notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "23bc97b1",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "468a1cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "using CSV, DataFrames, Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4398b052",
   "metadata": {},
   "outputs": [],
   "source": [
    "using Flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22456787",
   "metadata": {},
   "outputs": [],
   "source": [
    "using CUDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f49782f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "using Plots\n",
    "gr()\n",
    "theme(:juno);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb030cb",
   "metadata": {},
   "source": [
    "# Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e0f7b37",
   "metadata": {},
   "outputs": [],
   "source": [
    "function splitdf(df, pct)\n",
    "   @assert 0 <= pct <= 1\n",
    "   ids = collect(axes(df, 1))\n",
    "   shuffle!(ids)\n",
    "   sel = ids .<= nrow(df) .* pct\n",
    "   return view(df, sel, :), view(df, .!sel, :)\n",
    "end;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "140253ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = \"fashion-mnist_train.csv\" |> CSV.File |> DataFrame;\n",
    "df_train, df_valid = splitdf(df, 0.666)\n",
    "df_test = \"fashion-mnist_test.csv\" |> CSV.File |> DataFrame;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8016502b",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = Dict(\n",
    "    0 => \"T-shirt\",\n",
    "    1 => \"Trouser\",\n",
    "    2 => \"Pullover\",\n",
    "    3 => \"Dress\",\n",
    "    4 => \"Coat\",\n",
    "    5 => \"Sandal\",\n",
    "    6 => \"Shirt\",\n",
    "    7 => \"Sneaker\",\n",
    "    8 => \"Bag\",\n",
    "    9 => \"Ankle boot\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26e0cd70",
   "metadata": {},
   "outputs": [],
   "source": [
    "function load_y(df)\n",
    "    y = df[:, :label]\n",
    "    Flux.onehotbatch(y, 0:9)\n",
    "end\n",
    "\n",
    "y_train = load_y(df_train) |> y -> Flux.label_smoothing(y, 0.2f0) |> gpu\n",
    "y_valid = load_y(df_valid) |> gpu;\n",
    "y_test= load_y(df_test) |> gpu;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08d71fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "function load_x(df)\n",
    "    x = select(df, Not(:label)) |> Matrix |> permutedims\n",
    "    x = reshape(x, 28, 28, 1, :) / 255\n",
    "    convert(Array{Float32,4}, x) |> m -> permutedims(m, (2,1,3,4))\n",
    "end\n",
    "\n",
    "x_train = load_x(df_train) |> gpu\n",
    "x_valid = load_x(df_valid) |> gpu;\n",
    "x_test = load_x(df_test) |> gpu;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1581ce8",
   "metadata": {},
   "source": [
    "# Custom training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ff4b8e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "function train!(loss, ps, data, opt)\n",
    "\tlocal training_loss\n",
    "\tfor d in data\n",
    "    \tgs = gradient(ps) do\n",
    "      \t\ttraining_loss = loss(d...)\n",
    "      \t\treturn training_loss\n",
    "\t\tend\n",
    "    # Insert whatever code you want here that needs training_loss, e.g. logging.\n",
    "    # E.g. logging with TensorBoardLogger.jl as histogram so you can see if it is becoming huge.\n",
    "    Flux.update!(opt, ps, gs)\n",
    "    # Here you might like to check validation set accuracy, and break out to do early stopping.\n",
    "\tend\n",
    "    training_loss\n",
    "end;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6d2e362",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict(x) = labels[Flux.onecold(model(cat(x; dims=4)), 0:9)[1]]\n",
    "get_label(y) = labels[Flux.onecold(y, 0:9)[1]]\n",
    "\n",
    "function get_acc(model)\n",
    "    function acc(x, y) \n",
    "        ŷ = model(x) |> Flux.onecold\n",
    "        cy = y |> Flux.onecold\n",
    "        sum(ŷ .== cy) / length(cy)\n",
    "    end\n",
    "end;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d77e47",
   "metadata": {},
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ba169d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Chain(\n",
