3a_sf_models_deeplearning_colab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ICCCdaJx5Bkb",
    "outputId": "a471dfbe-59d6-495f-a0ec-167c5e02a101"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#import libareries\n",
    "import math\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression,SGDClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.metrics import accuracy_score, multilabel_confusion_matrix\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from keras.optimizers import Adam, Adadelta\n",
    "from keras.layers import LSTM, SimpleRNN, Dense, PReLU, Dropout, Activation\n",
    "from sklearn import model_selection\n",
    "from keras.models import Sequential\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "from keras import backend as K\n",
    "\n",
    "if 'tensorflow' == K.backend():\n",
    "  import tensorflow as tf\n",
    "  from keras.backend.tensorflow_backend import set_session\n",
    "  config = tf.ConfigProto()\n",
    "  config.gpu_options.allow_growth = True\n",
    "  config.gpu_options.visible_device_list = \"0\"\n",
    "  set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "X9wfxcD95djE",
    "outputId": "02fb15fc-12f5-4669-cbff-1ca09830ca73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25h  Building wheel for PyDrive (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "# Install the PyDrive wrapper & import libraries.\n",
    "# This only needs to be done once per notebook.\n",
    "!pip install -U -q PyDrive\n",
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials\n",
    "\n",
    "# Authenticate and create the PyDrive client.\n",
    "# This only needs to be done once per notebook.\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7bNgpUv_6Sy0"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# choose a local (colab) directory to store the data.\n",
    "local_root_path = os.path.expanduser(\"~/Business Intelligence\")\n",
    "try:\n",
    "  os.makedirs(local_root_path)\n",
    "except: pass\n",
    "\n",
    "def ListFolder(google_drive_id, destination):\n",
    "  file_list = drive.ListFile({'q': \"'%s' in parents and trashed=false\" % google_drive_id}).GetList()\n",
    "  counter = 0\n",
    "  for f in file_list:\n",
    "    # If it is a directory then, create the dicrectory and upload the file inside it\n",
    "    if f['mimeType']=='application/vnd.google-apps.folder': \n",
    "      folder_path = os.path.join(destination, f['title'])\n",
    "      os.makedirs(folder_path)\n",
    "      print('creating directory {}'.format(folder_path))\n",
    "      ListFolder(f['id'], folder_path)\n",
    "    else:\n",
    "      fname = os.path.join(destination, f['title'])\n",
    "      f_ = drive.CreateFile({'id': f['id']})\n",
    "      f_.GetContentFile(fname)\n",
    "      counter += 1\n",
    "  print('{} files were uploaded in {}'.format(counter, destination))\n",
    "\n",
    "ListFolder(\"Path to the Drive Folder\", local_root_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "wQfgBafX8lKG",
    "outputId": "9c70c839-9335-4db8-fd33-3d47a99ff5a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2_sf_models.ipynb\t\t       3_sf_models_plot.ipynb\t x_train\n",
      "3a_sf_models-deeplearning-colab.ipynb  crimeSF_NN_logodds.ipynb  y_test\n",
      "3a_sf_models-deeplearning_TPU.ipynb    test_data\t\t y_train\n",
      "3_sf_models.ipynb\t\t       x_test\n"
     ]
    }
   ],
   "source": [
    "!ls ~/\"Business Intelligence\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6cx3nv736nMY"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "99rQ7NHl5Bkj"
   },
   "outputs": [],
   "source": [
    "#load dataframes from preproc\n",
    "x_train = pd.read_pickle('~/Business Intelligence/x_train')\n",
    "x_test = pd.read_pickle('~/Business Intelligence/x_test')\n",
    "y_test = pd.read_pickle('~/Business Intelligence/y_test')\n",
    "y_train = pd.read_pickle('~/Business Intelligence/y_train')\n",
    "test_data = pd.read_pickle('~/Business Intelligence/test_data')\n",
    "\n",
    "labels = np.array(['ARSON', 'ASSAULT', 'BAD CHECKS', 'BRIBERY', 'BURGLARY',\n",
    "       'DISORDERLY CONDUCT', 'DRIVING UNDER THE INFLUENCE',\n",
    "       'DRUG/NARCOTIC', 'DRUNKENNESS', 'EMBEZZLEMENT', 'EXTORTION',\n",
    "       'FAMILY OFFENSES', 'FORGERY/COUNTERFEITING', 'FRAUD', 'GAMBLING',\n",
    "       'KIDNAPPING', 'LARCENY/THEFT', 'LIQUOR LAWS', 'LOITERING',\n",
    "       'MISSING PERSON', 'NON-CRIMINAL', 'OTHER OFFENSES',\n",
    "       'PORNOGRAPHY/OBSCENE MAT', 'PROSTITUTION', 'RECOVERED VEHICLE',\n",
    "       'ROBBERY', 'RUNAWAY', 'SECONDARY CODES', 'SEX OFFENSES FORCIBLE',\n",
    "       'SEX OFFENSES NON FORCIBLE', 'STOLEN PROPERTY', 'SUICIDE',\n",
    "       'SUSPICIOUS OCC', 'TREA', 'TRESPASS', 'VANDALISM', 'VEHICLE THEFT',\n",
    "       'WARRANTS', 'WEAPON LAWS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uBambT3c5Bkq"
