11- Predict move.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Why you cannot predict market move with vanilla ML\n",
    "This work illusturates a simple supervised setting where a ML model predicts the next market move given the current state. It seems pretty easy but nah :). \n",
    "What we do here surmises two things;\n",
    " \n",
    " - All information describing the market is hidden under the price values.\n",
    " - We go Semi-Markovian, meaning each prediction only depends on the present state.\n",
    "\n",
    "Ok run everthing and plot the final graph on which you see the predictions at the last excerpt of the test set. \n",
    "\n",
    "From the first sight, everyhing seems perfect. Very good accuracy - better than random or predicting the frequent class always-. However, once you inspect the plot, you see that our model is tend to predict the move at time t as memorizing the on at time t-1. Which is quite natural and it gives a pretty good accuracy. It is a good catch for Kaggler but not for a trader.\n",
    "\n",
    "Neverthless, the thing important for us to catch unexpected changes and we can already do what the model does here by eye seing. So this model unfortunately is not useful for real-life case. \n",
    "\n",
    "There are many tutorials on web tutoring market prediction following a similar pipeline depicted here, just measuring the success based on accuracy t. I try to illustrate here that they are far from reality and be aware. Best!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is downloaded from https://www.kaggle.com/mczielinski/bitcoin-historical-data/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eightbit/miniconda3/envs/dl/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: The finance module has been deprecated in mpl 2.0 and will be removed in mpl 2.2. Please use the module mpl_finance instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import finance as fin\n",
    "from utils.utils11 import (dateparse, compute_features, \n",
    "                           compute_labels, cm_analysis,\n",
    "                           model_performance, read_data,\n",
    "                           check_labels)\n",
    "import datetime\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " > There are 486359 rows\n"
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       "  </tbody>\n",
       "</table>\n",
       "<p>486357 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          timestamp         high          low        close       wprice  \\\n",
       "3        1417412160   300.000000   300.000000   300.000000   300.000000   \n",
       "6        1417412340   300.000000   300.000000   300.000000   300.000000   \n",
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       "18       1417413060   300.000000   300.000000   300.000000   300.000000   \n",
       "21       1417413240   300.000000   300.000000   300.000000   300.000000   \n",
       "24       1417413420   300.000000   300.000000   300.000000   300.000000   \n",
       "27       1417413600   300.000000   300.000000   300.000000   300.000000   \n",
       "30       1417413780   300.000000   300.000000   300.000000   300.000000   \n",
       "33       1417413960   300.000000   300.000000   300.000000   300.000000   \n",
       "36       1417414140   300.000000   300.000000   300.000000   300.000000   \n",
       "39       1417414320   300.000000   300.000000   300.000000   300.000000   \n",
       "42       1417414500   300.000000   300.000000   300.000000   300.000000   \n",
       "45       1417414680   300.000000   300.000000   300.000000   300.000000   \n",
       "48       1417414860   300.000000   300.000000   300.000000   300.000000   \n",
       "51       1417415040   370.000000   300.000000   370.000000   370.000000   \n",
       "54       1417415220   370.000000   370.000000   370.000000   370.000000   \n",
       "57       1417415400   370.000000   370.000000   370.000000   370.000000   \n",
       "60       1417415580   370.000000   370.000000   370.000000   370.000000   \n",
       "63       1417415760   370.000000   370.000000   370.000000   370.000000   \n",
       "66       1417415940   370.000000   370.000000   370.000000   370.000000   \n",
       "69       1417416120   370.000000   370.000000   370.000000   370.000000   \n",
       "72       1417416300   370.000000   370.000000   370.000000   370.000000   \n",
