+conv_block(in_channels, out_channels) = return Chain(
+    Conv((3,3), in_channels => out_channels, relu, pad = (1,1), stride = (1,1)), 
+    BatchNorm(out_channels))
+
+loss(x, y) = crossentropy(m(x), y)
+
+Chain(double_conv(2, 3))
+
+accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
+
+evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)
+
+opt = ADAM()
+
+Flux.train!(loss, params(m), train, opt, cb = evalcb)
diff --git a/vision/VGG/VGG19.ipynb b/vision/VGG/VGG19.ipynb
new file mode 100644
index 000000000..e39e6811d
--- /dev/null
+++ b/vision/VGG/VGG19.ipynb
@@ -0,0 +1,438 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:39.147000+04:00",
+     "start_time": "2020-08-11T13:13:16.198Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flux, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated, partition\n",
+    "using Metalhead:trainimgs, CIFAR10\n",
+    "using Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:39.377000+04:00",
+     "start_time": "2020-08-11T13:13:16.405Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flatten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:39.861000+04:00",
+     "start_time": "2020-08-11T13:13:16.558Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "getarray (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:45.622000+04:00",
+     "start_time": "2020-08-11T13:13:16.700Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "X = trainimgs(CIFAR10)\n",
+    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
+    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
+    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:46.662000+04:00",
+     "start_time": "2020-08-11T13:13:16.900Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
+       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
+       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valset = collect(49001:50000)\n",
+    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
+    "valY = labels[:, valset] |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:46.743000+04:00",
+     "start_time": "2020-08-11T13:13:17.068Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vgg19 (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vgg19() =  Chain(\n",
+    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  x -> reshape(x, :, size(x, 4)),\n",
+    "  Dense(512, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 10),\n",
+    "  softmax) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:46.553000+04:00",
+     "start_time": "2020-08-11T13:14:45.389Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #7, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = vgg19()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.788000+04:00",
+     "start_time": "2020-08-11T13:14:47.786Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "loss (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss(x, y) = crossentropy(m(x), y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.867000+04:00",
+     "start_time": "2020-08-11T13:14:47.866Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "accuracy (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.990000+04:00",
+     "start_time": "2020-08-11T13:14:47.906Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:48.860000+04:00",
+     "start_time": "2020-08-11T13:13:19.436Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt = ADAM()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:16:10.370000+04:00",
+     "start_time": "2020-08-11T13:14:58.340Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy(valX, valY) = 0.119\n"
+     ]
+    },
+    {
+     "ename": "LoadError",
+     "evalue": "InterruptException:",
+     "output_type": "error",
+     "traceback": [
+      "InterruptException:",
+      "",
+      "Stacktrace:",
+      " [1] BatchNorm at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/normalise.jl:199 [inlined]",
+      " [2] _pullback(::Zygote.Context, ::BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [3] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [4] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [5] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [6] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [7] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [8] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [9] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [10] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [11] Chain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38 [inlined]",
+      " [12] _pullback(::Zygote.Context, ::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [13] loss at ./In[17]:1 [inlined]",
+      " [14] _pullback(::Zygote.Context, ::typeof(loss), ::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [15] adjoint at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/lib/lib.jl:179 [inlined]",
+      " [16] _pullback at /home/subhaditya/.julia/packages/ZygoteRules/6nssF/src/adjoint.jl:47 [inlined]",
+      " [17] #17 at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:89 [inlined]",
+      " [18] _pullback(::Zygote.Context, ::Flux.Optimise.var\"#17#25\"{typeof(loss),Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [19] pullback(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:172",
+      " [20] gradient(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:53",
+      " [21] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:88 [inlined]",
+      " [22] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
+      " [23] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
+      " [24] top-level scope at In[21]:1",
+      " [25] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.5.0",
+   "language": "julia",
+   "name": "julia-1.5"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.5.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vision/VGG/VGG19.jl b/vision/VGG/VGG19.jl
new file mode 100644
index 000000000..34d004502
--- /dev/null
+++ b/vision/VGG/VGG19.jl
@@ -0,0 +1,75 @@
+using Flux, Statistics
+using Flux: onehotbatch, onecold, crossentropy, throttle
+using Base.Iterators: repeated, partition
+using Metalhead:trainimgs, CIFAR10
+using Images
+
+using Flatten
+
+getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))
+
+X = trainimgs(CIFAR10)
+imgs = [getarray(X[i].img) for i in 1:50000];
+labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);
+train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);
+
+valset = collect(49001:50000)
+valX = cat(imgs[valset]..., dims = 4) |> gpu
+valY = labels[:, valset] |> gpu
+
+vgg19() =  Chain(
+  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(64),
+  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(64),
+  MaxPool((2,2)),
+  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(128),
+  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(128),
+  MaxPool((2,2)),
+  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(256),
+  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(256),
+  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(256),
+  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
+  MaxPool((2,2)),
+  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  MaxPool((2,2)),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  BatchNorm(512),
+  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
+  MaxPool((2,2)),
+  x -> reshape(x, :, size(x, 4)),
+  Dense(512, 4096, relu),
+  Dropout(0.5),
+  Dense(4096, 4096, relu),
+  Dropout(0.5),
+  Dense(4096, 10),
+  softmax) |> gpu
+
+m = vgg19()
+
+loss(x, y) = crossentropy(m(x), y)
+
+accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
+
+evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)
+
+opt = ADAM()
+
+Flux.train!(loss, params(m), train, opt, cb = evalcb)
+
+
diff --git a/vision/cifar10/cifar10.jl b/vision/cifar10/cifar10.jl
deleted file mode 100644
index 236aec358..000000000
--- a/vision/cifar10/cifar10.jl
+++ /dev/null
@@ -1,136 +0,0 @@
-using Flux, Metalhead, Statistics
-using Flux: onehotbatch, onecold, crossentropy, throttle
-using Metalhead: trainimgs
-using Images: channelview
-using Statistics: mean
-using Base.Iterators: partition
-
-# VGG16 and VGG19 models
-
-vgg16() = Chain(
-  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  x -> maxpool(x, (2, 2)),
-  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  x -> maxpool(x, (2,2)),
-  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  x -> maxpool(x, (2, 2)),
-  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  x -> maxpool(x, (2, 2)),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  x -> maxpool(x, (2, 2)),
-  x -> reshape(x, :, size(x, 4)),
-  Dense(512, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 10),
-  softmax) |> gpu
-
-vgg19() = Chain(
-  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  x -> maxpool(x, (2, 2)),
-  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  x -> maxpool(x, (2, 2)),
-  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  x -> maxpool(x, (2, 2)),
-  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  x -> maxpool(x, (2, 2)),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  x -> maxpool(x, (2, 2)),
-  x -> reshape(x, :, size(x, 4)),
-  Dense(512, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 10),
-  softmax) |> gpu
-
-# Function to convert the RGB image to Float64 Arrays
-
-getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))
-
-# Fetching the train and validation data and getting them into proper shape
-
-X = trainimgs(CIFAR10)
-imgs = [getarray(X[i].img) for i in 1:50000]
-labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10)
-train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)])
-valset = collect(49001:50000)
-valX = cat(imgs[valset]..., dims = 4) |> gpu
-valY = labels[:, valset] |> gpu
-
-# Defining the loss and accuracy functions
-
-m = vgg16()
-
-loss(x, y) = crossentropy(m(x), y)
-
-accuracy(x, y) = mean(onecold(m(x), 1:10) .== onecold(y, 1:10))
-
-# Defining the callback and the optimizer
-
-evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)
-
-opt = ADAM()
-
-# Starting to train models
-
-Flux.train!(loss, params(m), train, opt, cb = evalcb)
-
-# Fetch the test data from Metalhead and get it into proper shape.
-# CIFAR-10 does not specify a validation set so valimgs fetch the testdata instead of testimgs
-
-test = valimgs(CIFAR10)
-
-testimgs = [getarray(test[i].img) for i in 1:10000]
-testY = onehotbatch([test[i].ground_truth.class for i in 1:10000], 1:10) |> gpu
-testX = cat(testimgs..., dims = 4) |> gpu
-
-# Print the final accuracy
-
-@show(accuracy(testX, testY))

From 1d0cf40e3ac278313873be85efc0a53dc9f51b27 Mon Sep 17 00:00:00 2001
From: SubhadityMukherjee <msubhaditya@gmail.com>
Date: Tue, 11 Aug 2020 17:23:33 +0400
Subject: [PATCH 2/7] added VGG models as a first part of restructuring

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 9a477bff9..2a87551a0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 notebooks
+*.checkpoints

From 5a6321ea5080f0267ceb3fc237370c4c666f128b Mon Sep 17 00:00:00 2001
From: SubhadityMukherjee <msubhaditya@gmail.com>
Date: Tue, 11 Aug 2020 17:23:52 +0400
Subject: [PATCH 3/7] added VGG models as a first part of restructuring

---
 .../.ipynb_checkpoints/VGG16-checkpoint.ipynb | 439 ------------------
 .../.ipynb_checkpoints/VGG19-checkpoint.ipynb | 438 -----------------
 2 files changed, 877 deletions(-)
 delete mode 100644 vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
 delete mode 100644 vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb

diff --git a/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb b/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
deleted file mode 100644
index 65f8ab4ee..000000000
--- a/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
+++ /dev/null
@@ -1,439 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "toc": true
-   },
-   "source": [
-    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
-    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T16:29:16.357000+04:00",
-     "start_time": "2020-08-11T12:28:53.552Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "using Flux, Statistics\n",
-    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
-    "using Base.Iterators: repeated, partition\n",
-    "using Metalhead:trainimgs, CIFAR10\n",
-    "using Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T16:55:19.835000+04:00",
-     "start_time": "2020-08-11T12:55:18.394Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Precompiling Flatten [4c728ea3-d9ee-5c9a-9642-b6f7d7dc04fa]\n",
-      "└ @ Base loading.jl:1278\n",
-      "WARNING: using Flatten.flatten in module Main conflicts with an existing identifier.\n"
-     ]
-    }
-   ],
-   "source": [
-    "using Flatten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T16:55:11.078000+04:00",
-     "start_time": "2020-08-11T12:55:11.076Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "getarray (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:07:39.471000+04:00",
-     "start_time": "2020-08-11T13:07:35.047Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "X = trainimgs(CIFAR10)\n",
-    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
-    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
-    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T16:29:25.375000+04:00",
-     "start_time": "2020-08-11T12:28:59.367Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
-       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
-       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "valset = collect(49001:50000)\n",
-    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
-    "valY = labels[:, valset] |> gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:05:05.168000+04:00",
-     "start_time": "2020-08-11T13:05:05.159Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "vgg16 (generic function with 2 methods)"
-      ]
-     },
-     "execution_count": 173,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vgg16() = Chain(\n",
-    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  MaxPool((2,2)),\n",
-    "  x -> reshape(x, :, size(x, 4)),\n",
-    "  Dense(512, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 10),\n",
-    "  softmax) |> gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:05:08.454000+04:00",
-     "start_time": "2020-08-11T13:05:08.040Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #36, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = vgg16()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:05:11.842000+04:00",
-     "start_time": "2020-08-11T13:05:11.839Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "loss (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loss(x, y) = crossentropy(m(x), y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:05:12.481000+04:00",
-     "start_time": "2020-08-11T13:05:12.447Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "accuracy (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 180,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:05:13.807000+04:00",
-     "start_time": "2020-08-11T13:05:13.721Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 181,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:05:14.409000+04:00",
-     "start_time": "2020-08-11T13:05:14.407Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "opt = ADAM()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:07:16.455000+04:00",
-     "start_time": "2020-08-11T13:05:14.799Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accuracy(valX, valY) = 0.112\n",
-      "accuracy(valX, valY) = 0.087\n",
-      "accuracy(valX, valY) = 0.087\n"
-     ]
-    },
-    {
-     "ename": "LoadError",
-     "evalue": "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
-     "output_type": "error",
-     "traceback": [
-      "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
-      "",
-      "Stacktrace:",
-      " [1] wait at ./task.jl:267 [inlined]",
-      " [2] threading_run(::Function) at ./threadingconstructs.jl:34",
-      " [3] macro expansion at ./threadingconstructs.jl:93 [inlined]",
-      " [4] conv_im2col!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}; col::Array{Float32,3}, alpha::Float32, beta::Float32) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:49",
-      " [5] conv_im2col! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:30 [inlined]",
-      " [6] #conv!#41 at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53 [inlined]",
-      " [7] conv!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53",
-      " [8] conv!(::Array{Float32,4}, ::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70",
-      " [9] conv! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70 [inlined]",
-      " [10] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:116",
-      " [11] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:114",
-      " [12] (::Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/conv.jl:61",
-      " [13] applychain(::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 (repeats 13 times)",
-      " [14] (::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38",
-      " [15] accuracy(::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at ./In[180]:1",
-      " [16] macro expansion at ./show.jl:641 [inlined]",
-      " [17] (::var\"#39#40\")() at ./In[181]:1",
-      " [18] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:302 [inlined]",
-      " [19] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:298 [inlined]",
-      " [20] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:93 [inlined]",
-      " [21] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
-      " [22] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
-      " [23] top-level scope at In[183]:1",
-      " [24] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
-     ]
-    }
-   ],
-   "source": [
-    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Julia 1.5.0",
-   "language": "julia",
-   "name": "julia-1.5"
-  },
-  "language_info": {
-   "file_extension": ".jl",
-   "mimetype": "application/julia",
-   "name": "julia",
-   "version": "1.5.0"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": false,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": true,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb b/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb
deleted file mode 100644
index e39e6811d..000000000
--- a/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb
+++ /dev/null
@@ -1,438 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "toc": true
-   },
-   "source": [
-    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
-    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:39.147000+04:00",
-     "start_time": "2020-08-11T13:13:16.198Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "using Flux, Statistics\n",
-    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
-    "using Base.Iterators: repeated, partition\n",
-    "using Metalhead:trainimgs, CIFAR10\n",
-    "using Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:39.377000+04:00",
-     "start_time": "2020-08-11T13:13:16.405Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "using Flatten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:39.861000+04:00",
-     "start_time": "2020-08-11T13:13:16.558Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "getarray (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:45.622000+04:00",
-     "start_time": "2020-08-11T13:13:16.700Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "X = trainimgs(CIFAR10)\n",
-    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
-    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
-    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:46.662000+04:00",
-     "start_time": "2020-08-11T13:13:16.900Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
-       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
-       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "valset = collect(49001:50000)\n",
-    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
-    "valY = labels[:, valset] |> gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:46.743000+04:00",
-     "start_time": "2020-08-11T13:13:17.068Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "vgg19 (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vgg19() =  Chain(\n",
-    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  MaxPool((2,2)),\n",
-    "  x -> reshape(x, :, size(x, 4)),\n",
-    "  Dense(512, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 10),\n",
-    "  softmax) |> gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:14:46.553000+04:00",
-     "start_time": "2020-08-11T13:14:45.389Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #7, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = vgg19()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:14:47.788000+04:00",
-     "start_time": "2020-08-11T13:14:47.786Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "loss (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loss(x, y) = crossentropy(m(x), y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:14:47.867000+04:00",
-     "start_time": "2020-08-11T13:14:47.866Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "accuracy (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:14:47.990000+04:00",
-     "start_time": "2020-08-11T13:14:47.906Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:48.860000+04:00",
-     "start_time": "2020-08-11T13:13:19.436Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "opt = ADAM()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T17:16:10.370000+04:00",
-     "start_time": "2020-08-11T13:14:58.340Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accuracy(valX, valY) = 0.119\n"
-     ]
-    },
-    {
-     "ename": "LoadError",
-     "evalue": "InterruptException:",
-     "output_type": "error",
-     "traceback": [
-      "InterruptException:",
-      "",
-      "Stacktrace:",
-      " [1] BatchNorm at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/normalise.jl:199 [inlined]",
-      " [2] _pullback(::Zygote.Context, ::BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [3] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
-      " [4] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [5] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
-      " [6] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [7] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
-      " [8] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [9] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
-      " [10] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [11] Chain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38 [inlined]",
-      " [12] _pullback(::Zygote.Context, ::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [13] loss at ./In[17]:1 [inlined]",
-      " [14] _pullback(::Zygote.Context, ::typeof(loss), ::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [15] adjoint at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/lib/lib.jl:179 [inlined]",
-      " [16] _pullback at /home/subhaditya/.julia/packages/ZygoteRules/6nssF/src/adjoint.jl:47 [inlined]",
-      " [17] #17 at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:89 [inlined]",
-      " [18] _pullback(::Zygote.Context, ::Flux.Optimise.var\"#17#25\"{typeof(loss),Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
-      " [19] pullback(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:172",
-      " [20] gradient(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:53",
-      " [21] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:88 [inlined]",
-      " [22] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
-      " [23] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
-      " [24] top-level scope at In[21]:1",
-      " [25] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
-     ]
-    }
-   ],
-   "source": [
-    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Julia 1.5.0",
-   "language": "julia",
-   "name": "julia-1.5"
-  },
-  "language_info": {
-   "file_extension": ".jl",
-   "mimetype": "application/julia",
-   "name": "julia",
-   "version": "1.5.0"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": false,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": true,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From c87e9eda4d4a403ef40876f7d4860b660d047551 Mon Sep 17 00:00:00 2001
From: SubhadityMukherjee <msubhaditya@gmail.com>
Date: Tue, 11 Aug 2020 23:09:51 +0400
Subject: [PATCH 4/7] refactor VGG and make a template for future use

---
 .../.ipynb_checkpoints/VGG16-checkpoint.ipynb | 552 ++++++++++++++++++
 .../.ipynb_checkpoints/VGG19-checkpoint.ipynb | 429 ++++++++++++++
 vision/VGG/Project.toml                       |   1 -
 vision/VGG/VGG16.ipynb                        | 353 ++++++-----
 vision/VGG/VGG16.jl                           | 119 ++--
 vision/VGG/VGG19.ipynb                        | 254 ++++----
 vision/VGG/VGG19.jl                           | 127 ++--
 7 files changed, 1457 insertions(+), 378 deletions(-)
 create mode 100644 vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
 create mode 100644 vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb

