dice_cnn_adam_wybrane.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D convolution network with Adam optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['blackOnBlack', 'whiteOnBlack', 'navyOnBlue', 'whiteOnRed', 'stainOnWhite', 'greenOnWhite', 'greenOnGreen', 'whiteOnRed_distance', 'redOnRed', 'woodOnRed', 'blackOnRed', 'redOnRed_white', 'hardLight', 'navyOnWhite', 'whiteOnBlue']\n",
      "69552\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.utils import to_categorical\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from keras import backend as K\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from PIL import Image\n",
    "\n",
    "import numpy as np\n",
    "from numpy.testing import assert_allclose, assert_equal\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# initialization and check if dataset got proper size\n",
    "\n",
    "# path = '/home/ubuntu/zdjecia/'\n",
    "path = '/home/oziomek/licencjat/kostki/zdjecia/'\n",
    "listing = os.listdir(path)\n",
    "print(listing)\n",
    "num_samples = 0\n",
    "for folder in listing:\n",
    "    for i in range(1, 7):\n",
    "        if folder == 'blackOnBlack' or \\\n",
    "            folder == 'greenOnGreen' or \\\n",
    "            folder == 'redOnRed' or \\\n",
    "            folder == 'redOnRed_white' or \\\n",
    "            folder == 'stainOnWhite':\n",
    "            continue\n",
    "        num_samples += len(os.listdir(path + folder + '/' + str(i)))\n",
    "assert_equal(69552, num_samples)\n",
    "print(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load all data into numpy array\n",
    "immatrix = np.array(\n",
    "    [np.array(Image.open(path + folder + '/' + str(i) + '/' + file)).flatten() \n",
    "         for folder in os.listdir(path)\n",
    "             for i in range(1, 7) \n",
    "                 for file in os.listdir(path + folder + '/' + str(i)) \n",
    "                     if folder != 'blackOnBlack' and \\\n",
    "                        folder != 'greenOnGreen'  and \\\n",
    "                        folder != 'redOnRed'  and \\\n",
    "                        folder != 'redOnRed_white'  and \\\n",
    "                        folder != 'stainOnWhite'], 'f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(69552, 4096)\n"
     ]
    }
   ],
   "source": [
    "print(immatrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = np.ones((num_samples, ), dtype=int)\n",
    "counter = 0\n",
    "for folder in os.listdir(path):\n",
    "    for i in range(1, 7):\n",
    "        samples = len(os.listdir(path + folder + '/' + str(i)))\n",
    "        labels[counter:counter+samples] = i-1\n",
    "        counter += samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# randomize data with const random_state\n",
    "data, label = shuffle(immatrix, labels, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1673a57b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 dots, number is 1716\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1673a64c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 dots, number is 698\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1673a25ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 dots, number is 6634\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1673a00080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 dots, number is 8206\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1673a3ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 dots, number is 9328\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJztnX+spOV1379nZu69e3cXll2D8ZolhhRkQpoYuysHCytgqCNiR0GqLDdOlNIIiT/iVqRNFaCVqqTqD/uPxraq1tKqdsIfrjH54UKQawdvQWmkGrOuIeaHMRhDWFhYWHZhf3B/zZz+Me+d95zzzHPuM3PnzkDe85Gu7jvzPu/zPPP+mDnnOef5PsTMCIKgWbRm3YEgCKZPPPhB0EDiwQ+CBhIPfhA0kHjwg6CBxIMfBA0kHvwgaCCbevCJ6HoiepKIniai2ybVqSAIthYaN4GHiNoAfgTgowAOA3gIwKeY+fHJdS8Igq2gs4ljPwjgaWZ+BgCI6E4ANwDIPvhzCzt4YfvuofuoJ184rcrvKa+cPYwyhd22zJeirEPt0uVozGRIln1RbY2bXVl4gpxi1Ctrm0ubMtXJ47zzlj03G9Ux1rkrvQHzfbbno/izef2Qt0Sm/uXTr2F1+fSGV2MzD/4FAJ4Xrw8D+AXvgIXtu3HFR24Zuq+1LM6M54DILwhbzvlS6M4Pr7Q7rwvKG729qq9Wr02inDjG3FwtcRy38teAuqb+ubpsr1Nvt7rjPfjZLztbzjnf7ZW6bfslID9bUodsWhymvuDNca