   },
   "outputs": [],
   "source": [
    "def modelToCsv_deeplearning(model):\n",
    "    \"\"\"Die Methode schreibt predictions des übergebenen Modells in eine CSV Datei Namens 'submit.csv'.\n",
    "    Die CSV Datei hat das von Kaggle gewünschte Format.\"\"\"\n",
    "    #Droppe Column Id, um die gleichen Features auf den Testdaten wie auf den Trainingsdaten zu haben\n",
    "    test_daten = test_data.drop(['Id'], axis = 1)\n",
    "    #füge eine Dimension hinzu, um prediction zu ermöglichen\n",
    "    test_daten = test_daten.values\n",
    "    test_daten = test_daten.reshape((test_daten.shape[0], 1, test_daten.shape[1]))\n",
    "    #Predicte das Ergebnis\n",
    "    predicted = model.predict_proba(test_daten)\n",
    "\n",
    "    \n",
    "    result = pd.DataFrame(predicted, columns = labels)\n",
    "    result.to_csv('submit.csv',index=True, index_label='Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1r4EQoFT5Bku",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Zusätzliche Datapreparation, um es im Neuronalen Netz nutzen zu können\n",
    "#Nur einmal Ausführen\n",
    "y_train = to_categorical(y_train)\n",
    "x_train = x_train.values\n",
    "\n",
    "y_test = to_categorical(y_test)\n",
    "x_test = x_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "A5wKrbOK5Bkz",
    "outputId": "2786fe4e-5b19-4897-dd69-20ff2c9d8b56"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0803 16:34:01.561525 140444664514432 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(39, use_bias=True, kernel_initializer=\"glorot_uniform\")`\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(39, use_bias=True, kernel_initializer=\"glorot_uniform\")`\n",
      "  if __name__ == '__main__':\n",
      "W0803 16:34:01.592282 140444664514432 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Die Erstellung des tiefen neuronalen Netzes\n",
    "def create_NeuralNet(layer, nodes):\n",
    "  \n",
    "  model = Sequential()\n",
    "  model.add(Dense(nodes, init='glorot_uniform', use_bias=True))\n",
    "  model.add(PReLU())\n",
    "  model.add(Dropout(0.2))\n",
    "  \n",
    "  for i in range(layer):\n",
    "    model.add(Dense(nodes, init='glorot_uniform', use_bias=True))\n",
    "    model.add(PReLU())\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "\n",
    "  model.add(Dense(39, activation='softmax'))\n",
    "\n",
    "\n",
    "  model.compile(loss='categorical_crossentropy', optimizer=Adadelta())\n",
    "  \n",
    "  return model\n",
    "\n",
    "model = create_NeuralNet(30, 39)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EDg679te5Bk4"
   },
   "outputs": [],
   "source": [
    "#options\n",
    "model.fit(x_train, \n",
    "          y_train,\n",
    "          validation_data=(x_test, y_test),\n",
    "          batch_size=40000,\n",
    "          epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2xXvuVBwb9tN"
   },
   "outputs": [],
   "source": [
    "\n",
    "x_train = x_train.reshape((x_train.shape[0], 1, x_train.shape[1]))\n",
    "x_test = x_test.reshape((x_test.shape[0], 1, x_test.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zTYVAOnBQjqo"
   },
   "outputs": [],
   "source": [
    "def create_LSTM(layer, nodes):\n",
    "  \n",
    "  model = Sequential()\n",
    "\n",
    "  model.add(LSTM(nodes,go_backwards= True, use_bias=True, kernel_initializer='glorot_uniform', input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=True))\n",
    "  model.add(PReLU())\n",
    "  model.add(Dropout(0.2))\n",
    "  \n",
    "\n",
    "  for i in range(layer):\n",
    "    model.add(LSTM(nodes, use_bias=True, kernel_initializer='glorot_uniform', input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=True))\n",
    "    model.add(PReLU())\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "\n",
    "\n",
    "  model.add(LSTM(39, use_bias=True, kernel_initializer='glorot_uniform', input_shape=(x_train.shape[1], x_train.shape[2]), return_sequences=False))\n",
    "  model.add(PReLU())\n",
    "  model.add(Dropout(0.2))\n",
    "  model.add(Dense(39, activation='softmax'))\n",
    "\n",
    "\n",
    "  model.compile(loss='categorical_crossentropy', optimizer=Adadelta())\n",
    "  \n",
    "  return model\n",
    "\n",
    "model = create_LSTM(50, 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "lDRmRM8gaX47",
    "outputId": "8d1911aa-b751-4df7-b113-2a96ef4faf55"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 700527 samples, validate on 175132 samples\n",
      "Epoch 1/200\n",
      "700527/700527 [==============================] - 96s 137us/step - loss: 3.6496 - val_loss: 3.6331\n",
      "Epoch 2/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.6191 - val_loss: 3.6026\n",
      "Epoch 3/200\n",
      "700527/700527 [==============================] - 34s 49us/step - loss: 3.5887 - val_loss: 3.5724\n",