       "75       1417416480   370.000000   370.000000   370.000000   370.000000   \n",
       "78       1417416660   370.000000   370.000000   370.000000   370.000000   \n",
       "81       1417416840   370.000000   370.000000   370.000000   370.000000   \n",
       "84       1417417020   370.000000   370.000000   370.000000   370.000000   \n",
       "87       1417417200   370.000000   370.000000   370.000000   370.000000   \n",
       "90       1417417380   370.000000   370.000000   370.000000   370.000000   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "1458984  1508452140  5709.950195  5705.000000  5709.950195  5709.110840   \n",
       "1458987  1508452320  5709.950195  5705.000000  5709.950195  5709.110840   \n",
       "1458990  1508452500  5709.950195  5705.000000  5709.950195  5709.110840   \n",
       "1458993  1508452680  5709.950195  5705.000000  5709.950195  5709.110840   \n",
       "1458996  1508452860  5729.990234  5705.000000  5727.770020  5728.121094   \n",
       "1458999  1508453040  5730.000000  5727.779785  5727.790039  5727.787598   \n",
       "1459002  1508453220  5730.000000  5727.779785  5730.000000  5729.998535   \n",
       "1459005  1508453400  5730.000000  5725.000000  5726.089844  5726.056152   \n",
       "1459008  1508453580  5726.100098  5725.009766  5725.089844  5725.910156   \n",
       "1459011  1508453760  5725.089844  5724.000000  5724.009766  5724.924316   \n",
       "1459014  1508453940  5724.009766  5690.000000  5711.709961  5707.477051   \n",
       "1459017  1508454120  5723.490234  5689.970215  5715.850098  5711.854004   \n",
       "1459020  1508454300  5714.770020  5696.810059  5713.189941  5713.245117   \n",
       "1459023  1508454480  5713.189941  5706.350098  5712.990234  5712.990234   \n",
       "1459026  1508454660  5713.200195  5712.990234  5713.200195  5713.173828   \n",
       "1459029  1508454840  5719.740234  5713.189941  5719.740234  5719.730957   \n",
       "1459032  1508455020  5719.740234  5719.729980  5719.740234  5719.740234   \n",
       "1459035  1508455200  5724.069824  5719.740234  5724.069824  5724.010742   \n",
       "1459038  1508455380  5724.990234  5724.970215  5724.979980  5724.987793   \n",
       "1459041  1508455560  5724.990234  5724.970215  5724.979980  5724.979492   \n",
       "1459044  1508455740  5724.979980  5710.000000  5710.009766  5713.041992   \n",
       "1459047  1508455920  5710.009766  5710.009766  5710.009766  5710.009766   \n",
       "1459050  1508456100  5710.020020  5710.000000  5710.020020  5710.019043   \n",
       "1459053  1508456280  5714.709961  5705.259766  5705.259766  5709.731445   \n",
       "1459056  1508456460  5705.259766  5705.250000  5705.250000  5705.251953   \n",
       "1459059  1508456640  5714.970215  5705.259766  5712.919922  5713.031738   \n",
       "1459062  1508456820  5712.919922  5705.279785  5707.470215  5706.121582   \n",
       "1459065  1508457000  5706.830078  5705.240234  5705.250000  5705.250000   \n",
       "1459068  1508457180  5705.250000  5701.009766  5701.009766  5704.880859   \n",
       "1459071  1508457360  5703.220215  5700.000000  5700.009766  5700.009766   \n",
       "\n",
       "                open         CN        WC         HO         LO         CO  \\\n",
       "3         300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "6         300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "9         300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "12        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "15        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "18        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "21        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "24        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "27        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "30        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "33        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "36        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "39        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "42        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "45        300.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "48        300.000000  70.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "51        300.000000   0.000000  0.000000  70.000000   0.000000  70.000000   \n",