01+61Qb8pzs7ZNNybrkP0dWmcO9VA516yXr987H/ZzS3Ln39478l61x6z34wf3fSHfkCxfVGoTENHNRHSIiA6tLZ/e6uaCIChgM7/4LwC4ULzeV72nYOYDAA4AwM49F3J3buNfIera18O/teWvYr+g2Nc25qD4iiPHMpDfsr227VimPtPfpF9Z8uXkr7ytX7ZtfxVY9tn5pVVWSi9/rvQvS946svvU+ZHWkfnFlPvc69mR1pb91RXb9l7J/JK7lkDLs2x0H3tzsmC+So/c+el18uWsu+NZFMPYzC/+QwAuJaKLiWgewK8BuGcT9QVBMCXG/sVn5jUi+mcAvgWgDeDLzPzYxHoWBMGWsRlTH8z8DQDfmFBfgiCYEpt68EeGa38sGclU/mh+dBeef+s4LtKvlz5zyzpmMrhg/MXcaK/ngye+tRMeU2W98E/bGU3PHWPLCb/eRiXs56n7lz9XdhxGfhbPR84d0y9bb3s+ubwu9hrJ41prdQO9+fx4QtovGdnIl5Oj7r25fD+SMaxM/fYzq/NorsV6NKM0/Bopu0HQQOLBD4IGMlVTn5jRXu7bMtYUypmGALR5L8JV1kRtL8lQSKFJuZYtlh6Xsb/ZhP1U6MmYdTL05CV5KNPZMUOtOajPo+iTOd/SLG0lfeSh2y5OWFR9TlPOhl2z1TsJU/L82+su93V6ZefUXmYvHJlzXWxmnfycLetaiZcy0SxxwTjvLqyf49IMyvjFD4IGEg9+EDSQePCDoIFMN5wHDHyrdAKCKGJ9rEx4KfE/lS9W2B0n/OGFniQ2DVkeZ/05z9/NhQFtP+T5SPovz4ET+pRXPvGzM6HP0ok4Ht4EGG9cRk0WsmMSonH7WWSdq9vrD2AnYMmJVbD3jvjcdrJXLuTYWdKVyOPaK3qf7KNMASbOX3d7LQavCydmxS9+EDSQePCDoIFM3dRfNzmtqaLmnxsTXpnLToYfdzIhpEwf+m3pfbLO3Jxn29/UNRHhsBHm0iuTT5q9Nrqp+qE7KfvvheVkaMia3+ociHCYLafMXJu5l3FHSjMNbZ2ee6OvZ/58e9dC1pGEC519ul/yINP2Wt6VKHZLC2dUlhC/+EHQQOLBD4IGMv1R/YrEJJOWbSLEUW97IhSlAhhS4CDVQsvXkZtsYkeSlRyTHWXNiHkAVnpLHDKSyMLwofZUzESYr1ZHrjfcpUkk4IrlpGQmY1mmIZCXtfKkt2yfcu6OzWRULo0T5Wit5t0Fea7s6L+apJMbkYeZpGPOlZZ003UMjivUGIxf/CBoIPHgB0EDiQc/CBrIVH18btFgVlgigyz8F0+QQYVWrHAjZ3xT6FlLWrhRtyVDh57QhxVCVP1wZpJ5Api6XF5eO5fJCJixE7lp/FY5Oy/1i4fXl3xmT5JavnTUTVX4cSU/7qN8cGdWo82Yk0i/3hNPcQVYPQFMQWnoMDmucGZkcu+v+f1J2ikrFgTB3yXiwQ+CBjJlzT0emECuPl5hyKdUFAHQ1qZeHUaX88JXyNaRn4hj+9RelR/G9FHskvVboY/SlXo8bT7PHVGfxwllKfehXaZPmPax3rYhtmzb+chk8ToDFnVObTivlTe/VXtuSNqpP0M6Ocv7AGkfPOIXPwgaSDz4QdBA4sEPggYy/ZTdyk1pL1ufUGybUFx3Qe6sNztvmpCg9L8KfR2LSg92/FR3ZVQpiuilUFqXttAfTXzhXPVy/YAkNFQ+a7Cu0NmXROIcn1we1hp+bQF/tlsxmfCpN+bhCo7YXb2y8ZZx1iCwuNd9XeBmUj4+EX2ZiI4S0aPivT1EdB8RPVX9H77ofRAEb0lKTP0/BnC9ee82AAeZ+VIAB6vXQRC8TdjQ1GfmvyKii8zbNwC4ptq+A8ADAG7dqC7i2qxJTBKVFWdm3bWHh0x63bwmWRJ6ckxzVYcMc8HWMby+BC87L5/EplwLyoiPjI1n6TuiKMU4oU/1duG577/OnAPvvJWeLKt774VxVWP51+qes30sPKduBmHu/hjWrw0Yd3DvfGY+Um2/BOD8MesJgmAGbHpUn5kZzvcNEd1MRIeI6NDqyunNNhcEwQQYd1T/ZSLay8xHiGgvgKO5gsx8AMABADjrnH28br7Z1UrliKUdge6ICMDaQv1dZU3SlmMP5kyodHLJcJ03wJiiqYLHUGzW3djhhgyjiXQM74YnaFI64Wgi7oiH51m1hm+7jNlfm3WXW+qseAk3ry1H4zB3bxbelmP/4t8D4MZq+0YAd49ZTxAEM6AknPdVAP8XwHuJ6DAR3QTgMwA+SkRPAfiH1esgCN4mlIzqfyqz67oJ9yUIgikxM119NxvK+i9yaSIhSu5n1jl9EMclwgpSAMOKUMp+eeEl4TN3zViGElpIMriGzxocZRnuXB1euM3OLmxlshe9GWely2R7IbtEPKU1fKzB4vrMGXFTT+zFzTRs29fic9Lwzwz4szlz4V+5nFa/2PC2+g1Us15jdl4QBDniwQ+CBjJdzT2igTnXXra2lti0EyiE+aIz8vLZea5IhwzZ9QptI+RNxWTlUmkO2uqHy94D8MQmTLhNTFqyq/Gq8Fsu820jpOnJZSaqt/Kv7l95xlnumiV1FmrJu+5TO+9WaH1/W6msv0zgJVm2rTv8OiWulRfSHOwsu5/jFz8IGkg8+EHQQOLBD4IGMrO184p9OwxLe80dV28X64s72uWWXJ2eT5isN5dbVnlI2XUSsc3Spbe9lFo5G9KuI0fSHy0QeMQGoThnrYLE55d1SC39wnUG/Nl/ecFL5YPb+h1x1uy4kvlYvfl8H3P3lSfwkhbONJwhfvGDoIHEgx8EDWTKpj7X5pAj/pBkdwnTSy8lbc1GUZ/JerJLWa9jTdnc0sz910OrGJKNJg8afgwwLCNveAM2+0+FjZLO5NvTbeXPo8z+Uvr+zvnw9vmmeFl2XqnunTsrUy5BbZe7XnDadt1S1ZNMn4D2kjiPToiXM6IzG/VpWrPzgiB4GxMPfhA0kKma+kpzzy4t5SwZ1V0Y/v00CREKb3JJaXveSLJ1JdzJJpBmdf1+x0iRe1lmXjZdrh/JslA03A1IohdOFqVyyZxValtr+RPSOS0F7URLiVsh6zcm9pt1/b35/IWaP+UsQSzbsi5jbhLQCKvxquo8ue5id2dj4hc/CBpIPPhB0EDiwQ+CBjKzzD1/mWxTVvQyt5wWAHTkjD87822EDL0cpbr63iwtN1MtIzwxyoy2fOWmH11nMKBV1oASoXBmu6n3zR3Xna8PlOM8/dcyPltvJufD+/nKLdFVKBxiy1oBDJlNp+6xUa5R5laaxD2bI37xg6CBxIMfBA1kdpN0DJ5pK7OR1CQXZzkjb3KMu0yRs2JtTksvCdk5ena5+izehI+xwpgjhILKMw/zGnByoosM+9kswe48JooNt7H8LM6kIoXZ5YVPszqMXp0T0CfMTWQLzb0gCLLEgx8EDSQe/CBoIDPz8b1QRSLWIPyojiNCoYQbMrrjI1EsIGk7kq+ydLluNdOrVHijrLqkH0lR6fOrlNS8eIUN07XWhquKJrPiHPFRda7m8uVUdVYoI9N/VxDERgszYphA/trYtSHdPubWdXRmMmbXWvAUOQUlS2hdSET3E9HjRPQYEd1Svb+HiO4joqeq/7uLWgyCYOaUmPprAH6XmS8HcCWATxPR5QBuA3CQmS8FcLB6HQTB24CStfOOADhSbZ8koicAXADgBgDXVMXuAPAAgFvdukiaL2UiF5ZxtfRyun02S9A7RrbtZQmqOgrFPFzKpf+z4cNSTTyvwWT5qEKNQ60pb4RPnFCfbM+dkaiP0vXLl8IsJ6tjWOh6elmU7IibeDqJnviLxBMcGVj4WyHEQUQXAXg/gAcBnF99KQDASwDOH6WuIAhmR/GDT0Q7AfwZgN9h5jfkPmZmZL5riOhmIjpERIfWlk9vqrNBEEyGogefiObQf+i/wsx/Xr39MhHtrfbvBXB02LHMfICZ9zPz/s7Cjkn0OQiCTbKhj09EBOBLAJ5g5j8Uu+4BcCOAz1T/7x6l4cTnlmIrxt+S/pJMlbW+Xm42F5D3nbxQWRLyyczE8vyypL3CMQo/ZdeZBSZfe6nJcqah2dnKhN/s55TnwPtcyle3vq/jW8u2vX7Ie8Lro7rHzExAta5eInxTtrZA+ThVfpzAW3rcq2N9ZmPpOoIlcfyrAPwmgB8Q0cPVe/8a/