      "Epoch 4/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.5588 - val_loss: 3.5428\n",
      "Epoch 5/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.5294 - val_loss: 3.5137\n",
      "Epoch 6/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.5006 - val_loss: 3.4852\n",
      "Epoch 7/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.4723 - val_loss: 3.4573\n",
      "Epoch 8/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.4447 - val_loss: 3.4299\n",
      "Epoch 9/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.4176 - val_loss: 3.4032\n",
      "Epoch 10/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.3911 - val_loss: 3.3770\n",
      "Epoch 11/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.3653 - val_loss: 3.3515\n",
      "Epoch 12/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.3400 - val_loss: 3.3265\n",
      "Epoch 13/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.3154 - val_loss: 3.3022\n",
      "Epoch 14/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.2914 - val_loss: 3.2785\n",
      "Epoch 15/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.2680 - val_loss: 3.2555\n",
      "Epoch 16/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.2452 - val_loss: 3.2330\n",
      "Epoch 17/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.2230 - val_loss: 3.2112\n",
      "Epoch 18/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.2015 - val_loss: 3.1899\n",
      "Epoch 19/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.1805 - val_loss: 3.1693\n",
      "Epoch 20/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.1602 - val_loss: 3.1493\n",
      "Epoch 21/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.1405 - val_loss: 3.1299\n",
      "Epoch 22/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.1214 - val_loss: 3.1111\n",
      "Epoch 23/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.1029 - val_loss: 3.0929\n",
      "Epoch 24/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.0850 - val_loss: 3.0753\n",
      "Epoch 25/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.0676 - val_loss: 3.0582\n",
      "Epoch 26/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.0508 - val_loss: 3.0417\n",
      "Epoch 27/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 3.0346 - val_loss: 3.0258\n",
      "Epoch 28/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.0190 - val_loss: 3.0104\n",
      "Epoch 29/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 3.0039 - val_loss: 2.9956\n",
      "Epoch 30/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.9893 - val_loss: 2.9813\n",
      "Epoch 31/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.9752 - val_loss: 2.9676\n",
      "Epoch 32/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.9617 - val_loss: 2.9543\n",
      "Epoch 33/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.9487 - val_loss: 2.9416\n",
      "Epoch 34/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.9362 - val_loss: 2.9293\n",
      "Epoch 35/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.9242 - val_loss: 2.9176\n",
      "Epoch 36/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.9126 - val_loss: 2.9063\n",
      "Epoch 37/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.9016 - val_loss: 2.8954\n",
      "Epoch 38/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8909 - val_loss: 2.8851\n",
      "Epoch 39/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8807 - val_loss: 2.8751\n",
      "Epoch 40/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8710 - val_loss: 2.8656\n",
      "Epoch 41/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.8617 - val_loss: 2.8565\n",
      "Epoch 42/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8527 - val_loss: 2.8478\n",
      "Epoch 43/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8442 - val_loss: 2.8395\n",
      "Epoch 44/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8361 - val_loss: 2.8316\n",
      "Epoch 45/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8283 - val_loss: 2.8240\n",
      "Epoch 46/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8209 - val_loss: 2.8168\n",
      "Epoch 47/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8138 - val_loss: 2.8099\n",
      "Epoch 48/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8071 - val_loss: 2.8034\n",
      "Epoch 49/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.8006 - val_loss: 2.7971\n",
      "Epoch 50/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7945 - val_loss: 2.7912\n",
      "Epoch 51/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7887 - val_loss: 2.7856\n",
      "Epoch 52/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7832 - val_loss: 2.7802\n",
      "Epoch 53/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7779 - val_loss: 2.7751\n",
      "Epoch 54/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7729 - val_loss: 2.7703\n",