diff --git a/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb b/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
new file mode 100644
index 000000000..293b9daca
--- /dev/null
+++ b/vision/VGG/.ipynb_checkpoints/VGG16-checkpoint.ipynb
@@ -0,0 +1,552 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T22:10:02.179000+04:00",
+     "start_time": "2020-08-11T18:09:21.430Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "┌ Info: Precompiling Flux [587475ba-b771-5e3f-ad9e-33799f191a9c]\n",
+      "└ @ Base loading.jl:1278\n",
+      "┌ Warning: Package Flux does not have Zygote in its dependencies:\n",
+      "│ - If you have Flux checked out for development and have\n",
+      "│   added Zygote as a dependency but haven't updated your primary\n",
+      "│   environment's manifest file, try `Pkg.resolve()`.\n",
+      "│ - Otherwise you may need to report an issue with Flux\n",
+      "└ Loading Zygote into Flux from project dependency, future warnings for Flux are suppressed.\n",
+      "┌ Warning: Package MacroTools does not have Markdown in its dependencies:\n",
+      "│ - If you have MacroTools checked out for development and have\n",
+      "│   added Markdown as a dependency but haven't updated your primary\n",
+      "│   environment's manifest file, try `Pkg.resolve()`.\n",
+      "│ - Otherwise you may need to report an issue with MacroTools\n",
+      "└ Loading Markdown into MacroTools from project dependency, future warnings for MacroTools are suppressed.\n",
+      "┌ Warning: The call to compilecache failed to create a usable precompiled cache file for ZygoteRules [700de1a5-db45-46bc-99cf-38207098b444]\n",
+      "│   exception = Required dependency MacroTools [1914dd2f-81c6-5fcd-8719-6d5c9610ff09] failed to load from a cache file.\n",
+      "└ @ Base loading.jl:1042\n",
+      "┌ Warning: Package MacroTools does not have Markdown in its dependencies:\n",
+      "│ - If you have MacroTools checked out for development and have\n",
+      "│   added Markdown as a dependency but haven't updated your primary\n",
+      "│   environment's manifest file, try `Pkg.resolve()`.\n",
+      "│ - Otherwise you may need to report an issue with MacroTools\n",
+      "└ Loading Markdown into MacroTools from project dependency, future warnings for MacroTools are suppressed.\n",
+      "┌ Warning: Package Requires does not have UUIDs in its dependencies:\n",
+      "│ - If you have Requires checked out for development and have\n",
+      "│   added UUIDs as a dependency but haven't updated your primary\n",
+      "│   environment's manifest file, try `Pkg.resolve()`.\n",
+      "│ - Otherwise you may need to report an issue with Requires\n",
+      "└ Loading UUIDs into Requires from project dependency, future warnings for Requires are suppressed.\n",
+      "┌ Warning: The call to compilecache failed to create a usable precompiled cache file for ChainRules [082447d4-558c-5d27-93f4-14fc19e9eca2]\n",
+      "│   exception = Required dependency Requires [ae029012-a4dd-5104-9daa-d747884805df] failed to load from a cache file.\n",
+      "└ @ Base loading.jl:1042\n",
+      "┌ Warning: Package Requires does not have UUIDs in its dependencies:\n",
+      "│ - If you have Requires checked out for development and have\n",
+      "│   added UUIDs as a dependency but haven't updated your primary\n",
+      "│   environment's manifest file, try `Pkg.resolve()`.\n",
+      "│ - Otherwise you may need to report an issue with Requires\n",
+      "└ Loading UUIDs into Requires from project dependency, future warnings for Requires are suppressed.\n",
+      "ERROR: LoadError: ArgumentError: Package SpecialFunctions does not have OpenSpecFun_jll in its dependencies:\n",
+      "- If you have SpecialFunctions checked out for development and have\n",
+      "  added OpenSpecFun_jll as a dependency but haven't updated your primary\n",
+      "  environment's manifest file, try `Pkg.resolve()`.\n",
+      "- Otherwise you may need to report an issue with SpecialFunctions\n",
+      "Stacktrace:\n",
+      " [1] require(::Module, ::Symbol) at ./loading.jl:906\n",
+      " [2] include(::Function, ::Module, ::String) at ./Base.jl:380\n",
+      " [3] include(::Module, ::String) at ./Base.jl:368\n",
+      " [4] top-level scope at none:2\n",
+      " [5] eval at ./boot.jl:331 [inlined]\n",
+      " [6] eval(::Expr) at ./client.jl:467\n",
+      " [7] top-level scope at ./none:3\n",
+      "in expression starting at /home/subhaditya/.julia/packages/SpecialFunctions/LC8dm/src/SpecialFunctions.jl:3\n",
+      "ERROR: LoadError: LoadError: LoadError: Failed to precompile SpecialFunctions [276daf66-3868-5448-9aa4-cd146d93841b] to /home/subhaditya/.julia/compiled/v1.5/SpecialFunctions/78gOt_L66GW.ji.\n",
+      "Stacktrace:\n",
+      " [1] error(::String) at ./error.jl:33\n",
+      " [2] compilecache(::Base.PkgId, ::String) at ./loading.jl:1290\n",
+      " [3] _require(::Base.PkgId) at ./loading.jl:1030\n",
+      " [4] require(::Base.PkgId) at ./loading.jl:928\n",
+      " [5] require(::Module, ::Symbol) at ./loading.jl:923\n",
+      " [6] include(::Function, ::Module, ::String) at ./Base.jl:380\n",
+      " [7] include at ./Base.jl:368 [inlined]\n",
+      " [8] include(::String) at /home/subhaditya/.julia/packages/Zygote/seGHk/src/forward/Forward.jl:1\n",
+      " [9] top-level scope at /home/subhaditya/.julia/packages/Zygote/seGHk/src/forward/Forward.jl:11\n",
+      " [10] include(::Function, ::Module, ::String) at ./Base.jl:380\n",
+      " [11] include at ./Base.jl:368 [inlined]\n",
+      " [12] include(::String) at /home/subhaditya/.julia/packages/Zygote/seGHk/src/Zygote.jl:1\n",
+      " [13] top-level scope at /home/subhaditya/.julia/packages/Zygote/seGHk/src/Zygote.jl:22\n",
+      " [14] include(::Function, ::Module, ::String) at ./Base.jl:380\n",
+      " [15] include(::Module, ::String) at ./Base.jl:368\n",
+      " [16] top-level scope at none:2\n",
+      " [17] eval at ./boot.jl:331 [inlined]\n",
+      " [18] eval(::Expr) at ./client.jl:467\n",
+      " [19] top-level scope at ./none:3\n",
+      "in expression starting at /home/subhaditya/.julia/packages/Zygote/seGHk/src/forward/number.jl:1\n",
+      "in expression starting at /home/subhaditya/.julia/packages/Zygote/seGHk/src/forward/Forward.jl:11\n",
+      "in expression starting at /home/subhaditya/.julia/packages/Zygote/seGHk/src/Zygote.jl:22\n",
+      "ERROR: LoadError: Failed to precompile Zygote [e88e6eb3-aa80-5325-afca-941959d7151f] to /home/subhaditya/.julia/compiled/v1.5/Zygote/4kbLI_L66GW.ji.\n",
+      "Stacktrace:\n",
+      " [1] error(::String) at ./error.jl:33\n",
+      " [2] compilecache(::Base.PkgId, ::String) at ./loading.jl:1290\n",
+      " [3] _require(::Base.PkgId) at ./loading.jl:1030\n",
+      " [4] require(::Base.PkgId) at ./loading.jl:928\n",
+      " [5] require(::Module, ::Symbol) at ./loading.jl:923\n",
+      " [6] include(::Function, ::Module, ::String) at ./Base.jl:380\n",
+      " [7] include(::Module, ::String) at ./Base.jl:368\n",
+      " [8] top-level scope at none:2\n",
+      " [9] eval at ./boot.jl:331 [inlined]\n",
+      " [10] eval(::Expr) at ./client.jl:467\n",
+      " [11] top-level scope at ./none:3\n",
+      "in expression starting at /home/subhaditya/.julia/packages/Flux/05b38/src/Flux.jl:7\n"
+     ]
+    },
+    {
+     "ename": "LoadError",
+     "evalue": "Failed to precompile Flux [587475ba-b771-5e3f-ad9e-33799f191a9c] to /home/subhaditya/.julia/compiled/v1.5/Flux/QdkVy_L66GW.ji.",
+     "output_type": "error",
+     "traceback": [
+      "Failed to precompile Flux [587475ba-b771-5e3f-ad9e-33799f191a9c] to /home/subhaditya/.julia/compiled/v1.5/Flux/QdkVy_L66GW.ji.",
+      "",
+      "Stacktrace:",
+      " [1] error(::String) at ./error.jl:33",
+      " [2] compilecache(::Base.PkgId, ::String) at ./loading.jl:1290",
+      " [3] _require(::Base.PkgId) at ./loading.jl:1030",
+      " [4] require(::Base.PkgId) at ./loading.jl:928",
+      " [5] require(::Module, ::Symbol) at ./loading.jl:923",
+      " [6] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "using Flux, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated, partition\n",
+    "using Metalhead:trainimgs, CIFAR10\n",
+    "using Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T21:52:29.839000+04:00",
+     "start_time": "2020-08-11T17:52:25.688Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "getarray (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T21:47:47.459000+04:00",
+     "start_time": "2020-08-11T17:47:47.448Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "LoadError",
+     "evalue": "UndefVarError: trainimgs not defined",
+     "output_type": "error",
+     "traceback": [
+      "UndefVarError: trainimgs not defined",
+      "",
+      "Stacktrace:",
+      " [1] top-level scope at In[7]:1",
+      " [2] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "X = trainimgs(CIFAR10)\n",
+    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
+    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
+    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T16:29:25.375000+04:00",
+     "start_time": "2020-08-11T12:28:59.367Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
+       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
+       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
+       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valset = collect(49001:50000)\n",
+    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
+    "valY = labels[:, valset] |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " chain(A::Tuple...) = Chain(mapreduce(collect, vcat, A)...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conv_block(in_channels, out_channels) = (\n",
+    "    Conv((3,3), in_channels => out_channels, relu, pad = (1,1), stride = (1,1)), \n",
+    "    BatchNorm(out_channels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "double_conv(in_channels, out_channels) = (\n",
+    "    conv_block(in_channels, out_channels),\n",
+    "    conv_block(out_channels, out_channels),\n",
+    "    MaxPool((2,2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:05.168000+04:00",
+     "start_time": "2020-08-11T13:05:05.159Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vgg16 (generic function with 2 methods)"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vgg16(initial_channels, num_classes) = chain(\n",
+    "    double_conv(initial_channels, 64),\n",
+    "    double_conv(64,128),\n",
+    "    conv_block(128, 256),\n",
+    "    double_conv(256, 256),  \n",
+    "    conv_block(256, 512),\n",
+    "    double_conv(512, 512),\n",
+    "    conv_block(512, 512),\n",
+    "    double_conv(512, 512),\n",
+    "    x -> reshape(x, :, size(x, 4)),\n",
+    "    Dense(512, 4096, relu),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(4096, 4096, relu),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(4096, num_classes), \n",
+    "    softmax\n",
+    "    ) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:08.454000+04:00",
+     "start_time": "2020-08-11T13:05:08.040Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #36, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = vgg16()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:11.842000+04:00",
+     "start_time": "2020-08-11T13:05:11.839Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "loss (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss(x, y) = crossentropy(m(x), y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:12.481000+04:00",
+     "start_time": "2020-08-11T13:05:12.447Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "accuracy (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:13.807000+04:00",
+     "start_time": "2020-08-11T13:05:13.721Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:05:14.409000+04:00",
+     "start_time": "2020-08-11T13:05:14.407Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt = ADAM()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:07:16.455000+04:00",
+     "start_time": "2020-08-11T13:05:14.799Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy(valX, valY) = 0.112\n",
+      "accuracy(valX, valY) = 0.087\n",
+      "accuracy(valX, valY) = 0.087\n"
+     ]
+    },
+    {
+     "ename": "LoadError",
+     "evalue": "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
+     "output_type": "error",
+     "traceback": [
+      "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
+      "",
+      "Stacktrace:",
+      " [1] wait at ./task.jl:267 [inlined]",
+      " [2] threading_run(::Function) at ./threadingconstructs.jl:34",
+      " [3] macro expansion at ./threadingconstructs.jl:93 [inlined]",
+      " [4] conv_im2col!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}; col::Array{Float32,3}, alpha::Float32, beta::Float32) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:49",
+      " [5] conv_im2col! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:30 [inlined]",
+      " [6] #conv!#41 at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53 [inlined]",
+      " [7] conv!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53",
+      " [8] conv!(::Array{Float32,4}, ::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70",
+      " [9] conv! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70 [inlined]",
+      " [10] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:116",
+      " [11] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:114",
+      " [12] (::Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/conv.jl:61",
+      " [13] applychain(::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 (repeats 13 times)",
+      " [14] (::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38",
+      " [15] accuracy(::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at ./In[180]:1",
+      " [16] macro expansion at ./show.jl:641 [inlined]",
+      " [17] (::var\"#39#40\")() at ./In[181]:1",
+      " [18] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:302 [inlined]",
+      " [19] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:298 [inlined]",
+      " [20] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:93 [inlined]",
+      " [21] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
+      " [22] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
+      " [23] top-level scope at In[183]:1",
+      " [24] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.5.0",
+   "language": "julia",
+   "name": "julia-1.5"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.5.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb b/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb
new file mode 100644
index 000000000..aac84acee
--- /dev/null
+++ b/vision/VGG/.ipynb_checkpoints/VGG19-checkpoint.ipynb
@@ -0,0 +1,429 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:23.051Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flux, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated, partition\n",
+    "using Metalhead:trainimgs, CIFAR10\n",
+    "using Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:23.691Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "using Flatten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:23.874Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:24.076Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "X = trainimgs(CIFAR10)\n",
+    "imgs = [getarray(X[i].img) for i in 1:50000];\n",
+    "labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);\n",
+    "train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:24.610Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "valset = collect(49001:50000)\n",
+    "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
+    "valY = labels[:, valset] |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:24.858Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    " chain(A::Tuple...) = Chain(mapreduce(collect, vcat, A)...)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:25.027Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "conv_block(in_channels, out_channels) = (\n",
+    "    Conv((3,3), in_channels => out_channels, relu, pad = (1,1), stride = (1,1)), \n",
+    "    BatchNorm(out_channels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "double_conv(in_channels, out_channels) = (\n",
+    "    conv_block(in_channels, out_channels),\n",
+    "    conv_block(out_channels, out_channels),\n",
+    "    MaxPool((2,2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-08-11T15:19:25.499Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vgg19() =  Chain(\n",
+    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(64),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(128),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(256),\n",
+    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  BatchNorm(512),\n",
+    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
+    "  MaxPool((2,2)),\n",
+    "  x -> reshape(x, :, size(x, 4)),\n",
+    "  Dense(512, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 4096, relu),\n",
+    "  Dropout(0.5),\n",
+    "  Dense(4096, 10),\n",
+    "  softmax) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:46.553000+04:00",
+     "start_time": "2020-08-11T13:14:45.389Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #7, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = vgg19()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.788000+04:00",
+     "start_time": "2020-08-11T13:14:47.786Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "loss (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss(x, y) = crossentropy(m(x), y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.867000+04:00",
+     "start_time": "2020-08-11T13:14:47.866Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "accuracy (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:14:47.990000+04:00",
+     "start_time": "2020-08-11T13:14:47.906Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) (generic function with 1 method)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:13:48.860000+04:00",
+     "start_time": "2020-08-11T13:13:19.436Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt = ADAM()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-08-11T17:16:10.370000+04:00",
+     "start_time": "2020-08-11T13:14:58.340Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy(valX, valY) = 0.119\n"
+     ]
+    },
+    {
+     "ename": "LoadError",
+     "evalue": "InterruptException:",
+     "output_type": "error",
+     "traceback": [
+      "InterruptException:",
+      "",
+      "Stacktrace:",
+      " [1] BatchNorm at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/normalise.jl:199 [inlined]",
+      " [2] _pullback(::Zygote.Context, ::BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [3] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [4] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [5] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [6] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [7] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [8] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [9] applychain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 [inlined]",
+      " [10] _pullback(::Zygote.Context, ::typeof(Flux.applychain), ::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [11] Chain at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38 [inlined]",
+      " [12] _pullback(::Zygote.Context, ::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},MaxPool{2,4},var\"#7#8\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [13] loss at ./In[17]:1 [inlined]",
+      " [14] _pullback(::Zygote.Context, ::typeof(loss), ::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [15] adjoint at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/lib/lib.jl:179 [inlined]",
+      " [16] _pullback at /home/subhaditya/.julia/packages/ZygoteRules/6nssF/src/adjoint.jl:47 [inlined]",
+      " [17] #17 at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:89 [inlined]",
+      " [18] _pullback(::Zygote.Context, ::Flux.Optimise.var\"#17#25\"{typeof(loss),Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}}}) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface2.jl:0",
+      " [19] pullback(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:172",
+      " [20] gradient(::Function, ::Zygote.Params) at /home/subhaditya/.julia/packages/Zygote/1GXzF/src/compiler/interface.jl:53",
+      " [21] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:88 [inlined]",
+      " [22] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
+      " [23] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#11#12\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
+      " [24] top-level scope at In[21]:1",
+      " [25] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
+     ]
+    }
+   ],
+   "source": [
+    "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Julia 1.5.0",
+   "language": "julia",
+   "name": "julia-1.5"
+  },
+  "language_info": {
+   "file_extension": ".jl",
+   "mimetype": "application/julia",
+   "name": "julia",
+   "version": "1.5.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": false,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vision/VGG/Project.toml b/vision/VGG/Project.toml
index 9c279df9e..403c36342 100644
--- a/vision/VGG/Project.toml
+++ b/vision/VGG/Project.toml
@@ -1,4 +1,3 @@
 [deps]
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
 Metalhead = "dbeba491-748d-5e0e-a39e-b530a07fa0cc"
diff --git a/vision/VGG/VGG16.ipynb b/vision/VGG/VGG16.ipynb
index 65f8ab4ee..e9d56b1f9 100644
--- a/vision/VGG/VGG16.ipynb
+++ b/vision/VGG/VGG16.ipynb
@@ -12,78 +12,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T16:29:16.357000+04:00",
-     "start_time": "2020-08-11T12:28:53.552Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "using Flux, Statistics\n",
-    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
-    "using Base.Iterators: repeated, partition\n",
-    "using Metalhead:trainimgs, CIFAR10\n",
-    "using Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-08-11T16:55:19.835000+04:00",
-     "start_time": "2020-08-11T12:55:18.394Z"
-    }
+     "end_time": "2020-08-11T22:10:02.179000+04:00",
+     "start_time": "2020-08-11T18:09:21.430Z"
+    },
+    "scrolled": true,
+    "tags": []
    },
    "outputs": [
     {
-     "name": "stderr",
      "output_type": "stream",
-     "text": [
-      "┌ Info: Precompiling Flatten [4c728ea3-d9ee-5c9a-9642-b6f7d7dc04fa]\n",
-      "└ @ Base loading.jl:1278\n",
-      "WARNING: using Flatten.flatten in module Main conflicts with an existing identifier.\n"
-     ]
+     "name": "stderr",
+     "text": "┌ Info: Precompiling Metalhead [dbeba491-748d-5e0e-a39e-b530a07fa0cc]\n└ @ Base loading.jl:1278\n"
     }
    ],
    "source": [
-    "using Flatten"
+    "using Flux, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated, partition\n",
+    "using Metalhead:trainimgs, CIFAR10\n",
+    "using Images"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T16:55:11.078000+04:00",
-     "start_time": "2020-08-11T12:55:11.076Z"
+     "end_time": "2020-08-11T21:52:29.839000+04:00",
+     "start_time": "2020-08-11T17:52:25.688Z"
     }
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "getarray (generic function with 1 method)"
-      ]
+      "text/plain": "getarray (generic function with 1 method)"
      },
-     "execution_count": 122,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
    "source": [
     "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CIFAR 10 dataset"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T17:07:39.471000+04:00",
-     "start_time": "2020-08-11T13:07:35.047Z"
+     "end_time": "2020-08-11T21:47:47.459000+04:00",
+     "start_time": "2020-08-11T17:47:47.448Z"
     },
     "scrolled": true
    },
@@ -97,33 +86,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T16:29:25.375000+04:00",
      "start_time": "2020-08-11T12:28:59.367Z"
-    }
+    },
+    "tags": []
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
-       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
-       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
-      ]
+      "text/plain": "10×1000 Flux.OneHotMatrix{CUDA.CuArray{Flux.OneHotVector,1}}:\n 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Warning: Performing scalar operations on GPU arrays: This is very slow, consider disallowing these operations with `allowscalar(false)`\n└ @ GPUArrays /home/subhaditya/.julia/packages/GPUArrays/PkHCM/src/host/indexing.jl:43\n"
     }
    ],
    "source": [
@@ -132,9 +115,90 @@
     "valY = labels[:, valset] |> gpu"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A block of Conv Relu Batchnorm based on input and output channels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "conv_block (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "conv_block(in_channels, out_channels) = (\n",
+    "    Conv((3,3), in_channels => out_channels, relu, pad = (1,1), stride = (1,1)), \n",
+    "    BatchNorm(out_channels))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two of the conv blocks which is common in VGG + Maxpool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "double_conv (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "double_conv(in_channels, out_channels) = (\n",
+    "    conv_block(in_channels, out_channels)...,\n",
+    "    conv_block(out_channels, out_channels)...,\n",
+    "    MaxPool((2,2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VGG Arch\n",
+    "- ... operator will help us unroll the previously defined blocks"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "3-element Array{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32}},1}:\n (Conv((3, 3), 256=>256, relu), BatchNorm(256))\n (Conv((3, 3), 256=>256, relu), BatchNorm(256))\n (Conv((3, 3), 256=>256, relu), BatchNorm(256))"
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ],
+   "source": [
+    "collect(Iterators.repeated(conv_block(256, 256),3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:05:05.168000+04:00",
@@ -143,61 +207,37 @@
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "vgg16 (generic function with 2 methods)"
-      ]
+      "text/plain": "vgg16 (generic function with 1 method)"
      },
-     "execution_count": 173,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 68
     }
    ],
    "source": [
-    "vgg16() = Chain(\n",
-    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  MaxPool((2,2)),\n",
-    "  x -> reshape(x, :, size(x, 4)),\n",
-    "  Dense(512, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 10),\n",
-    "  softmax) |> gpu"
+    "vgg16(initial_channels, num_classes) = Chain(\n",
+    "    double_conv(initial_channels, 64)...,\n",
+    "    double_conv(64,128)...,\n",
+    "    conv_block(128, 256)...,\n",
+    "    double_conv(256, 256)...,  \n",
+    "    conv_block(256, 512)...,\n",
+    "    double_conv(512, 512)...,\n",
+    "    conv_block(512, 512)...,\n",
+    "    double_conv(512, 512)...,\n",
+    "    x -> reshape(x, :, size(x, 4)),\n",
+    "    Dense(512, 4096, relu),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(4096, 4096, relu),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(4096, num_classes), \n",
+    "    softmax\n",
+    "    ) |> gpu"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 69,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:05:08.454000+04:00",
@@ -206,23 +246,21 @@
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #36, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
-      ]
+      "text/plain": "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #41, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
      },
-     "execution_count": 174,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 69
     }
    ],
    "source": [
-    "m = vgg16()"
+    "m = vgg16(3, 10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 70,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:05:11.842000+04:00",
@@ -231,14 +269,12 @@
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "loss (generic function with 1 method)"
-      ]
+      "text/plain": "loss (generic function with 1 method)"
      },
-     "execution_count": 178,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 70
     }
    ],
    "source": [
@@ -247,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 71,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:05:12.481000+04:00",
@@ -256,14 +292,12 @@
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "accuracy (generic function with 1 method)"
-      ]
+      "text/plain": "accuracy (generic function with 1 method)"
      },
-     "execution_count": 180,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 71
     }
    ],
    "source": [
@@ -272,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 72,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:05:13.807000+04:00",
@@ -281,14 +315,12 @@
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "(::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) (generic function with 1 method)"
-      ]
+      "text/plain": "(::Flux.var\"#throttled#42\"{Flux.var\"#throttled#38#43\"{Bool,Bool,var\"#43#44\",Int64}}) (generic function with 1 method)"
      },
-     "execution_count": 181,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 72
     }
    ],
    "source": [
@@ -297,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 73,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:05:14.409000+04:00",
@@ -306,14 +338,12 @@
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
-      ]
+      "text/plain": "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
      },
-     "execution_count": 182,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 73
     }
    ],
    "source": [
@@ -322,61 +352,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 74,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:07:16.455000+04:00",
      "start_time": "2020-08-11T13:05:14.799Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accuracy(valX, valY) = 0.112\n",
-      "accuracy(valX, valY) = 0.087\n",
-      "accuracy(valX, valY) = 0.087\n"
-     ]
-    },
-    {
-     "ename": "LoadError",
-     "evalue": "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
-     "output_type": "error",
-     "traceback": [
-      "TaskFailedException:\nInterruptException:\nStacktrace:\n [1] ntuple(::NNlib.var\"#4#5\"{Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},Tuple{Int64,Int64,Int64},NTuple{6,Int64},Tuple{Int64,Int64,Int64}}, ::Int64) at ./ntuple.jl:17\n [2] output_size at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/dim_helpers/ConvDims.jl:120 [inlined]\n [3] im2col!(::SubArray{Float32,2,Array{Float32,3},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::SubArray{Float32,4,Array{Float32,5},Tuple{Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Base.Slice{Base.OneTo{Int64}},Int64},true}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:190\n [4] macro expansion at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:53 [inlined]\n [5] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})(::Bool) at ./threadingconstructs.jl:81\n [6] (::NNlib.var\"#349#threadsfor_fun#160\"{Array{Float32,3},Float32,Float32,Array{Float32,5},Array{Float32,5},Array{Float32,5},DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false},Int64,Int64,Int64,UnitRange{Int64}})() at ./threadingconstructs.jl:48",
-      "",
-      "Stacktrace:",
-      " [1] wait at ./task.jl:267 [inlined]",
-      " [2] threading_run(::Function) at ./threadingconstructs.jl:34",
-      " [3] macro expansion at ./threadingconstructs.jl:93 [inlined]",
-      " [4] conv_im2col!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}; col::Array{Float32,3}, alpha::Float32, beta::Float32) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:49",
-      " [5] conv_im2col! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/impl/conv_im2col.jl:30 [inlined]",
-      " [6] #conv!#41 at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53 [inlined]",
-      " [7] conv!(::Array{Float32,5}, ::Array{Float32,5}, ::Array{Float32,5}, ::DenseConvDims{3,(3, 3, 1),256,256,(1, 1, 1),(1, 1, 1, 1, 0, 0),(1, 1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:53",
-      " [8] conv!(::Array{Float32,4}, ::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70",
-      " [9] conv! at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:70 [inlined]",
-      " [10] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:116",
-      " [11] conv(::Array{Float32,4}, ::Array{Float32,4}, ::DenseConvDims{2,(3, 3),256,256,(1, 1),(1, 1, 1, 1),(1, 1),false}) at /home/subhaditya/.julia/packages/NNlib/FAI3o/src/conv.jl:114",
-      " [12] (::Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/conv.jl:61",
-      " [13] applychain(::Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}, ::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:36 (repeats 13 times)",
-      " [14] (::Chain{Tuple{Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},Conv{2,2,typeof(relu),Array{Float32,4},Array{Float32,1}},BatchNorm{typeof(identity),Array{Float32,1},Array{Float32,1},Float32},MaxPool{2,4},var\"#36#37\",Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(relu),Array{Float32,2},Array{Float32,1}},Dropout{Float64,Colon},Dense{typeof(identity),Array{Float32,2},Array{Float32,1}},typeof(softmax)}})(::Array{Float32,4}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/layers/basic.jl:38",
-      " [15] accuracy(::Array{Float32,4}, ::Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}) at ./In[180]:1",
-      " [16] macro expansion at ./show.jl:641 [inlined]",
-      " [17] (::var\"#39#40\")() at ./In[181]:1",
-      " [18] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:302 [inlined]",
-      " [19] throttled at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/utils.jl:298 [inlined]",
-      " [20] macro expansion at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:93 [inlined]",
-      " [21] macro expansion at /home/subhaditya/.julia/packages/Juno/hEPx8/src/progress.jl:134 [inlined]",
-      " [22] train!(::typeof(loss), ::Zygote.Params, ::Array{Tuple{Array{Float32,4},Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}},1}, ::ADAM; cb::Flux.var\"#throttled#20\"{Flux.var\"#throttled#16#21\"{Bool,Bool,var\"#39#40\",Int64}}) at /home/subhaditya/.julia/packages/Flux/Fj3bt/src/optimise/train.jl:81",
-      " [23] top-level scope at In[183]:1",
-      " [24] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Flux.train!(loss, params(m), train, opt, cb = evalcb)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -436,4 +429,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/vision/VGG/VGG16.jl b/vision/VGG/VGG16.jl
index 07a98449f..1c321f9f0 100644
--- a/vision/VGG/VGG16.jl
+++ b/vision/VGG/VGG16.jl
@@ -1,76 +1,105 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %% [markdown]
+# <h1>Table of Contents<span class="tocSkip"></span></h1>
+# <div class="toc"><ul class="toc-item"></ul></div>
+
+# %%
 using Flux, Statistics
 using Flux: onehotbatch, onecold, crossentropy, throttle
 using Base.Iterators: repeated, partition
 using Metalhead:trainimgs, CIFAR10
 using Images
 
-using Flatten
 
+# %%
 getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))
 
+# %% [markdown]
+# ## CIFAR 10 dataset
+
+# %%
 X = trainimgs(CIFAR10)
 imgs = [getarray(X[i].img) for i in 1:50000];
 labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);
 train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);
 
+
+# %%
 valset = collect(49001:50000)
 valX = cat(imgs[valset]..., dims = 4) |> gpu
 valY = labels[:, valset] |> gpu
 
-vgg16() = Chain(
-  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  MaxPool((2,2)),
-  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  MaxPool((2,2)),
-  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  MaxPool((2,2)),
-  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  MaxPool((2,2)),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  MaxPool((2,2)),
-  x -> reshape(x, :, size(x, 4)),
-  Dense(512, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 10),
-  softmax) |> gpu
-
-m = vgg16()
-
-conv_block(in_channels, out_channels) = return Chain(
+# %% [markdown]
+# ## A block of Conv Relu Batchnorm based on input and output channels
+
+# %%
+conv_block(in_channels, out_channels) = (
     Conv((3,3), in_channels => out_channels, relu, pad = (1,1), stride = (1,1)), 
     BatchNorm(out_channels))
 
+# %% [markdown]
+# ## Two of the conv blocks which is common in VGG + Maxpool
+
+# %%
+double_conv(in_channels, out_channels) = (
+    conv_block(in_channels, out_channels)...,
+    conv_block(out_channels, out_channels)...,
+    MaxPool((2,2)))
+
+# %% [markdown]
+# ## VGG Arch
+# - ... operator will help us unroll the previously defined blocks
+
+# %%
+collect(Iterators.repeated(conv_block(256, 256),3))
+
+
+# %%
+vgg16(initial_channels, num_classes) = Chain(
+    double_conv(initial_channels, 64)...,
+    double_conv(64,128)...,
+    conv_block(128, 256)...,
+    double_conv(256, 256)...,  
+    conv_block(256, 512)...,
+    double_conv(512, 512)...,
+    conv_block(512, 512)...,
+    double_conv(512, 512)...,
+    x -> reshape(x, :, size(x, 4)),
+    Dense(512, 4096, relu),
+    Dropout(0.5),
+    Dense(4096, 4096, relu),
+    Dropout(0.5),
+    Dense(4096, num_classes), 
+    softmax
+    ) |> gpu
+
+
+# %%
+m = vgg16(3, 10)
+
+
+# %%
 loss(x, y) = crossentropy(m(x), y)
 
-Chain(double_conv(2, 3))
 
+# %%
 accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
 
+
+# %%
 evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)
 
+
+# %%
 opt = ADAM()
 
+
+# %%
 Flux.train!(loss, params(m), train, opt, cb = evalcb)
+
+
+# %%
+
+
+
diff --git a/vision/VGG/VGG19.ipynb b/vision/VGG/VGG19.ipynb
index e39e6811d..e6cd24f28 100644
--- a/vision/VGG/VGG19.ipynb
+++ b/vision/VGG/VGG19.ipynb
@@ -12,14 +12,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:39.147000+04:00",
-     "start_time": "2020-08-11T13:13:16.198Z"
-    }
+     "start_time": "2020-08-11T15:19:23.051Z"
+    },
+    "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Info: Precompiling Flux [587475ba-b771-5e3f-ad9e-33799f191a9c]\n└ @ Base loading.jl:1278\n┌ Info: Precompiling Metalhead [dbeba491-748d-5e0e-a39e-b530a07fa0cc]\n└ @ Base loading.jl:1278\n"
+    }
+   ],
    "source": [
     "using Flux, Statistics\n",
     "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
@@ -30,11 +36,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:39.377000+04:00",
-     "start_time": "2020-08-11T13:13:16.405Z"
+     "start_time": "2020-08-11T15:19:23.691Z"
     }
    },
    "outputs": [],
@@ -44,36 +49,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:39.861000+04:00",
-     "start_time": "2020-08-11T13:13:16.558Z"
+     "start_time": "2020-08-11T15:19:23.874Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "getarray (generic function with 1 method)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CIFAR 10 dataset"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:45.622000+04:00",
-     "start_time": "2020-08-11T13:13:16.700Z"
+     "start_time": "2020-08-11T15:19:24.076Z"
     },
     "scrolled": true
    },
@@ -87,110 +86,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:46.662000+04:00",
-     "start_time": "2020-08-11T13:13:16.900Z"
+     "start_time": "2020-08-11T15:19:24.610Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10×1000 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}:\n",
-       " 0  0  0  0  1  0  1  0  0  0  0  0  0  …  0  0  0  0  1  0  1  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  1  0  0  0  0  1  1\n",
-       " 0  0  0  0  0  0  0  0  1  0  0  0  0     0  0  0  1  0  0  0  1  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  1  0  0  0  0  0  0  0  0  0  0     0  0  1  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  1  0  0  0  0  0  0  0  …  1  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  1  0  0  0\n",
-       " 0  0  0  0  0  0  0  0  0  0  1  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 1  0  0  0  0  0  0  1  0  0  0  1  0     0  0  0  0  0  0  0  0  0  0  0  0\n",
-       " 0  1  0  1  0  0  0  0  0  0  0  0  1     0  0  0  0  0  0  0  0  0  1  0  0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "valset = collect(49001:50000)\n",
     "valX = cat(imgs[valset]..., dims = 4) |> gpu\n",
     "valY = labels[:, valset] |> gpu"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A block of Conv Relu Batchnorm based on input and output channels"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-08-11T17:13:46.743000+04:00",
-     "start_time": "2020-08-11T13:13:17.068Z"
+     "start_time": "2020-08-11T15:19:25.027Z"
     }
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "vgg19 (generic function with 1 method)"
-      ]
+      "text/plain": "conv_block (generic function with 1 method)"
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
    "source": [
-    "vgg19() =  Chain(\n",
-    "  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(64),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(128),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(256),\n",
-    "  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  MaxPool((2,2)),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  BatchNorm(512),\n",
-    "  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),\n",
-    "  MaxPool((2,2)),\n",
-    "  x -> reshape(x, :, size(x, 4)),\n",
-    "  Dense(512, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 4096, relu),\n",
-    "  Dropout(0.5),\n",
-    "  Dense(4096, 10),\n",
-    "  softmax) |> gpu"
+    "conv_block(in_channels, out_channels) = (\n",
+    "    Conv((3,3), in_channels => out_channels, relu, pad = (1,1), stride = (1,1)), \n",
+    "    BatchNorm(out_channels))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two of the conv blocks which is common in VGG + Maxpool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "double_conv (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "double_conv(in_channels, out_channels) = (\n",
+    "    conv_block(in_channels, out_channels),\n",
+    "    conv_block(out_channels, out_channels),\n",
+    "    MaxPool((2,2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modified with 2 conv blocks, 1 conv and max pool"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "triple_conv (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "triple_conv(in_channels, out_channels) = (\n",
+    "    conv_block(in_channels, out_channels),\n",
+    "    conv_block(out_channels, out_channels),\n",
+    "    Conv((3,3), out_channels => out_channels, relu, pad = (1,1), stride = (1,1)),\n",
+    "    MaxPool((2,2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VGG\n",
+    "- ... operator will help us unroll the previously defined blocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "vgg19 (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "vgg19(initial_channels, num_classes) = Chain(\n",
+    "    double_conv(initial_channels, 64)...,\n",
+    "    double_conv(64, 128)...,\n",
+    "    conv_block(128,256),\n",
+    "    triple_conv(256,256)...,\n",
+    "    conv_block(256,512),\n",
+    "    triple_conv(512,512)...,\n",
+    "    conv_block(512,512),\n",
+    "    triple_conv(512,512)...,\n",
+    "    x -> reshape(x, :, size(x, 4)),\n",
+    "    Dense(512, 4096, relu),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(4096, 4096, relu),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(4096, num_classes),\n",
+    "    softmax) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-11T17:14:46.553000+04:00",
@@ -199,18 +239,16 @@
    },
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "Chain(Conv((3, 3), 3=>64, relu), BatchNorm(64), Conv((3, 3), 64=>64, relu), BatchNorm(64), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 64=>128, relu), BatchNorm(128), Conv((3, 3), 128=>128, relu), BatchNorm(128), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 128=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), BatchNorm(256), Conv((3, 3), 256=>256, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 256=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), BatchNorm(512), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #7, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
-      ]
+      "text/plain": "Chain((Conv((3, 3), 3=>64, relu), BatchNorm(64)), (Conv((3, 3), 64=>64, relu), BatchNorm(64)), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), (Conv((3, 3), 64=>128, relu), BatchNorm(128)), (Conv((3, 3), 128=>128, relu), BatchNorm(128)), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), (Conv((3, 3), 128=>256, relu), BatchNorm(256)), (Conv((3, 3), 256=>256, relu), BatchNorm(256)), (Conv((3, 3), 256=>256, relu), BatchNorm(256)), Conv((3, 3), 256=>256, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), (Conv((3, 3), 256=>512, relu), BatchNorm(512)), (Conv((3, 3), 512=>512, relu), BatchNorm(512)), (Conv((3, 3), 512=>512, relu), BatchNorm(512)), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), (Conv((3, 3), 512=>512, relu), BatchNorm(512)), (Conv((3, 3), 512=>512, relu), BatchNorm(512)), (Conv((3, 3), 512=>512, relu), BatchNorm(512)), Conv((3, 3), 512=>512, relu), MaxPool((2, 2), pad = (0, 0, 0, 0), stride = (2, 2)), #3, Dense(512, 4096, relu), Dropout(0.5), Dense(4096, 4096, relu), Dropout(0.5), Dense(4096, 10), softmax)"
      },
-     "execution_count": 15,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 10
     }
    ],
    "source": [
-    "m = vgg19()"
+    "m = vgg19(3, 10)"
    ]
   },
   {
@@ -435,4 +473,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/vision/VGG/VGG19.jl b/vision/VGG/VGG19.jl
index 34d004502..234f911a5 100644
--- a/vision/VGG/VGG19.jl
+++ b/vision/VGG/VGG19.jl
@@ -1,75 +1,114 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %% [markdown]
+# <h1>Table of Contents<span class="tocSkip"></span></h1>
+# <div class="toc"><ul class="toc-item"></ul></div>
+
+# %%
 using Flux, Statistics
 using Flux: onehotbatch, onecold, crossentropy, throttle
 using Base.Iterators: repeated, partition
 using Metalhead:trainimgs, CIFAR10
 using Images
 
+
+# %%
 using Flatten
 
+
+# %%
 getarray(X) = Float32.(permutedims(channelview(X), (2, 3, 1)))
 
+# %% [markdown]
+# ## CIFAR 10 dataset
+
+# %%
 X = trainimgs(CIFAR10)
 imgs = [getarray(X[i].img) for i in 1:50000];
 labels = onehotbatch([X[i].ground_truth.class for i in 1:50000],1:10);
 train = gpu.([(cat(imgs[i]..., dims = 4), labels[:,i]) for i in partition(1:49000, 100)]);
 
+
+# %%
 valset = collect(49001:50000)
 valX = cat(imgs[valset]..., dims = 4) |> gpu
 valY = labels[:, valset] |> gpu
 
-vgg19() =  Chain(
-  Conv((3, 3), 3 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  Conv((3, 3), 64 => 64, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(64),
-  MaxPool((2,2)),
-  Conv((3, 3), 64 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  Conv((3, 3), 128 => 128, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(128),
-  MaxPool((2,2)),
-  Conv((3, 3), 128 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(256),
-  Conv((3, 3), 256 => 256, relu, pad=(1, 1), stride=(1, 1)),
-  MaxPool((2,2)),
-  Conv((3, 3), 256 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  MaxPool((2,2)),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  BatchNorm(512),
-  Conv((3, 3), 512 => 512, relu, pad=(1, 1), stride=(1, 1)),
-  MaxPool((2,2)),
-  x -> reshape(x, :, size(x, 4)),
-  Dense(512, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 4096, relu),
-  Dropout(0.5),
-  Dense(4096, 10),
-  softmax) |> gpu
-
-m = vgg19()
+# %% [markdown]
+# ## A block of Conv Relu Batchnorm based on input and output channels
+
+# %%
+conv_block(in_channels, out_channels) = (
+    Conv((3,3), in_channels => out_channels, relu, pad = (1,1), stride = (1,1)), 
+    BatchNorm(out_channels))
+
+# %% [markdown]
+# ## Two of the conv blocks which is common in VGG + Maxpool
+
+# %%
+double_conv(in_channels, out_channels) = (
+    conv_block(in_channels, out_channels),
+    conv_block(out_channels, out_channels),
+    MaxPool((2,2)))
+
+# %% [markdown]
+# ## Modified with 2 conv blocks, 1 conv and max pool
+
+# %%
+triple_conv(in_channels, out_channels) = (
+    conv_block(in_channels, out_channels),
+    conv_block(out_channels, out_channels),
+    Conv((3,3), out_channels => out_channels, relu, pad = (1,1), stride = (1,1)),
+    MaxPool((2,2)))
+
+# %% [markdown]
+# ## VGG
+# - ... operator will help us unroll the previously defined blocks
 
+# %%
+vgg19(initial_channels, num_classes) = Chain(
+    double_conv(initial_channels, 64)...,
+    double_conv(64, 128)...,
+    conv_block(128,256),
+    triple_conv(256,256)...,
+    conv_block(256,512),
+    triple_conv(512,512)...,
+    conv_block(512,512),
+    triple_conv(512,512)...,
+    x -> reshape(x, :, size(x, 4)),
+    Dense(512, 4096, relu),
+    Dropout(0.5),
+    Dense(4096, 4096, relu),
+    Dropout(0.5),
+    Dense(4096, num_classes),
+    softmax) |> gpu
+
+
+# %%
+m = vgg19(3, 10)
+
+
+# %%
 loss(x, y) = crossentropy(m(x), y)
 
+
+# %%
 accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
 
+
+# %%
 evalcb = throttle(() -> @show(accuracy(valX, valY)), 10)
 
+
+# %%
 opt = ADAM()
 
+
+# %%
 Flux.train!(loss, params(m), train, opt, cb = evalcb)
 
 
+# %%
+
+
+

From f55483ad274b029ec72b2db96ea46ad7b48a3b94 Mon Sep 17 00:00:00 2001
From: SubhadityMukherjee <msubhaditya@gmail.com>
Date: Tue, 11 Aug 2020 23:33:50 +0400
Subject: [PATCH 5/7] refactored CPPN and optimized loops