Qf+LiK6CcBzAD5Z1GIQBDOnZFT/r5H/7rlust0JgmAaTF1sc9308jTxx56NNkJGVwluhpXzviuiMWlKvYxRzqm0jp1sN/m5k3BV7mM7GYSJ+S37oeTmnfCjU39uOepR8DL+lICps8yXi5MpmWu3X7iw/orI1Q+CBhIPfhA0kOlP0qlMEjtZQwoo2NVbVcbcUt5GlcelevYZW2ikrLh6e5SRfEWh7ltuokxSndOP0pVj033543TBetNq7Mnzr661FZfwsh6l2duRLofTJWNSUy5qYOpQg+FOpMQ/b05mnbOCcs689z6nG70oIH7xg6CBxIMfBA0kHvwgaCCzE9tMxDBFWGTNlJUiicKP9zTObbiwLdafk76TFUyQ/qjN6svpyHvrpKXhH7ldOA7hhSltGI2yL8rask07M9NKxwK89ffgiYxIt1j6sF4o2La9kDkHVmxDjkM4S7gnvnSmbRvOU9mAXkZeV96n+YJJPyKcFwTBRsSDHwQNZKqmPrcIa4v97xprCiVabJvE017TO8xLT3QhV0VG5GN4/cPbspQux2TDYSrDzZuwIc1o72OKOtyJOIXhzbEzGeXnykxQ6Xckf1y53mFZHUD+HCf1u+d4eFtJN9yJUKOFl+MXPwgaSDz4QdBA4sEPggYyu3Ce5/PYtctECE+F6WwdPRF26RmHqFATX2nzO6E42bY309Dzs0uFOJPxj8ystS3BS2UtTQn2hhoKfdNx1xLMntMkpds5pnRYwhsLKBVPUWMZpvpJrK9QEb/4QdBA4sEPggYyXSGOHqPzZmW/eOaf1VcvXBZImdU2wWpOhul0n1Rb0vS02YXO7KtcP9zsNhtH62XadmZzJWS66JmeyaWYtHaIOyOxtJJ8mHUsd8ea0Wv1G2RDk/KlZ6aXUnj93NBn4u7E7LwgCDYgHvwgaCDTHdVnFGuxqV3SdF4V73ujnq7YQb4OhavhJ4QmrPhDV26XC16oFWelyIUXNbAmXmGmms5QzHbRNSGLs/W8KIRjRqvP6Y1oez9fmYk+braiU8co0agcVmjGk5ZXxdp5dycm6QRBsCHx4AdBA4kHPwgayHR9fELRV40r+DBuqCmncWH9VBVOKaujWDPdqcO252cQlok1lIbKRhF11B1xduWWpBph9lk2280RKbWfJbfMV7JMlrN+gHvPFc62lFhhUjdrUHbDuScmLrZJRNuI6LtE9AgRPUZEf1C9fzERPUhETxPR14hofqSWgyCYGSW/CcsArmXm9wG4AsD1RHQlgM8C+BwzXwLgOICbtq6bQRBMkpK18xjAqerlXPXHAK4F8OvV+3cA+H0AX3Trojo0NappUoSTqZZb3dYzDV1KM6y2ehKNoXzFVueYTIhtpCW0cu0at6hVuH6AbtjU6bg+6rUzYcqa36p+Jxyp7iVhinsTq7zlxlw3zkvkG3Gdh6LbhIja1Uq5RwHcB+DHAE4w87os5mEAF4zUchAEM6PowWfmLjNfAWAfgA8CuKy0ASK6mYgOEdGhteXTY3YzCIJJMlI4j5lPALgfwIcAnENE667CPgAvZI45wMz7mXl/Z2HHpjobBMFk2NDHJ6LzAKwy8wkiWgTwUfQH9u4H8AkAdwK4EcDdRS1Wrsi4IapiQQNL6ZDCOCmZNuw3Ab9epf06nRrHp++/zsfH1JoBhaGmVBSl3myt1i88/9amJnOnblCLiJoqVAqz2SfDbcKPT3zwMWfgqRAbOeMJ0sdf8SoUXTJ1qPERK1ZTnbvSmYolcfy9AO4gojb6FsJdzHwvET0O4E4i+vcAvg/gS2VNBkEwa0pG9f8GwPuHvP8M+v5+EARvM6YrxMG1mWO11pIZS4Ks5j7ly7Ez46w3J7Y7TruFoT1renbFsk3WrMtl5/X31WXV+TD1yzrJmt9ySfE3a9vQfk6V0WbM7/aZlcF2d2edl7W6PX+7dLfpjnTerNdBm3/5ZN3W8qoqxx1xEjr6hKyeV48JSfdvbZsuJ4VbvPUZSpc9GwV1z8k6EhEX0Za95aT36tyPkq5Z+m3QdqFoTeTqB0EDiQc/CBrIzIQ4XK27cauXWVSF1aWj3aWN1ZvWTZHmX7IarzDTu/P6e1eek86ZuiPzx5d1OWkuJ/0X2nEnz9Td3b5Nd39b7e/I0XOLMu9NseVdte28tmBNT1F/++zB9sJzr+lKjr4qOm/cBdGv1XdsH2y3V/SH7oqOucuZlWYrGlRZZ8RfCn3Y6IVa5dmT3h6TkYRFEL/4QdBI4sEPggYSD34QNJCZCXEks6jcMEShIIOX3ZVZmigR/fBmmWXq7zlClkkYTfj/Nlw4d6oOgbWW63hT6403dQOvvS4qNP7i9kXREc6W4/n60vfm9AdYE369zLqzdcjPdvzv68/SPas+bu61OiS459HzVbk93xEvjp3Q9W+vjzv9rnp77oz28b1MOHktpN/tusTerVi4z50lOCZK4HXN7kvLeMQvfhA0kHjwg6CBTNXUZ6IkhDXY52RV6YJjNq5M/cKJPk6GlaS9nA/ddIxZKvfZ7Ku5U/V2+1QdwqPT2tTnc88ZbJ++ZLfat7KzPr+Lx0T23DFdB71ZhwRbmFP7pAvSXqrrOL1vUZV77Wfr7auvelTt+7mzDg+2/+LIzw+2XzyzT5VbfPXcente92N598Jg+8z59efadkyft8VXpYtkz3fhRB84FE7ckiHp3tyQskOOAXzXUCLN+82upBu/+EHQQOLBD4IGEg9+EDSQ6YbzBFZkAF3K71PiBEPfrnaKfdaPyqQE0wREP1XICwC38t+nOZ33/muxb6n2wVd/6lxV7rmP1+mrl171rNrXEZU+eujiwfa+b29X5Xb88Ohg24Yj51brQRZ53pZ26YI/9Q9q0aVb935L7Xt3ux60ObJSj0l8w/j4Mh1Whu8AYO5M7dTOnaxv1c6SPnGUuT8AfW281OScrw6Y62LHnzj3olxohjKCozbE7a6ZyOb/BsQvfhA0kHjwg6CBTFmIgwembhJ+IM8UkttONpqzVFPx0tjqoGwXVVtWhEKytqj3zZ0SGW1vaFGKXNbV639Pm+nvu/pHg+27fvqg2ndkrY4J/uOl3xxsn37sXarcjieFa7Wk+6HEHMQsvm0n9Il75pk6C++/7b5a7btoWz3r7n899zOD7Z0v6jo6Z2rbmZZ0OtqcyFjcJfX3rIiLeN1d0Odbu1aOrqMy9fUupdVnbgpl+kuL3dzf3pLf6rqr02PKqTUOsCniFz8IGkg8+EHQQKY+qr9u1iQyyDKrypg4et/w9+1O15STZr8zwcaaZLI9mYFnhTikOW8n4si2PbNUIjPwAODhw/WiRZ/ffZHa9+xSHQE4fGTPYPs8M6mjd7ZwH3pmlHxNiHms1Afu/Nszqtx77q7FPR74gdZdZXFnnXW4tod3HNYZhC3RVstkKMp+tcUI/9pZOi1Oip2ky04Nl/ZOhGC8VYeF69MzT0xLRqA81Wxnjk52l7OCVla4pnAuUPziB0EDiQc/CBpIPPhB0ECm7uMPfCkbupIZXI4GPHvOkhPukH6aHEOw/rkq53wtKvFERxCktZz/nDbLrHNSi2qus/iiXmz03K/X4pX/5aXrdfUrdePnPla/v/uJU8jBC9pnprW6Hywy8OSMPgDY/sxSvf2kHkSg1fq1EvpcNeWEcCivrKh92HXWYLMntPTXto/weyXHZcR9lYR0ZZR4NT8uk82Yg59VOqoY5lAy41T9fo1WVfEZrJbK/j4R3Vu9vpiIHiSip4noa0Q0v1EdQRC8NRjF1L8FwBPi9WcBfI6ZLwFwHMBNk+xYEARbR5GpT0T7AHwcwH8A8C+JiABcC+DXqyJ3APh9AF/06mGigbllzWhv8ooyx4VVak0ymUXlhdHc953sP6ntJsU3rKmfy/ADoDKzErMxozvYOn5Svd71w/q47Ue1OIbMYlt4tQ6P2fCPnLBiNf14rr4tyJjmOZLw2LZaREOa98kSWtK87+oZMPRm7XLMHZOhRJ3JuLZduCM2K06EC3tCBCY593JNBntZPB07uRqvirflD0nc0FxozmvWLs22RZN0Pg/g91Dftu8AcIKZ16/oYQAXDDswCIK3Hhs++ET0KwCOMvP3xmmAiG4mokNEdGh1OT/AFATB9Cgx9a8C8KtE9DEA2wCcDeALAM4hok71q78PwAvDDmbmAwAOAMDO3RdOYmwzCIJNsuGDz8y3A7gdAIjoGgD