wildcard/mnist.ipynb

## mnist.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# imports"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import keras\nfrom keras.datasets import mnist\nfrom keras.layers import Dense\nfrom keras.models import Sequential\nfrom keras.optimizers import SGD\nfrom matplotlib import pyplot as plt\n%matplotlib inline",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from jupyterthemes import jtplot\njtplot.style(theme='solarizedd')\njtplot.style(context='talk', fscale=1.4, spines=False, gridlines='--')\njtplot.style(ticks=True, grid=False, figsize=(6, 4.5))",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# loading data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "(train_x, train_y), (test_x, test_y) = mnist.load_data()",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Training and Saving"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#train_x = train_x.astype('float32') / 255\n#test_x = test_x.astype('float32') / 255\nprint(train_x.shape)\nprint(train_y.shape)\nprint(test_x.shape)\nprint(test_y.shape)\ntrain_x = train_x.reshape(60000, 784)\ntest_x = test_x.reshape(10000, 784)\ntrain_y = keras.utils.to_categorical(train_y, 10)\ntest_y = keras.utils.to_categorical(test_y, 10)\nmodel = Sequential()\nmodel.add(Dense(units=128*2, activation=\"relu\", input_shape=(784,)))\n# model.add(Dense(units=128,activation=\"relu\"))\n# model.add(Dense(units=128,activation=\"relu\"))\nmodel.add(Dense(units=10, activation=\"softmax\"))\nmodel.compile(optimizer=SGD(0.001),\n              loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\nmodel.fit(train_x, train_y, batch_size=32, epochs=10, verbose=1)",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": "(60000, 28, 28)\n(60000,)\n(10000, 28, 28)\n(10000,)\nEpoch 1/10\n60000/60000 [==============================] - 2s 33us/step - loss: 10.8529 - acc: 0.3242\nEpoch 2/10\n60000/60000 [==============================] - 2s 32us/step - loss: 8.4678 - acc: 0.4714\nEpoch 3/10\n60000/60000 [==============================] - 2s 33us/step - loss: 7.0571 - acc: 0.5590\nEpoch 4/10\n60000/60000 [==============================] - 2s 36us/step - loss: 5.7439 - acc: 0.6400\nEpoch 5/10\n60000/60000 [==============================] - 2s 33us/step - loss: 4.7274 - acc: 0.7030\nEpoch 6/10\n60000/60000 [==============================] - 2s 34us/step - loss: 3.9789 - acc: 0.7498\nEpoch 7/10\n60000/60000 [==============================] - 2s 34us/step - loss: 2.5926 - acc: 0.8355\nEpoch 8/10\n60000/60000 [==============================] - 2s 35us/step - loss: 2.1119 - acc: 0.8658\nEpoch 9/10\n60000/60000 [==============================] - 2s 37us/step - loss: 2.0625 - acc: 0.8693\nEpoch 10/10\n60000/60000 [==============================] - 2s 36us/step - loss: 2.0234 - acc: 0.8718\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "<keras.callbacks.History at 0x11c56f898>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Saving model state to disk"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# model.save(\"./mnist.state\")",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Loading from disk"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# model = keras.models.load_model(\"./mnist.state\")\n# (train_x, train_y), (test_x, test_y) = mnist.load_data()",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Run Model on Test Data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# working will restoring model form disk\n# accuracy = model.evaluate(x=test_x.reshape(\n#     10000, 28*28), y=keras.utils.to_categorical(test_y, 10), batch_size=32)\n# print(\"Accuracy: \", accuracy[1])\n\naccuracy = model.evaluate(x=test_x, y=test_y, batch_size=32)\nprint(\"Accuracy: \", accuracy[1])",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": "10000/10000 [==============================] - 0s 14us/step\nAccuracy:  0.8703\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Debugging false predications"
    },
    {
      "metadata": {
        "code_folding": [
          3
        ],
        "trusted": true
      },
      "cell_type": "code",
      "source": "false_predications = [fp for fp in (\n    (i, model.predict(test_x[i].reshape(1, 784)).argmax())\n    for i in range(test_y.shape[0])) if fp[1] != test_y[fp[0]].argmax()\n]\n\nlen(false_predications)",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "1297"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "code_folding": [
          3
        ],
        "trusted": true
      },
      "cell_type": "code",