       "54        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "57        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "60        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "63        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "66        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "69        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "72        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "75        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "78        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "81        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "84        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "87        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "90        370.000000   0.000000  0.000000   0.000000   0.000000   0.000000   \n",
       "...              ...        ...       ...        ...        ...        ...   \n",
       "1458984  5705.000000   0.000000 -0.839355   4.950195   0.000000   4.950195   \n",
       "1458987  5705.000000   0.000000 -0.839355   4.950195   0.000000   4.950195   \n",
       "1458990  5705.000000   0.000000 -0.839355   4.950195   0.000000   4.950195   \n",
       "1458993  5705.000000  17.819824 -0.839355   4.950195   0.000000   4.950195   \n",
       "1458996  5705.000000   0.020020  0.351074  24.990234   0.000000  22.770020   \n",
       "1458999  5727.779785   2.209961 -0.002441   2.220215   0.000000   0.010254   \n",
       "1459002  5727.790039  -3.910156 -0.001465   2.209961  -0.010254   2.209961   \n",
       "1459005  5730.000000  -1.000000 -0.033691   0.000000  -5.000000  -3.910156   \n",
       "1459008  5726.100098  -1.080078  0.820312   0.000000  -1.090332  -1.010254   \n",
       "1459011  5725.089844 -12.299805  0.914551   0.000000  -1.089844  -1.080078   \n",
       "1459014  5724.009766   4.140137 -4.232910   0.000000 -34.009766 -12.299805   \n",
       "1459017  5711.709961  -2.660156 -3.996094  11.780273 -21.739746   4.140137   \n",
       "1459020  5714.370117  -0.199707  0.055176   0.399902 -17.560059  -1.180176   \n",
       "1459023  5713.189941   0.209961  0.000000   0.000000  -6.839844  -0.199707   \n",
       "1459026  5712.990234   6.540039 -0.026367   0.209961   0.000000   0.209961   \n",
       "1459029  5713.200195   0.000000 -0.009277   6.540039  -0.010254   6.540039   \n",
       "1459032  5719.740234   4.329590  0.000000   0.000000  -0.010254   0.000000   \n",
       "1459035  5719.740234   0.910156 -0.059082   4.329590   0.000000   4.329590   \n",
       "1459038  5724.979980   0.000000  0.007812   0.010254  -0.009766   0.000000   \n",
       "1459041  5724.990234 -14.970215 -0.000488   0.000000  -0.020020  -0.010254   \n",
       "1459044  5724.979980   0.000000  3.032227   0.000000 -14.979980 -14.970215   \n",
       "1459047  5710.009766   0.010254  0.000000   0.000000   0.000000   0.000000   \n",
       "1459050  5710.009766  -4.760254 -0.000977   0.010254  -0.009766   0.010254   \n",
       "1459053  5710.020020  -0.009766  4.471680   4.689941  -4.760254  -4.760254   \n",
       "1459056  5705.259766   7.669922  0.001953   0.000000  -0.009766  -0.009766   \n",
       "1459059  5705.259766  -5.449707  0.111816   9.710449   0.000000   7.660156   \n",
       "1459062  5712.919922  -2.220215 -1.348633   0.000000  -7.640137  -5.449707   \n",
       "1459065  5706.830078  -4.240234  0.000000   0.000000  -1.589844  -1.580078   \n",
       "1459068  5705.250000  -1.000000  3.871094   0.000000  -4.240234  -4.240234   \n",
       "1459071  5703.220215   4.000000  0.000000   0.000000  -3.220215  -3.210449   \n",
       "\n",
       "                WO  \n",
       "3         0.000000  \n",
       "6         0.000000  \n",
       "9         0.000000  \n",
       "12        0.000000  \n",
       "15        0.000000  \n",
       "18        0.000000  \n",
       "21        0.000000  \n",
       "24        0.000000  \n",
       "27        0.000000  \n",
       "30        0.000000  \n",
       "33        0.000000  \n",
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       "39        0.000000  \n",
       "42        0.000000  \n",
       "45        0.000000  \n",
       "48        0.000000  \n",
       "51       70.000000  \n",
       "54        0.000000  \n",
       "57        0.000000  \n",
       "60        0.000000  \n",
       "63        0.000000  \n",
       "66        0.000000  \n",
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       "78        0.000000  \n",
       "81        0.000000  \n",
       "84        0.000000  \n",