/ipl/g4j+BMAnANwJ4EYAd2/cHA9ENpL0VBkW8dIpvbRcUUmqay7rr7dtP2RKphfyKcUL4yR9lEIRbbFthD3kbLo5sxSd1MuX/nTiqws/PnFqZXOiH7JuAKA3a/88TZXNzIpbXFDFSPrWp3VKMC8JwdGl+rjWkh4L6MjJbSYU3BNp0Z6QpYc8zkvtVeNUExB4cbHjBIOGxzp8JG5Ff6DvafR9/i9toq4gCKbISAk8zPwAgAeq7WcAfNArHwTBW5Ppa+5VNomnO+aZUzJk52ZRGVNLhQ+dsIsNEeYoX4Y7X58bJpL71ozQmzC/Wye1Cd87qw7vqYy8dn796GQZsVV5kmXqm/F92nmDcenCXYPt03vr3K7F13R/t/+kXg6MlnXmXu+NOoxJe+r6Vs7J54pZ/UO9z7sW2V1qn52dp1xPGap1Zta5eCFBKtxXQOTqB0EDiQc/CBrIlDX38mIT0rz39MTy+mQbZMypgrJh25Hh9VlKTf2JYEbdPbeot1ib97252rxvGz07OeGGVvKae9yRWXHmhIu2T122R+16/uN1H3/uvT8ZbD/50jtVue1/VQuH7P22qf9E7QbwYm3en3q3vm3bwkOQy2n1+yy2ZRafWSHYdclkRqizvFvu3u7vzO9yzftcudy+kNcOgiBHPPhB0EDiwQ+CBjLdcB6j9svtjLbCcJ4SwzS+r6erbwU3BsWMz6Z8PW+fHJMY099PMvdklpycIWfDZmfq2XSnrnyP2vXiVbVP3j23dn7nXt6pyr3jkfqz7H7EiG0uiYw8oatvY1Stk3U/jv2svpX+09VfHWxfvfj8YPuhd2sf/1+88E8G2+/66/za0lL0s230OqRv3TKz8+RsRZbipsYX7uVmyBnsrLisT+0shV0s5uHNHE1mEGb6kSF+8YOggcSDHwQNZLqmPqHoq8YNi5SmQDlhOtUlJ4yThhUz5r2juWd3ypBS2p4oK01Pu/ruYq1hd+xyfQl/+5e/Odj+rV2PDrb/4ytXqXJ/sXLlYPusv9XCFu3XhWCF118R6rPn6nSvnlTzfLfe/s6pS1S5ba+KLMTXtODI2nI9Saf9yvHB9tnP7jD9kOlzuh89IdIhJ9skJrvEWa/BXVZN6kYa11KZ4oVh4kQ8xV0GjtO+OsQvfhA0kHjwg6CBxIMfBA1kdrr6BnemWray/C6bbqtmZjlpuV66cHbfhNJ3pU/HIt2W543uvRComD+h6zj0eh3e+8Dis4PtH586V5WbO1m31T6t42Otk7WOvworGqFMCB//Xd/Vdfxn+keD7ZWz65O862ldxXv+T72cdu+VV5GDz9QiHXMvv6H2dffUPn930YiFSNGVlXzYT7WVhFllhbZsthrTD3nT2Z1ldWTrAyJlNwiCjYkHPwgayPSXyc6ZWK6WnijWG26yWzivO+Hq+2VDdklHxLYr4pDRPx9SvwxLdcUsOzs7ry2Wk977gDaPn1q6bLD92xf8zGB7+4u6jgseqc1lOvyy7ofs70IdiuM1o9v3er2E1rZjx9Wui74jXog6esd1ua6oU7YFAK3FWlSE5oX4hhEEkRmP3qw7dclMqMy9Ls7MUelC5twKQF9bf/Zpfp8Kc9uJko7rMrSukUoHQfB3gnjwg6CBTF9zbx1vRN6ZxKBWqTUuQXtF2D+Flk8yOcMT6ciVs7scc02akTaioMxSNQpsRpm31WYvva4XKTnvnmOD7XdKM92IbfDJOkuuZ+W15VJWcrVcKyRnMwol0hwX5nxrp866w5ww4a0OoxQE2VlnF/Z26wlHcmksS6nAi3uphRltJ3Uh5yLY28qrYxzMZ+mtf87I3AuCIEc8+EHQQOLBD4IGMn0ff90H8ZwqR0RThjS680ZoUvj/7SVHpGNYf0r2iddd4Vemy4ENF+xIXnszA0X4p9fTscmWXP7KCGDqiYFyhp/xn8Uy1snKgJk3AAAL/klEQVQpyAiOoGNuF8//z5ST2X6Azgy0sDhOCoJ0t+ljlNiGEwpWjJAtpzIqneXddRy0vP7ss7CFuvpFDz4RPQvgJPqaNGvMvJ+I9gD4GoCLADwL4JPMfDxXRxAEbx1GMfU/wsxXMPP+6vVtAA4y86UADlavgyB4G7AZU/8GANdU23egv6berRseVWrWyF2ZrD5rYhevUOqZRYUTeKTenzX/3OpVWaNhJ5d/6mWLobezFuKgBTOBR06kkeHBVaNnJ0x9a24r/XwZljPhO6UR6IX2JG1bh5NiKUxsuV6AFaiQuCstl5ri3kQcT0uvmzH7N2KM+WmbraP0F58B/CURfY+Ibq7eO5+Zj1TbLwE4f7SmgyCYFaW/+B9m5heI6J0A7iOiH8qdzMxEw9edqb4obgaAhcVzNtXZIAgmQ9EvPjO/UP0/CuDr6C+P/TIR7QWA6v/RzLEHmHk/M++fm98xrEgQBFNmw198ItoBoMXMJ6vtXwLw7wDcA+BGAJ+p/t+9YWuM2hcpXUvMvJZ+vJfyurZY6HhbwQ45TuCkZ44iAlLUjxGQM9Dcb24ZVrTa/MKPT0Jq0q+XddjU4VK/XpKkSMt1+nR9PSVGIsYTPA18OxtSvhb18wg69DIFOxnPycwWTcaHxrnudjxBfG4vrb2EElP/fABfr+K0HQD/g5m/SUQPAbiLiG4C8ByAT47WdBAEs2LDB5+ZnwHwviHvHwNw3VZ0KgiCrWX6uvqVtSI1zi1KHw8mzCVITUPHnlLiG04xJ7OOCutQ9TnhHyuwITPQWnKXMamlu9Mz50C+kvtaq9pvIblstjFfi014afpbNyCzLoAVylAhQWPCy3vEXmvVjTH0Gt0l0G3Zwtl5+v4wYUX52UrNflffz2RihhBHEAQbEQ9+EDSQePCDoIHMTIEnmdEmfGbrfymFFW+GkqijveyEdeQhHcc3ddbE89cxy+Ppq0s/tteSfqX5LHKf+VxdGbJyfOS2N5vOCeGpYnLWWqLOI9sWqbemH16YS40HeNd9AkuWuzhhuuwh3izBMVN05X2WiLiu7wtd/SAIcsSDHwQNZLqmvszcS5a4krPAtJnU3SbMV2FStpe1jS3rsKYtZ8xBL4RESZbW8Prsh1FhFycL0SKPk59zpG/n3PoEZhKcErOwn1O4YeyEXZV574UtHTw9e7VPvO8uf1UqnjoJoQxbzBNgLfyc0oRPtP+dNSVGDWnGL34QNJB48IOggcxsVN+akD1hV9tRcrUckTOs2lsQdYypXS4FNmwGoTSv5Dem1f5To6/WLPUsssKvYTWabk3bjMmXmOwiUmJNylymZILSrC8Tx0giFNK0XTB6fJ7LVIq8ZqLtJHO0UJylGOv6eEXlNZPnypzTnnOOR01ejF/8IGgg8eAHQQOJBz8IGsj0l8mufJGe1YUQPmfbLDGsliJey4f9ijXVZX+szykmrblZWiLk1bFZiE6WmQpF2RlcMutOnB/rj3qZgqppL6NNTp6zoqUyaiT9f+tIZpagtm3LMQTrt0r/NlnbLnM57XWW94StQp67rhwDckQ5vZlu3vp7qpwNxTlCnOrzONdM1WHPTSvzfob4xQ+CBhIPfhA0kJmF85KQnRQ78GITGROyf6DTYCYiOAlJ80mhP3eZzZYIfUiz2ouCSo/DnkcpCFIa2ivE629aOLM9UoP1pndfeZOndDlTvSwrExlH0MTLLZ3uXbN8ZQVlEL/4QdBI4sEPggYSD34QNJAZ+viJpGG96c5gKxQtTOJLheXyWpgTSd30QlullIpNuOUyvmlShyNyOem20krFYW6qs5MuLMdzvDoKh1dUOBn6Gqqw6wQGj7wxCRvSHFWAJH7xg6CBxIMfBA1k6rr6ucwnT2QgS6npBsdUdAQkvJCMK6zQy+/TQhmFbduImpMZmOuXnZ0n2/b0D9lZxVpXWFjOHqZm+Ol9yezIgjrcUHBhmK5UUAMwH9vTBZwA6lyZ67K+pkRpZmFRMSI6h4j+lIh+SERPENGHiGgPEd1HRE9V/3eXNRkEwawpNfW/AOCbzHwZ+stpPQHgNgAHmflSAAer10EQvA0oWS13F4BfBPBPAYCZVwCsENENAK6pit0B4AEAtxa3PIoWXeGIpbeEkapPLZNl6pATTxxdM2VeOuVs13udzChwUtgxX8dQbnajKHZP6Ui4R+FxbrZi5np6k7GS0e7MZCebgedNolHumdVobA+/ntZN8VzDibAFQhwXA3gFwB8R0feJ6L9Xy2Wfz8