---
 .../Manifest.toml                             |   0
 .../Project.toml                              |   0
 vision/compositionalPatternNetwork/README.md  |   5 +
 vision/compositionalPatternNetwork/cppn.ipynb | 285 ++++++++++++++++++
 .../cppn.jl                                   |  53 +++-
 vision/cppn/sample.png                        | Bin 36791 -> 0 bytes
 6 files changed, 329 insertions(+), 14 deletions(-)
 rename vision/{cppn => compositionalPatternNetwork}/Manifest.toml (100%)
 rename vision/{cppn => compositionalPatternNetwork}/Project.toml (100%)
 create mode 100644 vision/compositionalPatternNetwork/README.md
 create mode 100644 vision/compositionalPatternNetwork/cppn.ipynb
 rename vision/{cppn => compositionalPatternNetwork}/cppn.jl (60%)
 delete mode 100644 vision/cppn/sample.png

diff --git a/vision/cppn/Manifest.toml b/vision/compositionalPatternNetwork/Manifest.toml
similarity index 100%
rename from vision/cppn/Manifest.toml
rename to vision/compositionalPatternNetwork/Manifest.toml
diff --git a/vision/cppn/Project.toml b/vision/compositionalPatternNetwork/Project.toml
similarity index 100%
rename from vision/cppn/Project.toml
rename to vision/compositionalPatternNetwork/Project.toml
diff --git a/vision/compositionalPatternNetwork/README.md b/vision/compositionalPatternNetwork/README.md
new file mode 100644
index 000000000..00b08a6ba
--- /dev/null
+++ b/vision/compositionalPatternNetwork/README.md
@@ -0,0 +1,5 @@
+# Generating Abstract Patterns with Compositional Pattern Producing Network
+
+- [Link to post](https://blog.otoro.net/2016/03/25/generating-abstract-patterns-with-tensorflow/)
+
+
diff --git a/vision/compositionalPatternNetwork/cppn.ipynb b/vision/compositionalPatternNetwork/cppn.ipynb
new file mode 100644
index 000000000..a6c335c9d
--- /dev/null
+++ b/vision/compositionalPatternNetwork/cppn.ipynb
@@ -0,0 +1,285 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "julia-1.5",
+   "display_name": "Julia 1.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Images\n",
+    "using Flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "262144"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "z_dim = 2\n",
+    "x_dim = 512\n",
+    "y_dim = 512\n",
+    "N = 14\n",
+    "hidden = 9\n",
+    "batch_size = 1024\n",
+    "n = x_dim * y_dim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## cast 0:x-1 to -0.5:0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "cast (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "\n",
+    "cast(x) = [range(-0.5, stop=0.5, step=1/(x - 1))...]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "262144-element Array{Float64,1}:\n 0.7071067811865476\n 0.7057243672141199\n 0.7043446771602189\n 0.702967727063262\n 0.7015935330555991\n 0.7002221113638016\n 0.6988534783089481\n 0.6974876503069023\n 0.6961246438685855\n 0.6947644756002421\n 0.6934071622036989\n 0.6920527204766176\n 0.6907011673127395\n ⋮\n 0.6920527204766176\n 0.6934071622036989\n 0.6947644756002421\n 0.6961246438685855\n 0.6974876503069023\n 0.6988534783089481\n 0.7002221113638016\n 0.7015935330555991\n 0.702967727063262\n 0.7043446771602189\n 0.7057243672141199\n 0.7071067811865476"
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "xs, ys = cast(x_dim), cast(y_dim)\n",
+    "xs = repeat(xs, inner=(y_dim))\n",
+    "ys = repeat(ys, outer=(x_dim))\n",
+    "rs = sqrt.(xs.^2 + ys.^2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "unit (generic function with 4 methods)"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "unit(in=N, out=N, f=tanh) = Dense(in, out, f, initW=randn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "11-element Array{Any,1}:\n Dense(5, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 1, σ)"
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "source": [
+    "layers = Any[unit(3 + z_dim), [unit() for _ in 1:hidden]..., unit(N, 1, σ)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- In essence, CPPN is just a function, c = f(x, y), that defines the intensity of the image for every point in space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "getColorAt (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "model = Chain(layers...)\n",
+    "getColorAt(x) = Flux.data(model(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make batches from the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "batch (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "function batch(arr, s)\n",
+    "    l = size(arr, 2)\n",
+    "    batches = [arr[:, i:min(i+s-1, l)] for i=1:s:l]\n",
+    "    batches\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create image with intensities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "getImage (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "function getImage(z)\n",
+    "    z = repeat(reshape(z, 1, z_dim), outer=(n, 1))\n",
+    "    coords = hcat(xs, ys, rs, z)'\n",
+    "    coords = batch(coords, batch_size)\n",
+    "    pixels = [Gray.(hcat(getColorAt.(coords)...))...]\n",
+    "    reshape(pixels, y_dim, x_dim)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "saveImg (generic function with 2 methods)"
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "function saveImg(z, image_path=\"sample.png\")\n",
+    "    imgg = getImage(z)\n",
+    "    save(image_path, imgg)\n",
+    "    imgg\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Will generate at random everytime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
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\"></td><td 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\"></td><td 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\"></td><td 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xpAyERm+eW2wY0gEPz1r13Xf+K1vDCf2K6Le6vyrg59bPvgoKQ7f+4/P7eShqOSLhCAh+OADjwa5uetf/LuPvXfDQbwLAqJWPAQRIQxnMeCe/e1X/ZSURnhiyVx99eAmSkzy1ue/tZ1Xt63FRaAPPogebPHeyuqvfHYQxYoAL8wUqb4hJgl5duSWDP+dz31v+qfRnRevbs6vv/SZQ+KoXPv8X28HANVlZD30zqUOz52wLYiEEh94MLQKpD1u7DWPCMv+/O8+H6Y+oWjl6ujc0o3zvcWDANb/818WAMrrZdQ4VL5xowc058v7RWKIpE9cbfaJWEX9p190jeYhQSZ/9XefyqY/Ugsnb2TDuY7+9BL27+Zbf/A/u0fQqXeHupe+t7vaRrGBQCTsXsl98IpZ1nYvfemf//bCfnMMu1//w1dG+3Stm0LYNuhu3gNAZMKNtr/wx5tH6VMHF7pv5tv3PzxOA6jzbjbMAhEgKZbv2QGDlVKqLIy57mt/8eSa3z+VTXKB78pgC4AEGae3O//lixtH53g2CrK7nj77YBV7CNJZi3ayAKKQ7vmHRr+3PvQUtU8sLy81jQxuXjx3oesO3GJSYtIA5JxtoYBQIdj54h92jrZovXy7G6Y99d2d1fG9880eOA8ENxzi5MrVp9bywoqVVgreuapWPm0GDQwDIMHtFQCqR7p/9IXNowFAL51Ow2gvfa+zOj6YX8xHIiJZGti2rqXWFLd4EvKjJ0IUu0EAgDACUF/1Suf3/9v2DADQ9/7s6ztxvLN78wN1bWD4ehhmFHxQmtG/tLedeVJcX0gdPh0nnA4DSAQWkBBCAAC59ntf6s0CAH3iw+qVtCH9jlAJQV4+J8ELwBDBIM/7juLEkHjnXNXLMDUpATDWj0YCAPoUEIJ3BoBc/p2vDmcCgDYrP37tXRXLUKi4NZerf9CRsv7NYJZhDtNoGob3Ls+d86HodqgcvvjLcDrKBQCiOYj3eRYJhXc/99UUs4dme+LMzYyShEiAIIMLf/R/A4n4QCCAOU/BLI6YwArE7HwoMEzIB7F3mSs4cBDv01EC2f7i47cCAA1unG70R3qZIBJGl5762vlMJITiyp5ZRh6EHE4xQQJALBAONM0tk/OFxyB9/cPWu8GohfTpPxvcCgA02CzPv30jX9tqDdzu2S+f7UsIEumRA0BELhdAPDwzUbGriozr9OWRkgh58AEEkuzP3/3kw2Evy+TK/7iBGefNEgFBNZud99Rfb334tWvXvv1WShTmf+aD3WfeERAxnAcVEa48ToTJ3qHaFimIBBECEL7/naf+7Ue3kcq3Xva45dBEUPlmFl27OP//nlsfCJA89NmH9FvvXHdEROKKSEAyrgrXd6TVkKoDDQAQ9tz5L8xTnGXP7kFIbomA3eBqLwgv2Gx7QIZXP/2ZU+RWF4tUKviqj0hQXlQJKhKqvqOiCaqKQUEEF775k4vZ5ve83PqwoYnyi+fSQNGJeDBQcXT6n/z8PIQ1CREx+SBlZwvKprrSUcf3yOWVMUKVLggwevl0lF++Jre0AkAzd59+1xO3Gm+d88ncXb/wyXkKIr4fiOppawzVbd14VNfmCJObhVy5sdD/5s6trQDQjO+/mAmZxpUn15P5ex/9xAKJCIYbwsW8tZwJCPt1QEQkYdI0ti6f+u5X/K2tANDone0IKF578UZj7n2PfnSRIEDodqSadlxMnrw8HgMoijSY7jrKL5y8fPE4FECPXn/VgzB6KV2av+MTH1lWEBH4d7rFwqaurPd1T1b/IhBT8FMI5J0nu/4YVgDom69uCcRvyB33nnzw769oEgFkeKFfHKr2q/ygfBTVxroNpxzZe1MtFzMQdK8NBEHQfviBO95/0pQyN857VZrB+K52SjyN7YGYC6+dcr1jCC8Q9K7vBQ/w+x+5c3XJlh0r7sLbxblOZB8Aqv8oe4wIzATZp4QfYOju+tALqP3JD8y1LJXE9Z7fLc0AFZX1+iYuWkiK/iFgoi9uYtw6HAHQN7wTkHr4Y8uxrRo0w6VXgqLSDCb7iGgfgMIIUFJwUODhzcfTn+oBAohOfvr2xLAUP6fhMzcqJdRNj+XXDwAgAmQcD28BgA5+qgNAiB/9UEMrERAJUfj+M04REaFqF5O6cXACQDFlcflxPCvYf2QsEAAE9VP/eNEUTQgILHuPXyvDXCGh6Lqqm5HrFZY9yYCEcBytHwoAGgS+69fusQoipLRQcM//jVdVNjCRDE4CkLrbqujpOAYDR5XHNYFWf+1nY0UCYqu9x7U/6TIzMaNoGaC6f3e8jLq7SUoKDomax7MMaNDir/xCWzNE2Gom6j92jhQRM43vFIFJBdAYglTNhj+cDRQI5v/pZ5cVQ4S0YbB/5muwVDjZFIDD5qi68Cef/AEBQM/96j9b1QwRUlGsvLv8WFjmNIXiqUi0f3esunqqk8WhUvd9fvhj+pFfX9EEEago0cTpN/IHlvILm8KlD07lWftPSweVcBSYo3+k71pVDBGwTTQB7w5+7s6Fb19UVVY0mY5OZCeT9B9JwTQJR1YwVMEA24YhYLD+oVMLm19PGUUP01SuewAAUCkCZXg42iKPrmAQRATKJgZCsrO82PKvXMgVwxeuKFQ321fT7PvvEZMfa1tC0acKUjbRAJCbM0l446n12JBLM6GSg6rFdyxubIBjIxins4egmVFDkUCso5iLZyL0L//Ja82Fpux2i1h7pDIPznngRH+8HAmkbGxLVrPnXnZnv5sl6W0rieTOlzuzYIKEioJbKvhIcPsQqCiKWAQi6zfT737tre7AY297D6cWdoeT29B+ALcQfFwrALRtKwlA2L34f17u3NxKA0CSbt5YTGI10Vx7GP90MED84BRAK8kQtt586ez5m8OizZ4EyLq9BUPTU4z76iaTxuNlhzMMQmfD7QvPvXBpK3Nl3VmEIGE4mB+HXkxqfiJlo5kx8LhaeP0/nL2044q3HEr7FYK4zI/3XJkKBXUfDtGxRc2yxKee8tVDUzuA5Jmvi+HT57WagvKdm2NszTMeYaHqan1qOcTZyJc99XLYDli1uR+HgZkPafGyDwAJAG1GqR9vPNPeV2ir7C46TtSZNTgcILhontP9tHzjEPveB5te3rFImAWSiVjt77YDqIl+XsV9mRV/aKpJ6ji07xtaReyyA+cN3c6Gfiz5YLJTGOF0Qeuotc/Gw43lpjowD0XNYRbGr7xMT1ukLmUb8CwJx7IQbs7p/SII1DQjV718tk8HUteOSgQ/GgCwNeKnnhcA3KbUH66Dwi0LL6gJmQmAZkNhhvcHnlDNwkkP6mD80s3kf394BgAW5w48SzpBOHSqIj2tX0ac4SaTJ/6ZQwd3kAKwJUZZQJ2adly8I9DRNy7TJYdbcOD9IcdOokiXIvbFqhJAPfWPygDAh557fRbV+clkPlZroLSDGfdOxx4shxWf3S41DE8mwqiK25NvpB6yY84k5HAEdWgf31mIuM0dU5wkZNodagB1ejR7+mOEZ9WORlmoijT19wLRMK3ekitxTSAoj8Lh7yJh1aQUF+/nTR4RR2vtrHo5scyEilpGDYBkv53+kEOTMnr6KpUECH0PV9eq61JCGQrrd1N+dPmAVsaY1GPylE4icEOuwp9U6WD5HnYB4O/CDQoEu26Ul9RPDIGbqp5UZjJhLuVtz+zTyzEg/n8PyfJwp5uL+QAAAABJRU5ErkJg\"></td><td 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\"></td><td 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gHChCBXskyxb7o5iGN+z2t4ucbTOOoezu1dmfDY6aB6bugSCMLfDoEyoNSdTdnzXwRF5U1YDF/n4zlOTjCsXr9RcFxzq1LhFqV1RWB0rvS9ze0GdsgBZA6evrvUk84nSp765jncr7LNg/f7982XQ7VlQMOTrlZ3DiR15ubg06huRdxK7xgJv+uqcG9QMG9BgUvBQPtLc3f4S9wgBgEg8+FLF6z6S6fBvTQVvzOkwZBi7PD15QYvba6MaDCusi4tH8/nqzKJnCFamkCugfu3WRpMqAQNMGQW5I0ryinHYt0ZV75pD6tDeuZ6UG7gaY7tZMtoAIewFq7DGJNjcpur7jf/6qAOzYO/zX31zww8Zwk6mnKBmQteqNDQAkC2NPdwSuyI/MS7RU8giNX4w381HAZSsxlKBikLV0Oe5tEOISNuGCZH74j/bJeHq1OLsauit+VrLwlAilooZ41Z8ABAUwGRbZR8KL106nBP1jR4IIv1g2m6V6ImI7KksDR15sCAAgMPQL+VEOxreCkHAeHFJjHo5aAZy7fqi3LMz6diCXV1uGgZEIl/xw5F2pt26mdijSi+f7+FT2A8/mlCCiASRkPbYETHgTD74iUSEKuSyjANCYttDmevPVQFpVTwReHFbjBzLSTbGBE23GjBAzvhpZ9YZ66h+Y0d87ZnXPt37PDn0O+LZRT+KSEji/PGEGNs1cO4iAMBoUc56hgUxb2MTavUvz9UN7NiGF3NM00zcVxDMHLIb1DzNgCx89Oupl8qjHTUL/bmf/ILyfXTKfX/8+Z99sNEIdOB7gbaOCk4PNfdM+xEEqucSdUBszbsDUK/ZFY8pbc2FiaSw7z+VBsCG2dU13wAUn3qiQI+HnTJwUL+ycMEdTPU/hnKfOHXu1etVz21W3fyXB8AqdzmTXo9AGyoXmqbllTaToBbyoYbYvbbqpNJTj4xLAMyspFd1QwBy+OOTYJKdjUdlo+z7IhbbvBOhzH1rS02WTlyf/ggxaIBkvhjpK1NtKFFviWELB1ozqLB7Pja8/6GD8UihmexGseRqBqWOP6JCmE7hhD/c8LWikYRntb1Mu6CVOblYcpnU+JMpgJHNqJwVOSpmXc43GcJgKwmKIe3Ywzx85MSBTOtZDKFnl+sBA9auxwdrFceWsqV5tZdd4yj7GNdaNR5Q20GLoY8V3w6lOjUhmMCJXCaV9Nt76PKOWIOl3sYjKQgrfmL3oRO7Mi0/y8zgWzPrzZAhCo/c37xhQck2uHMXGfFgak+MQ9PK8ENaLXiZxxYuxQoTDgCCnc4nM+X2kuXX8y5jOzEomRo88tihHWmnu8MJK9WLi1XPAPHDZ6yL3kTXB9efb7BQfKyQsGQracIchnEJgNwV64Gr1XwrzytT6WzGbrYfuja1ZiC3EYOa/NrO+4bjiU5pjzm8eda9WXRDhpr42K6z5x5wQhItgqYvSZbITMQt1VIDJsOhFycA7v/Jjo6fbYSRbohkIptMuG0xVCmzxmKbhUF95BvScRI9pRZ97Zl3DpbrvmEUTp689Gf+o0YIFXEQvtmwQmlyaSVFGwGITKgVQImb504luFQ10ZzxWDqVKoet5+q10aLZzhpEPp1IJHsA8NwPf1bzal6gOX7wwfV/88MFhEq1BF36gCQLk1JEBCJqOWKBgAGkJudevkLVtdaeNeYkUsm2H2Es52PUbbjo4cDYltVTcOP1n766eLrh+qERO4/Qnz4XVNdytt26Z2FZEMgoAtr7ACYQYJgA65hYgYOlIBps27FkIua2eW+4gw29jTUIGbN76q5ovvbCjR2pph+EJjuV/v4zPooNK9k2k5suMQBjuJM3IxCIWAOgg3mvUvUXmlEKxbIdJ55oP5zN/Lhsd9r1kiCE7PnJ4eVnruOA5/t+aE0Ov/cDF0iMFNpU6kXNzGBfm8hHtUAQwTCAnbuN6/pLkSJAWZYdi3cFvJhNEG3dvfSFT2yW//eHpQOOF/gB5ybqz5YYOHkg1r4eFsEaIDc0xnTbAQAiAwCZgxRof30j6oAQlq3suNN5eqMyInrr3B0EPQc3/vbtjeykF/heIMdT568ZQD3SbUkJm2wMGJ42zG0MEYSoWn6fZXRQXgoNAAhLSeU4HTHw7E7Z7nuh7RFweO7ZonuUgsD3dWKo+bYHYNeTPU1TmtgwyA+ZTTvx0qYPAB1KGq0btwLDzCBlSWHFurcvZBK0dRPZ22mhN/5mqTw24geBHyCVnL3FgHjqYM9oSWwABLqVeeJu8yADoF2DbIw352oGmKSSULGuItSLI9skVXoR+L94p85HTBCGgaaEvOACGPp6T4OYjJExAOkwSj+2xMBASxWHJqDZn6+GhgGSUjDZPd72xpjcqgi9/QdLP6pW9+QDrcPQiGzlGgN49FjPYJWSYCbSOiqJ9nQIRe42eUAYhCsboWYGhBQMq7vgYD4da1sDbYfAfelyQx3SoTZasz10tQzA/nyyF0E+KgawjgpTvS1HbABYhxUbXVoKtEGEgKXTnaJeHty6ke5eNkvPuvV9mVAbozWNjU2HAEZP9w0etKPWPNPbPwsAURIEdDDJxtTmQm04ytcbcmQX5OyI2NIZ09Oj+eo1154ymsEhcp/ZM88ADk/0DR6Kg4kI3Ar/eyvtDIAmB2HgznqhYTAJMgZ2jyItJO07cLD+c7e5N6uZYUzu5FPFOgBxItE3eiAdLQaduKiVsCNQlLUamoTh4FZVGwZICKPZsrtz1mqZLdbQ5eDydV8dYMNC6PToV3IvBQCsI/2qm8mLyLUT9T+JWmBSRwRzuLSutWGGINZGdr0im4WC2Jzx7swQTrvNiYJhIdkqPHG89C4DSOzpdx/xIUkkQIQtdh0pgnW/w8YUb2nNzCAyRosen4TlhGyX1rciqHl0ULBQIsw+8InEjVkASA32T6NGFBEBgtokdFoUogQOHc3DcGMm0AYMItaaYz3bq5ob37x37CAwQW3HMJOUzeSOTxfEOyUAsO1+DsSQJaIWHIrUofuwljXsmoSBd71pDDOTYGNMrKuKbFZTt9eDjeCgglQNO/7JMct7KQQAuWk0FRwpBEgQqHe30GU2f0KwCefXjYlOG23s3vayoiPoNhy41/NjJESTrONHbbF8FgAgNy8jmbiQFOlBj2+JelLYALAfibE2a3PaMBiC2IQq1jNlQ1u3kQKvzB6MkXBDe+ixhKRLUfc+6U1NuolUlMTryZhTWxGIGaATo9CmfiVaoCWx0ZTo6Usx1dgmReggOMu7pXQ9R31syBLmtUZ01t+03Y1lpCRuc9DfzR+JYfd9xDq4VmcQsxBsQpPobcSv9bf8dRBw8WfjWVFvxMz9U3FJ9dejQCtoatPXLqyyXQ6w6WUiVUx93GKtF5cZEpCK2ATx3lyDC7kdB1x94eJulBtxM/ZgxiIsfBhd9RqhadXio0PmZKQbbWNs+0gigAwD8rFh6KAxAxJgaRFr3+l5bda+1Y9cAQCv/tlrCVqhJGKnRxwiXFyJrvoNbZg1C9GOrnJWtwu3kxhqt/cxEzB1/7zxzU0jhWBpCxhfOL3v7FroOwQA1i9/rz7sUlqFp/YmBcG86UUTuiUd6tD3vHYJizKWAJhB20Q70cqffcKB79eaUgqWjgIHJt4bDXuSNseJbLzXaF8mk7Wb++/LSwLq77SuemVjdOC5rhe23jtpyVYs0CWga5XMgDwzDlOkdSVtljGLOPB7zZFD7k9vCjBT+f2DI4WcU8uf2mERgMUrrau6HArWge95LaOghCXAbNprU3fyrlPa/1HBpWaJySEZj0noptWrihxanXahCAERFir3F3J2TT6wP+Lrw3aPm171mn6tWPM8P4jkEFMEmK6N9hgFEWAYSD6VhTdjhxQnFYtJ4mZfIo99q8+MBECYH9+dEZX61Ml0FGu867aumgWvUmuUVyqeF0Q7PkcSYEKOTLKti20cBAbo9HHiaaEQUyqetIn9sLcBHKHsy20KAGatkNLlhaEzQ5Ftuu92EqazlUotMI21qu9HmmAJACbo2OHmeiIzMPzFGFbfEXBiMpaMSQR+HwdaS3T7tiAAdl+ZXrz2VuxLu1vuaXW6M3yj2ij7QG297kckRIrot3cJW+yBGVBPHkDwbBFWWsWSCUXsi95x7Av0sKAA3Hhp9ab/0HcOt5HOLHZuCCCKKSZTth3HlkxQghnG66mgUQ8NrRbXvV+47J9960nKKSeVinlh2AeUo6QG93Dw3K3a/PBvP9AWFp+vd0Zrrbyyz+RvlCOfoCzDMC63da/fPbd2A/ZX9qD0v5rIxpxkOq7I9HHFWkcRd8caN36i1fi/fLyjLcF7uvPMoEZUrQXM9bWar5khHc3MTd3ZddAWYTDo8Jcsfu59xLN2Mp2OiU0b5LaPj1gQwLnpWOGffLnrKysfdFcibwO2X2oEHJY2XM1gmTDM3DRoBwlbDmbA+cY+Wvy+q0bteCabsEj0Q2wnSJkACHg/D9O/+u1Yd8D8XPdvf03bolpqBqa5Wg0Mg5LaMNfDbll9MwUEBh3+hq1/cBaDGSedzcbk7TL7TIDA3LnYoad7IlL+sNj9Ea4FViwslpthWF5thoYpZQxz3e/7QKClAu3wlRnOrx+nhb9y4+N2MpfP2HILgm5vuTBvl7L/sOerFegLPVUrU/RkzG6sV5qhu1IODSPJ2qDhcVRl3ayKnWPvP8qYH09jZyqWGxhIWwpbjvZGS5TOmjOf7R3QvNRTwOG6S05SlNcqXlhZaWpj4ggNN11G3+SbADDUl35VzP7ITe2y0oXh4Wxsq+NoQxA3bo18M997ae1G7y/fg5VM6eJaxfOWS4HRDgWG3Vrv6ridY0Lh946Gz1zlPfF4fnhi1+DW/FH7ECve5+7rs9ZbfYU77ULGcwl3fb3uV5ZdHToy0OxVNbeksI1njPTi/j8YvvxyOTMhMwPjhw/a28zdSsm7k0/2hTD6Wq33uvFZ2qm8XV/daLrLJT+wkqHmoKj7HxP92dFFgKG++LR4/TrtteKpzMjeJG53KOfJHX30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\"></td><td 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\"></td></tr></tbody></table><div><small>(a vector displayed as a row to save space)</small></div>"
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ],
+   "source": [
+    "[saveImg(rand(z_dim)) for _ in 1:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/vision/cppn/cppn.jl b/vision/compositionalPatternNetwork/cppn.jl
similarity index 60%
rename from vision/cppn/cppn.jl
rename to vision/compositionalPatternNetwork/cppn.jl
index 0e2e7f237..68528a150 100644
--- a/vision/cppn/cppn.jl
+++ b/vision/compositionalPatternNetwork/cppn.jl
@@ -1,9 +1,11 @@
-# Ref: http://blog.otoro.net/2016/03/25/generating-abstract-patterns-with-tensorflow/
-
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
 using Images
 using Flux
 
-# set parameters
+
+# %%
 z_dim = 2
 x_dim = 512
 y_dim = 512
@@ -12,36 +14,47 @@ hidden = 9
 batch_size = 1024
 n = x_dim * y_dim
 
+
+# %%
 # cast 0:x-1 to -0.5:0.5
 cast(x) = [range(-0.5, stop=0.5, step=1/(x - 1))...]
 
+
+# %%
 xs, ys = cast(x_dim), cast(y_dim)
 xs = repeat(xs, inner=(y_dim))
 ys = repeat(ys, outer=(x_dim))
 rs = sqrt.(xs.^2 + ys.^2)
 
-# sample weigths from a gaussian distribution
+
+# %%
 unit(in=N, out=N, f=tanh) = Dense(in, out, f, initW=randn)
 
-# input -> [x, y, r, z...]
-layers = Any[unit(3 + z_dim)]
-for i=1:hidden
-    push!(layers, unit())
-end
-push!(layers, unit(N, 1, σ))
 
+# %%
+layers = Any[unit(3 + z_dim), [unit() for _ in 1:hidden]..., unit(N, 1, σ)]
+
+# %% [markdown]
+# - In essence, CPPN is just a function, c = f(x, y), that defines the intensity of the image for every point in space.
+
+# %%
 model = Chain(layers...)
 getColorAt(x) = Flux.data(model(x))
 
+# %% [markdown]
+# ## Make batches from the data
+
+# %%
 function batch(arr, s)
-    batches = []
     l = size(arr, 2)
-    for i=1:s:l
-        push!(batches, arr[:, i:min(i+s-1, l)])
-    end
+    batches = [arr[:, i:min(i+s-1, l)] for i=1:s:l]
     batches
 end
 
+# %% [markdown]
+# ## Create image with intensities
+
+# %%
 function getImage(z)
     z = repeat(reshape(z, 1, z_dim), outer=(n, 1))
     coords = hcat(xs, ys, rs, z)'
@@ -50,10 +63,22 @@ function getImage(z)
     reshape(pixels, y_dim, x_dim)
 end
 
+
+# %%
 function saveImg(z, image_path="sample.png")
     imgg = getImage(z)
     save(image_path, imgg)
     imgg
 end
 
+# %% [markdown]
+# ## Will generate at random everytime
+
+# %%
 saveImg(rand(z_dim))
+
+
+# %%
+
+
+
diff --git a/vision/cppn/sample.png b/vision/cppn/sample.png
deleted file mode 100644
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From 847e71260c9764e13d17692333590377c466c0e4 Mon Sep 17 00:00:00 2001
From: SubhadityMukherjee <msubhaditya@gmail.com>
Date: Wed, 12 Aug 2020 12:12:12 +0400
Subject: [PATCH 6/7] refactored VAE