       "87        0.000000  \n",
       "90        0.000000  \n",
       "...            ...  \n",
       "1458984   4.110840  \n",
       "1458987   4.110840  \n",
       "1458990   4.110840  \n",
       "1458993   4.110840  \n",
       "1458996  23.121094  \n",
       "1458999   0.007812  \n",
       "1459002   2.208496  \n",
       "1459005  -3.943848  \n",
       "1459008  -0.189941  \n",
       "1459011  -0.165527  \n",
       "1459014 -16.532715  \n",
       "1459017   0.144043  \n",
       "1459020  -1.125000  \n",
       "1459023  -0.199707  \n",
       "1459026   0.183594  \n",
       "1459029   6.530762  \n",
       "1459032   0.000000  \n",
       "1459035   4.270508  \n",
       "1459038   0.007812  \n",
       "1459041  -0.010742  \n",
       "1459044 -11.937988  \n",
       "1459047   0.000000  \n",
       "1459050   0.009277  \n",
       "1459053  -0.288574  \n",
       "1459056  -0.007812  \n",
       "1459059   7.771973  \n",
       "1459062  -6.798340  \n",
       "1459065  -1.580078  \n",
       "1459068  -0.369141  \n",
       "1459071  -3.210449  \n",
       "\n",
       "[486357 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_PATH = \"../data/bitcoin-historical-data/coinbaseUSD_1-min_data_2014-12-01_to_2017-10-20.csv.csv\"\n",
    "df = read_data(DATA_PATH, 3) \n",
    "\n",
    "df_feats = compute_features(df)\n",
    "df_feats.dropna(inplace=True)\n",
    "df_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(262541, 12)\n",
      "(53319, 12)\n"
     ]
    }
   ],
   "source": [
    "# Validation split\n",
    "train_split_point = time.mktime(datetime.datetime.strptime('2016-1-1 00:00:00', \"%Y-%m-%d %H:%M:%S\").timetuple())\n",
    "split_point = time.mktime(datetime.datetime.strptime('2017-7-1 00:00:00', \"%Y-%m-%d %H:%M:%S\").timetuple())\n",
    "\n",
    "df_train = df_feats[np.logical_and(df_feats['timestamp'] <  split_point, df_feats['timestamp'] > train_split_point)]\n",
    "df_test = df_feats[df_feats['timestamp'] >  split_point]\n",
    "\n",
    "print(df_train.shape)\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train, label_names = compute_labels(df_train)\n",
    "y_test, _ = compute_labels(df_test)\n",
    "\n",
    "check_labels(y_test.argmax(axis=1), df_test['close'].values)\n",
    "check_labels(y_train.argmax(axis=1), df_train['close'].values)\n",
    "\n",
    "assert y_train.shape[0] == df_train.shape[0]\n",
    "assert y_test.shape[0] == df_test.shape[0]\n",
    "\n",
    "X_train = df_train.iloc[:, -4:].values\n",
    "X_test = df_test.iloc[:, -4:].values\n",
    "\n",
    "assert y_train.shape[0] == X_train.shape[0]\n",
    "assert y_test.shape[0] == X_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<filter object at 0x7fd8202dfcc0>]\n",
      "[1]\n"
     ]
    }
   ],
   "source": [
    "# Compute class freq\n",
    "unique, counts = np.unique(filter(lambda v: v==v, np.argmax(y_test, axis=1)), return_counts=True)\n",
    "print(unique)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/eightbit/miniconda3/envs/dl/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(32, activation = 'tanh', input_dim = 4))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(32, activation = 'tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(32, activation = 'tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(3, activation = 'softmax')) \n",
    "# out shaped on df_Yt.shape[1]\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', \n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 262541 samples, validate on 53319 samples\n",
      "Epoch 1/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.9217 - acc: 0.4789 - val_loss: 0.9650 - val_acc: 0.4591\n",
      "Epoch 2/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.9094 - acc: 0.4856 - val_loss: 0.9479 - val_acc: 0.4464\n",
      "Epoch 3/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.9049 - acc: 0.4895 - val_loss: 0.9553 - val_acc: 0.4481\n",
      "Epoch 4/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.9021 - acc: 0.4930 - val_loss: 0.9545 - val_acc: 0.4592\n",
      "Epoch 5/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.9002 - acc: 0.4949 - val_loss: 0.9447 - val_acc: 0.4609\n",
      "Epoch 6/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8988 - acc: 0.4967 - val_loss: 0.9502 - val_acc: 0.4527\n",
      "Epoch 7/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8978 - acc: 0.4972 - val_loss: 0.9525 - val_acc: 0.4656\n",