xHqjIvob+qbhAEbwNKHvwOgA8A+CIzvx/AaRiznpkZme8cIrqZiA4R0aHVldOb7W8QBBOg5ME/DOAwMz9Yvf5T9L8IXiaivQBQ/T867GBmPsDM+5l5/9z8jkn0OQiCTbKhj8/MLxHR80T0XmZ+EsB1AB6v/m4E8Jnq/90btsa+iMSgmBMCs/XlSDTJM2Vd/9wIMqh+eYIJ8hjrj46bxZbDO1dK9NMU60qfWe8rXR5s0r5qIoqSu2aOiEbpvePO1HNCdt71VGFR575KBGRo+LYtp+7NZIxi+Ps5SuP4/xzAV4hoHsAzAH4LfWvhLiK6CcBzAD5ZWFcQBDOm6MFn5ocB7B+y67rJdicIgmnwFpqkI7CmUK4OV6xcvyyOdqhQX2lMSr9MhC1kPwpXUfV038ZZCsoNb9qVbnOm5xaHoUZZdVjimtFSonE1b4rnxDCGvdYHDt9OVnz2BEcK8TIUB5OHQogjCIIc8eAHQQOJBz8IGsgMZ+flQxVuFYVpuZ5DqkMmZp+s3/rjMpwiBBl4zvn+9GZ6OSG24jBaYeqz52MmPq3oh9SiT47zzn+hUKbnP+dCW0m7Top07lMn50MuUe6dU0fgxUvL5Y7X/+F1WLzl3QcCqYVDCfGLHwQNJB78IGggxGNPvxqjMaJX0E/2ORfAq1NreDhvhT4A0Q9L9EMzaj/ew8znbVRoqg/+oFGiQ8w8LCGoUX2IfkQ/ZtWPMPWDoIHEgx8EDWRWD/6BGbUreSv0AYh+WKIfmi3px0x8/CAIZkuY+kHQQKb64BPR9UT0JBE9TURTU+Uloi8T0VEielS8N3V5cCK6kIjuJ6LHiegxIrplFn0hom1E9F0ieqTqxx9U719MRA9W1+drlf7ClkNE7UrP8d5Z9YOIniWiHxDRw0R0qHpvFvfIVKTsp/bgE1EbwH8F8MsALgfwKSK6fErN/zGA6817s5AHXwPwu8x8OYArAXy6OgfT7ssygGuZ+X0ArgBwPRFdCeCzAD7HzJcAOA7gpi3uxzq3oC/Zvs6s+vERZr5ChM9mcY9MR8qemafyB+BDAL4lXt8O4PYptn8RgEfF6ycB7K229wJ4clp9EX24G8BHZ9kXANsB/D8Av4B+okhn2PXawvb3VTfztQDuRT/bfBb9eBbAuea9qV4XALsA/ATV2NtW9mOapv4FAJ4Xrw9X782KmcqDE9FFAN4P4MFZ9KUyrx9GXyT1PgA/BnCCmdeqItO6Pp8H8Huop6q8Y0b9YAB/SUTfI6Kbq/emfV2mJmUfg3vw5cG3AiLaCeDPAPwOM78xi74wc5eZr0D/F/eDAC7b6jYtRPQrAI4y8/em3fYQPszMH0DfFf00Ef2i3Dml67IpKftRmOaD/wKAC8XrfdV7s6JIHnzSENEc+g/9V5j5z2fZFwBg5hMA7kffpD6HiNanak/j+lwF4FeJ6FkAd6Jv7n9hBv0AM79Q/T8K4OvofxlO+7psSsp+FKb54D8E4NJqxHYewK8BuGeK7VvuQV8WHCiVB98kREQAvgTgCWb+w1n1hYjOI6Jzqu1F9McZnkD/C+AT0+oHM9/OzPuY+SL074f/zcy/Me1+ENEOIjprfRvALwF4FFO+Lsz8EoDniei91VvrUvaT78dWD5qYQYqPAfgR+v7kv5liu18FcATAKvrfqjeh70seBPAUgG8D2DOFfnwYfTPtbwA8XP19bNp9AfDzAL5f9eNRAP+2ev+nAXwXwNMA/gTAwhSv0TUA7p1FP6r2Hqn+Hlu/N2d0j1wB4FB1bf4ngN1b0Y/I3AuCBhKDe0HQQOLBD4IGEg9+EDSQePCDoIHEgx8EDSQe/CBoIPHgB0EDiQc/CBrI/wdCaEY+JHMThQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16739c5080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 dots, number is 8779\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1673a2d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 dots, number is 5574\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1673a07240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 dots, number is 5202\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16739000b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1 dots, number is 3827\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f167399ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 6 dots, number is 2930\n"
     ]
    }
   ],
   "source": [
    "# show randomly chosen image\n",
    "rows, cols = 64, 64\n",
    "labels_values = 6\n",
    "train_data = [data, label]\n",
    "import random\n",
    "for i in range(10):\n",
    "    random_image = random.randint(0, 10080)\n",
    "    img=immatrix[random_image].reshape(rows, cols)\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    print(\"There are {} dots, number is {}\".format(labels[random_image] + 1, random_image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(X, y) = (train_data[0], train_data[1])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55641, 1, 64, 64) (13911, 1, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "# reshape to [color_layers][pixels_height][pixels_width], color_layers equal to 2 because photos are in GRAYSCALE\n",
    "X_train = X_train.reshape(X_train.shape[0], 1, rows, cols).astype('float32')\n",
    "X_test = X_test.reshape(X_test.shape[0], 1, rows, cols).astype('float32')\n",
    "\n",
    "# normalize to 0-1\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55641, 6) (13911, 6)\n"
     ]
    }
   ],
   "source": [
    "# onehot\n",
    "y_train = to_categorical(y_train, 6)\n",
    "y_test = to_categorical(y_test, 6)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create sequential model with 2NN layers\n",
    "def base_model():\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(64, (5, 5), input_shape=(1, 64, 64), data_format='channels_first', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(Conv2D(32, (5, 5), activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(784, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(196, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dense(labels_values, activation='softmax'))\n",