---
 vision/VAE/Manifest.toml                      | 582 ++++++++++++++++++
 vision/VAE/Project.toml                       |   8 +
 vision/VAE/README.md                          |   4 +
 vision/VAE/vae.ipynb                          | 367 +++++++++++
 vision/VAE/vae.jl                             |  99 +++
 vision/VGG/VGG16.jl                           |   1 -
 vision/compositionalPatternNetwork/cppn.ipynb |  42 +-
 vision/mnist/vae.jl                           |  67 --
 8 files changed, 1081 insertions(+), 89 deletions(-)
 create mode 100644 vision/VAE/Manifest.toml
 create mode 100644 vision/VAE/Project.toml
 create mode 100644 vision/VAE/README.md
 create mode 100644 vision/VAE/vae.ipynb
 create mode 100644 vision/VAE/vae.jl
 delete mode 100644 vision/mnist/vae.jl

diff --git a/vision/VAE/Manifest.toml b/vision/VAE/Manifest.toml
new file mode 100644
index 000000000..d35de90e9
--- /dev/null
+++ b/vision/VAE/Manifest.toml
@@ -0,0 +1,582 @@
+# This file is machine-generated - editing it directly is not advised
+
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diff --git a/vision/VAE/Project.toml b/vision/VAE/Project.toml
new file mode 100644
index 000000000..23178a679
--- /dev/null
+++ b/vision/VAE/Project.toml
@@ -0,0 +1,8 @@
+[deps]
+BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+Juno = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
diff --git a/vision/VAE/README.md b/vision/VAE/README.md
new file mode 100644
index 000000000..ce5a7edfe
--- /dev/null
+++ b/vision/VAE/README.md
@@ -0,0 +1,4 @@
+# Variational Auto Encoder
+
+- Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
+- [Paper link](https://arxiv.org/pdf/1312.6114.pdf?source=post_page---------------------------)
diff --git a/vision/VAE/vae.ipynb b/vision/VAE/vae.ipynb
new file mode 100644
index 000000000..87d6560e1
--- /dev/null
+++ b/vision/VAE/vae.ipynb
@@ -0,0 +1,367 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "julia-1.5",
+   "display_name": "Julia 1.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Flux, Flux.Data.MNIST, Statistics, Flux.Optimise\n",
+    "using Flux: throttle, params\n",
+    "using Images\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n"
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "784×60000 BitArray{2}:\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n ⋮              ⋮              ⋮        ⋱        ⋮              ⋮           \n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "X = (float.(hcat(vec.(MNIST.images())...)) .> 0.5) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "(60000, 100)"
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "N, M = size(X, 2), 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "600-element Array{BitArray{2},1}:\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n ⋮\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "data = [X[:,i] for i in Iterators.partition(1:N,M)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "(Dense(784, 500, tanh), Dense(500, 5), Dense(500, 5))"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "Dz, Dh = 5, 500\n",
+    "A, μ, logσ = Dense(28^2, Dh, tanh) , Dense(Dh, Dz) , Dense(Dh, Dz) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "g (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "g(X) = (h = A(X); (μ(h), logσ(h)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "sample_z (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "function sample_z(μ, logσ)\n",
+    "    eps = randn(Float32, size(μ)) \n",
+    "    return μ + exp.(logσ) .* eps\n",
+    "end\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "Chain(Dense(5, 500, tanh), Dense(500, 784, σ))"
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "f = Chain(Dense(Dz, Dh, tanh), Dense(Dh, 28^2, σ))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "kl_q_p (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "kl_q_p(μ, logσ) = 0.5f0 * sum(exp.(2f0 .* logσ) + μ.^2 .- 1f0 .+ logσ.^2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "logp_x_z (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "source": [
+    "function logp_x_z(x, z)\n",
+    "    p = f(z)\n",
+    "    ll = x .* log.(p .+ eps(Float32)) + (1f0 .- x) .* log.(1 .- p .+ eps(Float32))\n",
+    "    return sum(ll)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "L̄ (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "L̄(X) = ((μ̂, logσ̂) = g(X); (logp_x_z(X, sample_z(μ̂, logσ̂)) - kl_q_p(μ̂, logσ̂)) * 1 // M)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "loss (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ],
+   "source": [
+    "loss(X) = -L̄(X) + 0.01f0 * sum(x->sum(x.^2), params(f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "modelsample (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "function modelsample()  \n",
+    "  ϕ = zeros(Float32, Dz)\n",
+    "  p = f(sample_z(ϕ, ϕ))\n",
+    "  u = rand(Float32, size(p))\n",
+    "  return (u .< p) \n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "Params([Float32[-0.04325612 -0.04432942 … 0.024764992 0.0025093316; -0.06396034 0.0077259494 … -0.006176046 0.016506964; … ; 8.820455f-5 0.064784825 … 0.060753915 0.023969032; 0.009093919 0.05117704 … -0.0029312368 -0.05824799], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float32[-0.00041203704 -0.0528699 … -0.03941686 0.06000133; 0.05124626 -0.06446281 … 0.0718011 -0.04769289; … ; -0.024800759 0.068491444 … 0.101761356 -0.07139413; 0.033019643 0.010317117 … -0.019728636 0.05537383], Float32[0.0, 0.0, 0.0, 0.0, 0.0], Float32[0.10620293 0.023587517 … 0.08458545 -0.020418482; -0.038067207 0.010741732 … 0.028195212 0.012534944; … ; -0.014529563 -0.087974004 … 0.028156698 0.045931697; -0.102924176 -0.032970604 … 0.029116375 0.035560943], Float32[0.0, 0.0, 0.0, 0.0, 0.0], Float32[-0.09521466 -0.08829451 … 0.020895256 0.0067901523; 0.108671494 -0.009676283 … 0.07440227 -0.0760235; … ; -0.0069366717 -0.011213775 … 0.06583009 0.10229878; 0.045926213 0.07811487 … 0.07023905 -0.10748437], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float32[0.053993654 -0.0059612063 … 0.009265504 -0.062752746; 0.0022692461 -0.03440574 … 0.05393037 0.04595501; … ; 0.004562907 -0.043307588 … -0.004600816 0.041631848; -0.066888765 -0.032768432 … -0.036583245 0.010277884], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])"
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(-L̄(X[:, rand(1:N, M)])), 10)\n",
+    "opt = ADAM()\n",
+    "ps = params(A, μ, logσ, f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Info: Epoch 1\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 544.0159f0\n-(L̄(X[:, rand(1:N, M)])) = 188.8044f0\n-(L̄(X[:, rand(1:N, M)])) = 163.6948f0\n-(L̄(X[:, rand(1:N, M)])) = 170.76443f0\n-(L̄(X[:, rand(1:N, M)])) = 159.46655f0\n-(L̄(X[:, rand(1:N, M)])) = 168.12997f0\n-(L̄(X[:, rand(1:N, M)])) = 160.12689f0\n-(L̄(X[:, rand(1:N, M)])) = 165.35117f0\n-(L̄(X[:, rand(1:N, M)])) = 158.74713f0\n-(L̄(X[:, rand(1:N, M)])) = 156.7229f0\n┌ Info: Epoch 2\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 163.61983f0\n-(L̄(X[:, rand(1:N, M)])) = 161.33939f0\n-(L̄(X[:, rand(1:N, M)])) = 158.21996f0\n-(L̄(X[:, rand(1:N, M)])) = 151.2085f0\n-(L̄(X[:, rand(1:N, M)])) = 146.22623f0\n-(L̄(X[:, rand(1:N, M)])) = 150.20674f0\n-(L̄(X[:, rand(1:N, M)])) = 156.2229f0\n-(L̄(X[:, rand(1:N, M)])) = 143.83652f0\n┌ Info: Epoch 3\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 142.15207f0\n-(L̄(X[:, rand(1:N, M)])) = 142.52904f0\n-(L̄(X[:, rand(1:N, M)])) = 150.46121f0\n-(L̄(X[:, rand(1:N, M)])) = 148.28822f0\n-(L̄(X[:, rand(1:N, M)])) = 143.29193f0\n-(L̄(X[:, rand(1:N, M)])) = 139.69719f0\n-(L̄(X[:, rand(1:N, M)])) = 135.97562f0\n-(L̄(X[:, rand(1:N, M)])) = 142.71591f0\n-(L̄(X[:, rand(1:N, M)])) = 140.0783f0\n┌ Info: Epoch 4\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 139.62978f0\n-(L̄(X[:, rand(1:N, M)])) = 140.09804f0\n-(L̄(X[:, rand(1:N, M)])) = 140.85718f0\n-(L̄(X[:, rand(1:N, M)])) = 143.96771f0\n-(L̄(X[:, rand(1:N, M)])) = 139.85089f0\n-(L̄(X[:, rand(1:N, M)])) = 141.64473f0\n-(L̄(X[:, rand(1:N, M)])) = 136.50128f0\n-(L̄(X[:, rand(1:N, M)])) = 131.97739f0\n-(L̄(X[:, rand(1:N, M)])) = 129.70644f0\n┌ Info: Epoch 5\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 137.03206f0\n-(L̄(X[:, rand(1:N, M)])) = 142.41936f0\n-(L̄(X[:, rand(1:N, M)])) = 135.6215f0\n-(L̄(X[:, rand(1:N, M)])) = 139.77963f0\n-(L̄(X[:, rand(1:N, M)])) = 137.5791f0\n-(L̄(X[:, rand(1:N, M)])) = 129.35582f0\n-(L̄(X[:, rand(1:N, M)])) = 131.18831f0\n-(L̄(X[:, rand(1:N, M)])) = 128.95619f0\n┌ Info: Epoch 6\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 135.28596f0\n-(L̄(X[:, rand(1:N, M)])) = 133.43881f0\n-(L̄(X[:, rand(1:N, M)])) = 131.18222f0\n-(L̄(X[:, rand(1:N, M)])) = 130.74548f0\n-(L̄(X[:, rand(1:N, M)])) = 131.1535f0\n-(L̄(X[:, rand(1:N, M)])) = 136.14282f0\n-(L̄(X[:, rand(1:N, M)])) = 141.14491f0\n-(L̄(X[:, rand(1:N, M)])) = 130.80042f0\n-(L̄(X[:, rand(1:N, M)])) = 136.48291f0\n┌ Info: Epoch 7\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 129.31302f0\n-(L̄(X[:, rand(1:N, M)])) = 139.49428f0\n-(L̄(X[:, rand(1:N, M)])) = 130.39726f0\n-(L̄(X[:, rand(1:N, M)])) = 135.66624f0\n-(L̄(X[:, rand(1:N, M)])) = 139.52274f0\n-(L̄(X[:, rand(1:N, M)])) = 129.02013f0\n-(L̄(X[:, rand(1:N, M)])) = 132.18924f0\n-(L̄(X[:, rand(1:N, M)])) = 137.77753f0\n-(L̄(X[:, rand(1:N, M)])) = 137.5309f0\n┌ Info: Epoch 8\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 130.02092f0\n-(L̄(X[:, rand(1:N, M)])) = 127.53695f0\n-(L̄(X[:, rand(1:N, M)])) = 134.24876f0\n-(L̄(X[:, rand(1:N, M)])) = 126.24858f0\n-(L̄(X[:, rand(1:N, M)])) = 134.23547f0\n-(L̄(X[:, rand(1:N, M)])) = 128.36131f0\n-(L̄(X[:, rand(1:N, M)])) = 122.926f0\n┌ Info: Epoch 9\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 128.51785f0\n-(L̄(X[:, rand(1:N, M)])) = 130.70897f0\n-(L̄(X[:, rand(1:N, M)])) = 126.51974f0\n-(L̄(X[:, rand(1:N, M)])) = 126.55835f0\n-(L̄(X[:, rand(1:N, M)])) = 133.80797f0\n-(L̄(X[:, rand(1:N, M)])) = 137.55035f0\n-(L̄(X[:, rand(1:N, M)])) = 129.17249f0\n-(L̄(X[:, rand(1:N, M)])) = 133.11034f0\n-(L̄(X[:, rand(1:N, M)])) = 122.74662f0\n┌ Info: Epoch 10\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 129.06119f0\n-(L̄(X[:, rand(1:N, M)])) = 134.97176f0\n-(L̄(X[:, rand(1:N, M)])) = 125.834236f0\n-(L̄(X[:, rand(1:N, M)])) = 127.66348f0\n-(L̄(X[:, rand(1:N, M)])) = 125.22201f0\n-(L̄(X[:, rand(1:N, M)])) = 130.18465f0\n-(L̄(X[:, rand(1:N, M)])) = 128.43158f0\n-(L̄(X[:, rand(1:N, M)])) = 125.08065f0\n"
+    }
+   ],
+   "source": [
+    "for i = 1:10\n",
+    "  @info \"Epoch $i\"\n",
+    "  Flux.train!(loss, ps, zip(data), opt, cb=evalcb)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "28×280 Array{Gray{Bool},2} with eltype Gray{Bool}:\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n ⋮                                     ⋱  \n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)",
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ],
+   "source": [
+    "img(x) = Gray.(reshape(x, 28, 28))\n",
+    "sample = hcat(img.([modelsample() for i = 1:10])...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "28×280 Array{Gray{Bool},2} with eltype Gray{Bool}:\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n ⋮                                     ⋱  \n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)",
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ],
+   "source": [
+    "sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save(\"sample.png\", sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/vision/VAE/vae.jl b/vision/VAE/vae.jl
new file mode 100644
index 000000000..377ad40c9
--- /dev/null
+++ b/vision/VAE/vae.jl
@@ -0,0 +1,99 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
+using Flux, Flux.Data.MNIST, Statistics, Flux.Optimise
+using Flux: throttle, params
+using Images
+
+
+# %%
+X = (float.(hcat(vec.(MNIST.images())...)) .> 0.5) 
+
+
+# %%
+N, M = size(X, 2), 100
+
+
+# %%
+data = [X[:,i] for i in Iterators.partition(1:N,M)]
+
+
+# %%
+Dz, Dh = 5, 500
+A, μ, logσ = Dense(28^2, Dh, tanh) , Dense(Dh, Dz) , Dense(Dh, Dz) 
+
+
+# %%
+g(X) = (h = A(X); (μ(h), logσ(h)))
+
+
+# %%
+function sample_z(μ, logσ)
+    eps = randn(Float32, size(μ)) 
+    return μ + exp.(logσ) .* eps
+end
+
+
+# %%
+f = Chain(Dense(Dz, Dh, tanh), Dense(Dh, 28^2, σ))
+
+
+# %%
+kl_q_p(μ, logσ) = 0.5f0 * sum(exp.(2f0 .* logσ) + μ.^2 .- 1f0 .+ logσ.^2)
+
+
+# %%
+function logp_x_z(x, z)
+    p = f(z)
+    ll = x .* log.(p .+ eps(Float32)) + (1f0 .- x) .* log.(1 .- p .+ eps(Float32))
+    return sum(ll)
+end
+
+
+# %%
+L̄(X) = ((μ̂, logσ̂) = g(X); (logp_x_z(X, sample_z(μ̂, logσ̂)) - kl_q_p(μ̂, logσ̂)) * 1 // M)
+
+
+# %%
+loss(X) = -L̄(X) + 0.01f0 * sum(x->sum(x.^2), params(f))
+
+
+# %%
+function modelsample()  
+  ϕ = zeros(Float32, Dz)
+  p = f(sample_z(ϕ, ϕ))
+  u = rand(Float32, size(p))
+  return (u .< p) 
+end
+
+
+# %%
+evalcb = throttle(() -> @show(-L̄(X[:, rand(1:N, M)])), 10)
+opt = ADAM()
+ps = params(A, μ, logσ, f)
+
+
+# %%
+for i = 1:10
+  @info "Epoch $i"
+  Flux.train!(loss, ps, zip(data), opt, cb=evalcb)
+end
+
+
+# %%
+img(x) = Gray.(reshape(x, 28, 28))
+sample = hcat(img.([modelsample() for i = 1:10])...)
+
+
+# %%
+sample
+
+
+# %%
+save("sample.png", sample)
+
+
+# %%
+
+
+
diff --git a/vision/VGG/VGG16.jl b/vision/VGG/VGG16.jl
index 1c321f9f0..a62fcac8e 100644
--- a/vision/VGG/VGG16.jl
+++ b/vision/VGG/VGG16.jl
@@ -52,7 +52,6 @@ double_conv(in_channels, out_channels) = (
 # - ... operator will help us unroll the previously defined blocks
 
 # %%
-collect(Iterators.repeated(conv_block(256, 256),3))
 
 
 # %%
diff --git a/vision/compositionalPatternNetwork/cppn.ipynb b/vision/compositionalPatternNetwork/cppn.ipynb
index a6c335c9d..d3f14dbab 100644
--- a/vision/compositionalPatternNetwork/cppn.ipynb
+++ b/vision/compositionalPatternNetwork/cppn.ipynb
@@ -23,7 +23,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -42,7 +42,7 @@
       "text/plain": "262144"
      },
      "metadata": {},
-     "execution_count": 3
+     "execution_count": 4
     }
    ],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -73,7 +73,7 @@
       "text/plain": "cast (generic function with 1 method)"
      },
      "metadata": {},
-     "execution_count": 4
+     "execution_count": 2
     }
    ],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -113,7 +113,7 @@
       "text/plain": "unit (generic function with 4 methods)"
      },
      "metadata": {},
-     "execution_count": 6
+     "execution_count": 45
     }
    ],
    "source": [
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +131,7 @@
       "text/plain": "11-element Array{Any,1}:\n Dense(5, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 1, σ)"
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 46
     }
    ],
    "source": [
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -156,7 +156,7 @@
       "text/plain": "getColorAt (generic function with 1 method)"
      },
      "metadata": {},
-     "execution_count": 13
+     "execution_count": 47
     }
    ],
    "source": [
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +182,7 @@
       "text/plain": "batch (generic function with 1 method)"
      },
      "metadata": {},
-     "execution_count": 15
+     "execution_count": 48
     }
    ],
    "source": [
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -211,7 +211,7 @@
       "text/plain": "getImage (generic function with 1 method)"
      },
      "metadata": {},
-     "execution_count": 16
+     "execution_count": 49
     }
    ],
    "source": [
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +235,7 @@
       "text/plain": "saveImg (generic function with 2 methods)"
      },
      "metadata": {},
-     "execution_count": 17
+     "execution_count": 50
     }
    ],
    "source": [
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 51,
    "metadata": {
     "tags": []
    },
@@ -263,11 +263,11 @@
     {
      "output_type": "execute_result",
      "data": {
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Gray{Float64}(0.9158487702719089) Gray{Float64}(0.9231819529375643) … Gray{Float64}(0.672909516787164) Gray{Float64}(0.671720138604213); … ; Gray{Float64}(0.0006547176463694403) Gray{Float64}(0.0006339610402856328) … Gray{Float64}(7.760013279088737e-5) Gray{Float64}(7.733330843616922e-5); Gray{Float64}(0.0006209573405352554) Gray{Float64}(0.000607837698818018) … Gray{Float64}(7.799381741515829e-5) Gray{Float64}(7.773003638801709e-5)]",
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\"></td><td 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\"></td><td 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zFjt0m5cvWffwbE1I6hK90Gkeb0aRJljdHIgXbFg9nNfu6ICyvr5itxu/P59Y7cGIEuXY53H3WTywMH9pUH5KEDSm1O7gzBnmhVAFjvGeS7ff83H6DG8lRCGAcAU2w4jSetOzyNtd9Ndh47sswPXJZ0Fwa/cHDM25LwFAP7CA9514l9unywCBIomMLd14jLAkMFmHew1d/1qmt5qy97DD+wxgzf8ykUAki22xrSVGykA3Hd2t+nGyf5f3XdiNhGo+uFHdv/gZTeCBBoGALOlysjC9Mry6dnK4QePDgNAcvkKAHDcqkDRAgDQ+IXDO7O4me78wAMXpxdlx1sONWq7ktd4y/m4nr3LVm6kO31t+iez9sAjGxgAd15egRAgWaa52yEAZsfSiQ9kt65LNDZ2pBurRkOxrn9k/vodAcAAMtKb+usXXn+u2dBH3rrbDt/hpeccAJAh57uZyUUQTrR+8p7uxaW91UDJBLQiJk0PfegLsdzJRZitpu7SqWd/eAvlxtv2hRuCofz0GwAAXbHOtaL7r8SeKrXSxZV0tjpVN1QkkYFRUnvfm8+PWEsZogBGRGgEiOz0d7+3IOiOH94AgH33p0sAgLBuXCfWpiwe1ZJtzUXJvnKgCcIipJQmu+djpxfvRIKSkbdl+rkfzrNIUh8f3tv1Lp094QBA16pIY5cstzOPmlV0k1EySimltDbGBFG52njXz6s7+UjT52UYwPzzb9wEAXmjcAXiOokoKAPXfPYMA4BtWJ9m0mnmWptxo8Nrh0ygetaFlA6MhgQf+/Hcpj3dYadplKJNmijxc+1XPAC4qRAA3/zxsxeWvVaVPQfGVl64BQCqUlNZ5tCNqVQOJ7UOF7UtMici0qYIt8zj7/uyu4MmKr2ZgeRE69osAMBHZaD11J++0bN0OqrqdiwA6Xok3gnHPgjHa+Ow4S0VFvElKW2t1QQhVfvwczfvNBc2A0gvnT30VM+B5lpu/bcn2v01atfpKmEAFDWMc144g9K2GnEQxDoqwmQyQRgaEmYhdfx9T+W318TNl3j+8s4K10tWE9kSz/7eX8SBGYheWQCYRhXeCzEr+CzL0sAmXCkAaBuVIwVttAIq79h/e09tNl2R5EZ+bPVgd6GV5Lzr2PJ//WK9jLQV9xPCQhyVCeudF/FMkq6qxXZgur5KBECZsGQ7HV+rCrOzteNz7WES5E4IZq9TZfWovzbfTMz7H/n8F9myCtllgy9SvcK583mSCnzKebiwx3jUPAAoG9lWM3WupCRntePee8/528hhM4Lu9078hqnd70gF/v7fWP5c16yEgSbrZMB0UCBpniXNlIvNNXVlNwlVugBIB2HScsyp1y6mYGz/sVtLtzEKBn3X1PvL3/kfHztaiQxnXNv5oQd//zzY52JIK0+0hoF8Knm8mjIXi1or5x8WVqVMBGQianuXA8JprHzL7znUym7PgfSXa0DApU/UPjxB1riujd76lnNPewh7AkF7IbW2KJ4mvtvJXa9wQtKLM5m2oRUhZYPU+TSzRmexszdfeqNdn5jbmoQBKZBAKHt68T8/pqFpx8Gde/fTly4DwsJEorUXkOotJGVJ0s3ZgQgEAs+eatWrOmCBCnTKeexMoBJvrz/z4s00Mia/Kz0gIXnj2+/8FQPAVO5RY+Erf+0BMCtWQloYAiIFYXASZ+IFEAKRIH5ZvauSWwcoAy9ZwrUQzpz+fz9ZcrJqaOv0xQDrjoGw8n87Hx8HQGTHjO3+2TUAgFckJEqDIUICESVpBmZAIKQA4uuNXwi8cSKkRMTlKoiEXv/sT2MBwd/GJAzPBfe9Fz/wjkIrtFZ47msCEgh7IhCpYqECENLiRLgI+YWJAPfoPyDSYC1CBEEQofPKn7yaoe+KtvLRQwjk1Fcqv1aE5aQIK3+62LvBTIoUehs0UEozCxdqwSIgQumfVJ3SStjlJaOUrZbS1/781WIO3DZ/MQX7RVt68vqvH+srBeSZv+u9KuwLjwNiAUBKFTqhDXxv0+jwcdFEJhOXidG2MhaeeeIn/UkoRVY7koZBDrJvvXzgn60HhQufaQ14A4IiqKJOhlTRF1kLV2zbmftKMFqZjH3qjI5K9Zmnnk02jjYKglmngF/9Vv7RvWt3+FsvrLMn7IvKEwUBiFBIQ2tF3gNAWBNVTrU1TqVZqCth8t3vrQ5JeCtJDPjGG1+6+fafX/89+7nOwPvMzEKkFKleTZgAREFgDQFQ9QnRQUnrUNhlpCv25W/MbPxgGpkZqDUKWk+9Mf6b9bUb/muvCAaUhJlFiEgRFUUEAJGuVa0mAMHenVC2bHRo2GdC9urXT98+ZxtA0Bsh+84Ps48+uH7jyl/Gg4iLAiFBb8W52GcQXa0GmgCqHpwUUBhAR8yZ4/iZv9+oBLJFcthn3b/wN4vv+dC6XmZPvr6BRGFmKTbJIUWxH8gGgSZAT95TYWGKiELFWeze/M7C6Om/GUR/VfzkF64d/FeN9eHe/OKGFWKBMDEU9cEIQRjaEAAzNRUwi5RIjM3TJf72mZF7nqNagUDOf/Zc498cXL/c/ovzm5Md4cJ/ivQ23Nj7QBEAM17VIhBrcgrTdGnuR+1R3z+SFgUAcvF/nzC/8471ecB/9zf5KMZkvUEAcVkxKUw1JAhEBUKBym794OrdL/IrAHLhj1/Gb39wwDhd/eO5AfqHxu9XEBaVid2ijsFEvZdDkIn46qvxljqw6bMMwKc//ar6rY+G6xe7n/t7HvF6bwFsYOue4161jVWAELElqMifmrlDpjaMwP/0T94Mfus3y+vX/N9+Pr67tzntehEpTASE2Cgo07wwcqtHaLRfyJ757KXax391AICc+6MbGwvFQCACEQ1SKcR5XFRsFjdIlALRzbm7NEYFgmd/Orfn3/3igAiw+MmXBnoYiI+pF5KtBbcQn9pi96/gQKA9+Fp7G0KAubT7kX//Vj1wJf78U9n6dw5QQIVN7n1xEaGyUwICC4gAYdGeshvZFghGSyH6pY8PpVXu259eGfUg9RsKDorevGcAwgwqClsVIV7cSggjrbL5xf9UG/zNr/zBVRl4uu+d1gCQDHAA8V4AMAMKwuwI6Cxva6NJ7R4CIBd//8SoL6B+RX9fFXt/mYtySl8UOftMAclWm57rK0iDZGzInW994vv5ZsJoSAa9Kz3OBADYg0jAPgYh3nLPceTi6TCC1p8/NWwJ+kX9pEYD6OWS7ASAsO/kkCTfnhQGf6RPfWakFq5RsLmcs0j12AlBhH07E3Hb2/gdROB/9KmbPNx/f3xFIBq18im9Yx29kxHNJolsRcHa9aF+BtyRnPqjs1vMo2KFrE8BrS/D9mxnH4Fv0fg2NzwHOJDZT7/Yj+839DFyA6A/EBHAOUNExHcusCiFO7VBltaf7j75jS2taX/FbwtTBckZYBZ2FzMYi+20NQTu+b+a33pfauOmwPqvAlvmUZS7X2kj3ArB6O77COTqExe2Xnrs18T08AzeKBxCgQCMm9cQhlt1s3WUBkjnmy90R+vqwOsyojtS1DvixCxCrTMSlrZSxU11ecB6rPzGtxe3XvCSoq5vIOJfX1AqTHWWFzWUCqddVMF2WsGBLP/thXSEIVm3fIRN87z4qZQCJMshnkm0Pd8Kx27HwWgEkp96efV2FX09/7PZ2BBpowjIMxHvITq4cTOYuPN03IBAuPnK9ew2tlT6acIICIHtIYD3xDpqnta79Fb9bIFAJL9ypj2agr4H9KC1irCh3igMNQCXsrAj1pE/4fYG2+NA2LfPzw0FVpv2JISL801Dx54AgHQp1ApwCYNzsIro9Mru6vY48D5dvNa6Q2DFvq8IawfkiqbDwBDgEhZ2QlLSM9NTU9vigPO0e2shGzHqBhJoDYIMnFe0RgcK8LGH5N5kke68Udm/Hd+ksnbSWurwlh6tv5bFIKwpwrrtCiBWAxw7iMvDOLB80h/TdzV2D0G73WmtZsO7niNIYF4Tw5A4A2JrCBznIi4rpWRxaf5YafRgMkoVVJp0u4knNXRmdEjZiiu+L4ahimsKjNI9BODM6thi8eL+ndvhQNI4y6HUkFcfIRH2Qv1DZgOvByrSAUHiVIQznlzRlJxqHN3Ks4xGkHruHT28DQkiXKRnQ9ZZtI29CRUkThicZXu7THwme3wbIYLizAtprfWdgiuWvhgGN85peVFbA4ljD3HJVCUjujr3yOQWnYygQYkTUtroDfuOo0joq0RfEURgeXUpDwJI0vECn5buTwnL0/seuPv5qJhJaVsU4w2OLgMuuLi65p4GLkZ5Eq/akJC2ckBS96gC0jPBO8LbDTrUrxLR2lij9dAJ6l5INBQVMa/nCP3HosxzE6FGtpoyOI+P7BDiM/Hju++eA5AuTqAOH6AeAbx//KEf6QigSjkojSOLfCVmwMcTDwI0fWvvY3ftoRW0Mcb2SLgtX73ZMHhRh86WVcsEcMsdJ+AU7zDA0rnSu6p3HruHgLTRtqhMVdiKhaH9wAEx2JAb+2upi5RfaeYCyeNHp4D8VXno0LYQGBsYU5w9vA0JhYseUkUbYcc/fG/QDYw0lxIP+HjncUDeWNr5dnPHsfsIjDE2CKwxRg9Mhg2FU8MkrJ0WC2p6zyP/4nhGgXTmO14gGd5tQTfORG+/WxetlNHaBNYavcEsjuBjODQAEOwef2DfQx+p5IGK51Yzgbjk0V1A/CIffnhknru5mROfCd3q8sJKJ85yD9n85IAN9gIREFP/JGzrzdVXZ8ZX3AokO8WTAQRWswA/KpvmXdaq/n8rCrFhs8XwygAAAABJRU5ErkJg\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td></tr></tbody></table><div><small>(a vector displayed as a row to save space)</small></div>"
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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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ZjemS0Ui8HUQQCSTFZ3+YDBY28f0/IDQpkNVFSPCNqZbaqyhwBpqa457X/uvc2qooyKAzQCot2RAQEUEyjp5feKUBn//X2wt3n6r/Pnu7YBA4F0UIDRZ0R0MhoN+r5PnWRt+q+dvDgFUSJQ3ZBQyVWDxtbUHV77wfxrtSVFBLQMPMsoyIkFAyjp2OQtgtn7jdwb1wx8+vnb9YCGIGn0UJMqKbn9rOOz3ukWy37Tl2Jai5oQVFRVlpWUQItZe2NfGHH3+/geSZFMFUAvgC5Nt9XNBQJPnerKIaLZ/67d7bvrmO93P2GMHCNFHIGOKzmC4tTXod4s8y0wb/c3DYyrI7YcUcaXuASICBInO2Mngq+NpEqAUFKxyIOicPT8FRpOhn1WCducf/eOuKz/4m/kXLx+cRACOgmSyTn+4NRr2e50is9asgu/USGWjNQM6zQQAg0Zf28Nrr/05a1OdwKoPMWbnr8+OvGpYLoNStvPrv9319dGbH1x4CY9LBY6Cxua94dZoa9DrdnJrDVHj+tM96QpF4gq0Tn+qChAlODs//Lm794Ub91l1flFlu08/KmNVlU7QdM5843f6zs1u/0RfPXM0CSqiaLNOf2u0tdXvdYrcGqJWHdA1O9yIxhUlRAI1jXoiqsAcnD0e/dK3grRsnavFo5x6/cnhovKimI3O/+LvbDm/OPjJoxeu6/GcVZVM3h1ujUbDfrcorDW0oQmtZH3YZMCrPh2R1DRJSkVV2dvy4JkXfsIMgKhg4/zRvfc//Zn5nUMvCtS7cO76b+04X57c+Vn/s6OjSS2qZIr+cDTaGva6qZvFDUWsYefrrnAjOFsIalRyEWHRKNHbk+NffOhLRQRAG44fzMJbN5aPnQJme0+PBn/vqeCr+cHb4y9d5Ok0qJIt+lvbo61Br1tkyQFJkeTmv3WxXoX+mi8mL6hVSYyFJfj66FOf+4GLCoho/cJ2l/fvawA0/UuXC//6c+zrxcmd986/0D8el6xkOoMGQJ5Zg7guPTGEECNzKhYm5R5MSWJ1CxoIqVaIgnBw84PP3q6nqohgHV0uY+kUKd+7ugezz30BfF0uDn66fH2XpxMnYDuD0fb2Vr/XzTNjEBVUhZmj93VVVc5HVkRjbJZnWVMnUqFeVVskJWNFmEU0aPT1eO8L09IJAdqlvejixCMNL13o8PT8V3uurhYnt24/e7U7GS8imKI/2t4eDXqdPDMGAUCUU6+6mM0XpQ8sAEjG5nlRdFK1Ni1bwRaCUbHCwizKEv3yyfX36n1RBMvgYq8/h91ruxSm9suXgquWy8c/1c8P4mRcCWa9rdH2aNDrphhM9COGupxPJtNF7Vmk4SzGZnmn2+t125RNiOtYIKOSpVBQiW4y+/nD+UIIrIxnrGTyiyMO8/jpV8FVy3Ly3oNXz/TGh7OgpjPYGg37vW6eWUJQVZYYfTWfHo1PSs+tPAwAQERZXvT6/X6vW+TWtkJ26wUrwsyirBzKJy9crysWMOSPjuvgB3sQl+7MN6/6clnOb/2l/doWPH54EqAYjnZGw363yGy6BCwxuOXJ4eP9yTKp28lKombBuaqq6jSG3hh+bqSqhmZCce2BK4ENhNopc6cLvs4//yUql8vlox89+PLFreP7+6XY3mhne6vfLfIWALN3y8nBk8NZzWgTQzS06lWEo6+r2oWYIJzuFFv+AIAIF2U+i2yIVFGl6Iin8197ql6W1fjHP37q9a48fHDisRhu74wGvSK3BgFUmGOoF5OD/fHCq8kyYAVspgttuyIxeOd8ZNWPN7RJZAUABHP9Ic+iKXIFEu0WrJ3nv2iWZXXyzl/V39waHdw7qMR0RysACKINgMOD42UAk5nohQOrpsF+cwEa8cRFlo8g0JbEJf17bzBelDYrREEAWG1+tnC1m9/+68PPXimq/cMygim63W4nz0wThJsAKKPoMdfAAYiI0KZQExZl4eCqsnLDXiezhihFo1G1aT4aVcLi0fXb9cRaiwQKzEocfainN//i3vBL0rv7ZObZmKKbMjGkGIihXkzHR9MyqrHEXrMMTYjMjEREaIwRYWZWFg7e1W5r0M0zk0oAIjW5UYFB3PHeF6ZnTd86RAbKgKPu+Cc//fM7+uVPDRe3HywCmO5oZ6vfyaxJvuXglrPx4WQRhKwVL7bIV4VCpBklEREhgEhMLRueIiq65jSql90HpouBVEEFZbH/8OFP3njEl36NBvduHzsB29vaHvaKzKQpCgdXzo6PpgsvaCzECFmR2aZVgMQAFDHR53QvIkujnSGcorMAACj2xltWIiCgcuVI4sRyhM6XzO744bhmIUBjUggIiHBw1Xw6OVl6BjTIUY0xxgAhETGzqLIgGyJDTBFZ2UmMzCwdVUNNQ6aNsMDK9cHuczYqISCpsIpiADDPPGfx0eNlGpU0T6eqIsHXy9n0ZOlYiUgjAxlCSiNHjtRsAQgZg5YMxSjiU1FWVTWEDQSb2LponH9Y2YhGAbF1IWD/y/WzD+9PvLZuVVUBEIm+Luez2dJFASRg1qYCpl6PopA0/0CMIUTCyBoSO1JV0BWExNiiiB9PrQDFVgtRULQvbY/ig8elAIBKDJGZGRWEg68W89myDqKEqMLQkmUkAERkSnRFWVUNGURE1linXAxgk86ERCZL5yBcVVapIflIigq4+5nqpdt3pxEQVCV4HyIzokr0dblclHUqxqicsooqAmKSZRgFpeWDJq1XSAMBQFUNbh6EAkCIFs2aUyJg59N8pb7zxAGRgrCvKhcCASkHV1XLsvZR0uhGtQ0RVFSkxo+cdCQBACKrGlmjWzV2KTWRGmlDQSxiq7wjIphLl/HSz+7OxFijGDWUy6qXGyBh7+qqdj6KKjbsX1VEEHBFQ5oVEKYWglFV0ehadcVq8hYaFauqIGppnSwQcfSi/2x5a9+jLTLwdYjVYtnvWEANztW183FVdFOJEUEgbaUPoVR7BFUl5UBRVQnYCBxgFFFTLDS30gKsxkAAnU/t0qW/vbsEW/QL8LSM9Xwx7BpACd55HyKLquJKcBKRjSfQJCwqpe5Vmp0EVgk1mZQ7TeMoIjJWRdEi0kqUthc/Vb9W3jqKmPeGHfJGlrGcj3oZEIcQ0iS7efgUqUyYFKGm6q/2UFBBhTQNnlUl1KnTBFBKQmJaEhKwAKQqAApAo5e6fPW9+6VmneHu2d7JEwl1PV8McsB0L5uLDYliqKAwAiC18tFm26qg0nbPKtxAALWKmJ4Am4oKaBRVBUx+/cqTF+LtccSsv/PCc4PZz96ufVguyhyMBmaWVrMWadgII4CSfmTAvVGG2q+FfdVCSCsojSKIFoQISAXV7jwfqmc/fFiq7QyfffWsOTP0y7Kq54sCLcRkHxsEikSiggxgVGg15l4R1xStK20JOPrKphrbND2NoGdVtJGl86dHN88X944CmM7Zl88g07nPH5zUfjErMEdNMlpz5MI2QQBQJZKWrELbyraxmjyioMrR18kJQgRNL60AVpM8RWj6l48ff/Hkw4WA6T9zASKwufzpx/N6edIhscoKSY1BAUERIlJQAVVK5akhoWnFpRUHW0CoysElJ1hDK0KhDQJUJBrZd3T78YFTyrafySU3EswL746dmxekOakAtlcqecMANzsVJE2LthYKtOXOrU9EOLhENxPtlgTBapNeAAdP7j0THp9Exez8GS26pOR2X7q/cMvMQDdDUSQyhkhUhRENAaR6jLISdNbnAGk5RKWNX+HorSHUXIgAIKmeapt9OhWSO3M4OigFsHOhYzoZKTA//7OJ93MC7lhUIGOMYQRQBkAigLTO2e6arNcdoN2LkZWGq8LRG0JQNq0TWMS22Q1wuh+XD4+9EPXOUZYZUugsd175sCxrBOHCAqCxNjlBAdESAeLa5S2ENHeixK2kFS9BlWMwRCCZTQ2oCosaYyCJQll5ArmZzBTzq58f9nKDiMAyPJrUkUVTom20GGnSIq3FzNM7LohIhCqrHLaW9aABxsKRmQ9NWuYBBSq9yXVRqyluvNLvZemn+072pPSc9r82BkirYrYpKevGN4lwzY6gHbuv5lDMzMyR+dACAIIqUAhgdVkJohmlFgmQsiw8+8o8zrlSlW5ugIy1qf1NH0MEqJpaRGxWjNpqwCIimxlSmCKCShMIqipiAVKSQQHK8xiVMBsZSwgIiqYInVf3HZdcg4hkBshm0jIxVVFDSKDUSmrtgnDqstfrk0lwVWFEBOFWDVMVC0mWAQCyncFCFTAbkmlGUWQzf/a1aZCKa1XpZoRkU4Vqfpk0XNrYVQYEaISOVW5axYowgookBNAgAFBVBAOcD0tAxKKf1BpARZMHvH5QRnHsALSTEZqsXVAXVBKR1CIhrqXVZsaxtt9e1lRJVKVBACq2YVyK1gbbtwCInS6tmBNZ67ufPvKsnh2oSoZoMhUA4HT5hVvhClq2o2sB86PNswq0CJoiZ5tsimAysoUBBOx2VgAQTRZk75WpZ4nsVFUtorECqgCioIwqzUrgqXspbT5uXdDMPyQ5fS2LW2gnxNRBYxEAsV+YNXtEa3125fll4GVkD6C5JbKNiKoCCozSSpmrpNRwwI+P2hUSHRJaI2jnHZCLJVUEM8hog72S9dy7cVhHriMHaLyQ/pE0ujo2WXgDwnr0jes4aBG004fGB20ja00GrIg03CTQgGTY7t6YuCjKDAAqlsgoAGJEkXYlphm8N2Y29v8216DSZEUAmmPA5IMm5gzmHBTBDswmACSKml+8ughRVBiSF8gAAABjs+qIzRx65ewNEe2j+kEaQ7WnZtu/SjlI4aICZH3aKDMKSMhmePWoDqxBGNpUCIBIEddX7uOa0YYTTnuhHb8rgl0FXMHamQcAzLp0+uchCti9iyc+sqgyQKpuZAERmXEj6DbmTB/5atML2G6IAiI0p4CmmznK5gwANk8sqPFYqhvUfepw6QMrazoIQ4gGARFXHAA+8YMb4bgGgJq2DRCtAoAiFgOtR7wQRLQGRRUbd7X5PhudnbgQpYWQGQICi4jCuN68hf8fjM0zSo5BxPQWCWbDouTh3BMAoELDB1pGCwCAnb3tpQ8sKsqgoNYSIAGioAitX2Y6HQaf7IYWAhJheofDDvu+7HUfISmiOoyRmpIpLe9EO9iZ1iGyRtGUkA1hg0EEN/uEdvr4MTecvhWISAYtgKIdbMlcz3hPQAA8BwmmlcdYJG0yUTEcLFyRWkeJGxCaxqxFoJ/khtNQGoCIRGRVgfrbsojnd/YhAKqGI9f1BISoIMIcRQFVwfT6nTqkc1CJCqpEBICkANh4QbHtrHW91PEJLtBGcyC0ANTfkUU8/+zJ8sQTgbqD4yvRqU0b/swsSqCqmHWLzGYxLaAJgKppxVJKJDK9gpIufRPx/68zSH+OhBawtyNzuHr9+MHYY04gbnz3fBaUDaKm0VDT51GWZ9ZaywrSpHc1hAiKACQApKutgmaki/pJh3AqXI3t78E8e/HpB++PfUP8M3N2SLEls6JIievX5bIO0k73AFYz34/YQfjEONxckk3JiIjMzBTnab71uc7fPoCuEQZEAIN2u6B0ACKYkg5HXy3Lev0GGzQLTKAff8RN/XbjEHB9MVulG+dmu7+48Mrhz+xzL1/pR8fpPbxoui1FQERl5hi9K5fL2icdI5Ue1I/Y+tijn3LBhm9wNaSdmw5ee/q92Stf/fy1s0Msq9QBSg0WGi7IzJFjDK5cLpaNDzb4Xxv47QyphXNqZbCdCLefDdALK9cH71z8hU8N0BdSzqu5CnGJ4meXtjtZM+IW5Rjq5bLy3IrNaZEPBEiSn9qf2AoGHzkfbHf32hYbVFFVhe0Fc/iVL5y3EgCw2N7xAQXCkt388Pxuv0gYRDj4qpyXLqbnTxqiJrlKoH09pOEaqnAqNWH7KsVqDNA0UyAibE+e+a2X+sDsq8Uyds/4mbIyaHTzg53tQa+whgAkRueqsqp9bMWk5qKtBBuEzUJ2CkD7gim1b5qoCEdmURFm++w/uZpx8NXiZOa7F4ajBydlVGEQvzzu93vdTmYJVTgE713wqzdENhKP0MbdPPWmAgIgrF50wpUMbUGF09Kc2BufghhcOZ9V2ahn/Xj3nUPHoKLCvpzmRZEn6UVSQKZ9v3Xqx83kuwFgNUZJvS00BTU1Z2jIkDVZCIHV5uRdvZzNdXdvWEDY6wGdlEEVRCX62lhrjSVEAFFJHa/IKUKyWZFP74VgMzPgZiLbXCIksjbLMlNkwZOV4MrFyTI/d7ZvgbGzfWVusqVrGgEmn17VwvXagZx6nW+z0LQIEoD2FR5Ik9LVLU7jAJsVRZZldma5KmcnVf/S2a4xJJlkxWCJhJ4TKxDE2G6gwkoqk48OlBOj2oiCdkcshawqUvs6TaqaIsHZoigs2bA8mdbbV/e6Racw6rs87hVRYelWa7UouG5H2p75lA90cyMNNqgzIqIx1oBEV8fI6xBGNKQu+iJaN5nUu9f2uoNBxyJwP6v2xy5XVZd6wxRrGwhWEmGb3lensI6BlB0QyRTdbk5cL+plHQUxDYVVVYBMZgEi22pc7V7b629tFQgA1lqZHFac9VQ9rzIs6nr5bK0UtCUOcP399fY4EuW9rWGXwnJ2fFJGNJ1OkVtS9q72UTwYWxiobbnYfnqvtz3KNKTVs4ufOTwJkvVUVdrdJm22OZpeaHXQpzs13bSPphjubPeNm48Pj0u2W1ujrUE3Myjsyvl0svBeKesUMxu6V890t0dZKBeOim4ny5/5uYOKTa6qQU5fudVvDQBcOWYdhOlDphjunRkYd3K0f7SE7s65s9uDVGdUOFTz8f6TkzBHk5PtXD7X3Rpl9fH+ibf9ne1B3nnxaPKhaq66DKCbI5jN+9/uiX9MyQQANPnwzLmRcceHjw9L2r1w+dyomxlcuY7DhcuP7z1aztCKvf9HvbyfazWbe6Ws2+vmBMc3n3hRCT7q+tk/ueZvbGZuqHk2IswxVItZyabXh+M5tdOxFNQqoWYL7rgO/xeO3FvX9it/CgAAAABJRU5ErkJg\"></td><td 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\"></td></tr></tbody></table><div><small>(a vector displayed as a row to save space)</small></div>"
      },
      "metadata": {},
-     "execution_count": 21
+     "execution_count": 51
     }
    ],
    "source": [
diff --git a/vision/mnist/vae.jl b/vision/mnist/vae.jl
deleted file mode 100644
index 66c821dd4..000000000
--- a/vision/mnist/vae.jl
+++ /dev/null
@@ -1,67 +0,0 @@
-using Flux, Flux.Data.MNIST, Statistics
-using Flux: throttle, params
-using Juno: @progress
-
-# Extend distributions slightly to have a numerically stable logpdf for `p` close to 1 or 0.
-using Distributions
-import Distributions: logpdf
-logpdf(b::Bernoulli, y::Bool) = y * log(b.p + eps(Float32)) + (1f0 - y) * log(1 - b.p + eps(Float32))
-
-# Load data, binarise it, and partition into mini-batches of M.
-X = float.(hcat(vec.(MNIST.images())...)) .> 0.5
-N, M = size(X, 2), 100
-data = [X[:,i] for i in Iterators.partition(1:N,M)]
-
-
-################################# Define Model #################################
-
-# Latent dimensionality, # hidden units.
-Dz, Dh = 5, 500
-
-# Components of recognition model / "encoder" MLP.
-A, μ, logσ = Dense(28^2, Dh, tanh), Dense(Dh, Dz), Dense(Dh, Dz)
-g(X) = (h = A(X); (μ(h), logσ(h)))
-z(μ, logσ) = μ + exp(logσ) * randn(Float32)
-
-# Generative model / "decoder" MLP.
-f = Chain(Dense(Dz, Dh, tanh), Dense(Dh, 28^2, σ))
-
-
-####################### Define ways of doing things with the model. #######################
-
-# KL-divergence between approximation posterior and N(0, 1) prior.
-kl_q_p(μ, logσ) = 0.5f0 * sum(exp.(2f0 .* logσ) + μ.^2 .- 1f0 .+ logσ.^2)
-
-# logp(x|z) - conditional probability of data given latents.
-logp_x_z(x, z) = sum(logpdf.(Bernoulli.(f(z)), x))
-
-# Monte Carlo estimator of mean ELBO using M samples.
-L̄(X) = ((μ̂, logσ̂) = g(X); (logp_x_z(X, z.(μ̂, logσ̂)) - kl_q_p(μ̂, logσ̂)) * 1 // M)
-
-loss(X) = -L̄(X) + 0.01f0 * sum(x->sum(x.^2), params(f))
-
-# Sample from the learned model.
-modelsample() = rand.(Bernoulli.(f(z.(zeros(Dz), zeros(Dz)))))
-
-
-################################# Learn Parameters ##############################
-
-evalcb = throttle(() -> @show(-L̄(X[:, rand(1:N, M)])), 30)
-opt = ADAM()
-ps = params(A, μ, logσ, f)
-
-@progress for i = 1:20
-  @info "Epoch $i"
-  Flux.train!(loss, ps, zip(data), opt, cb=evalcb)
-end
-
-
-################################# Sample Output ##############################
-
-using Images
-
-img(x) = Gray.(reshape(x, 28, 28))
-
-cd(@__DIR__)
-sample = hcat(img.([modelsample() for i = 1:10])...)
-save("sample.png", sample)