    "    Conv((3, 3), 1 => 32, relu)\n",
    "    , AdaptiveMaxPool((14, 14))\n",
    "    , Conv((3, 3), 32 => 32, relu)\n",
    "    , Flux.Dropout(0.2)\n",
    "    , Flux.flatten\n",
    "    , Dense(4608, 32, relu)\n",
    "    , Dense(32, 10)\n",
    "    , softmax\n",
    ") |> gpu\n",
    "\n",
    "ps = params(model)\n",
    "\n",
    "loss(x, y) = Flux.mse(model(x), y)\n",
    "\n",
    "opt = ADAM()\n",
    "\n",
    "data = Flux.DataLoader((x_train, y_train), batchsize=512, shuffle=true) |> gpu;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6f6ff40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "287.748686 seconds (222.56 M allocations: 12.519 GiB, 1.71% gc time, 26.95% compilation time)\n"
     ]
    }
   ],
   "source": [
    "training_error = []\n",
    "# vde = []\n",
    "# tse = []\n",
    "# ats = []\n",
    "cnn_acc = get_acc(model)\n",
    "@time for epoch=1:75\n",
    "\tpush!(training_error, train!(loss, ps, data, opt))\n",
    "\t# push!(vde, loss(x_valid, y_valid))\n",
    "\t# push!(tse, loss(x_test, y_test))\n",
    "\t# push!(ats, cnn_acc(x_test, y_test))\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "72e52e15",
   "metadata": {},
   "outputs": [
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       "</svg>\n"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(\n",
    "    plot(training_error, title=\"Training error\")\n",
    "    # , plot(vde, title=\"Validation error\")\n",
    "    # , plot(tse, title=\"Test error\")\n",
    "    # , plot(ats, title=\"Test accuracy\")\n",
    "    , legend = false\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f59c73bf",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bebbd7b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8947"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_acc(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7c4cc35b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = 21\n",
    "xi = x_test[:, :, 1, idx]\n",
    "ŷi = xi |> predict\n",
    "yi = y_test[:, idx] |> get_label\n",
    "plot(Gray.(xi), title=\"y=$(yi); ŷ=$ŷi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bddaf77e",
   "metadata": {},
   "source": [
    "# Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0a6c4183",
   "metadata": {},
   "outputs": [],
   "source": [
    "function x_autoenc(df)\n",
    "    select(df, Not(:label)) |> Matrix |> permutedims |> m -> convert(Matrix{Float32}, m / 255) |> gpu;\n",
    "end;\n",
    "\n",
    "x_train_autoenc = df_train |>  x_autoenc\n",
    "x_test_autoenc = df_test |> x_autoenc;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c5cbef",
   "metadata": {},
   "source": [
    "## Compression and decompression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "53ebfab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = Chain(\n",
    "    Dense(784, 1024, relu)\n",
    "    , Dense(1024, 256, relu)\n",
    "    , Dense(256, 16, relu)\n",
    ") |> gpu\n",
    "\n",
    "knw = Chain(\n",
    "    Dense(16, 16, σ)\n",
    ") |> gpu\n",
    "\n",
    "dec = Chain(\n",
    "    Dense(16, 256, relu)\n",
    "    , Dense(256, 1024, relu)\n",
    "    , Dense(1024, 784, relu)\n",
    ") |> gpu\n",
    "\n",
    "auto = Chain(\n",
    "    enc\n",
    "    , knw\n",
    "    , dec\n",
    ") |> gpu;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9135d083",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_autoenc = Flux.DataLoader((x_train_autoenc, x_train_autoenc), batchsize=1024, shuffle=true) |> gpu;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0db1dd44",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_autoenc(x, y) = Flux.mse(auto(x), y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c94081ea",
   "metadata": {},
   "source": [
    "First I train the encoding, knowledge and decoding networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e628151a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 83.250845 seconds (40.67 M allocations: 2.749 GiB, 2.35% gc time, 0.09% compilation time)\n"
     ]
    }
   ],
   "source": [
    "h = []\n",
    "@time for e=1:200\n",