      "source": "false_predications_with_img = [{\n    \"index\": fp[0],\n    \"img\": test_x[fp[0]],\n    \"classname\": fp[1]\n    } for fp in false_predications\n]\n\n# print(false_predications_with_img[0])\n# print(i, classname, test_y[i].argmax())]",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Insepect a random false predication"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from random import *\nimport pandas as pd\ndf = pd.DataFrame.from_dict(data=false_predications_with_img)\n# print(false_predications_with_img)\n\n# print(df)\n\nfor false_predication in false_predications_with_img[:10]:\n    # false_predication_index = randint(1, len(false_predications_with_img))\n    # false_predication = false_predications_with_img[false_predication_index]\n    index = false_predication[\"index\"]\n    img = false_predication[\"img\"]\n    classname = false_predication[\"classname\"]\n    plt.imshow(img.reshape(28, 28))\n    plt.title(f\"Index: {index}, Value: {classname}\")\n    plt.show()\n#     print(classname)",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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TpEbd6SKshbyzVVhL/9NaWLP8vxPCmvgqRvLIuXxD1y2UCG6JiSTHEBNJjiEmkhxDTCQ5hphIcgwxkeQKNMXUplF99G7fFt39JgMAnGxtMP3bAXB3cUJEbBzGL1qJK3fulUijZZHivVrCWrC7+HpXW546Cmv2624Ka+Lf8VBppvWW+IvWLTF72GC8eDnhuSOG4sjFy6jfcyBW796HhWOGwchQWRJ9EpGAViH27emNz72aY+XOPeqxag52cLKzwfLtvyMrOxvbDh5B8pMnaFrHo8SaJaKXaRXioN370N1vMh5Ex6rHXO3tEBEbj6zsf3fC7kfFwNXetvi7JCIhrT4T53WpUhNjI6Slp2uMqdLTYWJkJFyP94de6NrGS2NMTyE+YTkR5a/Qx06r0tNhbGioMWZsZIQUlUq4TPD+EATvD9EYK29qgjNB4lPNENGrFXqK6V5kNBxtKkNP798tqYu9Le5HxRRLY0SknUJvie9FRiMqPgHfdOmIJdt24tPmTVDR3Awnr4hPqEYFc2uw+IR3lfVNhTW/Mx2FNbeki0XqiUqfIh3sMeTnX1D/nbdwOmgxerdvi8HTA6DKyCiu3ohICwXaEm8POYrtIUfVtyNi4/H1xOnF3hQRaY+HXRJJjiEmkhxDTCQ5hphIcjxRXimmb1K43xUZ3hNPTdGbh1tiIskxxESSY4iJJMcQE0mOISaSHENMJDlOMZVivzTcKKyFZ6UKay5bkoQ1ngzvzcMtMZHkGGIiyTHERJJjiIkkxxATSY4hJpIcp5h0TN/SUlirrhRPFS171ERYy7IwFj9gkzpa9VUQ+pfvCms5KSnF/nikiVtiIskxxESSY4iJJMcQE0mOISaSHENMJLkCTTG1aVQfvdu3RXe/yQCAjxrXx5zhQ5CRmaW+T58ff8KlW+IpB9L0uE1NYa2awQFhbXLlS+KVbnlFrQT4J4ovLL97QQthrfL6K8Iap6a0p3WIv2jdEhP/1wtX791Xj7m7OGHVzj2YtTa4RJojovxptTvt29Mbn3s1x8qdezTG3V2ccPNBeIk0RkTa0SrEQbv3obvfZDyIjtUYd3d2QseWzXBsxXzsnT8TnVqJd52IqGRotTudkPz4pTFDpRIPY+OwPeQo/jwVgHequWCJ3wjEJCThZGje1yj2/tALXdt4aYzpKRR53peItFPoY6czMjPRc8JU9e2Lt+5g15ETaNWgrjDEwftDELw/RGOsvKkJzgQtKmwbRGVeoaeY7KwrYXiPrhpjhkoDZGRmFrkpItJeobfEf6ekottHrRCTmIRNfx5Evbdrol2zxurpJ9KO+e0nwlrnux8La9FPzYW1xPsVC9VLOXtxL/1qnBDWJlqFims/iGv1P/1SWLP5MlJY4/STpkKH+GlqGgZNm41xfb/CqK+/RHxyMsYtWMZvq4leswKFeHvIUWwPOaq+feHmHXQZ9UOxN0VE2uNhl0SSY4iJJMcQE0mOISaSHE+Up2O5F/M+MAYA0t4XL2eJuFfUit8+M0dhbVf9D8QLjksQls6+J77WVM0fvxHWqvmeEj9eGcQtMZHkGGIiyTHERJJjiIkkxxATSY4hJpIcp5hIKzlPxL9wMopIFtbet7lRqMczSuL2RVt8pYgkxxATSY4hJpIcQ0wkOYaYSHIMMZHkOMVEWknr2EBYazc5RFjzrXhLWBsU2VxYc1p6U1jLFlbKJm6JiSTHEBNJjiEmkhxDTCQ5hphIcgwxkeS0mmJq7FELo77+Ek62NohJTMKc9Ztx4MwF1K5eDf6D+sDZrgpu3A/HmHlL8DBGfAI3Kj56xsbCWo5KJazpm4uv4XTj57eEtaOfzBHWbPVNhbVhMQ2FtbBx4sczSDovrJGmfLfEFS3METjKB4GbtuK9rwZg6sp1mPn9YLg52mPBmO+xauce1PtqII5duoLAkT6vo2ciekG+Iba3tsIfx08h5NxF5Obm4sTlq3gQHQsPN1ekpKZhx+HjyMrOxqJfd8DO2go1ncSnNiWi4pfv7nTo3TCE3g1T33a0sYabgz1sKlkiLCpaPZ6Tk4vw2Di42tvh1sOIkumWiF5SoMMurSpYYOl4X2w9eBgKKJCWnqFRV6VnwNjIULi894de6NrGS2NMT6EoSAtE9B9ah9jN0R5Lx/vi2MVQ+C9bg97t28LYUDOwxkaGSH3FlyrB+0MQvF/zONvypiY4E7SogG0T0XNaTTG9514DG6ZOwMa9BzBx8Urk5uYiLDIaznZV/l2RngJOtja4HxVTYs0S0cvy3RLbVKqIRWOHY0bQBmw9cEQ9furqdVQwK49OrVpg15HjGNCpPaLjE3E7PLJEG37T6FeqKKzFflFTWHviIl5ndpV0YW1m41+FtY7lDglryTnijz01gsXXTao59bawxmmk4pFviLu2bokKZuUxvl8vjO/XSz3uvywIA6bMgv+gvhjfvyduPQjH0JmBJdosEb0s3xDPC96GecHbhPXOoyYWa0NEVDA87JJIcgwxkeQYYiLJMcREkuOJ8oqJwkD8Uob51xfWLvQKENYWPX4orPW3CBXWzPXEv3DKesVp5kbENhbWro6oLay5HTolrPGkdiWPW2IiyTHERJJjiIkkxxATSY4hJpIcQ0wkOU4xFZNb8+sKa3fbL3jFkuKTKAy3vCOspeWK//6OiXtPWDs2U3ziOvON4qkifVwQ1ki3uCUmkhxDTCQ5hphIcgwxkeQYYiLJMcREkuMUUzGxPqUvrDWw7yasNa4i/qXSiRXiaSubNZeFtZzUVGHNHOJpJJITt8REkmOIiSTHEBNJjiEmkhxDTCQ5hphIclpNMTX2qIVRX38JJ1sbxCQmYc76zThw5gL6dvgEw3t0RWZWlvq+Hw3xRXzy4xJruLSyXH1SXFwtLol/pwRYQ7zOnHw7orIi3xBXtDBH4CgfjPplMQ6dv4TGtWth/ujv0GG4H9xdnDB1xVps3HfgdfRKRHnId3fa3toKfxw/hZBzF5Gbm4sTl6/iQXQsPKq5wN2lKm4+CH8dfRKRQL5b4tC7YQi9G6a+7WhjDTcHe9wOj4SLnS0GdGoPz5rVkPDoMeas34JD5y+VaMNEpKlAh11aVbDA0vG+2HrwMFLSVLhw8w7W/vEnfGbeQFNPDwSM+BadR05AmOBC494feqFrGy+NMT2F+Lq3RJQ/rUPs5miPpeN9cexiKPyXrUFubi56Tpiqrh86dwmnr15Hi7p1hCEO3h+C4P0hGmPlTU1wJmhRIdsnIq2mmN5zr4ENUydg494DmLh4JXJzc+HuXBX9O7bTuJ+hUomMzCzBWoioJOQbYptKFbFo7HDMCNqAZdt3q8dTVOkY2q0TvOq9C4VCgbZNGsCzhhv2nz5Xog0TkaZ8d6e7tm6JCmblMb5fL4zv10s97r8sCMNmL4BvT28EjBiChzFxGDRtDhLK4BwxkS7lG+J5wdswL3ibsH7wLE9lSqRLPOySSHIMMZHkGGIiyTHERJJjiIkkxxATSY4hJpIcQ0wkOYaYSHIMMZHkSs1lXIzLGem6BSKdK0wOdB7icibGAAD/g7467oSo9ChnYoynqWla3VeBWg1yS7iffNlUskRKmgoAsNZ/HHpOnKbjjkofvi55exNfl3ImxohLStb6/jrfEgPQaDgnN1frv0BlCV+XvL2Jr0tBnw+/2CKSHENMJDmGmEhy+qhsP0nXTbxIAeBa2ANdt1Hq8HXJG1+XUvLtNBEVHneniSTHEBNJjiEmkhxDTCS5UnHEFgDUrl4N/oP6wNmuCm7cD8eYeUvwMCZO123pVJtG9dG7fVt095sMAHCytcH0bwfA3cUJEbFxGL9oJa7cuafjLl8f0cXuy/p7p1RsiQ2VSiwY8z1W7dyDel8NxLFLVxA40kfXbenUF61bYvawwXjxopFzRwzFkYuXUb/nQKzevQ8LxwyDkaFSd02+Rs8vdh+4aSve+2oApq5ch5nfD4abo32Zf++UihA38nBHSmoadhw+jqzsbCz6dQfsrK1Q08lR163phG9Pb3zu1Rwrd+5Rj1VzsIOTnQ2Wb/8dWdnZ2HbwCJKfPEHTOh467PT1EV7s3s21zL93SkWIXe3tEBYVrb6dk5OL8Ng4uNrb6bAr3QnavQ/d/SbjQXSseszV3g4RsfHIys5Wj92PioGrva0uWnztQu+G4YfFq9S3n1/s3qaSZZl/75SKEJsYGSEtPUNjTJWeAWMjQx11pFt5XZTOxNgIaenpGmOq9HSYGJW9kym8eLF7BRRl/r1TKkKsysiAsaHmi25sZIhUlUpHHZU+qvT0PF4jI6SUsdfIzdEem2dMwtlrt+C/bA3fOyglIQ6LjIazXRX1bT09BZxsbXA/KkaHXZUu9yKj4WhTGXp6/37T5WJvW6Zeo7wuds/3TikJ8amr11HBrDw6tWoBpYE+BnfpgOj4RNwOj9R1a6XGvchoRMUn4JsuHaE00MfnXs1R0dwMJ69c03Vrr4XoYvd875SiH0DUcnWG/6C+cHGwxa0H4Rgzb2mZmuvLy+dezdH1w5b4ctyzeWLHKpUx5Zt+8Kjmioi4eEwoQ/PEQ707YWi3TurTOD3nvywItx9GlOn3TqkJMREVTqnYnSaiwmOIiSTHEBNJjiEmkhxDTCQ5hphIcgwxkeQYYiLJMcREkvt/xaqoteXqPrUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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1sMf0L/rB290VtxOTMH7xKpy/EfFcGn1Z5bWMVH3XXWHN1GWkhzrxT5WanewrrJVbZyesldnyp7DmBPGB5Er6r4NkYfRM/FHrlpg9fCCePp3wvJFDcOjMOTQM6I8fd+3BD2OHw9JC9Tz6JCIBo0IcGOCPTr7NsWrHbv2Yp5Marmp7rNj2K7Kys7F1/yHce/AAb9b1eW7NEtGzjApxyK496B40GdHxifoxD0c1bifeQVZ2tn4sKi4BHo4ORd8lEQkZ9Zk4t1OVWltZIl1r+PlKo9XC2lK8757/O77o2sbXYEypyOPEsESUL5P3ndZotbCysDAYs7K0RKpGI7xN6N4whO41PPOyrY01ToQsNrUNolLP5CWmiNh4ONtXhVL5ZCZ1d3RAVFxCkTRGRMYxeSaOiI1H3J1kDOrSEUu37sAHzZuiYlk7HDt/qSj7e+mV+SlTWJtd7YRJ2xwS31RYi+7lIqypL1826f6oeBVqZ4/B332PhrVfwZ8hS9C7fTsMnD4XmoyMouqNiIxQoJl4W9hhbAt78jvW24l30Gvi9CJvioiMx90uiSTHEBNJjiEmkhxDTCQ5HijvBdC+21BYm+I8L49bWgsrr53sIaw5+F0XbzInjxpJiTMxkeQYYiLJMcREkmOIiSTHEBNJjiEmkhyXmF6AQd9vFtY8zcXLSHX/DBDWnLrdFNZ0OdnCGr18OBMTSY4hJpIcQ0wkOYaYSHIMMZHkGGIiyXGJqYgo67wirDW0PCKs/Zsj/jtaZltZYU1R011Yi2lfUVjTqMXLTxPf3iasrY1tLKyl7HAS1tS/iY9+mn0zSlgj43EmJpIcQ0wkOYaYSHIMMZHkGGIiyTHERJIr0BJTm8YN0bt9O3QPmgwAaNukIeaMGIyMzCz9dfp88y3OXhP/wuZlFelfQVhzMbcR1v7NEZ9FcsOUmcKaKo8zwjqaie/vbEaWsBadWVlYW1x9o7DmOUb8S6wLw8XnmvpkyXBhzXHGUWGNDBkd4o9at8TEz3viYsSTtT1vd1es3rEbs9aGPpfmiCh/Rr2dDgzwRyff5li1Y7fBuLe7K65GxzyXxojIOEaFOGTXHnQPmozo+ESDcW83V3Rs2QxHVi7E7wtnwq9Vi+fSJBGJGfV2Ovne/WfGLFQq3EpMwraww/jj+FzU9nTH0qCRSEhOwbELuZ+j2P8dX3Rt42swplTk8eGOiPJl8r7TGZmZCJgwVX/5zLUb2HnoKFq9UV8Y4tC9YQjdG2YwZmtjjRMhi01