      "Epoch 8/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8968 - acc: 0.4994 - val_loss: 0.9402 - val_acc: 0.4677\n",
      "Epoch 9/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8958 - acc: 0.5006 - val_loss: 0.9459 - val_acc: 0.4466\n",
      "Epoch 10/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8953 - acc: 0.5010 - val_loss: 0.9408 - val_acc: 0.4514\n",
      "Epoch 11/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8949 - acc: 0.5009 - val_loss: 0.9415 - val_acc: 0.4726\n",
      "Epoch 12/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8943 - acc: 0.5011 - val_loss: 0.9404 - val_acc: 0.4702\n",
      "Epoch 13/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8943 - acc: 0.5017 - val_loss: 0.9352 - val_acc: 0.4678\n",
      "Epoch 14/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8938 - acc: 0.5022 - val_loss: 0.9384 - val_acc: 0.4599\n",
      "Epoch 15/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8933 - acc: 0.5025 - val_loss: 0.9445 - val_acc: 0.4518\n",
      "Epoch 16/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8934 - acc: 0.5028 - val_loss: 0.9366 - val_acc: 0.4690\n",
      "Epoch 17/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8933 - acc: 0.5027 - val_loss: 0.9422 - val_acc: 0.4705\n",
      "Epoch 18/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8931 - acc: 0.5031 - val_loss: 0.9343 - val_acc: 0.4631\n",
      "Epoch 19/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8927 - acc: 0.5020 - val_loss: 0.9311 - val_acc: 0.4664\n",
      "Epoch 20/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8927 - acc: 0.5027 - val_loss: 0.9371 - val_acc: 0.4709\n",
      "Epoch 21/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8924 - acc: 0.5040 - val_loss: 0.9388 - val_acc: 0.4721\n",
      "Epoch 22/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8922 - acc: 0.5030 - val_loss: 0.9394 - val_acc: 0.4694\n",
      "Epoch 23/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8921 - acc: 0.5037 - val_loss: 0.9349 - val_acc: 0.4686\n",
      "Epoch 24/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8918 - acc: 0.5050 - val_loss: 0.9375 - val_acc: 0.4681\n",
      "Epoch 25/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8920 - acc: 0.5039 - val_loss: 0.9340 - val_acc: 0.4711\n",
      "Epoch 26/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8916 - acc: 0.5042 - val_loss: 0.9393 - val_acc: 0.4669\n",
      "Epoch 27/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8918 - acc: 0.5037 - val_loss: 0.9284 - val_acc: 0.4727\n",
      "Epoch 28/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8910 - acc: 0.5046 - val_loss: 0.9236 - val_acc: 0.4771\n",
      "Epoch 29/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8914 - acc: 0.5046 - val_loss: 0.9282 - val_acc: 0.4732\n",
      "Epoch 30/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8915 - acc: 0.5028 - val_loss: 0.9267 - val_acc: 0.4732\n",
      "Epoch 31/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8911 - acc: 0.5044 - val_loss: 0.9283 - val_acc: 0.4731\n",
      "Epoch 32/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8911 - acc: 0.5041 - val_loss: 0.9385 - val_acc: 0.4687\n",
      "Epoch 33/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8905 - acc: 0.5056 - val_loss: 0.9256 - val_acc: 0.4768\n",
      "Epoch 34/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8908 - acc: 0.5045 - val_loss: 0.9373 - val_acc: 0.4712\n",
      "Epoch 35/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8907 - acc: 0.5049 - val_loss: 0.9358 - val_acc: 0.4650\n",
      "Epoch 36/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8911 - acc: 0.5037 - val_loss: 0.9277 - val_acc: 0.4718\n",
      "Epoch 37/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8906 - acc: 0.5049 - val_loss: 0.9348 - val_acc: 0.4701\n",
      "Epoch 38/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8907 - acc: 0.5056 - val_loss: 0.9347 - val_acc: 0.4643\n",
      "Epoch 39/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8906 - acc: 0.5049 - val_loss: 0.9327 - val_acc: 0.4624\n",
      "Epoch 40/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8905 - acc: 0.5042 - val_loss: 0.9386 - val_acc: 0.4675\n",
      "Epoch 41/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8904 - acc: 0.5052 - val_loss: 0.9271 - val_acc: 0.4741\n",
      "Epoch 42/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8904 - acc: 0.5052 - val_loss: 0.9289 - val_acc: 0.4698\n",