From 79b8002270a0e210ab8702e5383328fc3e3d5f3d Mon Sep 17 00:00:00 2001
From: SubhadityMukherjee <msubhaditya@gmail.com>
Date: Wed, 12 Aug 2020 13:20:34 +0400
Subject: [PATCH 7/7] refactored Vision fully

---
 .../60-minute-blitz.jl                        |   0
 .../Manifest.toml                             |   0
 .../Project.toml                              |   0
 vision/{VAE => Auto Encoder}/Manifest.toml    |   0
 vision/{VAE => Auto Encoder}/Project.toml     |   0
 vision/Auto Encoder/README.md                 |   3 +
 vision/Auto Encoder/autoencoder.ipynb         | 184 ++++++
 vision/Auto Encoder/autoencoder.jl            |  81 +++
 .../Manifest.toml                             |   0
 .../Project.toml                              |   0
 .../README.md                                 |   0
 .../cppn.ipynb                                |   0
 .../cppn.jl                                   |   0
 .../Manifest.toml                             |   0
 .../Project.toml                              |   0
 vision/Multi Layer Perceptron/README.md       |   3 +
 vision/Multi Layer Perceptron/mlp.ipynb       | 275 ++++++++
 .../{mnist => Multi Layer Perceptron}/mlp.jl  |  61 +-
 .../Manifest.toml                             | 528 +++++++++++++++
 .../Project.toml                              |   3 +
 .../Compositional Pattern Network/README.md   |   5 +
 .../Compositional Pattern Network/cppn.ipynb  | 285 ++++++++
 .../VGG/Compositional Pattern Network/cppn.jl |  84 +++
 .../VGG/Multi Layer Perceptron/Manifest.toml  | 582 ++++++++++++++++
 .../VGG/Multi Layer Perceptron/Project.toml   |   8 +
 vision/VGG/Multi Layer Perceptron/README.md   |   3 +
 vision/VGG/Multi Layer Perceptron/mlp.ipynb   | 275 ++++++++
 vision/VGG/Multi Layer Perceptron/mlp.jl      |  88 +++
 .../Variational Auto Encoder/Manifest.toml    | 582 ++++++++++++++++
 .../VGG/Variational Auto Encoder/Project.toml |   8 +
 .../Variational Auto Encoder}/README.md       |   0
 .../Variational Auto Encoder}/vae.ipynb       |  49 ++
 .../Variational Auto Encoder}/vae.jl          |   0
 vision/{ => VGG}/mnist/.gitignore             |   0
 vision/VGG/mnist/Manifest.toml                | 582 ++++++++++++++++
 vision/VGG/mnist/Project.toml                 |   8 +
 vision/{ => VGG}/mnist/autoencoder.jl         |   0
 vision/{ => VGG}/mnist/cuda/Manifest.toml     |   0
 vision/{ => VGG}/mnist/cuda/Project.toml      |   0
 vision/Variational Auto Encoder/Manifest.toml | 582 ++++++++++++++++
 vision/Variational Auto Encoder/Project.toml  |   8 +
 vision/Variational Auto Encoder/README.md     |   4 +
 vision/Variational Auto Encoder/vae.ipynb     | 416 ++++++++++++
 vision/Variational Auto Encoder/vae.jl        |  99 +++
 vision/cuda/Manifest.toml                     | 624 ++++++++++++++++++
 vision/cuda/Project.toml                      |   9 +
 vision/mnist/conv.jl                          | 126 ----
 47 files changed, 5434 insertions(+), 131 deletions(-)
 rename {tutorials => Introduction to Flux}/60-minute-blitz.jl (100%)
 rename {tutorials => Introduction to Flux}/Manifest.toml (100%)
 rename {tutorials => Introduction to Flux}/Project.toml (100%)
 rename vision/{VAE => Auto Encoder}/Manifest.toml (100%)
 rename vision/{VAE => Auto Encoder}/Project.toml (100%)
 create mode 100644 vision/Auto Encoder/README.md
 create mode 100644 vision/Auto Encoder/autoencoder.ipynb
 create mode 100644 vision/Auto Encoder/autoencoder.jl
 rename vision/{compositionalPatternNetwork => Compositional Pattern Network}/Manifest.toml (100%)
 rename vision/{compositionalPatternNetwork => Compositional Pattern Network}/Project.toml (100%)
 rename vision/{compositionalPatternNetwork => Compositional Pattern Network}/README.md (100%)
 rename vision/{compositionalPatternNetwork => Compositional Pattern Network}/cppn.ipynb (100%)
 rename vision/{compositionalPatternNetwork => Compositional Pattern Network}/cppn.jl (100%)
 rename vision/{mnist => Multi Layer Perceptron}/Manifest.toml (100%)
 rename vision/{mnist => Multi Layer Perceptron}/Project.toml (100%)
 create mode 100644 vision/Multi Layer Perceptron/README.md
 create mode 100644 vision/Multi Layer Perceptron/mlp.ipynb
 rename vision/{mnist => Multi Layer Perceptron}/mlp.jl (61%)
 create mode 100644 vision/VGG/Compositional Pattern Network/Manifest.toml
 create mode 100644 vision/VGG/Compositional Pattern Network/Project.toml
 create mode 100644 vision/VGG/Compositional Pattern Network/README.md
 create mode 100644 vision/VGG/Compositional Pattern Network/cppn.ipynb
 create mode 100644 vision/VGG/Compositional Pattern Network/cppn.jl
 create mode 100644 vision/VGG/Multi Layer Perceptron/Manifest.toml
 create mode 100644 vision/VGG/Multi Layer Perceptron/Project.toml
 create mode 100644 vision/VGG/Multi Layer Perceptron/README.md
 create mode 100644 vision/VGG/Multi Layer Perceptron/mlp.ipynb
 create mode 100644 vision/VGG/Multi Layer Perceptron/mlp.jl
 create mode 100644 vision/VGG/Variational Auto Encoder/Manifest.toml
 create mode 100644 vision/VGG/Variational Auto Encoder/Project.toml
 rename vision/{VAE => VGG/Variational Auto Encoder}/README.md (100%)
 rename vision/{VAE => VGG/Variational Auto Encoder}/vae.ipynb (96%)
 rename vision/{VAE => VGG/Variational Auto Encoder}/vae.jl (100%)
 rename vision/{ => VGG}/mnist/.gitignore (100%)
 create mode 100644 vision/VGG/mnist/Manifest.toml
 create mode 100644 vision/VGG/mnist/Project.toml
 rename vision/{ => VGG}/mnist/autoencoder.jl (100%)
 rename vision/{ => VGG}/mnist/cuda/Manifest.toml (100%)
 rename vision/{ => VGG}/mnist/cuda/Project.toml (100%)
 create mode 100644 vision/Variational Auto Encoder/Manifest.toml
 create mode 100644 vision/Variational Auto Encoder/Project.toml
 create mode 100644 vision/Variational Auto Encoder/README.md
 create mode 100644 vision/Variational Auto Encoder/vae.ipynb
 create mode 100644 vision/Variational Auto Encoder/vae.jl
 create mode 100644 vision/cuda/Manifest.toml
 create mode 100644 vision/cuda/Project.toml
 delete mode 100644 vision/mnist/conv.jl

diff --git a/tutorials/60-minute-blitz.jl b/Introduction to Flux/60-minute-blitz.jl
similarity index 100%
rename from tutorials/60-minute-blitz.jl
rename to Introduction to Flux/60-minute-blitz.jl
diff --git a/tutorials/Manifest.toml b/Introduction to Flux/Manifest.toml
similarity index 100%
rename from tutorials/Manifest.toml
rename to Introduction to Flux/Manifest.toml
diff --git a/tutorials/Project.toml b/Introduction to Flux/Project.toml
similarity index 100%
rename from tutorials/Project.toml
rename to Introduction to Flux/Project.toml
diff --git a/vision/VAE/Manifest.toml b/vision/Auto Encoder/Manifest.toml
similarity index 100%
rename from vision/VAE/Manifest.toml
rename to vision/Auto Encoder/Manifest.toml
diff --git a/vision/VAE/Project.toml b/vision/Auto Encoder/Project.toml
similarity index 100%
rename from vision/VAE/Project.toml
rename to vision/Auto Encoder/Project.toml
diff --git a/vision/Auto Encoder/README.md b/vision/Auto Encoder/README.md
new file mode 100644
index 000000000..5c4e4881d
--- /dev/null
+++ b/vision/Auto Encoder/README.md	
@@ -0,0 +1,3 @@
+# Simple Auto Encoder
+
+- Encoder decoder architecture
\ No newline at end of file
diff --git a/vision/Auto Encoder/autoencoder.ipynb b/vision/Auto Encoder/autoencoder.ipynb
new file mode 100644
index 000000000..01da4cc0c
--- /dev/null
+++ b/vision/Auto Encoder/autoencoder.ipynb	
@@ -0,0 +1,184 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Flux, Flux.Data.MNIST\n",
+    "using Flux: @epochs, onehotbatch, mse, throttle\n",
+    "using Base.Iterators\n",
+    "using CuArrays\n",
+    "using Images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Encode MNIST images as compressed vectors that can later be decoded back into images.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "imgs = MNIST.images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Partition into batches of size 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [float(hcat(vec.(imgs)...)) for imgs in partition(imgs, 1000)]\n",
+    "data = gpu.(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 32 # Size of the encoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- You can try to make the encoder/decoder network larger\n",
+    "- Also, the output of encoder is a coding of the given input.\n",
+    "- In this case, the input dimension is 28^2 and the output dimension of\n",
+    "- encoder is 32. This implies that the coding is a compressed representation.\n",
+    "- We can make lossy compression via this `encoder`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder = Dense(28^2, N, leakyrelu) |> gpu\n",
+    "decoder = Dense(N, 28^2, leakyrelu) |> gpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = Chain(encoder, decoder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss(x) = mse(m(x), x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "evalcb = throttle(() -> @show(loss(data[1])), 5)\n",
+    "opt = ADAM()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@epochs 10 Flux.train!(loss, params(m), zip(data), opt, cb = evalcb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sample output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img(x::Vector) = Gray.(reshape(clamp.(x, 0, 1), 28, 28))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "function sample()\n",
+    "  # 20 random digits\n",
+    "  before = [imgs[i] for i in rand(1:length(imgs), 20)]\n",
+    "  # Before and after images\n",
+    "  after = img.(map(x -> cpu(m)(float(vec(x))).data, before))\n",
+    "  # Stack them all together\n",
+    "  hcat(vcat.(before, after)...)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save(\"sample.png\", sample())"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/vision/Auto Encoder/autoencoder.jl b/vision/Auto Encoder/autoencoder.jl
new file mode 100644
index 000000000..30b413184
--- /dev/null
+++ b/vision/Auto Encoder/autoencoder.jl	
@@ -0,0 +1,81 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
+using Flux, Flux.Data.MNIST
+using Flux: @epochs, onehotbatch, mse, throttle
+using Base.Iterators
+using CuArrays
+using Images
+
+# %% [markdown]
+# ## Encode MNIST images as compressed vectors that can later be decoded back into images.
+# 
+
+# %%
+
+imgs = MNIST.images()
+
+# %% [markdown]
+# # Partition into batches of size 1000
+
+# %%
+data = [float(hcat(vec.(imgs)...)) for imgs in partition(imgs, 1000)]
+data = gpu.(data)
+
+
+# %%
+N = 32 # Size of the encoding
+
+# %% [markdown]
+# - You can try to make the encoder/decoder network larger
+# - Also, the output of encoder is a coding of the given input.
+# - In this case, the input dimension is 28^2 and the output dimension of
+# - encoder is 32. This implies that the coding is a compressed representation.
+# - We can make lossy compression via this `encoder`.
+
+# %%
+encoder = Dense(28^2, N, leakyrelu) |> gpu
+decoder = Dense(N, 28^2, leakyrelu) |> gpu
+
+
+# %%
+m = Chain(encoder, decoder)
+
+
+# %%
+loss(x) = mse(m(x), x)
+
+
+# %%
+
+evalcb = throttle(() -> @show(loss(data[1])), 5)
+opt = ADAM()
+
+# %% [markdown]
+# ## Train
+
+# %%
+@epochs 10 Flux.train!(loss, params(m), zip(data), opt, cb = evalcb)
+
+# %% [markdown]
+# # Sample output
+
+# %%
+img(x::Vector) = Gray.(reshape(clamp.(x, 0, 1), 28, 28))
+
+
+# %%
+function sample()
+  # 20 random digits
+  before = [imgs[i] for i in rand(1:length(imgs), 20)]
+  # Before and after images
+  after = img.(map(x -> cpu(m)(float(vec(x))).data, before))
+  # Stack them all together
+  hcat(vcat.(before, after)...)
+end
+
+
+# %%
+save("sample.png", sample())
+
+
diff --git a/vision/compositionalPatternNetwork/Manifest.toml b/vision/Compositional Pattern Network/Manifest.toml
similarity index 100%
rename from vision/compositionalPatternNetwork/Manifest.toml
rename to vision/Compositional Pattern Network/Manifest.toml
diff --git a/vision/compositionalPatternNetwork/Project.toml b/vision/Compositional Pattern Network/Project.toml
similarity index 100%
rename from vision/compositionalPatternNetwork/Project.toml
rename to vision/Compositional Pattern Network/Project.toml
diff --git a/vision/compositionalPatternNetwork/README.md b/vision/Compositional Pattern Network/README.md
similarity index 100%
rename from vision/compositionalPatternNetwork/README.md
rename to vision/Compositional Pattern Network/README.md
diff --git a/vision/compositionalPatternNetwork/cppn.ipynb b/vision/Compositional Pattern Network/cppn.ipynb
similarity index 100%
rename from vision/compositionalPatternNetwork/cppn.ipynb
rename to vision/Compositional Pattern Network/cppn.ipynb
diff --git a/vision/compositionalPatternNetwork/cppn.jl b/vision/Compositional Pattern Network/cppn.jl
similarity index 100%
rename from vision/compositionalPatternNetwork/cppn.jl
rename to vision/Compositional Pattern Network/cppn.jl
diff --git a/vision/mnist/Manifest.toml b/vision/Multi Layer Perceptron/Manifest.toml
similarity index 100%
rename from vision/mnist/Manifest.toml
rename to vision/Multi Layer Perceptron/Manifest.toml
diff --git a/vision/mnist/Project.toml b/vision/Multi Layer Perceptron/Project.toml
similarity index 100%
rename from vision/mnist/Project.toml
rename to vision/Multi Layer Perceptron/Project.toml
diff --git a/vision/Multi Layer Perceptron/README.md b/vision/Multi Layer Perceptron/README.md
new file mode 100644
index 000000000..4ea3069f7
--- /dev/null
+++ b/vision/Multi Layer Perceptron/README.md	
@@ -0,0 +1,3 @@
+# A simple MLP
+
+- A good place to start with Deep Learning using Flux
\ No newline at end of file
diff --git a/vision/Multi Layer Perceptron/mlp.ipynb b/vision/Multi Layer Perceptron/mlp.ipynb
new file mode 100644
index 000000000..1772c9f66
--- /dev/null
+++ b/vision/Multi Layer Perceptron/mlp.ipynb	
@@ -0,0 +1,275 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "julia-1.5",
+   "display_name": "Julia 1.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Flux, Flux.Data.MNIST, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated\n",
+    "using CuArrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classify MNIST digits with a simple multi-layer-perceptron\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "60000-element Array{Array{ColorTypes.Gray{FixedPointNumbers.Normed{UInt8,8}},2},1}:\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); 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Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n ⋮\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); 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Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "imgs = MNIST.images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Stack images into one large batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "60000-element Array{Int64,1}:\n 5\n 0\n 4\n 1\n 9\n 2\n 1\n 3\n 1\n 4\n 3\n 5\n 3\n ⋮\n 7\n 8\n 9\n 2\n 9\n 5\n 1\n 8\n 3\n 5\n 6\n 8"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "\n",
+    "X = hcat(float.(reshape.(imgs, :))...) |> gpu\n",
+    "\n",
+    "labels = MNIST.labels()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# One-hot-encode the labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "10×60000 Flux.OneHotMatrix{CuArray{Flux.OneHotVector,1,Nothing}}:\n 0  1  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  1  0  0  1  0  1  0  0  0  0     0  0  0  0  0  0  1  0  0  0  0  0\n 0  0  0  0  0  1  0  0  0  0  0  0  0     0  0  0  1  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  1  0  0  1  0  1     0  0  0  0  0  0  0  0  1  0  0  0\n 0  0  1  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 1  0  0  0  0  0  0  0  0  0  0  1  0  …  0  0  0  0  0  1  0  0  0  1  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  1  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     1  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  0  0  1  0  0  0  1\n 0  0  0  0  1  0  0  0  0  0  0  0  0     0  0  1  0  1  0  0  0  0  0  0  0"
+     },
+     "metadata": {},
+     "execution_count": 5
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Warning: Performing scalar operations on GPU arrays: This is very slow, consider disallowing these operations with `allowscalar(false)`\n└ @ GPUArrays /home/subhaditya/.julia/packages/GPUArrays/JqOUg/src/host/indexing.jl:43\n"
+    }
+   ],
+   "source": [
+    "\n",
+    "Y = onehotbatch(labels, 0:9) |> gpu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "Chain(Dense(784, 32, relu), Dense(32, 10), softmax)"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "m = Chain(\n",
+    "  Dense(28^2, 32, relu),\n",
+    "  Dense(32, 10),\n",
+    "  softmax) |> gpu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "loss (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "\n",
+    "loss(x, y) = crossentropy(m(x), y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "accuracy (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "\n",
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "\n",
+    "dataset = repeated((X, Y), 200)\n",
+    "evalcb = () -> @show(loss(X, Y))\n",
+    "opt = ADAM()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "loss(X, Y) = 2.2866402f0\n"
+    }
+   ],
+   "source": [
+    "\n",
+    "Flux.train!(loss, params(m), dataset, opt, cb = throttle(evalcb, 10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "accuracy(X, Y)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Test set accuracy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tX = hcat(float.(reshape.(MNIST.images(:test), :))...) |> gpu\n",
+    "tY = onehotbatch(MNIST.labels(:test), 0:9) |> gpu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "accuracy(tX, tY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/vision/mnist/mlp.jl b/vision/Multi Layer Perceptron/mlp.jl
similarity index 61%
rename from vision/mnist/mlp.jl
rename to vision/Multi Layer Perceptron/mlp.jl
index 06aeaad75..edd843690 100644
--- a/vision/mnist/mlp.jl
+++ b/vision/Multi Layer Perceptron/mlp.jl	
@@ -1,37 +1,88 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
 using Flux, Flux.Data.MNIST, Statistics
 using Flux: onehotbatch, onecold, crossentropy, throttle
 using Base.Iterators: repeated
-# using CuArrays
+using CuArrays
 