tg6jUM3mJSV2lEkb06GowZqEyR0am+NtIIip6Js/E/6am4eO2rZBwNwWb/tiPBq/WxPvNmuiXn0qbtm1PmXS7skorYW1a8uvC2s9nxTXXn8UfUcpcShTWsm7dFtbMXm0nrF0ZIf611YV2C4W1Ff0XCGt9lUOENafpXH56mskhfpiWjgHTZmPcp59gdK9uuHPvHsYtWs5vq4lesAKFeFvYYWwLO6y/fPrqDXQZ/XWRN0VExuNul0SSY4iJJMcQE0mOISaSHA+UV0Qi2lcS1mrO6iuslTsoXmKy3yb+NViNZNOWtMS/Ycpb9uXr4l4+E9+uzoIvhbUbfuKdfKr4xos3+m0ee/npdOLaS4ozMZHkGGIiyTHERJJjiIkkxxATSY4hJpIcl5iKSFaC+NdBnj3EtbyIz5okD6skM5Nut6/WVmHtA9u3hLWcBw9Muj+ZcSYmkhxDTCQ5hphIcgwxkeQYYiLJMcREkuMSExWasvYrwlov/70mbbP15U7CmsVDHsftaZyJiSTHEBNJjiEmkhxDTCQ5hphIcgwxkeSMWmJq4lMLo3t1g6uDPRLupmDO+s3Yd+I06lT3RPCAPnBTV8OVqBiMXbAUtxKSnnfPVAzSOjUS1ibMXCWsvW2tFdZOasUHtVNNqiBuRndLXCuF8p2JK5Yri/mjh2L+pi14/ZN+mLpqHWYOGwgvZ0csGjsMq3fsRoNP+uPI2fOYP2roi+iZiJ6Sb4gdq1TGb+HHEXbqDHQ6HY6eu4jo+ET4eHkgNS0d2w+GIys7G4t/3g51lcqo6er8Ivomov/k+3b6ws1IXLgZqb/sbF8FXk6OsK9UAZFxT44NnJOjQ0xiEjwc1biWxykyiahoFWi3y8rly2HZ+EBs2X8QCiiQrs0wqGu0GbCytBDe3v8dX3Rt42swplTkcSBwIsqX0SH2cnbEsvGBOHLmAoKXr0Hv9u1gZWEYWCtLC6RpNMJthO4NQ+jeMIMxWxtrnAgRnwmAiPJm1BLT6941sGHqBGz8fR8mLlkFnU6HyNh4uKmrPdmQUgFXB3tExSU8t2aJ6Fn5zsT2lSpi8VcjMCNkA7bsO6QfP37xMsrb2cKvVQvsPBSOfn7tEX/nLq7HxD7Xhil/ZpXF54WK7VVTWHNpHyWs7fb6XlizVog/QoVrxfNEYPBAYa1C+DFhjQzlG+KurVuivJ0txvftifF9e+rHg5eHoN+UWQge8CnGfxaAa9ExGDJz/nNtloielW+IF4RuxYJQ8eFDO4+eWKQNEVHBcLdLIskxxESSY4iJJMcQE0mOB8orALManuKiTvyLnLzEdK4mrGXaibeZ7SbeqWZawzy+iCxj2oHrIrLEZ4Zqu7efsOb93T1hrcJ1LiMVBc7ERJJjiIkkxxATSY4hJpIcQ0wkOYaYSHJcYiqARXtDhLWKZmbCmhnEBz6wVKiEtX9yxMtImXksaaXlsdrV+WZnYS1il3gJzXlXsrBW48opYU28MEVFhTMxkeQYYiLJMcREkmOIiSTHEBNJjiEmkhyXmApggGszYS2nWT1hTWcu/luprSheYrK9+Y+wpvz7gbCWFRsnrAHic2Wp86hxqajk4kxMJDmGmEhyDDGR5BhiIskxxESSY4iJJGfUElMTn1oY3asbXB3skXA3BXPWb8a+E6fxaYf3MKJHV2RmZemv23ZwIO7cu//cGi6plEfOmnQ7mzxqOSbWqHTJN8QVy5XF/NFDMfr7JTjw11k0qVMLC8d8iQ4jguDt7oqpK9di4559L6JXIspFvm+nHatUxm/hxxF26gx0Oh2OnruI6PhE+Hi6w9vdBVejY15En0QkkO9MfOFmJC7cjNRfdravAi8nR1yPiYW72gH9/NqjXk1PJP99H3PW/4QDf5n2tpKITFOg3S4rly+HZeMDsWX/QaSma3D66g2s/e0PDJ15BW/W88HckV+g86gJiBScaNz/HV90beNrMKZUiI96QUT5MzrEXs6OWDY+EEfOXEDw8jXQ6XQImDBVXz9w6iz+vHgZLerXFYY4dG8YQveGGYzZ2ljjRMhiE9snIqOWmF73roENUydg4+/7MHHJKuh0Oni7ueCzju8bXM9CpUJGZpZgK0T0POQbYvtKFbH4qxGYEbIBy7ft0o+narQY8rEffBu8BoVCgXZN30C9Gl7Y+6f4oGlEVPTyfTvdtXVLlLezxfi+PTG+b0/9ePDyEAyfvQiBAf6YO3IwbiUkYcC0OUguhWvERMUp3xAvCN2KBaHis+ztP3m6SBsiooLhbpdEkmOIiSTHEBNJjiEmkhxDTCQ5hphIcgwxkeQYYiLJMcREkmOIiSRXYk7jYlXGsrhbICp2puSg2ENcxtoKABC8P7CYOyEqOcpYW+FhWrpR11Wg1hu659xPvuwrVUBqugYAsDZ4HAImTivmjkoePi+5exmflzLWVkhKuWf09Yt9JgZg0HCOTmf0X6DShM9L7l7G56Wgj4dfbBFJjiEmkhxDTCQ5M1R1nFTcTTxNAeBSZHRxt1Hi8HnJHZ+XEvLtNBGZjm+niSTHEBNJjiEmkhxDTCS5ErHHFgDUqe6J4AF94KauhitRMRi7YCluJSQVd1vFqk3jhujdvh26B00GALg62GP6F/3g7e6K24lJGL94Fc7fiCjmLl8c0cnuS/trp0TMxBYqFRaNHYbVO3ajwSf9ceTsecwfNbS42ypWH7VuidnDB+Lpk0bOGzkEh86cQ8OA/vhx1x78MHY4LC1UxdfkC/T4ZPfzN23B65/0w9RV6zBz2EB4OTuW+tdOiQhxYx9vpKalY/vBcGRlZ2Pxz9uhrlIZNV2di7u1YhEY4I9Ovs2xasdu/Zinkxquanus2PYrsrKzsXX/Idx78ABv1vUpxk5fHOHJ7r08Sv1rp0SE2MNRjci4eP3lnBwdYhKT4OGoLsauik/Irj3oHjQZ0fGJ+jEPRzVuJ95BVna2fiwqLgEejg7F0eILd+FmJL5eslp/+fHJ7u0rVSj1r50SEWJrS0ukazMMxjTaDFhZWhRTR8Urt5PSWVtZIl2rNRjTaLWwtix9B1N4+mT3CihK/WunRIRYk5EBKwvDJ93K0gJpGk0xdVTyaLTaXJ4jS6SWsufIy9kRm2dMwslL1xC8fA1fOyghIY6MjYebupr+slKpgKuDPaLiEoqxq5IlIjYezvZVoVQ++abL3dGhVD1HuZ3snq+dEhLi4xcvo7ydLfxatYDK3AwDu3RA/J27uB4TW9ytlRgRsfGIu5OMQV06QmVuhk6+zVGxrB2Onb9U3K29EKKT3fO1U4J+AFHLww3BAz6Fu5MDrkXHYOyCZaVqrS83nXybo+s7LdFt3KN1YudqVTFlUF/4eHrgdtIdTChF68RD/P0w5GM//WGcHgteHoLrt26X6tdOiQkxEZmmRLydJiLTMcREkmOIiSTHEBNJjiEmkhxDTCQ5hphIcgwxkeQYYiLJ/T8628D3p5ejFgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 345.6x259.2 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Scratchpad"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# keras.utils.to_categorical([[8, 2, 3]], 10)",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/c42d3c8a15bb8391fee8a5e82ec185d8"
    },
    "gist": {
      "id": "c42d3c8a15bb8391fee8a5e82ec185d8",
      "data": {
        "description": "mnist.ipynb",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.0",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "toc": {
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "base_numbering": 1,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "varInspector": {
      "window_display": false,
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "library": "var_list.py",
          "delete_cmd_prefix": "del ",
          "delete_cmd_postfix": "",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "library": "var_list.r",
          "delete_cmd_prefix": "rm(",
          "delete_cmd_postfix": ") ",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
        "module",
        "function",
        "builtin_function_or_method",
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