      "Epoch 43/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8904 - acc: 0.5049 - val_loss: 0.9344 - val_acc: 0.4668\n",
      "Epoch 44/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8901 - acc: 0.5055 - val_loss: 0.9268 - val_acc: 0.4740\n",
      "Epoch 45/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8902 - acc: 0.5043 - val_loss: 0.9296 - val_acc: 0.4729\n",
      "Epoch 46/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8899 - acc: 0.5058 - val_loss: 0.9317 - val_acc: 0.4695\n",
      "Epoch 47/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8899 - acc: 0.5048 - val_loss: 0.9283 - val_acc: 0.4705\n",
      "Epoch 48/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8901 - acc: 0.5054 - val_loss: 0.9319 - val_acc: 0.4732\n",
      "Epoch 49/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8901 - acc: 0.5052 - val_loss: 0.9235 - val_acc: 0.4709\n",
      "Epoch 50/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8899 - acc: 0.5058 - val_loss: 0.9364 - val_acc: 0.4699\n",
      "Epoch 51/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8900 - acc: 0.5045 - val_loss: 0.9263 - val_acc: 0.4715\n",
      "Epoch 52/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8901 - acc: 0.5049 - val_loss: 0.9279 - val_acc: 0.4664\n",
      "Epoch 53/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8899 - acc: 0.5044 - val_loss: 0.9275 - val_acc: 0.4738\n",
      "Epoch 54/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8897 - acc: 0.5060 - val_loss: 0.9252 - val_acc: 0.4646\n",
      "Epoch 55/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8897 - acc: 0.5060 - val_loss: 0.9236 - val_acc: 0.4753\n",
      "Epoch 56/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8895 - acc: 0.5055 - val_loss: 0.9284 - val_acc: 0.4737\n",
      "Epoch 57/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8897 - acc: 0.5056 - val_loss: 0.9282 - val_acc: 0.4698\n",
      "Epoch 58/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8897 - acc: 0.5053 - val_loss: 0.9298 - val_acc: 0.4785\n",
      "Epoch 59/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8897 - acc: 0.5061 - val_loss: 0.9251 - val_acc: 0.4774\n",
      "Epoch 60/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8895 - acc: 0.5059 - val_loss: 0.9340 - val_acc: 0.4727\n",
      "Epoch 61/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8895 - acc: 0.5056 - val_loss: 0.9253 - val_acc: 0.4717\n",
      "Epoch 62/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8895 - acc: 0.5064 - val_loss: 0.9324 - val_acc: 0.4718\n",
      "Epoch 63/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8897 - acc: 0.5056 - val_loss: 0.9260 - val_acc: 0.4711\n",
      "Epoch 64/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8893 - acc: 0.5061 - val_loss: 0.9281 - val_acc: 0.4744\n",
      "Epoch 65/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8892 - acc: 0.5050 - val_loss: 0.9278 - val_acc: 0.4601\n",
      "Epoch 66/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8894 - acc: 0.5054 - val_loss: 0.9211 - val_acc: 0.4754\n",
      "Epoch 67/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8893 - acc: 0.5055 - val_loss: 0.9272 - val_acc: 0.4623\n",
      "Epoch 68/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8892 - acc: 0.5064 - val_loss: 0.9321 - val_acc: 0.4667\n",
      "Epoch 69/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8892 - acc: 0.5061 - val_loss: 0.9230 - val_acc: 0.4711\n",
      "Epoch 70/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8893 - acc: 0.5059 - val_loss: 0.9224 - val_acc: 0.4720\n",
      "Epoch 71/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8891 - acc: 0.5063 - val_loss: 0.9283 - val_acc: 0.4735\n",
      "Epoch 72/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8890 - acc: 0.5058 - val_loss: 0.9240 - val_acc: 0.4773\n",
      "Epoch 73/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8893 - acc: 0.5044 - val_loss: 0.9285 - val_acc: 0.4744\n",
      "Epoch 74/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8891 - acc: 0.5058 - val_loss: 0.9238 - val_acc: 0.4658\n",
      "Epoch 75/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8891 - acc: 0.5067 - val_loss: 0.9256 - val_acc: 0.4780\n",
      "Epoch 76/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8891 - acc: 0.5064 - val_loss: 0.9281 - val_acc: 0.4717\n",
      "Epoch 77/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8889 - acc: 0.5046 - val_loss: 0.9278 - val_acc: 0.4753\n",