-# Classify MNIST digits with a simple multi-layer-perceptron
+# %% [markdown]
+# # Classify MNIST digits with a simple multi-layer-perceptron
+# 
 
+# %%
 imgs = MNIST.images()
-# Stack images into one large batch
+
+# %% [markdown]
+# 
+# # Stack images into one large batch
+
+# %%
+
 X = hcat(float.(reshape.(imgs, :))...) |> gpu
 
 labels = MNIST.labels()
-# One-hot-encode the labels
+
+# %% [markdown]
+# # One-hot-encode the labels
+
+# %%
+
 Y = onehotbatch(labels, 0:9) |> gpu
 
+
+# %%
+
 m = Chain(
   Dense(28^2, 32, relu),
   Dense(32, 10),
   softmax) |> gpu
 
+
+# %%
+
 loss(x, y) = crossentropy(m(x), y)
 
+
+# %%
+
 accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
 
+
+# %%
+
 dataset = repeated((X, Y), 200)
 evalcb = () -> @show(loss(X, Y))
 opt = ADAM()
 
+# %% [markdown]
+# # Train
+
+# %%
+
 Flux.train!(loss, params(m), dataset, opt, cb = throttle(evalcb, 10))
 
+
+# %%
+
 accuracy(X, Y)
 
-# Test set accuracy
+# %% [markdown]
+# 
+# # Test set accuracy
+# 
+
+# %%
 tX = hcat(float.(reshape.(MNIST.images(:test), :))...) |> gpu
 tY = onehotbatch(MNIST.labels(:test), 0:9) |> gpu
 