    "    push!(h, train!(loss_autoenc, params(auto), data_autoenc, ADAM()))\n",
    "end;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5f059c64",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h |> plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "33cf1668",
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og = x_test_autoenc[:, 97]\n",
    "en = enc(og)\n",
    "kn = knw(en)\n",
    "dc = dec(kn)\n",
    "plot(\n",
    "    plot(\n",
    "        Gray.(reshape(og, 28, 28)'), title=\"Original\"\n",
    "    ),\n",
    "    plot(\n",
    "        Gray.(reshape(en, 4, 4)'), title=\"Encoded\"\n",
    "    ),\n",
    "    plot(\n",
    "        Gray.(reshape(kn, 4, 4)'), title=\"Knowledge\"\n",
    "    ),\n",
    "    plot(\n",
    "        Gray.(reshape(dc, 28, 28)'), title=\"Decoded\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47c61fd",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0b960ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = Chain(\n",
    "    Dense(16, 32, relu)\n",
    "    , Dense(32, 10)\n",
    "    , softmax\n",
    ") |> gpu\n",
    "\n",
    "m2 = Chain(\n",
    "    enc\n",
    "    , knw\n",
    "    , clf\n",
    ") |> gpu;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e7cb934f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_autoenc_classif = Flux.DataLoader((knw(enc(x_train_autoenc)), y_train), batchsize=1024, shuffle=true) |> gpu;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "202bf623",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_autoenc_classif(x, y) = Flux.mse(clf(x), y);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be38018e",
   "metadata": {},
   "source": [
    "Then I train the classification network with the training data compressed by the encoding and knowledge networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b2dc977d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  5.598522 seconds (13.26 M allocations: 897.456 MiB, 2.91% gc time, 1.39% compilation time)\n"
     ]
    }
   ],
   "source": [
    "hc = []\n",
    "@time for e=1:200\n",
    "    push!(hc, train!(loss_autoenc_classif, params(clf), data_autoenc_classif, ADAM()))\n",
    "end;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cc2b6d81",
   "metadata": {},
   "outputs": [
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       "  350.745,783.059 361.013,798.591 371.282,1020.45 381.55,1040.34 391.818,727.386 402.086,933.936 412.355,983.064 422.623,1083.05 432.891,928.508 443.16,697.952 \n",
       "  453.428,969.27 463.696,1213.75 473.965,1208.16 484.233,938.447 494.501,1055.68 504.769,1011.12 515.038,1169.66 525.306,733.756 535.574,994.504 545.843,1244.29 \n",
       "  556.111,1050.31 566.379,950.631 576.648,1003.35 586.916,975.336 597.184,833.836 607.452,849.247 617.721,1218.98 627.989,898.048 638.257,1245.64 648.526,1119.12 \n",
       "  658.794,1124.84 669.062,1269.12 679.331,1091.27 689.599,971.914 699.867,1024.71 710.136,949.342 720.404,957.975 730.672,1232.33 740.94,1205.48 751.209,1135.26 \n",
       "  761.477,970.972 771.745,1018.6 782.014,1224.54 792.282,1033.28 802.55,889.474 812.819,1096.55 823.087,1113.36 833.355,1105.59 843.623,1244.29 853.892,965.687 \n",
       "  864.16,1231.16 874.428,887.644 884.697,1125.57 894.965,1120.79 905.233,1100.75 915.502,1121.23 925.77,1197.28 936.038,1289.1 946.306,838.717 956.575,1218.2 \n",
       "  966.843,1002.77 977.111,853.699 987.38,1130.22 997.648,1156.73 1007.92,1179.38 1018.18,873.303 1028.45,1139.08 1038.72,1009.25 1048.99,1108.18 1059.26,1168.81 \n",
       "  1069.53,943.03 1079.79,947.553 1090.06,1079.23 1100.33,1158.94 1110.6,1168.39 1120.87,1019.19 1131.14,1040.79 1141.4,1106.91 1151.67,1083.43 1161.94,1191.84 \n",
       "  1172.21,1113.75 1182.48,1166.93 1192.75,1271.46 1203.01,1335.52 1213.28,1073.11 1223.55,1020.58 1233.82,1134.56 1244.09,999.62 1254.36,1155.18 1264.62,1344.63 \n",
       "  1274.89,915.796 1285.16,1205.42 1295.43,983.246 1305.7,995.001 1315.97,930.81 1326.23,995.052 1336.5,1127.98 1346.77,1275.39 1357.04,1099.17 1367.31,824.814 \n",
       "  1377.58,1140.01 1387.84,1009.62 1398.11,1220.56 1408.38,1162.31 1418.65,1167.1 1428.92,957.48 1439.19,1355.16 1449.45,1351.29 1459.72,1056.48 1469.99,1128 \n",