      "Epoch 78/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8889 - acc: 0.5060 - val_loss: 0.9279 - val_acc: 0.4690\n",
      "Epoch 79/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8894 - acc: 0.5053 - val_loss: 0.9268 - val_acc: 0.4663\n",
      "Epoch 80/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8887 - acc: 0.5064 - val_loss: 0.9249 - val_acc: 0.4746\n",
      "Epoch 81/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8888 - acc: 0.5064 - val_loss: 0.9281 - val_acc: 0.4765\n",
      "Epoch 82/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8888 - acc: 0.5062 - val_loss: 0.9278 - val_acc: 0.4710\n",
      "Epoch 83/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8887 - acc: 0.5062 - val_loss: 0.9245 - val_acc: 0.4778\n",
      "Epoch 84/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8887 - acc: 0.5065 - val_loss: 0.9257 - val_acc: 0.4734\n",
      "Epoch 85/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8890 - acc: 0.5054 - val_loss: 0.9254 - val_acc: 0.4803\n",
      "Epoch 86/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8887 - acc: 0.5061 - val_loss: 0.9274 - val_acc: 0.4629\n",
      "Epoch 87/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8886 - acc: 0.5072 - val_loss: 0.9306 - val_acc: 0.4681\n",
      "Epoch 88/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8885 - acc: 0.5065 - val_loss: 0.9264 - val_acc: 0.4764\n",
      "Epoch 89/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8885 - acc: 0.5067 - val_loss: 0.9244 - val_acc: 0.4755\n",
      "Epoch 90/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8887 - acc: 0.5063 - val_loss: 0.9264 - val_acc: 0.4735\n",
      "Epoch 91/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8888 - acc: 0.5057 - val_loss: 0.9243 - val_acc: 0.4742\n",
      "Epoch 92/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8885 - acc: 0.5074 - val_loss: 0.9245 - val_acc: 0.4761\n",
      "Epoch 93/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8887 - acc: 0.5063 - val_loss: 0.9183 - val_acc: 0.4791\n",
      "Epoch 94/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8887 - acc: 0.5063 - val_loss: 0.9238 - val_acc: 0.4671\n",
      "Epoch 95/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8886 - acc: 0.5063 - val_loss: 0.9219 - val_acc: 0.4733\n",
      "Epoch 96/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8884 - acc: 0.5065 - val_loss: 0.9275 - val_acc: 0.4684\n",
      "Epoch 97/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8886 - acc: 0.5069 - val_loss: 0.9198 - val_acc: 0.4780\n",
      "Epoch 98/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8884 - acc: 0.5065 - val_loss: 0.9216 - val_acc: 0.4761\n",
      "Epoch 99/1000\n",
      "262541/262541 [==============================] - 3s 12us/step - loss: 0.8882 - acc: 0.5059 - val_loss: 0.9226 - val_acc: 0.4736\n",
      "Epoch 100/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8886 - acc: 0.5062 - val_loss: 0.9190 - val_acc: 0.4795\n",
      "Epoch 101/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8884 - acc: 0.5061 - val_loss: 0.9221 - val_acc: 0.4741\n",
      "Epoch 102/1000\n",
      "262541/262541 [==============================] - 3s 11us/step - loss: 0.8882 - acc: 0.5077 - val_loss: 0.9240 - val_acc: 0.4802\n",
      "Epoch 103/1000\n",
      " 39936/262541 [===>..........................] - ETA: 2s - loss: 0.8865 - acc: 0.5090"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-a485dd3625f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m    958\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    959\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 960\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m    961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    962\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1655\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1656\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1657\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1659\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1211\u001b[0m                     \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1212\u001b[0m                     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1213\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1214\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2355\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2356\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2357\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2358\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1318\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1322\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1324\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1325\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1300\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1301\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 512 # Total 'blocks/snapshot' in a day\n",