    "    \n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1264: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:2885: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1349: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 64, 60, 60)        1664      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 30, 60)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 28, 26, 32)        48032     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 28, 26, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 23296)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 784)               18264848  \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 196)               153860    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 196)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                12608     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 6)                 390       \n",
      "=================================================================\n",
      "Total params: 18,481,402\n",
      "Trainable params: 18,481,402\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# summarize model and apply checkpoints\n",
    "model = base_model()\n",
    "print(model.summary())\n",
    "filepath = 'model3adam-{epoch:02d}-{loss:.4f}.h5'\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
    "callbacks_list = [checkpoint]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# visualization of a model\n",
    "plot_model(model, to_file='model3adam_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 55641 samples, validate on 13911 samples\n",
      "Epoch 1/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 1.7930 - acc: 0.1740Epoch 00001: loss improved from inf to 1.79300, saving model to model3adam-01-1.7930.h5\n",
      "55641/55641 [==============================] - 26s 470us/step - loss: 1.7930 - acc: 0.1742 - val_loss: 1.7897 - val_acc: 0.1786\n",
      "Epoch 2/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 1.7678 - acc: 0.2048Epoch 00002: loss improved from 1.79300 to 1.76731, saving model to model3adam-02-1.7673.h5\n",
      "55641/55641 [==============================] - 22s 396us/step - loss: 1.7673 - acc: 0.2053 - val_loss: 1.6831 - val_acc: 0.2653\n",
      "Epoch 3/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 1.5831 - acc: 0.2964Epoch 00003: loss improved from 1.76731 to 1.58235, saving model to model3adam-03-1.5824.h5\n",
      "55641/55641 [==============================] - 22s 399us/step - loss: 1.5824 - acc: 0.2969 - val_loss: 1.3919 - val_acc: 0.4102\n",
      "Epoch 4/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 1.3558 - acc: 0.4047Epoch 00004: loss improved from 1.58235 to 1.35559, saving model to model3adam-04-1.3556.h5\n",
      "55641/55641 [==============================] - 22s 402us/step - loss: 1.3556 - acc: 0.4050 - val_loss: 1.2534 - val_acc: 0.4668\n",
      "Epoch 5/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 1.2231 - acc: 0.4726Epoch 00005: loss improved from 1.35559 to 1.22238, saving model to model3adam-05-1.2224.h5\n",
      "55641/55641 [==============================] - 22s 403us/step - loss: 1.2224 - acc: 0.4732 - val_loss: 1.0968 - val_acc: 0.5447\n",
      "Epoch 6/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 1.1370 - acc: 0.5076Epoch 00006: loss improved from 1.22238 to 1.13685, saving model to model3adam-06-1.1369.h5\n",
      "55641/55641 [==============================] - 22s 402us/step - loss: 1.1369 - acc: 0.5077 - val_loss: 1.0191 - val_acc: 0.5715\n",
      "Epoch 7/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 1.0261 - acc: 0.5584Epoch 00007: loss improved from 1.13685 to 1.02626, saving model to model3adam-07-1.0263.h5\n",
      "55641/55641 [==============================] - 22s 404us/step - loss: 1.0263 - acc: 0.5582 - val_loss: 1.1710 - val_acc: 0.4882\n",
      "Epoch 8/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.9958 - acc: 0.5698Epoch 00008: loss improved from 1.02626 to 0.99512, saving model to model3adam-08-0.9951.h5\n",