+
+# %%
+
 accuracy(tX, tY)
+
+
+# %%
+
+
+
diff --git a/vision/VGG/Compositional Pattern Network/Manifest.toml b/vision/VGG/Compositional Pattern Network/Manifest.toml
new file mode 100644
index 000000000..5203234cc
--- /dev/null
+++ b/vision/VGG/Compositional Pattern Network/Manifest.toml	
@@ -0,0 +1,528 @@
+[[AbstractFFTs]]
+deps = ["Compat", "LinearAlgebra"]
+git-tree-sha1 = "8d59c3b1463b5e0ad05a3698167f85fac90e184d"
+uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
+version = "0.3.2"
+
+[[AbstractTrees]]
+deps = ["Markdown", "Test"]
+git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b"
+uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
+version = "0.2.1"
+
+[[Adapt]]
+deps = ["LinearAlgebra", "Test"]
+git-tree-sha1 = "53d8fec4f662088c1202530e338a11a919407f3b"
+uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+version = "0.4.2"
+
+[[AxisAlgorithms]]
+deps = ["Compat", "WoodburyMatrices"]
+git-tree-sha1 = "99dabbe853e4f641ab21a676131f2cf9fb29937e"
+uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950"
+version = "0.3.0"
+
+[[AxisArrays]]
+deps = ["Compat", "Dates", "IntervalSets", "IterTools", "Random", "RangeArrays", "Test"]
+git-tree-sha1 = "2e2536e9e6f27c4f8d09d8442b61a7ae0b910c28"
+uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
+version = "0.3.0"
+
+[[Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+
+[[BinDeps]]
+deps = ["Compat", "Libdl", "SHA", "URIParser"]
+git-tree-sha1 = "12093ca6cdd0ee547c39b1870e0c9c3f154d9ca9"
+uuid = "9e28174c-4ba2-5203-b857-d8d62c4213ee"
+version = "0.8.10"
+
+[[BinaryProvider]]
+deps = ["Libdl", "Pkg", "SHA", "Test"]
+git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e"
+uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
+version = "0.5.3"
+
+[[CatIndices]]
+deps = ["CustomUnitRanges", "OffsetArrays", "Test"]
+git-tree-sha1 = "254cf73ea369d2e39bfd6c5eb27a2296cfaed68c"
+uuid = "aafaddc9-749c-510e-ac4f-586e18779b91"
+version = "0.2.0"
+
+[[CodecZlib]]
+deps = ["BinaryProvider", "Libdl", "Test", "TranscodingStreams"]
+git-tree-sha1 = "e3df104c84dfc108f0ca203fd7f5bbdc98641ae9"
+uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
+version = "0.5.1"
+
+[[ColorTypes]]
+deps = ["FixedPointNumbers", "Random", "Test"]
+git-tree-sha1 = "f73b0e10f2a5756de7019818a41654686da06b09"
+uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
+version = "0.7.5"
+
+[[ColorVectorSpace]]
+deps = ["ColorTypes", "Colors", "FixedPointNumbers", "LinearAlgebra", "SpecialFunctions", "Statistics", "StatsBase", "Test"]
+git-tree-sha1 = "a890f08e61b40e9843d7177206da61229a3603c8"
+uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4"
+version = "0.6.2"
+
+[[Colors]]
+deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Printf", "Reexport", "Test"]
+git-tree-sha1 = "9f0a0210450acb91c730b730a994f8eef1d3d543"
+uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
+version = "0.9.5"
+
+[[CommonSubexpressions]]
+deps = ["Test"]
+git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0"
+uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
+version = "0.2.0"
+
+[[Compat]]
+deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
+git-tree-sha1 = "ec61a16eed883ad0cfa002d7489b3ce6d039bb9a"
+uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
+version = "1.4.0"
+
+[[ComputationalResources]]
+deps = ["Test"]
+git-tree-sha1 = "89e7e7ed20af73d9f78877d2b8d1194e7b6ff13d"
+uuid = "ed09eef8-17a6-5b46-8889-db040fac31e3"
+version = "0.3.0"
+
+[[Conda]]
+deps = ["Compat", "JSON", "VersionParsing"]
+git-tree-sha1 = "fb86fe40cb5b35990e368709bfdc1b46dbb99dac"
+uuid = "8f4d0f93-b110-5947-807f-2305c1781a2d"
+version = "1.1.1"
+
+[[CoordinateTransformations]]
+deps = ["Compat", "Rotations", "StaticArrays"]
+git-tree-sha1 = "47f05d0b7f4999609f92e657147df000818c1f24"
+uuid = "150eb455-5306-5404-9cee-2592286d6298"
+version = "0.5.0"
+
+[[CustomUnitRanges]]
+deps = ["Test"]
+git-tree-sha1 = "0a106457a1831555857e18ac9617279c22fc393b"
+uuid = "dc8bdbbb-1ca9-579f-8c36-e416f6a65cce"
+version = "0.2.0"
+
+[[DataStructures]]
+deps = ["InteractiveUtils", "OrderedCollections", "Random", "Serialization", "Test"]
+git-tree-sha1 = "ca971f03e146cf144a9e2f2ce59674f5bf0e8038"
+uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
+version = "0.15.0"
+
+[[Dates]]
+deps = ["Printf"]
+uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
+
+[[DelimitedFiles]]
+deps = ["Mmap"]
+uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
+
+[[DiffResults]]
+deps = ["Compat", "StaticArrays"]
+git-tree-sha1 = "db8acf46717b13d6c48deb7a12007c7f85a70cf7"
+uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
+version = "0.0.3"
+
+[[DiffRules]]
+deps = ["Random", "Test"]
+git-tree-sha1 = "c49ec69428ffea0c1d1bbdc63d1a70f5df5860ad"
+uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
+version = "0.0.7"
+
+[[Distances]]
+deps = ["LinearAlgebra", "Printf", "Random", "Statistics", "Test"]
+git-tree-sha1 = "0e37d8a95bafbeb9c800ef27ab6f443d29e17610"
+uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+version = "0.7.4"
+
+[[Distributed]]
+deps = ["LinearAlgebra", "Random", "Serialization", "Sockets"]
+uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+
+[[FFTViews]]
+deps = ["CustomUnitRanges", "FFTW", "Test"]
+git-tree-sha1 = "9d7993227ca7c0fdb6b31deef193adbba11c8f4e"
+uuid = "4f61f5a4-77b1-5117-aa51-3ab5ef4ef0cd"
+version = "0.2.0"
+
+[[FFTW]]
+deps = ["AbstractFFTs", "BinaryProvider", "Compat", "Conda", "Libdl", "LinearAlgebra", "Reexport", "Test"]
+git-tree-sha1 = "29cda58afbf62f35b1a094882ad6c745a47b2eaa"
+uuid = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
+version = "0.2.4"
+
+[[FileIO]]
+deps = ["Pkg", "Random", "Test"]
+git-tree-sha1 = "c94b0787956629036fb2b20fccde9e52b89d079a"
+uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
+version = "1.0.5"
+
+[[FixedPointNumbers]]
+deps = ["Test"]
+git-tree-sha1 = "b8045033701c3b10bf2324d7203404be7aef88ba"
+uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
+version = "0.5.3"
+
+[[Flux]]
+deps = ["AbstractTrees", "Adapt", "CodecZlib", "Colors", "DiffRules", "ForwardDiff", "Juno", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Pkg", "Printf", "Random", "Reexport", "Requires", "SpecialFunctions", "Statistics", "StatsBase", "Test", "ZipFile"]
+git-tree-sha1 = "cc0b3a248448d6ebecca4d1588c8fd54b96c5939"
+uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+version = "0.7.0"
+
+[[ForwardDiff]]
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+git-tree-sha1 = "b91250044374764e7c29af59a774c4b8d6100b6e"
+uuid = "f6369f11-7733-5829-9624-2563aa707210"
+version = "0.10.1"
+
+[[Graphics]]
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+git-tree-sha1 = "e3ead4211073d4117a0d2ef7d1efc5c8092c8412"
+uuid = "a2bd30eb-e257-5431-a919-1863eab51364"
+version = "0.4.0"
+
+[[IdentityRanges]]
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+uuid = "bbac6d45-d8f3-5730-bfe4-7a449cd117ca"
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+
+[[ImageAxes]]
+deps = ["AxisArrays", "Colors", "FixedPointNumbers", "ImageCore", "MappedArrays", "Reexport", "SimpleTraits", "Test"]
+git-tree-sha1 = "5735ec90843acaa67a4624611921c686cdf4efbf"
+uuid = "2803e5a7-5153-5ecf-9a86-9b4c37f5f5ac"
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+
+[[ImageCore]]
+deps = ["ColorTypes", "Colors", "FFTW", "FixedPointNumbers", "Graphics", "MappedArrays", "OffsetArrays", "PaddedViews", "Random", "Statistics", "Test"]
+git-tree-sha1 = "5e7b1f49c80541860e08a7ea91805a24c1641f19"
+uuid = "a09fc81d-aa75-5fe9-8630-4744c3626534"
+version = "0.7.3"
+
+[[ImageDistances]]
+deps = ["Colors", "Distances", "LinearAlgebra", "ProgressMeter", "Test"]
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+uuid = "51556ac3-7006-55f5-8cb3-34580c88182d"
+version = "0.1.1"
+
+[[ImageFiltering]]
+deps = ["CatIndices", "ColorVectorSpace", "Colors", "ComputationalResources", "DataStructures", "FFTViews", "FFTW", "FixedPointNumbers", "ImageCore", "LinearAlgebra", "Logging", "MappedArrays", "OffsetArrays", "Random", "StaticArrays", "Statistics", "Test", "TiledIteration"]
+git-tree-sha1 = "9bb70bff423fb26b45817da5f127d71e662bfe50"
+uuid = "6a3955dd-da59-5b1f-98d4-e7296123deb5"
+version = "0.5.2"
+
+[[ImageMetadata]]
+deps = ["AxisArrays", "ColorVectorSpace", "Colors", "Dates", "FixedPointNumbers", "ImageAxes", "ImageCore", "IndirectArrays", "Statistics", "Test"]
+git-tree-sha1 = "e8763955937114ea15cf3bfb5ae00588e19b58c3"
+uuid = "bc367c6b-8a6b-528e-b4bd-a4b897500b49"
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+
+[[ImageMorphology]]
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+uuid = "787d08f9-d448-5407-9aad-5290dd7ab264"
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+
+[[ImageShow]]
+deps = ["Base64", "ColorTypes", "Colors", "FileIO", "FixedPointNumbers", "ImageCore", "OffsetArrays", "Requires", "Test"]
+git-tree-sha1 = "98eb96a852fd2d6f0905cbe4d215ec2113805b46"
+uuid = "4e3cecfd-b093-5904-9786-8bbb286a6a31"
+version = "0.1.2"
+
+[[ImageTransformations]]
+deps = ["AxisAlgorithms", "ColorTypes", "ColorVectorSpace", "Colors", "CoordinateTransformations", "FixedPointNumbers", "IdentityRanges", "ImageCore", "Interpolations", "LinearAlgebra", "OffsetArrays", "StaticArrays", "Test"]
+git-tree-sha1 = "18ae1c0a8df31549a9452ceac93751d4aa166071"
+uuid = "02fcd773-0e25-5acc-982a-7f6622650795"
+version = "0.7.1"
+
+[[Images]]
+deps = ["AxisArrays", "Base64", "ColorTypes", "ColorVectorSpace", "Colors", "FileIO", "FixedPointNumbers", "Graphics", "ImageAxes", "ImageCore", "ImageDistances", "ImageFiltering", "ImageMetadata", "ImageMorphology", "ImageShow", "ImageTransformations", "IndirectArrays", "LinearAlgebra", "MappedArrays", "OffsetArrays", "Random", "Reexport", "SIUnits", "SparseArrays", "StaticArrays", "Statistics", "StatsBase", "Test", "TiledIteration"]
+git-tree-sha1 = "01291372b2c3a67191a8cc2ff291f33f5485ac8b"
+uuid = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
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+[[IndirectArrays]]
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+
+[[InteractiveUtils]]
+deps = ["LinearAlgebra", "Markdown"]
+uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+
+[[Interpolations]]
+deps = ["AxisAlgorithms", "LinearAlgebra", "OffsetArrays", "Random", "Ratios", "SharedArrays", "SparseArrays", "StaticArrays", "Test", "WoodburyMatrices"]
+git-tree-sha1 = "1ec5b78dbfb5ae2b3d7a4987a9287fed4cc31ded"
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+
+[[IntervalSets]]
+deps = ["Compat"]
+git-tree-sha1 = "9dc556002f23740de13946e8c2e41798e09a9249"
+uuid = "8197267c-284f-5f27-9208-e0e47529a953"
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+
+[[IterTools]]
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+
+[[JSON]]
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+
+[[Juno]]
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+
+[[LibGit2]]
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+[[Libdl]]
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+
+[[LinearAlgebra]]
+deps = ["Libdl"]
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+
+[[Logging]]
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+
+[[MacroTools]]
+deps = ["Compat"]
+git-tree-sha1 = "c443e1c8d58a4e9f61b708ad0a88286c7042145b"
+uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
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+
+[[MappedArrays]]
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+version = "0.2.1"
+
+[[Markdown]]
+deps = ["Base64"]
+uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
+
+[[Media]]
+deps = ["MacroTools", "Test"]
+git-tree-sha1 = "75a54abd10709c01f1b86b84ec225d26e840ed58"
+uuid = "e89f7d12-3494-54d1-8411-f7d8b9ae1f27"
+version = "0.5.0"
+
+[[Missings]]
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+uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
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+
+[[Mmap]]
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+
+[[NNlib]]
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+git-tree-sha1 = "51330bb45927379007e089997bf548fbe232589d"
+uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+version = "0.4.3"
+
+[[NaNMath]]
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+[[OffsetArrays]]
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+
+[[OrderedCollections]]
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+
+[[PaddedViews]]
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+
+[[Pkg]]
+deps = ["Dates", "LibGit2", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
+uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+
+[[Printf]]
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+uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+
+[[Profile]]
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+
+[[ProgressMeter]]
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+
+[[REPL]]
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+uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
+
+[[Random]]
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+
+[[RangeArrays]]
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+uuid = "b3c3ace0-ae52-54e7-9d0b-2c1406fd6b9d"
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+
+[[Ratios]]
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+
+[[Reexport]]
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+
+[[Rotations]]
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+
+[[SHA]]
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+
+[[SIUnits]]
+deps = ["Compat", "TexExtensions"]
+git-tree-sha1 = "224d83b62711fe7e429454aace2c97eb2cf36031"
+uuid = "b9d75638-96e3-5676-bdf0-e9c958f63a55"
+version = "0.1.0"
+
+[[Serialization]]
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+[[SharedArrays]]
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+uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
+
+[[SimpleTraits]]
+deps = ["InteractiveUtils", "MacroTools", "Test"]
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+[[Sockets]]
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+[[SparseArrays]]
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+[[SpecialFunctions]]
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+
+[[StaticArrays]]
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+
+[[Statistics]]
+deps = ["LinearAlgebra", "SparseArrays"]
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+
+[[StatsBase]]
+deps = ["DataStructures", "DelimitedFiles", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "Test"]
+git-tree-sha1 = "7b596062316c7d846b67bf625d5963a832528598"
+uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
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+[[Test]]
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+
+[[TexExtensions]]
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+[[TiledIteration]]
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+[[TranscodingStreams]]
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+[[URIParser]]
+deps = ["Test", "Unicode"]
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+uuid = "30578b45-9adc-5946-b283-645ec420af67"
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+[[UUIDs]]
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+[[Unicode]]
+uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
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+[[VersionParsing]]
+deps = ["Compat"]
+git-tree-sha1 = "c9d5aa108588b978bd859554660c8a5c4f2f7669"
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+[[WoodburyMatrices]]
+deps = ["LinearAlgebra", "Random", "SparseArrays", "Test"]
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+[[ZipFile]]
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diff --git a/vision/VGG/Compositional Pattern Network/Project.toml b/vision/VGG/Compositional Pattern Network/Project.toml
new file mode 100644
index 000000000..5a4b19ee1
--- /dev/null
+++ b/vision/VGG/Compositional Pattern Network/Project.toml	
@@ -0,0 +1,3 @@
+[deps]
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
diff --git a/vision/VGG/Compositional Pattern Network/README.md b/vision/VGG/Compositional Pattern Network/README.md
new file mode 100644
index 000000000..00b08a6ba
--- /dev/null
+++ b/vision/VGG/Compositional Pattern Network/README.md	
@@ -0,0 +1,5 @@
+# Generating Abstract Patterns with Compositional Pattern Producing Network
+
+- [Link to post](https://blog.otoro.net/2016/03/25/generating-abstract-patterns-with-tensorflow/)
+
+
diff --git a/vision/VGG/Compositional Pattern Network/cppn.ipynb b/vision/VGG/Compositional Pattern Network/cppn.ipynb
new file mode 100644
index 000000000..d3f14dbab
--- /dev/null
+++ b/vision/VGG/Compositional Pattern Network/cppn.ipynb	
@@ -0,0 +1,285 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "julia-1.5",
+   "display_name": "Julia 1.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Images\n",
+    "using Flux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "262144"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "z_dim = 2\n",
+    "x_dim = 512\n",
+    "y_dim = 512\n",
+    "N = 14\n",
+    "hidden = 9\n",
+    "batch_size = 1024\n",
+    "n = x_dim * y_dim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## cast 0:x-1 to -0.5:0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "cast (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "\n",
+    "cast(x) = [range(-0.5, stop=0.5, step=1/(x - 1))...]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "262144-element Array{Float64,1}:\n 0.7071067811865476\n 0.7057243672141199\n 0.7043446771602189\n 0.702967727063262\n 0.7015935330555991\n 0.7002221113638016\n 0.6988534783089481\n 0.6974876503069023\n 0.6961246438685855\n 0.6947644756002421\n 0.6934071622036989\n 0.6920527204766176\n 0.6907011673127395\n ⋮\n 0.6920527204766176\n 0.6934071622036989\n 0.6947644756002421\n 0.6961246438685855\n 0.6974876503069023\n 0.6988534783089481\n 0.7002221113638016\n 0.7015935330555991\n 0.702967727063262\n 0.7043446771602189\n 0.7057243672141199\n 0.7071067811865476"
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "xs, ys = cast(x_dim), cast(y_dim)\n",
+    "xs = repeat(xs, inner=(y_dim))\n",
+    "ys = repeat(ys, outer=(x_dim))\n",
+    "rs = sqrt.(xs.^2 + ys.^2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "unit (generic function with 4 methods)"
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ],
+   "source": [
+    "unit(in=N, out=N, f=tanh) = Dense(in, out, f, initW=randn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "11-element Array{Any,1}:\n Dense(5, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 14, tanh)\n Dense(14, 1, σ)"
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ],
+   "source": [
+    "layers = Any[unit(3 + z_dim), [unit() for _ in 1:hidden]..., unit(N, 1, σ)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- In essence, CPPN is just a function, c = f(x, y), that defines the intensity of the image for every point in space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "getColorAt (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ],
+   "source": [
+    "model = Chain(layers...)\n",
+    "getColorAt(x) = Flux.data(model(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make batches from the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "batch (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ],
+   "source": [
+    "function batch(arr, s)\n",
+    "    l = size(arr, 2)\n",
+    "    batches = [arr[:, i:min(i+s-1, l)] for i=1:s:l]\n",
+    "    batches\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create image with intensities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "getImage (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ],
+   "source": [
+    "function getImage(z)\n",
+    "    z = repeat(reshape(z, 1, z_dim), outer=(n, 1))\n",
+    "    coords = hcat(xs, ys, rs, z)'\n",
+    "    coords = batch(coords, batch_size)\n",
+    "    pixels = [Gray.(hcat(getColorAt.(coords)...))...]\n",
+    "    reshape(pixels, y_dim, x_dim)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "saveImg (generic function with 2 methods)"
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ],
+   "source": [
+    "function saveImg(z, image_path=\"sample.png\")\n",
+    "    imgg = getImage(z)\n",
+    "    save(image_path, imgg)\n",
+    "    imgg\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Will generate at random everytime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "10-element Array{Array{Gray{Float64},2},1}:\n [Gray{Float64}(0.5946810419058337) Gray{Float64}(0.5844071218782684) … Gray{Float64}(0.15290018571840844) Gray{Float64}(0.15713981219146686); Gray{Float64}(0.6045090537341339) Gray{Float64}(0.5949466340725956) … Gray{Float64}(0.14036278476817127) Gray{Float64}(0.1443107836337645); … ; Gray{Float64}(0.24997947367144818) Gray{Float64}(0.3941396770541056) … Gray{Float64}(0.9202468729908154) Gray{Float64}(0.9346036508108042); Gray{Float64}(0.322760625913554) Gray{Float64}(0.419712705310896) … Gray{Float64}(0.9263213744240756) Gray{Float64}(0.9397232772490718)]\n [Gray{Float64}(0.11819501133927904) Gray{Float64}(0.11675908787762) … Gray{Float64}(0.7648690894451594) Gray{Float64}(0.7641494669450892); Gray{Float64}(0.11122510245225029) Gray{Float64}(0.10999837637012912) … Gray{Float64}(0.7760557634698783) Gray{Float64}(0.7774299862906915); … ; Gray{Float64}(0.9884111268623222) Gray{Float64}(0.9878463147302998) … Gray{Float64}(0.040437062420612985) Gray{Float64}(0.040251734266504434); 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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\"></td><td 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ZVmWdRNbALpwMWOMc00TBYi8AVCxkUhxQZw7NyAyBJj3h97CZBaGfrxDq+e2DUozC4lq51cf7bz0nVe++PT15vfs/piDKKB1RurZvJzP52XdhFZs6SpievMYmRXIOWuNMp/2bwiKgJgULENESNob5bYpGSnPN/PByCPEphQAAMxfPtr83ODV2eCv7/7O80dDZgUgY0nq2Xw+nc7KqmkjsTUAICESkYiIIrm8yC2RIdPyFGiFJaL2CohIfW9cWDDKgHkvzz2ChrpGVMXB5zab5ju/dGHyJz9a++2GGgVAMpa4ns0nJ8fTedWEzgW6BoSQiCjxU1tVdfRIRAsAgNrlwxYAAemwsEjGRLU+cwQgdSM9anjliyO+ePuV89W3/3f8t70nkQiQyGCsZpPDg6PpvI7c2r/tytLxhowqIMUQQmTtdFNI5Spl5HR8+w3fzywgWWYBJAQNVY251dFn3Hy73ho2P/yTePXLD6rcABIZaKqTg8dPjuY1c5uIF70PQhuOooCpbi+3Dpi8AFL3mDAkYcNbRHTIWBMq8Uldi/Vr18z+Zsh78PH/0PkfPL27YRGJSJvpwe7uwayJS8p99/aAwEhkohVF0+ZggLZrWHTs1EHoOCxZi4RgVWQ2QSNRAg42zuOjNZ2u4w/+I1fPX/v+iidEBK6OHt1/PK1F2orbNScLlRoEo4ksYFiBiLAF0BlDsXXEBYlGBLJoEosKsQG06NdWNuDxwIae/8Z/2Ni5+Yc/np13CKhxunv30XHNXa1TUekUxta4iAhMzIJOgIzBVBO0uwzsssapfyCQRVIVJAJFVbGj9QHr2DD2/9e/X3n5LZgcXBs7Qi2f3r17ULKoJFquorK4BkBEwqRKiDCD7QlZwlSTlkdyi7BZQAIbp/N5U2znMXMxgBm72h7eGxYr7/+X6lfKW70P//GFdcPVwf1P9mZRRFsFv7sDXUhCXUokZBbwYyEC5QUtac+HxVwh2QAQ7d7rT8r+S+dzK1wJuqzC42/vrXx28MMHX9j8xnxta+eMmz25f/fRSSMiwi0/XYywTtUABCQ0xhrDomawLajKMSZRZlnSOpW4EADA3nodVy9e7pNjVTA2hPhn79Zbv/rwPfjS/Y/hzJltfnj//u5RzSrCMXG91gOW5man/QFZa5nVb5wTjbGbKnTC1emtLf7R1qHIe1I6QiNsZa5//mYjsPqDN18afqvqnd/GH99+fFKzqHBk5tPz23dbTgoCwNAY6xzTvXObFOq6ibwIzEVyeMYSlogP37nXc/nOixizefjmjxlkVb558rn92zo8u/qTd04aURXhGPnU/qeq4LNDJAXhULtM7t89Z6p5OyrBzlJda3E6KFQLzPXxJ4D23FpfOfzdG0JRN2+9+fzqj6e4cmbvjRMBUGFuJ6mqzxza5eXTwYOqcmwC3rqcV9OKtS2KLVzVZ34YFWysWuIvLhjzo+8HGxXG7/Arkw+UVkc/2kcESDegLfmE5ZvtIqGDkHQjmcudj9ZxPlM0hNRJoqDPQgBFtRIRiQz5s8Oj8e3vloYAsHhwffutA6DV41uBECTpEvKMMLe4gkVgtWgUlESb/Xe3zscqkrXGIAGCAoLgMxAUACyQsdYibHzOjgY3SjKIYHz1SrzJQINbJ8aASksE9NlHTxewCO1OQURFRdX69utf6IcarLPWmNTRI6ksN5kICtbmznorxavP2fHuzAkBgPPFmbt7AAh3G0OgC/Mvh3X73yUAi+8qAopOfna4tZobY5231iABogIuZyjQpK2rye3wlS/0XP0ICmRWyLP1+GAKgOVRFAJ49vjTNN9NbqFr2FsvTcD0pH5gds6MMhe8s8YgIgm2eVpTpVIEW81G2eDSK+e9L28f8TCfzEVXipUPnzQAUDUCip9+9G5q3M5cUo/a7RtAy9yJECqSvTuXzwxyn3lniUhpyQZtm23FDa984UoPbfz76eyp64cJy1lHDw4VQMNiDvGpP93xbUe83D22xN1Ya0nU7T85f2mjH7LMW2MIcMFdFjkxP/vlF/wsju3b3z738N5oVWc1XpRH5aEqQmylsJ//DaggnLKu6DN5qiMC1npncDD98OmVs+NeZO/UaHcLSwi2f327UW+y8v/cPrlXzo7jflw9U97UKQBqJFoaTnQ2aJ8+hhBFoa3TAEBdRgAFZY5Ue+9pgLvT40vrA2YRZ5AW/L5DcHGN+wPq2XfeOHo0p6mJx5NsfONowooIoQv0ZQBJnAtNYFXo6j91WWlJzmDhEDLbw4MPZ1e2o6iCIp3eQvIwq9Ib9bUI3/6kRIsEYT7n7J3itigSBLfw9tOJlQrHponcJjikpYKfZu2nmnrD0Wf52pM7dX02TRGJl28BEay6fi9Xe+d7T9l5IKnKsFbdHBwrkJFoAfHZ45VjCE2Udh3I0IJqJCsoLLFIVRbmzK89ud+2uEi2dQRd1MZeP7Oqf/9BSSYAxLKBcx9zrAAdBRY8zfit9VvzgwKaJOR0Mbggfyh4uosTVTK/+uShpGaMbMcYWgjWDwuHdPDNA7YooqEWf+Xd9X1V4zWq0mJgqK0sGTkdSESIyQ/b+tRObhABgTQdqKqs6rPxwW5q2ozjT0dj3xuAt3/c2IyCSNPAyuattbmLple1tl1+/CiwSDqJicqSgpr6AaTTWo0AKgFcr57skkFEsjFJHp0nWJ9ZgNlf7pl+FlRizbSJbv3MW/d9Pl8Sy5Iqy5ICgogIIIm5gEDtwouCtBsmuIgbAFCJYEfV/JG1RMYFlmdsYDwpvPta7A+1UQlB4SoMr1770ZOMoknXDMrcNKHhTqYlg6lkqwKRIUrWFlEVQEEiJEydauJHEsGt7s0eOEvkinjaQgCANaRQ/vmDfDWbsXAtaF4Y6OVzL78OdeibNvqapmlY0p2klC/MLApkrU2bfZh4lKgqqpAsVIN0ERL98Gh6P3fGFc2SIyBYBKG3vqlrq01kCQHN5iswH5ueqUv2hKAqoanruHQ+gTJHEUDrLaXlFAQyTmKIzKqKStpOvdv6wyjjeXN8NzO2GEeF04JuNdLkTx6NtrKy4ViDsS/c6uVTOCirAJ5AlZu6CrFt8tAQpacVQeO8SecrgCqRNbYJyNJujS02ggBVRdzqXnia5y4frS07go1i/vbbfmd9WjXcRKRi97//4pfgzTtVzc6jQmyqKggQdhoBqnCMomi9JxUA0+YElVaxiAIg2GJIjAlARYaTWbNXeDfc5I6sI4BV+/C/TXfO2qfzEBswJp+Em787+vZHzOocqISqDIqUulNDCMoxsijazJMIkKF0V6ogSA4RIQooCAkJpd0VQFUVWCulfFT4/uZm/5RuWBf/9O3xxY3DaRlrJnJArhnbjx8bAG9AY1UFRUpBZgghdU6AxnsSSboUQuqnQQEtqCokCNrlPVQEVR70J3zysMjWzqwsTAAW3vhTe/6SfXhSNw0ayputXzhw5bS2gDkpN3XQ1G4nHxRJFiCfGUkKryEAEGFGBQWyiQGoggppxyUAVBQ2ZhIPimx0bitfJAz76D8fnL1+fvdwUtdqKFNY/73b9mlsAGyGwE0QIoLUnGNLigTQOpPErcSDlQSRRUEpQeBu3LLIC6rCo/5U68dFsX1hiB1JsN8y42ufNx/sTytGY7Pp+vjFyyfHEACsR4mBkWhhARVhZgE01pEkmd4QAYC0tQOS8i5p7qKKp/lXRWBrBjLbyz++vOk6CPTU7fzC5eaTvXkEY/KovaJYoWhDRO9QY9QlAF3nAGisAYBWGFyIUymHkumGD6cyT1da1nqIfLJ398aRdq0rudFLz1c3PjyJQMbbsjCeEMELU2ZQmVsvTHNDaRfGUl7GNvo6PzW08BhsVdwOQrKBitlCgubw4Y1boeteya6+TB98/1EAND4vbV+QgciDYmYARDvdhdolu5YXEQG2lbjdeFwSyejUBqetJYCK8E5GKOXTOz/Z7xpo0vOr+298VCqSy6OuWOEGEDMAsoiwTMLaiU2r5UM7Z8KFmNaqtaeELVkBFlZQYe6vk0Ge7b/7TpU2s4H85uzdtyeKaHOoNgqVEBTJYxpcJzF68Y5Ls8XlHS9sCUqrIi9cr5MLuteJiF4gRxpO7v3dXW0RmOpnrz+IAJTRvL8CAgIA6JMIjB37aof4Cw1ROxMs6TLUCtmLnq4FsagAqsJxc2gcSn3w3t9NiUABaPaTv/rpTJEyU9qxZwXMFNEjKLe333JaWDhW6tIWmhx0Y6ZOtO/OXmwLp+yTNlb9Ocyscrn7vffEECjQ0U/ePRYkTyX1R4FVjQPEjECDorGnRmz9Kv0toskbu1Z1sXtLi0vocCw9gcQol4z3JHF682/2kBCUwqQWNE4ryvtuxoAGWwSNAlmUpXtsTaoqwgKtibB1CtOmJ4TFuvrS5LlzhBDX1yC3IOHwB9+vLQFQeqbQmCLrz2pF0zMAmFnQJiJ5syybLaQg5RilE3LTgPYUwJKY/Gzfr6ocg70ieUbK9YO//AAsAkHaV3GFLeKMAcxqDoi5BQ0NkPP6LLdOOpIwxyhA7d5jys42rQMuImYxDF8QMgCJQS46U1jUOH//L54Yi5Q+ieAK8lQJKLocATBzoFwqugxFuyBYehaJIbB2MxuzAJAY/OkIurNf57Mam7iyE3s5qoaj1/62tqk3Jldg20mg9wiIeQYgMwZTGF4aJXQtpwjHELiboKYhZhsbCzm7DZslCyTSa69F13eo2jz4s/eACADIF4hZq3K5PiJQloNKVQHlubAsPVFrWOGY2pduwka0GIXzM3v7i+MT75dQy/ke9wpS4fL9//kICCkBsO02sF1BQPR9BG1mAq5P3Mk0CxPoAgIvjbrax++Ub/iU4dJPcqh5eLHK+g5V+fi1v5haJDckcRkll0PjEYD8EFXlZNVQv5gxABA8k91AuWtThQC03XlI9GFJ+nsmJyZHqIO99hH054FV4t6fO2sH4zwIYduAojGAiG5EAlLOMvLDMqbkfPpmCiDAnYqH7dpH0v7bkcJCLwNY1sJAYl3r9vpsMKgqBWk+Vrv+0mB+NI+dukPkAJDsio2q4WjkzWAyRQvaZppTI4hwwE5OIwJQbtX3xWdYls4+pWpNLb1rPxj1Zw2D8pxp8/oGpDWdNrtmgEB2xQOoTKaK2YqJi8nKklqlzBxD0zQhhBBDjDFGXnyO5TR3dKuybWLmpormUtbkwwxVlSPZavewjouHI9NDQDTjHgJoOAhAw5HGduVNFpebtvJjDCG0EELgbgIiC3O1lWwpJDRWAVYvTk2/Z0BVlOaPjmKi02kgRzkgoh0OEVTlZCLoVguOkU99rONfIsIcYwwhhhDTHchiQ/E0iT/jihKrWv1zEophhgAKVIds0M8z0xJKQgOIaPvrCAAaHjdK/TXLHGOMz1xEawdhbu+flwJxgUE/jUG5rpm2t6am37egoJRtbKxvbq2Pspa1kQFAtH7LAqjK5EDBrKwQt7uHn3q7T63aLKUiPY1GXeaKyk3FUDxfxWKYEyjYYoTCsTo5OI6oAGgtIKJzO3mjoBD3RyPMNuqJyKmKDO3c5ln7/tz/pWhcZLJ01SqhCoU5P56u9QdNo2D90ILGOtcqAgBQQmDM1niiAKDlbt9ifzOWLJ/KMF0yWKKmi1nuoiyi6qlqhAoAEqpGafDcT8b5sIwM5Af9XtHrj1YKAlRE6xARrVnbNKnMHDwlNKsbhYE2zrpPMkCi6IYMmTTsbKvD6aeNOka3mM0CqHLdMNhLvantDTwCudw5a30xKAgBAJ0DRCTXv+BTAIeHpUG/tVoYPB20dgp+WgF3zjnrrFuspHd8ebEstlzbua4YcHTthPNhz4Alo0IAkmdJJXMmIbAXh2W6uOn9l0n7Z0FnC57c7lJY55x33lrbdvCxa+RJcEGSBAVO5RRQaeqYobl8c7JaDOtgERUQ0ThPDIDoCBCRPJ3dOQgJwuP9qxHWHemcF1uPaIxxznnvvUsfV0j1siVJQooLRxBUoaXSEKoggMOr7w+zflnabpvI2FR/MgOAiI5Wr945Sc8R75/drOh8YffmnTSLxljnvM98lhBgi6CpDS5CJo2yVAVIaEESJNaNINCl2ydrxfDEdiwqcUyk3AAioTPZc+/MQ3rk8sGV1Zlf6/ndNMFEMtam8zPffoiuRVDX3plUxUDa9CkAQHKqTnPdiFHtX3136Hveaqs7IgEqEBUGkRCN5/PXHqcNMaRKrhzPis/0PtirBJFaAD7zHYCEQGJo6mru0zpWTCubIKSSxh/aFac6GlQ8f+doM3P2meYWiApKHVA2H7/0ySeNAJBxxfByfFj2etlHjwMYa1sX9N65FAAtQ+MY6nren2beGqqxlW9VSADSIyKASlMFD8rFtbeGmbXL00sAMv02xXi0V1+ePg2K5Prr62d6w0fNKPO3Dtl47713PoVgG4Btb80x1vV8NjgpTqamSlMgABCSFEAAqiChagpUlp21w22yS5kDAKwdUYp1Z2Xt8yc3TgLa/ub5rWKj8I95i8wnE3V5nmXeeWvd4mOKLU8WiTHU5WxyfHR4PC3ryCl/JGkPhABBJdY1IzLbaz+cgV30QZKqwqCdo5r8pH+pNHcnmq1efm7DmVXFI9gGejBD63yWZc5Za2ybCrtOWpU5NPV8enxwcHA8qZqYyrmQICAJqaqEqgoeNeDamb1oF5mLAQCdH0M7q8hP4vB6tvUk5BuXrqxasKtB536bYa9KC242TZS7ZNx9Zi19dKRaWz968uTp0bQK3O0fSAKgEquysYgi9vLezHYdprACGJeNWvJN3tfZqt84bvx4Y7NPCn6l1jjYjvo0KAsngadd9XtmAU1FOIzXpltPH+89OZrWUbrliFROJNbzKkPUKP1L+7bzAomKYH1/mOIP0RRl3R8PdsTk/b5HUMqH9VyGm0FOosQYLZuUzRZjXlrGEMdrmzs7e7v7x7OGU3nUJI9wrKazwhBw1HO3bEdkmAEwd+MethAod3Pby3tonHeECmDzIkQ/qgPMmEMKAELpPijRfjxoIfjwYLy6eWbv4aPHx/MoraYkAACxnp70+oaA1a/ajtJIVMCh3cg7PyBXVFMsvLGmLXVonRXIhlXQSjkQtVtWjHhaME8HjyrcG47XtrYePHyaPjsjKiCggM3sqGfQgDCgvZ2uIVaPZwCwT3OjEgMzCzdz9s6adgPWksayjspNOa8aATKLQGhvYLHuthi+inCo55PjSdmIAqh0Rejoyf2eDU0UevL/AKlCMaImxqiPAAAAAElFTkSuQmCC\"></td><td 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PvH4ySsHiNXN645i09B7QARTRhwJqXSduNXTzncCasdHOoWxhzyp/U3vI5Bj9r17mWVgWw6u/1Q+t8JA2HwCpuT1MADvHhGICIlxNk3b4Hwbmuquk465rD/RQKgpQARedO8fOtEv/esvJuzKnVcHn19lrNPqGsLHYwAAAF9KEr3aS5PSAQCgn0QAAFQBO5jiYNqrIyL3utfHLPDJ//Bv/tM/VezswSv55+cegvAQhok6VX0thQSzF1oL4hknrbCOQLDesCKV1YPK4CEi69P3BwKgf39NH/vygrBz2692XwpA+TpVxQCIPJoEv4Iv71HwxLm5hcHYP3ioqVYbJtVwwJUQqvUReCW670CAemEQLM2CMNvr1x4769BXj2srBY+ImRsSPASk5ZdOrsAdAwAAOgpVPcJgVDTRQm/5m9u46MzgEADEXhlp3BkBiMjwCl0kpqrp38iveuhpyyj+QTwFSBeeOjV/Z8NHXzoJVdWgsxm2sYFQTXtU2MUtzW9ZBAD+7v+5d+svt0FExN3du9TLsBo8wCkVgkddNQTUi0904S1FHkErVtWOsiM7R87LAImloQARnD6pdwURUTb+/bfLnxWe7Kz/7FN/FSdI3LR1J5Znymo3/Qwf+OiVZbNx5RnlzVArCcgTaIfDpWgsIAzITHWbFgFB7Pzpg6EnljfuoxNfXm91Zl78QT/SglSTVkUMIBMDWZe36gYnUizys22/B1CnaVgRaA53FmYbp9e0+wEBHC8dPyirtqM4J4BIFCy88PTchWdGQyGtfF/F93YaORwJtH2LGZFQW9t/3Ybk90KrEykEBGB7uLl2dqsUJq41uILApV5LhiyARFA1OEj3zn3qty+33As/2YtahELscGpSCKa9RA3AUwAE5fsfqNBLX7W6sSYEEHHjza3HFgiqppCI+NkaFGN659AQCJCua5tq7vl/8gdPzQbJ2mNmuxBSSgdaT/Rxmv5pCIiIxNkbB0HDQS8NCUEAXL5zM3iqBb5hyXUADeBKPrMEcaJJ6SgOCIQFdbz61LmeQlz4naXxdi6olK4kUVu1I1GF78oQEiLo8f6bNtI+JaZ4phMqBAQR019fOLv3y1wAuJ4sAgE2RXqp7ZbXtiVqh+PN3cxhMHv5c793IhJXFtHzt75xCAuJJkD2bX5ozPDEa1bzHoiIoMe3blBYzQBQ1OvFvv/Hdrz5fvy0u5oJAHuPhQJscnfyTNB+/Dgn7ai4/vOO6q08dvnsjCotuGx7Mz997NYhz7c1KUJEB5NqDB7Vg8qHgI6uHCwGhAAoiEFvLg2UExDgsr9Ol5+K3z3kek+LsC3K1uU53e1FcaTEfuKLNggUEbGAuOLgxnt7ZUxm6GwvrJ7RVX79gTSibo8BaHnHKE3Vn4POfCfyhpltvsvF2RO9a/fKysgym7LEM+dbQdJqRQrFlaUTWxohrZWILfKDDw4PiW22beZiIh9nMlSzDtUsGE7q7gAA+t4tULqeQVDthdlEEwsIsAEuDu4tnz9278AIiDhnrYWFp44HOk5CAmeLQlCYHRMSAej2ifGgnwM4gD2z2Kp9PU+rQt1pb5pD+oMBKKoVhZL5hfZ+jgIgDIbteG+9101TU+RFaZ0D1XnmYkcHmphtUbJSJKKRSWlCUmErjYNf5KVzIn17rFMZe9sUJ7FevqnqIej7mKh6mhApnDvW2/bVRGERZ7KDe1oppRAEKKT0wrPHQyK0ZTayYRIQAul67AUgiXUQ/Wx9JGxl6LinUSEg2Lo1O+UapM50dB4hAlfqSkF3aeHe0FR7iIFrm4Iq7szMLJxaW061QlfsbslcopEQyKc4iAjINL/mCG5mzCKjuzyrkTRCBUEmTcG62wMgmgFRWMQPhun28aX1flVMq+bo/MCKyOzlM0sLvZgIeHT36mBlVimtcMrIAaAOu6ul8M1cwEl2lxcU+eW4aVXUoQOT3yRaAME59iqCFC+sLu5mHjM2qYqAMMxcODebBsAg+ebr78ULURR5T1rdGoFFMGgfu2DteinAkt+FBU1+4mjauQmIn4IRANAACM5OxDCzurw5MgyMjY/3Zj3fK+N2QpbF9N/4ydZaEioQoUk2JOKscRh2Fh4r7YYRECjuwqIiUNWDTJRRhB0iAoEoHcYzK6fmY1XnR/Zwf1g6nmRaVWQgMn9qNiYu7ejKq9fL0xe75Ky1rgoi2TlrjTHWMkuI/UwEBTjTydRcFFStcQBA8L3iPQ0AXJauNnwqmj+1sZdZsSw45dMAZLS+dwKRy/zWzz8a9OL8wMVaKaWJyIuW2VljBYPWzLlhue8EhfMNNUeooCmN10UgVxsEDQhcFsZrIgDqzurZ7ZFhgRpCfdndfUYoh5tvXd+35A7CshVq5WddqqyO2TljREUza4NyyAAC+R01g0BKBJyAMEHFNTv/iwYQV2ZNV4lUvHBme1A4KNFPjmHNnuQ5kjncfOfqdgbl3jbmrShQhADCLFw7X3a2FN1dPTAFgwCMb+s2kg9mBaCCIOIqajWAuHxc2FpTKeisnOvnDsBAXWKvhmWEAsruX3nz3qDEwU01mm0FGoGdcww+3qloZluYcGbgjKAAjO6cSarZE26yPvSUiLAGEFuMMsOVhEhFc2cOMyuAxjFPLDqi7iS89e7r66NScLwxvN1LA2LngOJ2r5PGgUKxpsiyvDBlcchRyE5QgA/ungqQlM9+hamaXhK2AsAaQWw+HJdOV5pAQXp8nBlByowFrstCiJguRsOrr90YWwGw42JPKwIEFbbnVVcH2odlbIvh/u7wYDh0pIQFBXgnOa6AqgkcqCGAH1bxUjg8zK23LghIYXclN4yEGVjBJsdSi8u0/uNrIycAIMzWA1NBSUmiXaSVn+6Io+7C7sadsSChd8b2fquHJIp9799DQABxtRTyw/6oqBM2JB3PnjQOEHGqHo8Unlkcvfb2sKrWVD4fkBl23SCNtFZah2EUJ3HYimYWb3605ZwACApkd+MISJQvd3kIxN7zaAC2xeH+YZ7oqmhLErTmjHXMzjlXFxSIupfSd364d8TDISCSIjcsD7QftdFKqSBud9vHe4vv39wvHYAgSH9nWUGlCn5DACIQC4sGEVcc7h0stHRdvyAJ0nlTlmVZFFRtGiK1erH40YdA0kQ7gDpq97pJQLU9EDaGiIa7oJJu+mTvw83MioiA2+610cf+ACII5HWORTQAu2K0t3ssDaiuD5IK07lsOBgcjurwmih8YvnDVwoFrtoZqNLF1aU2MQOiiLPGOMvMIlYA5PAeKz3L+5lhFoZsN0VExbVVaCCABhFXjva3jnciharqZJEKW7352TSgyvORUrPPqR+vK2K/oVU4f+7CcW0chnEcKmRX5lmWZUVprGVmQWI7LKAVlNZY52RgYiFQwuxjwBoCagF2JutvbfUiBSFR/YBBnLZCqjQRSamzFzd/bLSfLKZo8alnlyR3abvbTQIFzM6U+Xg8Gmd5XhSlMc6xDlMBNkWej7NSmFhQ1ZWABgJqEGGbD7bv9hINoBVJpWKkUFw1GkqBTp6d/+YNAmYBVJ0LL13QmW315ruJQhARdmxNJ8+yPC+LssiLvCiNcyIqTiPJ+9vjjjaMoLgioYGgQcTZYrh3t5MEBKFWiFVcXGRZNaKNOlDzz5pXx9rP9/ae+exJl+H84mysvClmRCYVhHErL8rSGlOWeZ7nubFihjy7EuW7o8ORFRJdZ/bEQCiIGkDEleODzSRAAWBF3rDno/3dg7Gf81ZhSOfObbyLICyoZp59abm04dyxdrWBxU/wIpEKwqIwxjpnbVkWRZ5lpSt3Rp10rqU4czyBwMQAJKBBgMHmh2GAtlia9ZmrM1n/3q2N3ZEVAaQw0cGTMy9voYigmn3yk3OlRAvHEtVEvoAgSH4ULiiMcY7ZOWOtLcbDsSkPskSnVgoLIJodg6AwiTCBBgFgiyNFUo72j821E01is/07779/Z79w4ksrMvO4XCkUCKje+cc7RkUzCzE25UKpTwMRIBFpZS2zcORYhMvD/mFROq0Sh7n124G9MgKJaK8VthiimHF/Z7bbihQXg607t27vjK0IoG51wnL5zPC6gAi1Vk+3nAtas5E/jNFU3j0V5I1HSZaZRIsA0lw+2OvnxlEMmFtRmp1308IAngMBMABiy2LcT2ONLj/c29rcqwHMdJlPz93aFBHQM0uJMOh2LMLNAZ6pWi2SaAAgdK6qHpNq9eb2dwe5wwgwB1FKVySQiD9BAQwWQFgAXBkpcMVoMC4ciwCqpDsTZeHJ1u4hAEAw0wIW0jEK01Q+PBVSEiifFTIDgiCiCqJ2b2/3sMRAAKRSRk+CaP8xnx8ob98QKIiTJCAEIK0VsI2WdL8UEQhaSgRAK2HiyUjmESBIokAAHIgIIoig0kGS7u6PJRARYaUUC3sS/CkSAXBgVGEYdRQqcFGggq6T4dahcwOXcKdHvgGkAmH/4Sacn9RFmpeVKKAujjNRqIIo3htkOhLxk8rekICuKjyCgNYxBX7UOWl1OmkkBzfee/tXu/0spBgV+XjaTzs7B1LFD03oV+fjAogkSjwNPpxB1AnpIDgYcsDCTrMTBqlP0oBP150TdmrxxLHZdqwJnDXx/JMvfOd7G2PHSJ1AmMA5ds7a0jqoomQfJjdzwj4p8+5NBJg9CahQhSkQoTCzc04pBgEm0ZUaowgzF/n8Zy7OhgTirHXshOLVJ27sDiArYLGzJwKlYWdtmRehj4rZ+eMYkyptNaTutQGqUhojKtRVPszM7JxicQDeJvpSgoiY/NwfXoj9qQupUsl45Vh6aIYDOL6yzoLl2FpTqvEoAn9MwvrMDRBpMo6LFQuV3fEZFaH2KmCdDly9RiUFT50rTv7xZV0X8hEFEYCOn+ntZoP7dv7JN3MBO7DWlESDFmiqDirVJ5WUH4muzowKVMdd6l6HIGmOnbXWucBZp5hRBBSpOicC6PzL3wvYTtJ8r9r2g/2cT/2DdPzToQDIXKgUMUYaqiStbC5jrHWTo4R1bQhrNal2j7NWfMAmApmiuo4GgJ/5Sq8JTaEiBphu3R+X3U/PJ1dvOgDXapMicTpAnzFXEIwx1hhjm3M6TWVjaqjdt4jZGifAPm6eIACEzld/S7juMzftdoGDG+MMnj/dMu8MBYRnA0KwEpKIMFvrk3Z/LsQa57yIpRm3P3KI15cObMkA/rjjFAKAZ77SZaooq+rOAiJsrx2Oi1NPpZ27N0qAMmkjAhsM/CkZ5+oDIV7E1vGEBESgqfF6BBQQdsZYBq9wHoG37/rL/7Dp9TefEAGRW5vjPHx+vtW6vu2Ay26IKMYq7etBk0Mh7Kr/CXNVt/LDE80ghZ/sd7Y0DsSxCIwVUVUGkuNfXUUiD2AyjS8i3P9wnOfnzyZd/KjPUEKHENk6oqYV6BxXbQB2dTugPhiB9enZigSPwAowi8hY+fkSBIBPfTlBaggghLprx/baIB8nT8wkM+b2UCALW77Mr6r3q7XZDyGwTOxkfXK3OWTgbbQpS8Mi7ETGVWcLQYI/frE6CNnARQBgEYGNjaIYnzmZtGbKe2PhcRIhiiuFsOqu8tRXlZoJ1Kv7qbJmmkfYlWVhnAizwKjSRARY/NMTapquytMIi0h2tSxH6vxsnHbt5lhM1goQxZXsj54DiDBU9QGoa5WNoabmqEVV03ImLwrLwswynCB45p+1VTXbX5/M8buRRfCjHZcfLpxsRWkXtsdSZi2NINY4wqZGNZlirYvI1SEJpPrJAAEF2JV5VloWmSBAEPWlz2qqO2DTkyzCAji6CmZUrC7Ecasb7gw596m2Ld1UlHC03V+dEvOeYrIbfXaQZYV1LI7lsEYAnT85r6r21+Rcgm/4sCDd2FHFAJd7UZy02wcDybNYI4gzTo70lrGu3FcM+A5sDQCr1kUxGhXV+cIJgjN/Mqc8BXXqWNeAmQFV/h5ydhAtpmESp3PjA85HoUYEV9rp5Wv750WglPKnmGoRAID3wcNRYXykNND1Kf3Rf2vRhKmpwI+dc1xujQ6lWP9fV1dSsoM90U7285lQEYAKo1ARAfgGeVXIR/QtvyDQSjXzLCAgws6MR6PSsHPOucP/B0wEbJ4fRcc2AAAAAElFTkSuQmCC\"></td><td 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\"></td><td 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\"></td></tr></tbody></table><div><small>(a vector displayed as a row to save space)</small></div>"
+     },
+     "metadata": {},
+     "execution_count": 51
+    }
+   ],
+   "source": [
+    "[saveImg(rand(z_dim)) for _ in 1:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/vision/VGG/Compositional Pattern Network/cppn.jl b/vision/VGG/Compositional Pattern Network/cppn.jl
new file mode 100644
index 000000000..68528a150
--- /dev/null
+++ b/vision/VGG/Compositional Pattern Network/cppn.jl	
@@ -0,0 +1,84 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
+using Images
+using Flux
+
+
+# %%
+z_dim = 2
+x_dim = 512
+y_dim = 512
+N = 14
+hidden = 9
+batch_size = 1024
+n = x_dim * y_dim
+
+
+# %%
+# cast 0:x-1 to -0.5:0.5
+cast(x) = [range(-0.5, stop=0.5, step=1/(x - 1))...]
+
+
+# %%
+xs, ys = cast(x_dim), cast(y_dim)
+xs = repeat(xs, inner=(y_dim))
+ys = repeat(ys, outer=(x_dim))
+rs = sqrt.(xs.^2 + ys.^2)
+
+
+# %%
+unit(in=N, out=N, f=tanh) = Dense(in, out, f, initW=randn)
+
+
+# %%
+layers = Any[unit(3 + z_dim), [unit() for _ in 1:hidden]..., unit(N, 1, σ)]