    "epochs = 1000\n",
    "\n",
    "model.fit(X_train, y_train, validation_data=[X_test, y_test], batch_size=batch_size, epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "         UP     0.4999    0.6983    0.5826     25027\n",
      "         DN     0.4540    0.2735    0.3413     22390\n",
      "       FLAT     0.3840    0.3168    0.3472      5902\n",
      "\n",
      "avg / total     0.4678    0.4777    0.4552     53319\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# test performance\n",
    "model_performance(model, label_names, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  timestamp     0.4694    1.0000    0.6389     25027\n",
      "       high     0.0000    0.0000    0.0000     22390\n",
      "        low     0.0000    0.0000    0.0000      5902\n",
      "\n",
      "avg / total     0.2203    0.4694    0.2999     53319\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eightbit/miniconda3/envs/dl/lib/python3.6/site-packages/sklearn/metrics/classification.py:1428: UserWarning: labels size, 3, does not match size of target_names, 12\n",
      "  .format(len(labels), len(target_names))\n",
      "/home/eightbit/miniconda3/envs/dl/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# If you predict always move up\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "preds = np.ones([y_test.shape[0]], dtype='int')*0\n",
    "labels = np.argmax(y_test, axis = 1)\n",
    "classreport= classification_report(labels, preds, \n",
    "                                 target_names = df_test.columns,\n",
    "                                 digits = 4)\n",
    "print(classreport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  timestamp     0.4732    0.3385    0.3947     25027\n",
      "       high     0.4262    0.3354    0.3754     22390\n",
      "        low     0.1112    0.3353    0.1670      5902\n",
      "\n",
      "avg / total     0.4134    0.3368    0.3614     53319\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random prediction\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "rnd_preds = np.random.randint(0, 3, size=y_test.shape[0])\n",
    "labels = np.argmax(y_test, axis = 1)\n",
    "classreport= classification_report(labels, rnd_preds, \n",
    "                                 target_names = df_test.columns,\n",
    "                                 digits = 4)\n",
    "print(classreport)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53312/53319 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78f3dc4850>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f789c1f4b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See predictions on the plot \n",
    "preds = model.predict_classes(X_test)\n",
    "\n",
    "plt.figure(figsize=[200, 20])\n",
    "plt.plot(df_test['close'][-1000:])\n",
    "df_test['close'][-1000:][preds[-1000:]==0].plot(style='go')\n",
    "df_test['close'][-1000:][preds[-1000:]==1].plot(style='ro')\n",
    "df_test['close'][-1000:][preds[-1000:]==2].plot(style='bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MjCRJSlAGRvFMjCRJkiImRpIkJajtLe6aiEyMJEmSIhZGkiRJEQsjSZKkiD1G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\nEwHYnLOZTVlZLFmylKSkGJN+nMIeJUt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78f3dc0990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_analysis(preds, y_test.argmax(axis=1), [0, 1, 2], ymap=label_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    feats: [c-o, h-o, l-o, w-o]\n",
    "      precision    recall  f1-score   support\n",
    "\n",
    "         UP     0.6384    0.8045    0.7119      3371\n",
    "         DN     0.6502    0.3068    0.4169      3048\n",
    "       FLAT     0.7311    0.7871    0.7581      9577\n",
    "\n",
    "    avg / total     0.6962    0.6992    0.6833     15996"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "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"
  },
  "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": 2
}