       "  1480.26,1225.69 1490.53,1299.77 1500.79,1445.72 1511.06,1019.57 1521.33,1073.32 1531.6,1126.7 1541.87,1116.54 1552.14,1087.96 1562.4,941.192 1572.67,1058.08 \n",
       "  1582.94,1267.4 1593.21,1227.55 1603.48,1070.9 1613.75,1233.63 1624.01,1257.86 1634.28,1096.09 1644.55,1174.93 1654.82,1040.49 1665.09,1358.17 1675.36,1331.78 \n",
       "  1685.62,1165 1695.89,1149.5 1706.16,1309.66 1716.43,1337.31 1726.7,1242.19 1736.97,1010.92 1747.23,1117.86 1757.5,1265.73 1767.77,1290.02 1778.04,936.048 \n",
       "  1788.31,1103.07 1798.58,1207.49 1808.84,1118.09 1819.11,1123.96 1829.38,1426.04 1839.65,1116.34 1849.92,1270.48 1860.19,1173.05 1870.45,1195.12 1880.72,881.103 \n",
       "  1890.99,1127.56 1901.26,1228.42 1911.53,1288.25 1921.8,1303.92 1932.06,1429.98 1942.33,832.407 1952.6,1173.39 1962.87,1111.36 1973.14,1178.63 1983.41,1179.19 \n",
       "  1993.67,1088.08 2003.94,1321.62 2014.21,1387.76 2024.48,1107.04 2034.75,1151.57 2045.01,1262.74 2055.28,1010.85 2065.55,1116.09 2075.82,1245.88 2086.09,1289.14 \n",
       "  2096.36,965.546 2106.62,1021.25 2116.89,1103.05 2127.16,1178.64 2137.43,1242.35 2147.7,1092.1 2157.97,1209.95 2168.23,1290.48 2178.5,1180.34 2188.77,1309.34 \n",
       "  2199.04,1081.24 2209.31,1143.68 2219.58,1174.57 2229.84,1129.46 2240.11,1211.74 2250.38,1169.47 2260.65,1157.27 2270.92,1191.86 2281.19,1272 2291.45,1241.05 \n",
       "  \n",
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       "  1988.13,198.898 2280.56,198.898 2280.56,95.2176 1988.13,95.2176 1988.13,198.898 \n",
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      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hc |> plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c12904",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ae2b11",
   "metadata": {},
   "source": [
    "At this point I could use the m2 or the clf models.\n",
    "\n",
    "The m2 model takes a 28x28 image as an input, compresses it with the encoding and knowledge networks, and then classifies it.\n",
    "\n",
    "The clf model takes a compressed 4x4 image and classifies it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4dbd5df1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16×10000 CuArray{Float32, 2, CUDA.Mem.DeviceBuffer}:\n",
       " 0.940425  0.369556   0.304466  0.342902   …  0.534504   0.647912   0.729091\n",
       " 0.169327  0.476021   0.187799  0.0720671     0.363696   0.0770572  0.0871172\n",
       " 0.341197  0.0354029  0.538205  0.338364      0.814999   0.48862    0.540134\n",
       " 0.803808  0.400577   0.49707   0.232363      0.0970822  0.6102     0.216925\n",
       " 0.25419   0.622433   0.39763   0.370179      0.311225   0.31504    0.537176\n",
       " 0.13793   0.963279   0.253031  0.642356   …  0.79141    0.0390155  0.0916268\n",
       " 0.28535   0.0985808  0.380226  0.190825      0.310181   0.168932   0.0828913\n",
       " 0.528243  0.768223   0.463189  0.176289      0.0415898  0.0342672  0.588168\n",
       " 0.579654  0.0354131  0.733973  0.828916      0.137161   0.84145    0.753332\n",
       " 0.083266  0.30258    0.494886  0.09353       0.0138732  0.157423   0.0921301\n",
       " 0.127386  0.104193   0.45664   0.145326   …  0.0288254  0.151292   0.0605917\n",
       " 0.156033  0.225684   0.316097  0.392282      0.526755   0.287957   0.0411403\n",
       " 0.953783  0.349266   0.450268  0.88071       0.933882   0.95299    0.867055\n",
       " 0.42933   0.556401   0.650649  0.706159      0.0509424  0.271593   0.567137\n",
       " 0.392897  0.356192   0.416983  0.44909       0.292131   0.163725   0.318824\n",
       " 0.111558  0.750463   0.376389  0.348271   …  0.875475   0.599237   0.678546"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compressed_x = x_test_autoenc |> enc |> knw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "28489b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8382"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_autoenc = get_acc(clf)\n",
    "accuracy_autoenc(compressed_x, y_test)"
   ]
  }
 ],
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