      "55641/55641 [==============================] - 22s 404us/step - loss: 0.9951 - acc: 0.5701 - val_loss: 0.8637 - val_acc: 0.6340\n",
      "Epoch 9/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.8694 - acc: 0.6283Epoch 00009: loss improved from 0.99512 to 0.86901, saving model to model3adam-09-0.8690.h5\n",
      "55641/55641 [==============================] - 23s 411us/step - loss: 0.8690 - acc: 0.6288 - val_loss: 0.7676 - val_acc: 0.6739\n",
      "Epoch 10/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.7539 - acc: 0.6816Epoch 00010: loss improved from 0.86901 to 0.75358, saving model to model3adam-10-0.7536.h5\n",
      "55641/55641 [==============================] - 23s 408us/step - loss: 0.7536 - acc: 0.6818 - val_loss: 0.6924 - val_acc: 0.7058\n",
      "Epoch 11/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.6627 - acc: 0.7227Epoch 00011: loss improved from 0.75358 to 0.66269, saving model to model3adam-11-0.6627.h5\n",
      "55641/55641 [==============================] - 22s 404us/step - loss: 0.6627 - acc: 0.7228 - val_loss: 0.5845 - val_acc: 0.7702\n",
      "Epoch 12/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.5675 - acc: 0.7668Epoch 00012: loss improved from 0.66269 to 0.56743, saving model to model3adam-12-0.5674.h5\n",
      "55641/55641 [==============================] - 23s 406us/step - loss: 0.5674 - acc: 0.7668 - val_loss: 0.5410 - val_acc: 0.7744\n",
      "Epoch 13/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.5002 - acc: 0.7954Epoch 00013: loss improved from 0.56743 to 0.50000, saving model to model3adam-13-0.5000.h5\n",
      "55641/55641 [==============================] - 23s 404us/step - loss: 0.5000 - acc: 0.7954 - val_loss: 0.4805 - val_acc: 0.8032\n",
      "Epoch 14/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.4349 - acc: 0.8200Epoch 00014: loss improved from 0.50000 to 0.43470, saving model to model3adam-14-0.4347.h5\n",
      "55641/55641 [==============================] - 23s 405us/step - loss: 0.4347 - acc: 0.8201 - val_loss: 0.4056 - val_acc: 0.8349\n",
      "Epoch 15/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.3793 - acc: 0.8453Epoch 00015: loss improved from 0.43470 to 0.37916, saving model to model3adam-15-0.3792.h5\n",
      "55641/55641 [==============================] - 22s 403us/step - loss: 0.3792 - acc: 0.8455 - val_loss: 0.3911 - val_acc: 0.8325\n",
      "Epoch 16/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.3439 - acc: 0.8615Epoch 00016: loss improved from 0.37916 to 0.34399, saving model to model3adam-16-0.3440.h5\n",
      "55641/55641 [==============================] - 23s 408us/step - loss: 0.3440 - acc: 0.8614 - val_loss: 0.3753 - val_acc: 0.8431\n",
      "Epoch 17/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.3086 - acc: 0.8752Epoch 00017: loss improved from 0.34399 to 0.30841, saving model to model3adam-17-0.3084.h5\n",
      "55641/55641 [==============================] - 23s 408us/step - loss: 0.3084 - acc: 0.8753 - val_loss: 0.3324 - val_acc: 0.8612\n",
      "Epoch 18/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.2803 - acc: 0.8859Epoch 00018: loss improved from 0.30841 to 0.28006, saving model to model3adam-18-0.2801.h5\n",
      "55641/55641 [==============================] - 22s 404us/step - loss: 0.2801 - acc: 0.8859 - val_loss: 0.3133 - val_acc: 0.8650\n",
      "Epoch 19/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.2665 - acc: 0.8928Epoch 00019: loss improved from 0.28006 to 0.26684, saving model to model3adam-19-0.2668.h5\n",
      "55641/55641 [==============================] - 23s 406us/step - loss: 0.2668 - acc: 0.8927 - val_loss: 0.3153 - val_acc: 0.8695\n",
      "Epoch 20/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.2417 - acc: 0.9036Epoch 00020: loss improved from 0.26684 to 0.24172, saving model to model3adam-20-0.2417.h5\n",
      "55641/55641 [==============================] - 23s 407us/step - loss: 0.2417 - acc: 0.9035 - val_loss: 0.2999 - val_acc: 0.8761\n",
      "Epoch 21/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.2178 - acc: 0.9110Epoch 00021: loss improved from 0.24172 to 0.21781, saving model to model3adam-21-0.2178.h5\n",