      "Epoch 55/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7682 - val_loss: 2.7657\n",
      "Epoch 56/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7637 - val_loss: 2.7613\n",
      "Epoch 57/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7594 - val_loss: 2.7572\n",
      "Epoch 58/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7553 - val_loss: 2.7532\n",
      "Epoch 59/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7515 - val_loss: 2.7495\n",
      "Epoch 60/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7478 - val_loss: 2.7460\n",
      "Epoch 61/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7443 - val_loss: 2.7426\n",
      "Epoch 62/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7410 - val_loss: 2.7394\n",
      "Epoch 63/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7379 - val_loss: 2.7364\n",
      "Epoch 64/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7350 - val_loss: 2.7336\n",
      "Epoch 65/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7322 - val_loss: 2.7309\n",
      "Epoch 66/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7295 - val_loss: 2.7283\n",
      "Epoch 67/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7270 - val_loss: 2.7259\n",
      "Epoch 68/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7246 - val_loss: 2.7236\n",
      "Epoch 69/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7224 - val_loss: 2.7214\n",
      "Epoch 70/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7203 - val_loss: 2.7194\n",
      "Epoch 71/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7182 - val_loss: 2.7174\n",
      "Epoch 72/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7163 - val_loss: 2.7156\n",
      "Epoch 73/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7145 - val_loss: 2.7139\n",
      "Epoch 74/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7128 - val_loss: 2.7122\n",
      "Epoch 75/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7112 - val_loss: 2.7107\n",
      "Epoch 76/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.7097 - val_loss: 2.7092\n",
      "Epoch 77/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7083 - val_loss: 2.7078\n",
      "Epoch 78/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7069 - val_loss: 2.7065\n",
      "Epoch 79/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7056 - val_loss: 2.7053\n",
      "Epoch 80/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7044 - val_loss: 2.7041\n",
      "Epoch 81/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7033 - val_loss: 2.7030\n",
      "Epoch 82/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7022 - val_loss: 2.7020\n",
      "Epoch 83/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7012 - val_loss: 2.7010\n",
      "Epoch 84/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.7002 - val_loss: 2.7000\n",
      "Epoch 85/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6993 - val_loss: 2.6991\n",
      "Epoch 86/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6984 - val_loss: 2.6983\n",
      "Epoch 87/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.6976 - val_loss: 2.6975\n",
      "Epoch 88/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.6968 - val_loss: 2.6968\n",
      "Epoch 89/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6961 - val_loss: 2.6961\n",
      "Epoch 90/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6954 - val_loss: 2.6954\n",
      "Epoch 91/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6947 - val_loss: 2.6947\n",
      "Epoch 92/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6941 - val_loss: 2.6941\n",
      "Epoch 93/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6935 - val_loss: 2.6936\n",
      "Epoch 94/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6930 - val_loss: 2.6930\n",
      "Epoch 95/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6924 - val_loss: 2.6925\n",
      "Epoch 96/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6919 - val_loss: 2.6920\n",
      "Epoch 97/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6915 - val_loss: 2.6916\n",
      "Epoch 98/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6910 - val_loss: 2.6911\n",
      "Epoch 99/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6906 - val_loss: 2.6907\n",
      "Epoch 100/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6902 - val_loss: 2.6904\n",
      "Epoch 101/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6898 - val_loss: 2.6900\n",
      "Epoch 102/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6894 - val_loss: 2.6896\n",
      "Epoch 103/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6891 - val_loss: 2.6893\n",
      "Epoch 104/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6888 - val_loss: 2.6890\n",