+
+# %% [markdown]
+# - In essence, CPPN is just a function, c = f(x, y), that defines the intensity of the image for every point in space.
+
+# %%
+model = Chain(layers...)
+getColorAt(x) = Flux.data(model(x))
+
+# %% [markdown]
+# ## Make batches from the data
+
+# %%
+function batch(arr, s)
+    l = size(arr, 2)
+    batches = [arr[:, i:min(i+s-1, l)] for i=1:s:l]
+    batches
+end
+
+# %% [markdown]
+# ## Create image with intensities
+
+# %%
+function getImage(z)
+    z = repeat(reshape(z, 1, z_dim), outer=(n, 1))
+    coords = hcat(xs, ys, rs, z)'
+    coords = batch(coords, batch_size)
+    pixels = [Gray.(hcat(getColorAt.(coords)...))...]
+    reshape(pixels, y_dim, x_dim)
+end
+
+
+# %%
+function saveImg(z, image_path="sample.png")
+    imgg = getImage(z)
+    save(image_path, imgg)
+    imgg
+end
+
+# %% [markdown]
+# ## Will generate at random everytime
+
+# %%
+saveImg(rand(z_dim))
+
+
+# %%
+
+
+
diff --git a/vision/VGG/Multi Layer Perceptron/Manifest.toml b/vision/VGG/Multi Layer Perceptron/Manifest.toml
new file mode 100644
index 000000000..d35de90e9
--- /dev/null
+++ b/vision/VGG/Multi Layer Perceptron/Manifest.toml	
@@ -0,0 +1,582 @@
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+
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diff --git a/vision/VGG/Multi Layer Perceptron/Project.toml b/vision/VGG/Multi Layer Perceptron/Project.toml
new file mode 100644
index 000000000..23178a679
--- /dev/null
+++ b/vision/VGG/Multi Layer Perceptron/Project.toml	
@@ -0,0 +1,8 @@
+[deps]
+BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
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diff --git a/vision/VGG/Multi Layer Perceptron/README.md b/vision/VGG/Multi Layer Perceptron/README.md
new file mode 100644
index 000000000..4ea3069f7
--- /dev/null
+++ b/vision/VGG/Multi Layer Perceptron/README.md	
@@ -0,0 +1,3 @@
+# A simple MLP
+
+- A good place to start with Deep Learning using Flux
\ No newline at end of file
diff --git a/vision/VGG/Multi Layer Perceptron/mlp.ipynb b/vision/VGG/Multi Layer Perceptron/mlp.ipynb
new file mode 100644
index 000000000..1772c9f66
--- /dev/null
+++ b/vision/VGG/Multi Layer Perceptron/mlp.ipynb	
@@ -0,0 +1,275 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "julia-1.5",
+   "display_name": "Julia 1.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Flux, Flux.Data.MNIST, Statistics\n",
+    "using Flux: onehotbatch, onecold, crossentropy, throttle\n",
+    "using Base.Iterators: repeated\n",
+    "using CuArrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classify MNIST digits with a simple multi-layer-perceptron\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
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Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n ⋮\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]\n [Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); … ; Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0); Gray{N0f8}(0.0) Gray{N0f8}(0.0) … Gray{N0f8}(0.0) Gray{N0f8}(0.0)]"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "imgs = MNIST.images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Stack images into one large batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "60000-element Array{Int64,1}:\n 5\n 0\n 4\n 1\n 9\n 2\n 1\n 3\n 1\n 4\n 3\n 5\n 3\n ⋮\n 7\n 8\n 9\n 2\n 9\n 5\n 1\n 8\n 3\n 5\n 6\n 8"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "\n",
+    "X = hcat(float.(reshape.(imgs, :))...) |> gpu\n",
+    "\n",
+    "labels = MNIST.labels()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# One-hot-encode the labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "10×60000 Flux.OneHotMatrix{CuArray{Flux.OneHotVector,1,Nothing}}:\n 0  1  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  1  0  0  1  0  1  0  0  0  0     0  0  0  0  0  0  1  0  0  0  0  0\n 0  0  0  0  0  1  0  0  0  0  0  0  0     0  0  0  1  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  1  0  0  1  0  1     0  0  0  0  0  0  0  0  1  0  0  0\n 0  0  1  0  0  0  0  0  0  1  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 1  0  0  0  0  0  0  0  0  0  0  1  0  …  0  0  0  0  0  1  0  0  0  1  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  1  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     1  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  1  0  0  0  0  0  1  0  0  0  1\n 0  0  0  0  1  0  0  0  0  0  0  0  0     0  0  1  0  1  0  0  0  0  0  0  0"
+     },
+     "metadata": {},
+     "execution_count": 5
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Warning: Performing scalar operations on GPU arrays: This is very slow, consider disallowing these operations with `allowscalar(false)`\n└ @ GPUArrays /home/subhaditya/.julia/packages/GPUArrays/JqOUg/src/host/indexing.jl:43\n"
+    }
+   ],
+   "source": [
+    "\n",
+    "Y = onehotbatch(labels, 0:9) |> gpu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "Chain(Dense(784, 32, relu), Dense(32, 10), softmax)"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "m = Chain(\n",
+    "  Dense(28^2, 32, relu),\n",
+    "  Dense(32, 10),\n",
+    "  softmax) |> gpu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "loss (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "\n",
+    "loss(x, y) = crossentropy(m(x), y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "accuracy (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "\n",
+    "accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "ADAM(0.001, (0.9, 0.999), IdDict{Any,Any}())"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "\n",
+    "dataset = repeated((X, Y), 200)\n",
+    "evalcb = () -> @show(loss(X, Y))\n",
+    "opt = ADAM()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "loss(X, Y) = 2.2866402f0\n"
+    }
+   ],
+   "source": [
+    "\n",
+    "Flux.train!(loss, params(m), dataset, opt, cb = throttle(evalcb, 10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "accuracy(X, Y)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Test set accuracy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tX = hcat(float.(reshape.(MNIST.images(:test), :))...) |> gpu\n",
+    "tY = onehotbatch(MNIST.labels(:test), 0:9) |> gpu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "accuracy(tX, tY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/vision/VGG/Multi Layer Perceptron/mlp.jl b/vision/VGG/Multi Layer Perceptron/mlp.jl
new file mode 100644
index 000000000..edd843690
--- /dev/null
+++ b/vision/VGG/Multi Layer Perceptron/mlp.jl	
@@ -0,0 +1,88 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
+using Flux, Flux.Data.MNIST, Statistics
+using Flux: onehotbatch, onecold, crossentropy, throttle
+using Base.Iterators: repeated
+using CuArrays
+
+# %% [markdown]
+# # Classify MNIST digits with a simple multi-layer-perceptron
+# 
+
+# %%
+imgs = MNIST.images()
+
+# %% [markdown]
+# 
+# # Stack images into one large batch
+
+# %%
+
+X = hcat(float.(reshape.(imgs, :))...) |> gpu
+
+labels = MNIST.labels()
+
+# %% [markdown]
+# # One-hot-encode the labels
+
+# %%
+
+Y = onehotbatch(labels, 0:9) |> gpu
+
+
+# %%
+
+m = Chain(
+  Dense(28^2, 32, relu),
+  Dense(32, 10),
+  softmax) |> gpu
+
+
+# %%
+
+loss(x, y) = crossentropy(m(x), y)
+
+
+# %%
+
+accuracy(x, y) = mean(onecold(m(x)) .== onecold(y))
+
+
+# %%
+
+dataset = repeated((X, Y), 200)
+evalcb = () -> @show(loss(X, Y))
+opt = ADAM()
+
+# %% [markdown]
+# # Train
+
+# %%
+
+Flux.train!(loss, params(m), dataset, opt, cb = throttle(evalcb, 10))
+
+
+# %%
+
+accuracy(X, Y)
+
+# %% [markdown]
+# 
+# # Test set accuracy
+# 
+
+# %%
+tX = hcat(float.(reshape.(MNIST.images(:test), :))...) |> gpu
+tY = onehotbatch(MNIST.labels(:test), 0:9) |> gpu
+
+
+# %%
+
+accuracy(tX, tY)
+
+
+# %%
+
+
+
diff --git a/vision/VGG/Variational Auto Encoder/Manifest.toml b/vision/VGG/Variational Auto Encoder/Manifest.toml
new file mode 100644
index 000000000..d35de90e9
--- /dev/null
+++ b/vision/VGG/Variational Auto Encoder/Manifest.toml	
@@ -0,0 +1,582 @@
+# This file is machine-generated - editing it directly is not advised
+
+[[AbstractFFTs]]
+deps = ["Compat", "LinearAlgebra"]
+git-tree-sha1 = "8d59c3b1463b5e0ad05a3698167f85fac90e184d"
+uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
+version = "0.3.2"
+
+[[AbstractTrees]]
+deps = ["Markdown", "Test"]
+git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b"
+uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
+version = "0.2.1"
+
+[[Adapt]]
+deps = ["LinearAlgebra", "Test"]
+git-tree-sha1 = "53d8fec4f662088c1202530e338a11a919407f3b"
+uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+version = "0.4.2"
+
+[[Arpack]]
+deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Random", "SparseArrays", "Test"]
+git-tree-sha1 = "1ce1ce9984683f0b6a587d5bdbc688ecb480096f"
+uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
+version = "0.3.0"
+
+[[AxisAlgorithms]]
+deps = ["Compat", "WoodburyMatrices"]
+git-tree-sha1 = "99dabbe853e4f641ab21a676131f2cf9fb29937e"
+uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950"
+version = "0.3.0"
+
+[[AxisArrays]]
+deps = ["Compat", "Dates", "IntervalSets", "IterTools", "Random", "RangeArrays", "Test"]
+git-tree-sha1 = "2e2536e9e6f27c4f8d09d8442b61a7ae0b910c28"
+uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
+version = "0.3.0"
+
+[[BSON]]
+deps = ["Test"]
+git-tree-sha1 = "922b43e731601b73f0e53f1cc94ea719b94f673a"
+uuid = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+version = "0.2.1"
+
+[[Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+
+[[BinDeps]]
+deps = ["Compat", "Libdl", "SHA", "URIParser"]
+git-tree-sha1 = "12093ca6cdd0ee547c39b1870e0c9c3f154d9ca9"
+uuid = "9e28174c-4ba2-5203-b857-d8d62c4213ee"
+version = "0.8.10"
+
+[[BinaryProvider]]
+deps = ["Libdl", "Pkg", "SHA", "Test"]
+git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e"
+uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
+version = "0.5.3"
+
+[[CatIndices]]
+deps = ["CustomUnitRanges", "OffsetArrays", "Test"]
+git-tree-sha1 = "254cf73ea369d2e39bfd6c5eb27a2296cfaed68c"
+uuid = "aafaddc9-749c-510e-ac4f-586e18779b91"
+version = "0.2.0"
+
+[[CodecZlib]]
+deps = ["BinaryProvider", "Libdl", "Test", "TranscodingStreams"]
+git-tree-sha1 = "e3df104c84dfc108f0ca203fd7f5bbdc98641ae9"
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+[[StatsBase]]
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diff --git a/vision/VGG/Variational Auto Encoder/Project.toml b/vision/VGG/Variational Auto Encoder/Project.toml
new file mode 100644
index 000000000..23178a679
--- /dev/null
+++ b/vision/VGG/Variational Auto Encoder/Project.toml	
@@ -0,0 +1,8 @@
+[deps]
+BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+Juno = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
diff --git a/vision/VAE/README.md b/vision/VGG/Variational Auto Encoder/README.md
similarity index 100%
rename from vision/VAE/README.md
rename to vision/VGG/Variational Auto Encoder/README.md
diff --git a/vision/VAE/vae.ipynb b/vision/VGG/Variational Auto Encoder/vae.ipynb
similarity index 96%
rename from vision/VAE/vae.ipynb
rename to vision/VGG/Variational Auto Encoder/vae.ipynb
index 87d6560e1..f04775d8e 100644
--- a/vision/VAE/vae.ipynb
+++ b/vision/VGG/Variational Auto Encoder/vae.ipynb	
@@ -32,6 +32,13 @@
     "using Images\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing MNIST"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -93,6 +100,13 @@
     "data = [X[:,i] for i in Iterators.partition(1:N,M)]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defining our network and parameters"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -130,6 +144,13 @@
     "g(X) = (h = A(X); (μ(h), logσ(h)))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sample latent space"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -169,6 +190,13 @@
     "f = Chain(Dense(Dz, Dh, tanh), Dense(Dh, 28^2, σ))\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KL divergence and loss functions we need\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -245,6 +273,13 @@
     "loss(X) = -L̄(X) + 0.01f0 * sum(x->sum(x.^2), params(f))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate a sample\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -268,6 +303,13 @@
     "end"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -308,6 +350,13 @@
     "end"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Outputs"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 21,
diff --git a/vision/VAE/vae.jl b/vision/VGG/Variational Auto Encoder/vae.jl
similarity index 100%
rename from vision/VAE/vae.jl
rename to vision/VGG/Variational Auto Encoder/vae.jl
diff --git a/vision/mnist/.gitignore b/vision/VGG/mnist/.gitignore
similarity index 100%
rename from vision/mnist/.gitignore
rename to vision/VGG/mnist/.gitignore
diff --git a/vision/VGG/mnist/Manifest.toml b/vision/VGG/mnist/Manifest.toml
new file mode 100644
index 000000000..d35de90e9
--- /dev/null
+++ b/vision/VGG/mnist/Manifest.toml
@@ -0,0 +1,582 @@
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+
+[[AbstractFFTs]]
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diff --git a/vision/VGG/mnist/Project.toml b/vision/VGG/mnist/Project.toml
new file mode 100644
index 000000000..23178a679
--- /dev/null
+++ b/vision/VGG/mnist/Project.toml
@@ -0,0 +1,8 @@
+[deps]
+BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
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diff --git a/vision/mnist/autoencoder.jl b/vision/VGG/mnist/autoencoder.jl
similarity index 100%
rename from vision/mnist/autoencoder.jl
rename to vision/VGG/mnist/autoencoder.jl
diff --git a/vision/mnist/cuda/Manifest.toml b/vision/VGG/mnist/cuda/Manifest.toml
similarity index 100%
rename from vision/mnist/cuda/Manifest.toml
rename to vision/VGG/mnist/cuda/Manifest.toml
diff --git a/vision/mnist/cuda/Project.toml b/vision/VGG/mnist/cuda/Project.toml
similarity index 100%
rename from vision/mnist/cuda/Project.toml
rename to vision/VGG/mnist/cuda/Project.toml
diff --git a/vision/Variational Auto Encoder/Manifest.toml b/vision/Variational Auto Encoder/Manifest.toml
new file mode 100644
index 000000000..d35de90e9
--- /dev/null
+++ b/vision/Variational Auto Encoder/Manifest.toml	
@@ -0,0 +1,582 @@
+# This file is machine-generated - editing it directly is not advised
+
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+[[TexExtensions]]
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+[[URIParser]]
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+[[UUIDs]]
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+[[Unicode]]
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+[[VersionParsing]]
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+[[WoodburyMatrices]]
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+[[ZipFile]]
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diff --git a/vision/Variational Auto Encoder/Project.toml b/vision/Variational Auto Encoder/Project.toml
new file mode 100644
index 000000000..23178a679
--- /dev/null
+++ b/vision/Variational Auto Encoder/Project.toml	
@@ -0,0 +1,8 @@
+[deps]
+BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+Juno = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
diff --git a/vision/Variational Auto Encoder/README.md b/vision/Variational Auto Encoder/README.md
new file mode 100644
index 000000000..ce5a7edfe
--- /dev/null
+++ b/vision/Variational Auto Encoder/README.md	
@@ -0,0 +1,4 @@
+# Variational Auto Encoder
+
+- Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
+- [Paper link](https://arxiv.org/pdf/1312.6114.pdf?source=post_page---------------------------)
diff --git a/vision/Variational Auto Encoder/vae.ipynb b/vision/Variational Auto Encoder/vae.ipynb
new file mode 100644
index 000000000..f04775d8e
--- /dev/null
+++ b/vision/Variational Auto Encoder/vae.ipynb	
@@ -0,0 +1,416 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "julia-1.5",
+   "display_name": "Julia 1.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "using Flux, Flux.Data.MNIST, Statistics, Flux.Optimise\n",
+    "using Flux: throttle, params\n",
+    "using Images\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing MNIST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n┌ Info: Downloading MNIST dataset\n└ @ Flux.Data.MNIST /home/subhaditya/.julia/packages/Flux/Fj3bt/src/data/mnist.jl:24\n"
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "784×60000 BitArray{2}:\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n ⋮              ⋮              ⋮        ⋱        ⋮              ⋮           \n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0  …  0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0\n 0  0  0  0  0  0  0  0  0  0  0  0  0     0  0  0  0  0  0  0  0  0  0  0  0"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "X = (float.(hcat(vec.(MNIST.images())...)) .> 0.5) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "(60000, 100)"
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "N, M = size(X, 2), 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "600-element Array{BitArray{2},1}:\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n ⋮\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]\n [0 0 … 0 0; 0 0 … 0 0; … ; 0 0 … 0 0; 0 0 … 0 0]"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "data = [X[:,i] for i in Iterators.partition(1:N,M)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defining our network and parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "(Dense(784, 500, tanh), Dense(500, 5), Dense(500, 5))"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "Dz, Dh = 5, 500\n",
+    "A, μ, logσ = Dense(28^2, Dh, tanh) , Dense(Dh, Dz) , Dense(Dh, Dz) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "g (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "g(X) = (h = A(X); (μ(h), logσ(h)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sample latent space"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "sample_z (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "function sample_z(μ, logσ)\n",
+    "    eps = randn(Float32, size(μ)) \n",
+    "    return μ + exp.(logσ) .* eps\n",
+    "end\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "Chain(Dense(5, 500, tanh), Dense(500, 784, σ))"
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "f = Chain(Dense(Dz, Dh, tanh), Dense(Dh, 28^2, σ))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# KL divergence and loss functions we need\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "kl_q_p (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "kl_q_p(μ, logσ) = 0.5f0 * sum(exp.(2f0 .* logσ) + μ.^2 .- 1f0 .+ logσ.^2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "logp_x_z (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "source": [
+    "function logp_x_z(x, z)\n",
+    "    p = f(z)\n",
+    "    ll = x .* log.(p .+ eps(Float32)) + (1f0 .- x) .* log.(1 .- p .+ eps(Float32))\n",
+    "    return sum(ll)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "L̄ (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "L̄(X) = ((μ̂, logσ̂) = g(X); (logp_x_z(X, sample_z(μ̂, logσ̂)) - kl_q_p(μ̂, logσ̂)) * 1 // M)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "loss (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ],
+   "source": [
+    "loss(X) = -L̄(X) + 0.01f0 * sum(x->sum(x.^2), params(f))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate a sample\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "modelsample (generic function with 1 method)"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "function modelsample()  \n",
+    "  ϕ = zeros(Float32, Dz)\n",
+    "  p = f(sample_z(ϕ, ϕ))\n",
+    "  u = rand(Float32, size(p))\n",
+    "  return (u .< p) \n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "Params([Float32[-0.04325612 -0.04432942 … 0.024764992 0.0025093316; -0.06396034 0.0077259494 … -0.006176046 0.016506964; … ; 8.820455f-5 0.064784825 … 0.060753915 0.023969032; 0.009093919 0.05117704 … -0.0029312368 -0.05824799], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float32[-0.00041203704 -0.0528699 … -0.03941686 0.06000133; 0.05124626 -0.06446281 … 0.0718011 -0.04769289; … ; -0.024800759 0.068491444 … 0.101761356 -0.07139413; 0.033019643 0.010317117 … -0.019728636 0.05537383], Float32[0.0, 0.0, 0.0, 0.0, 0.0], Float32[0.10620293 0.023587517 … 0.08458545 -0.020418482; -0.038067207 0.010741732 … 0.028195212 0.012534944; … ; -0.014529563 -0.087974004 … 0.028156698 0.045931697; -0.102924176 -0.032970604 … 0.029116375 0.035560943], Float32[0.0, 0.0, 0.0, 0.0, 0.0], Float32[-0.09521466 -0.08829451 … 0.020895256 0.0067901523; 0.108671494 -0.009676283 … 0.07440227 -0.0760235; … ; -0.0069366717 -0.011213775 … 0.06583009 0.10229878; 0.045926213 0.07811487 … 0.07023905 -0.10748437], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], Float32[0.053993654 -0.0059612063 … 0.009265504 -0.062752746; 0.0022692461 -0.03440574 … 0.05393037 0.04595501; … ; 0.004562907 -0.043307588 … -0.004600816 0.041631848; -0.066888765 -0.032768432 … -0.036583245 0.010277884], Float32[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0  …  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])"
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "evalcb = throttle(() -> @show(-L̄(X[:, rand(1:N, M)])), 10)\n",
+    "opt = ADAM()\n",
+    "ps = params(A, μ, logσ, f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": "┌ Info: Epoch 1\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 544.0159f0\n-(L̄(X[:, rand(1:N, M)])) = 188.8044f0\n-(L̄(X[:, rand(1:N, M)])) = 163.6948f0\n-(L̄(X[:, rand(1:N, M)])) = 170.76443f0\n-(L̄(X[:, rand(1:N, M)])) = 159.46655f0\n-(L̄(X[:, rand(1:N, M)])) = 168.12997f0\n-(L̄(X[:, rand(1:N, M)])) = 160.12689f0\n-(L̄(X[:, rand(1:N, M)])) = 165.35117f0\n-(L̄(X[:, rand(1:N, M)])) = 158.74713f0\n-(L̄(X[:, rand(1:N, M)])) = 156.7229f0\n┌ Info: Epoch 2\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 163.61983f0\n-(L̄(X[:, rand(1:N, M)])) = 161.33939f0\n-(L̄(X[:, rand(1:N, M)])) = 158.21996f0\n-(L̄(X[:, rand(1:N, M)])) = 151.2085f0\n-(L̄(X[:, rand(1:N, M)])) = 146.22623f0\n-(L̄(X[:, rand(1:N, M)])) = 150.20674f0\n-(L̄(X[:, rand(1:N, M)])) = 156.2229f0\n-(L̄(X[:, rand(1:N, M)])) = 143.83652f0\n┌ Info: Epoch 3\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 142.15207f0\n-(L̄(X[:, rand(1:N, M)])) = 142.52904f0\n-(L̄(X[:, rand(1:N, M)])) = 150.46121f0\n-(L̄(X[:, rand(1:N, M)])) = 148.28822f0\n-(L̄(X[:, rand(1:N, M)])) = 143.29193f0\n-(L̄(X[:, rand(1:N, M)])) = 139.69719f0\n-(L̄(X[:, rand(1:N, M)])) = 135.97562f0\n-(L̄(X[:, rand(1:N, M)])) = 142.71591f0\n-(L̄(X[:, rand(1:N, M)])) = 140.0783f0\n┌ Info: Epoch 4\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 139.62978f0\n-(L̄(X[:, rand(1:N, M)])) = 140.09804f0\n-(L̄(X[:, rand(1:N, M)])) = 140.85718f0\n-(L̄(X[:, rand(1:N, M)])) = 143.96771f0\n-(L̄(X[:, rand(1:N, M)])) = 139.85089f0\n-(L̄(X[:, rand(1:N, M)])) = 141.64473f0\n-(L̄(X[:, rand(1:N, M)])) = 136.50128f0\n-(L̄(X[:, rand(1:N, M)])) = 131.97739f0\n-(L̄(X[:, rand(1:N, M)])) = 129.70644f0\n┌ Info: Epoch 5\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 137.03206f0\n-(L̄(X[:, rand(1:N, M)])) = 142.41936f0\n-(L̄(X[:, rand(1:N, M)])) = 135.6215f0\n-(L̄(X[:, rand(1:N, M)])) = 139.77963f0\n-(L̄(X[:, rand(1:N, M)])) = 137.5791f0\n-(L̄(X[:, rand(1:N, M)])) = 129.35582f0\n-(L̄(X[:, rand(1:N, M)])) = 131.18831f0\n-(L̄(X[:, rand(1:N, M)])) = 128.95619f0\n┌ Info: Epoch 6\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 135.28596f0\n-(L̄(X[:, rand(1:N, M)])) = 133.43881f0\n-(L̄(X[:, rand(1:N, M)])) = 131.18222f0\n-(L̄(X[:, rand(1:N, M)])) = 130.74548f0\n-(L̄(X[:, rand(1:N, M)])) = 131.1535f0\n-(L̄(X[:, rand(1:N, M)])) = 136.14282f0\n-(L̄(X[:, rand(1:N, M)])) = 141.14491f0\n-(L̄(X[:, rand(1:N, M)])) = 130.80042f0\n-(L̄(X[:, rand(1:N, M)])) = 136.48291f0\n┌ Info: Epoch 7\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 129.31302f0\n-(L̄(X[:, rand(1:N, M)])) = 139.49428f0\n-(L̄(X[:, rand(1:N, M)])) = 130.39726f0\n-(L̄(X[:, rand(1:N, M)])) = 135.66624f0\n-(L̄(X[:, rand(1:N, M)])) = 139.52274f0\n-(L̄(X[:, rand(1:N, M)])) = 129.02013f0\n-(L̄(X[:, rand(1:N, M)])) = 132.18924f0\n-(L̄(X[:, rand(1:N, M)])) = 137.77753f0\n-(L̄(X[:, rand(1:N, M)])) = 137.5309f0\n┌ Info: Epoch 8\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 130.02092f0\n-(L̄(X[:, rand(1:N, M)])) = 127.53695f0\n-(L̄(X[:, rand(1:N, M)])) = 134.24876f0\n-(L̄(X[:, rand(1:N, M)])) = 126.24858f0\n-(L̄(X[:, rand(1:N, M)])) = 134.23547f0\n-(L̄(X[:, rand(1:N, M)])) = 128.36131f0\n-(L̄(X[:, rand(1:N, M)])) = 122.926f0\n┌ Info: Epoch 9\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 128.51785f0\n-(L̄(X[:, rand(1:N, M)])) = 130.70897f0\n-(L̄(X[:, rand(1:N, M)])) = 126.51974f0\n-(L̄(X[:, rand(1:N, M)])) = 126.55835f0\n-(L̄(X[:, rand(1:N, M)])) = 133.80797f0\n-(L̄(X[:, rand(1:N, M)])) = 137.55035f0\n-(L̄(X[:, rand(1:N, M)])) = 129.17249f0\n-(L̄(X[:, rand(1:N, M)])) = 133.11034f0\n-(L̄(X[:, rand(1:N, M)])) = 122.74662f0\n┌ Info: Epoch 10\n└ @ Main In[17]:2\n-(L̄(X[:, rand(1:N, M)])) = 129.06119f0\n-(L̄(X[:, rand(1:N, M)])) = 134.97176f0\n-(L̄(X[:, rand(1:N, M)])) = 125.834236f0\n-(L̄(X[:, rand(1:N, M)])) = 127.66348f0\n-(L̄(X[:, rand(1:N, M)])) = 125.22201f0\n-(L̄(X[:, rand(1:N, M)])) = 130.18465f0\n-(L̄(X[:, rand(1:N, M)])) = 128.43158f0\n-(L̄(X[:, rand(1:N, M)])) = 125.08065f0\n"
+    }
+   ],
+   "source": [
+    "for i = 1:10\n",
+    "  @info \"Epoch $i\"\n",
+    "  Flux.train!(loss, ps, zip(data), opt, cb=evalcb)\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "28×280 Array{Gray{Bool},2} with eltype Gray{Bool}:\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n ⋮                                     ⋱  \n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)",
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ],
+   "source": [
+    "img(x) = Gray.(reshape(x, 28, 28))\n",
+    "sample = hcat(img.([modelsample() for i = 1:10])...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "28×280 Array{Gray{Bool},2} with eltype Gray{Bool}:\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n ⋮                                     ⋱  \n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)  …  Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)\n Gray{Bool}(false)  Gray{Bool}(false)     Gray{Bool}(false)",
+      "image/png": "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"
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ],
+   "source": [
+    "sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save(\"sample.png\", sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/vision/Variational Auto Encoder/vae.jl b/vision/Variational Auto Encoder/vae.jl
new file mode 100644
index 000000000..377ad40c9
--- /dev/null
+++ b/vision/Variational Auto Encoder/vae.jl	
@@ -0,0 +1,99 @@
+# To add a new cell, type '# %%'
+# To add a new markdown cell, type '# %% [markdown]'
+# %%
+using Flux, Flux.Data.MNIST, Statistics, Flux.Optimise
+using Flux: throttle, params
+using Images
+
+
+# %%
+X = (float.(hcat(vec.(MNIST.images())...)) .> 0.5) 
+
+
+# %%
+N, M = size(X, 2), 100
+
+
+# %%
+data = [X[:,i] for i in Iterators.partition(1:N,M)]
+
+
+# %%
+Dz, Dh = 5, 500
+A, μ, logσ = Dense(28^2, Dh, tanh) , Dense(Dh, Dz) , Dense(Dh, Dz) 
+
+
+# %%
+g(X) = (h = A(X); (μ(h), logσ(h)))
+
+
+# %%
+function sample_z(μ, logσ)
+    eps = randn(Float32, size(μ)) 
+    return μ + exp.(logσ) .* eps
+end
+
+
+# %%
+f = Chain(Dense(Dz, Dh, tanh), Dense(Dh, 28^2, σ))
+
+
+# %%
+kl_q_p(μ, logσ) = 0.5f0 * sum(exp.(2f0 .* logσ) + μ.^2 .- 1f0 .+ logσ.^2)
+
+
+# %%
+function logp_x_z(x, z)
+    p = f(z)
+    ll = x .* log.(p .+ eps(Float32)) + (1f0 .- x) .* log.(1 .- p .+ eps(Float32))
+    return sum(ll)
+end
+
+
+# %%
+L̄(X) = ((μ̂, logσ̂) = g(X); (logp_x_z(X, sample_z(μ̂, logσ̂)) - kl_q_p(μ̂, logσ̂)) * 1 // M)
+
+
+# %%
+loss(X) = -L̄(X) + 0.01f0 * sum(x->sum(x.^2), params(f))
+
+
+# %%
+function modelsample()  
+  ϕ = zeros(Float32, Dz)
+  p = f(sample_z(ϕ, ϕ))
+  u = rand(Float32, size(p))
+  return (u .< p) 
+end
+
+
+# %%
+evalcb = throttle(() -> @show(-L̄(X[:, rand(1:N, M)])), 10)
+opt = ADAM()
+ps = params(A, μ, logσ, f)
+
+
+# %%
+for i = 1:10
+  @info "Epoch $i"
+  Flux.train!(loss, ps, zip(data), opt, cb=evalcb)
+end
+
+
+# %%
+img(x) = Gray.(reshape(x, 28, 28))
+sample = hcat(img.([modelsample() for i = 1:10])...)
+
+
+# %%
+sample
+
+
+# %%
+save("sample.png", sample)
+
+
+# %%
+
+
+
diff --git a/vision/cuda/Manifest.toml b/vision/cuda/Manifest.toml
new file mode 100644
index 000000000..a0ba0c673
--- /dev/null
+++ b/vision/cuda/Manifest.toml
@@ -0,0 +1,624 @@
+# This file is machine-generated - editing it directly is not advised
+
+[[AbstractFFTs]]
+deps = ["Compat", "LinearAlgebra"]
+git-tree-sha1 = "8d59c3b1463b5e0ad05a3698167f85fac90e184d"
+uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
+version = "0.3.2"
+
+[[AbstractTrees]]
+deps = ["Markdown", "Test"]
+git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b"
+uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
+version = "0.2.1"
+
+[[Adapt]]
+deps = ["LinearAlgebra", "Test"]
+git-tree-sha1 = "53d8fec4f662088c1202530e338a11a919407f3b"
+uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+version = "0.4.2"
+
+[[Arpack]]
+deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Random", "SparseArrays", "Test"]
+git-tree-sha1 = "1ce1ce9984683f0b6a587d5bdbc688ecb480096f"
+uuid = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
+version = "0.3.0"
+
+[[AxisAlgorithms]]
+deps = ["Compat", "WoodburyMatrices"]
+git-tree-sha1 = "99dabbe853e4f641ab21a676131f2cf9fb29937e"
+uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950"
+version = "0.3.0"
+
+[[AxisArrays]]
+deps = ["Compat", "Dates", "IntervalSets", "IterTools", "Random", "RangeArrays", "Test"]
+git-tree-sha1 = "2e2536e9e6f27c4f8d09d8442b61a7ae0b910c28"
+uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
+version = "0.3.0"
+
+[[BSON]]
+deps = ["Test"]
+git-tree-sha1 = "922b43e731601b73f0e53f1cc94ea719b94f673a"
+uuid = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+version = "0.2.1"
+
+[[Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+
+[[BinDeps]]
+deps = ["Compat", "Libdl", "SHA", "URIParser"]
+git-tree-sha1 = "12093ca6cdd0ee547c39b1870e0c9c3f154d9ca9"
+uuid = "9e28174c-4ba2-5203-b857-d8d62c4213ee"
+version = "0.8.10"
+
+[[BinaryProvider]]
+deps = ["Libdl", "Pkg", "SHA", "Test"]
+git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e"
+uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
+version = "0.5.3"
+
+[[CUDAapi]]
+deps = ["Libdl", "Logging", "Test"]
+git-tree-sha1 = "350cde12f25d297609369a9acb4c6214211675db"
+uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
+version = "0.5.4"
+
+[[CUDAdrv]]
+deps = ["CUDAapi", "Libdl", "Printf", "Test"]
+git-tree-sha1 = "dfe527ba231b6b699f879d1d384c1d39b49fc005"
+uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
+version = "1.0.1"
+
+[[CUDAnative]]
+deps = ["Adapt", "CUDAapi", "CUDAdrv", "InteractiveUtils", "LLVM", "Libdl", "Pkg", "Printf", "Statistics", "Test"]
+git-tree-sha1 = "92e3ec4f4458e43cc17be4388b68690dbef24f31"
+uuid = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
+version = "1.0.1"
+
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diff --git a/vision/cuda/Project.toml b/vision/cuda/Project.toml
new file mode 100644
index 000000000..bb4fdf153
--- /dev/null
+++ b/vision/cuda/Project.toml
@@ -0,0 +1,9 @@
+[deps]
+BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+CuArrays = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
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diff --git a/vision/mnist/conv.jl b/vision/mnist/conv.jl
deleted file mode 100644
index 1e92d9b78..000000000
--- a/vision/mnist/conv.jl
+++ /dev/null
@@ -1,126 +0,0 @@
-# Classifies MNIST digits with a convolutional network.
-# Writes out saved model to the file "mnist_conv.bson".
-# Demonstrates basic model construction, training, saving,
-# conditional early-exit, and learning rate scheduling.
-#
-# This model, while simple, should hit around 99% test
-# accuracy after training for approximately 20 epochs.
-
-using Flux, Flux.Data.MNIST, Statistics
-using Flux: onehotbatch, onecold, crossentropy, throttle
-using Base.Iterators: repeated, partition
-using Printf, BSON
-
-# Load labels and images from Flux.Data.MNIST
-@info("Loading data set")
-train_labels = MNIST.labels()
-train_imgs = MNIST.images()
-
-# Bundle images together with labels and group into minibatchess
-function make_minibatch(X, Y, idxs)
-    X_batch = Array{Float32}(undef, size(X[1])..., 1, length(idxs))
-    for i in 1:length(idxs)
-        X_batch[:, :, :, i] = Float32.(X[idxs[i]])
-    end
-    Y_batch = onehotbatch(Y[idxs], 0:9)
-    return (X_batch, Y_batch)
-end
-batch_size = 128
-mb_idxs = partition(1:length(train_imgs), batch_size)
-train_set = [make_minibatch(train_imgs, train_labels, i) for i in mb_idxs]
-
-# Prepare test set as one giant minibatch:
-test_imgs = MNIST.images(:test)
-test_labels = MNIST.labels(:test)
-test_set = make_minibatch(test_imgs, test_labels, 1:length(test_imgs))
-
-# Define our model.  We will use a simple convolutional architecture with
-# three iterations of Conv -> ReLU -> MaxPool, followed by a final Dense
-# layer that feeds into a softmax probability output.
-@info("Constructing model...")
-model = Chain(
-    # First convolution, operating upon a 28x28 image
-    Conv((3, 3), 1=>16, pad=(1,1), relu),
-    x -> maxpool(x, (2,2)),
-
-    # Second convolution, operating upon a 14x14 image
-    Conv((3, 3), 16=>32, pad=(1,1), relu),
-    x -> maxpool(x, (2,2)),
-
-    # Third convolution, operating upon a 7x7 image
-    Conv((3, 3), 32=>32, pad=(1,1), relu),
-    x -> maxpool(x, (2,2)),
-
-    # Reshape 3d tensor into a 2d one, at this point it should be (3, 3, 32, N)
-    # which is where we get the 288 in the `Dense` layer below:
-    x -> reshape(x, :, size(x, 4)),
-    Dense(288, 10),
-
-    # Finally, softmax to get nice probabilities
-    softmax,
-)
-
-# Load model and datasets onto GPU, if enabled
-train_set = gpu.(train_set)
-test_set = gpu.(test_set)
-model = gpu(model)
-
-# Make sure our model is nicely precompiled before starting our training loop
-model(train_set[1][1])
-
-# `loss()` calculates the crossentropy loss between our prediction `y_hat`
-# (calculated from `model(x)`) and the ground truth `y`.  We augment the data
-# a bit, adding gaussian random noise to our image to make it more robust.
-function loss(x, y)
-    # We augment `x` a little bit here, adding in random noise
-    x_aug = x .+ 0.1f0*gpu(randn(eltype(x), size(x)))
-
-    y_hat = model(x_aug)
-    return crossentropy(y_hat, y)
-end
-accuracy(x, y) = mean(onecold(model(x)) .== onecold(y))
-
-# Train our model with the given training set using the ADAM optimizer and
-# printing out performance against the test set as we go.
-opt = ADAM(0.001)
-
-@info("Beginning training loop...")
-best_acc = 0.0
-last_improvement = 0
-for epoch_idx in 1:100
-    global best_acc, last_improvement
-    # Train for a single epoch
-    Flux.train!(loss, params(model), train_set, opt)
-
-    # Calculate accuracy:
-    acc = accuracy(test_set...)
-    @info(@sprintf("[%d]: Test accuracy: %.4f", epoch_idx, acc))
-    
-    # If our accuracy is good enough, quit out.
-    if acc >= 0.999
-        @info(" -> Early-exiting: We reached our target accuracy of 99.9%")
-        break
-    end
-
-    # If this is the best accuracy we've seen so far, save the model out
-    if acc >= best_acc
-        @info(" -> New best accuracy! Saving model out to mnist_conv.bson")
-        BSON.@save "mnist_conv.bson" model epoch_idx acc
-        best_acc = acc
-        last_improvement = epoch_idx
-    end
-
-    # If we haven't seen improvement in 5 epochs, drop our learning rate:
-    if epoch_idx - last_improvement >= 5 && opt.eta > 1e-6
-        opt.eta /= 10.0
-        @warn(" -> Haven't improved in a while, dropping learning rate to $(opt.eta)!")
-
-        # After dropping learning rate, give it a few epochs to improve
-        last_improvement = epoch_idx
-    end
-
-    if epoch_idx - last_improvement >= 10
-        @warn(" -> We're calling this converged.")
-        break
-    end
-end