      "55641/55641 [==============================] - 22s 403us/step - loss: 0.2178 - acc: 0.9110 - val_loss: 0.2757 - val_acc: 0.8866\n",
      "Epoch 22/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.1926 - acc: 0.9227Epoch 00022: loss improved from 0.21781 to 0.19286, saving model to model3adam-22-0.1929.h5\n",
      "55641/55641 [==============================] - 22s 404us/step - loss: 0.1929 - acc: 0.9226 - val_loss: 0.3278 - val_acc: 0.8690\n",
      "Epoch 23/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.2063 - acc: 0.9170Epoch 00023: loss did not improve\n",
      "55641/55641 [==============================] - 22s 402us/step - loss: 0.2065 - acc: 0.9170 - val_loss: 0.2612 - val_acc: 0.8928\n",
      "Epoch 24/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.1673 - acc: 0.9325Epoch 00024: loss improved from 0.19286 to 0.16711, saving model to model3adam-24-0.1671.h5\n",
      "55641/55641 [==============================] - 23s 404us/step - loss: 0.1671 - acc: 0.9325 - val_loss: 0.2459 - val_acc: 0.9005\n",
      "Epoch 25/25\n",
      "55296/55641 [============================>.] - ETA: 0s - loss: 0.1603 - acc: 0.9364Epoch 00025: loss improved from 0.16711 to 0.16036, saving model to model3adam-25-0.1604.h5\n",
      "55641/55641 [==============================] - 23s 405us/step - loss: 0.1604 - acc: 0.9364 - val_loss: 0.2646 - val_acc: 0.8924\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=25, batch_size=2048, verbose=1, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13911/13911 [==============================] - 3s 201us/step\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'val_acc', 'acc', 'val_loss'])\n",
      "Accuracy: 89.24%\n",
      "Test loss: 0.2645845416999979\n",
      "Test accuracy 0.8923873193746809\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "print(\"Accuracy: %.2f%%\" % (score[1]*100))\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training results:\n",
      "loss: 0.2645845416999979\n",
      "acc: 0.8923873193746809\n"
     ]
    }
   ],
   "source": [
    "print('training results:')\n",
    "for i in range(len(model.metrics_names)):\n",
    "    print(str(model.metrics_names[i]) + \": \" + str(score[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15d72045f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#summarize for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15d72041d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#summarize for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model weights\n"
     ]
    }
   ],
   "source": [
    "model_json = model.to_json()\n",
    "with open('model3adam_model.json', 'w') as json_file:\n",
    "    json_file.write(model_json)\n",
    "    \n",
    "model.save_weights('model3adam_weights.h5')\n",
    "print('Saved model weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_model = load_model('model3adam-25-1.0456.h5')\n",
    "# check in models are similar to the given tolerance\n",
    "assert_allclose(model.predict(X_train),\n",
    "               new_model.predict(X_train),\n",
    "               1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filepath = 'model3adam_continue-{epoch:02d}-{loss:.4f}.h5'\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
    "callbacks_list = [checkpoint]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_history = new_model.fit(X_train, y_train, epochs=10, batch_size=2048, callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_score = new_model.evaluate(X_test, y_test, verbose=1)\n",
    "print('training results:')\n",
    "for i in range(len(new_model.metrics_names)):\n",
    "    print(str(new_model.metrics_names[i]) + \": \" + str(new_score[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot(new_history.history['acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot(new_history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_json = model.to_json()\n",
    "with open('model3adam_continue.json', 'w') as json_file:\n",
    "    json_file.write(model_json)\n",
    "    \n",
    "model.save_weights('model3adam_weights_continue.h5')\n",
    "print('Saved model weights')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}