      "Epoch 105/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6884 - val_loss: 2.6887\n",
      "Epoch 106/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6881 - val_loss: 2.6884\n",
      "Epoch 107/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6879 - val_loss: 2.6881\n",
      "Epoch 108/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6876 - val_loss: 2.6878\n",
      "Epoch 109/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6873 - val_loss: 2.6876\n",
      "Epoch 110/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6871 - val_loss: 2.6873\n",
      "Epoch 111/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6869 - val_loss: 2.6871\n",
      "Epoch 112/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6866 - val_loss: 2.6869\n",
      "Epoch 113/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6864 - val_loss: 2.6867\n",
      "Epoch 114/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6862 - val_loss: 2.6865\n",
      "Epoch 115/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6860 - val_loss: 2.6863\n",
      "Epoch 116/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6858 - val_loss: 2.6861\n",
      "Epoch 117/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6857 - val_loss: 2.6859\n",
      "Epoch 118/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.6855 - val_loss: 2.6858\n",
      "Epoch 119/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6853 - val_loss: 2.6856\n",
      "Epoch 120/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6852 - val_loss: 2.6855\n",
      "Epoch 121/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6850 - val_loss: 2.6853\n",
      "Epoch 122/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6849 - val_loss: 2.6852\n",
      "Epoch 123/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6848 - val_loss: 2.6850\n",
      "Epoch 124/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6846 - val_loss: 2.6849\n",
      "Epoch 125/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6845 - val_loss: 2.6848\n",
      "Epoch 126/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.6844 - val_loss: 2.6847\n",
      "Epoch 127/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6843 - val_loss: 2.6846\n",
      "Epoch 128/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6842 - val_loss: 2.6845\n",
      "Epoch 129/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6841 - val_loss: 2.6843\n",
      "Epoch 130/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6840 - val_loss: 2.6842\n",
      "Epoch 131/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6839 - val_loss: 2.6841\n",
      "Epoch 132/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6838 - val_loss: 2.6841\n",
      "Epoch 133/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6837 - val_loss: 2.6840\n",
      "Epoch 134/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6836 - val_loss: 2.6839\n",
      "Epoch 135/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6835 - val_loss: 2.6838\n",
      "Epoch 136/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6834 - val_loss: 2.6837\n",
      "Epoch 137/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6833 - val_loss: 2.6837\n",
      "Epoch 138/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6833 - val_loss: 2.6836\n",
      "Epoch 139/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6832 - val_loss: 2.6835\n",
      "Epoch 140/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6831 - val_loss: 2.6834\n",
      "Epoch 141/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6831 - val_loss: 2.6834\n",
      "Epoch 142/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6830 - val_loss: 2.6833\n",
      "Epoch 143/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6829 - val_loss: 2.6833\n",
      "Epoch 144/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6829 - val_loss: 2.6832\n",
      "Epoch 145/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6828 - val_loss: 2.6831\n",
      "Epoch 146/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6828 - val_loss: 2.6831\n",
      "Epoch 147/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6827 - val_loss: 2.6830\n",
      "Epoch 148/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6827 - val_loss: 2.6830\n",
      "Epoch 149/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6826 - val_loss: 2.6829\n",
      "Epoch 150/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6826 - val_loss: 2.6829\n",
      "Epoch 151/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6825 - val_loss: 2.6828\n",
      "Epoch 152/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6825 - val_loss: 2.6828\n",
      "Epoch 153/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6824 - val_loss: 2.6827\n",
      "Epoch 154/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6824 - val_loss: 2.6827\n",
      "Epoch 155/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6824 - val_loss: 2.6827\n",
      "Epoch 156/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6823 - val_loss: 2.6826\n",
      "Epoch 157/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6823 - val_loss: 2.6826\n",
      "Epoch 158/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6822 - val_loss: 2.6825\n",
      "Epoch 159/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6822 - val_loss: 2.6825\n",
      "Epoch 160/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6822 - val_loss: 2.6825\n",
      "Epoch 161/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6821 - val_loss: 2.6824\n",
      "Epoch 162/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6821 - val_loss: 2.6824\n",
      "Epoch 163/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6821 - val_loss: 2.6824\n",
      "Epoch 164/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6820 - val_loss: 2.6823\n",
      "Epoch 165/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6820 - val_loss: 2.6823\n",
      "Epoch 166/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6820 - val_loss: 2.6823\n",
      "Epoch 167/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6819 - val_loss: 2.6822\n",
      "Epoch 168/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6819 - val_loss: 2.6822\n",
      "Epoch 169/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6819 - val_loss: 2.6822\n",
      "Epoch 170/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6819 - val_loss: 2.6822\n",
      "Epoch 171/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6818 - val_loss: 2.6821\n",
      "Epoch 172/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6818 - val_loss: 2.6821\n",
      "Epoch 173/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6818 - val_loss: 2.6821\n",
      "Epoch 174/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6818 - val_loss: 2.6821\n",
      "Epoch 175/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6817 - val_loss: 2.6820\n",
      "Epoch 176/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6817 - val_loss: 2.6820\n",
      "Epoch 177/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6817 - val_loss: 2.6820\n",
      "Epoch 178/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6817 - val_loss: 2.6820\n",
      "Epoch 179/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6816 - val_loss: 2.6820\n",
      "Epoch 180/200\n",
      "700527/700527 [==============================] - 35s 49us/step - loss: 2.6816 - val_loss: 2.6819\n",
      "Epoch 181/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6816 - val_loss: 2.6819\n",
      "Epoch 182/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6816 - val_loss: 2.6819\n",
      "Epoch 183/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6816 - val_loss: 2.6819\n",
      "Epoch 184/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6816 - val_loss: 2.6819\n",
      "Epoch 185/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6815 - val_loss: 2.6818\n",
      "Epoch 186/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6815 - val_loss: 2.6818\n",
      "Epoch 187/200\n",
      "700527/700527 [==============================] - 35s 50us/step - loss: 2.6815 - val_loss: 2.6818\n",
      "Epoch 188/200\n",
      "320000/700527 [============>.................] - ETA: 17s - loss: 2.6819"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-a4369f86affc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m           epochs=200)\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-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   1037\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, 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    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\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[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\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/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\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/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1457\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\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[0;32m-> 1458\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1459\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1460\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[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#options\n",
    "model.fit(x_train, \n",
    "          y_train,\n",
    "          validation_data=(x_test, y_test),\n",
    "          batch_size=40000,\n",
    "          epochs=200)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "3a_sf_models-deeplearning-colab.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.3"
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 },
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