t81_558_class_10_5_temporal_cnn.ipynb

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    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 10: Time Series in Keras**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 10 Material\n",
    "\n",
    "* Part 10.1: Time Series Data Encoding for Deep Learning [[Video]](https://www.youtube.com/watch?v=dMUmHsktl04&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_1_timeseries.ipynb)\n",
    "* Part 10.2: Programming LSTM with Keras and TensorFlow [[Video]](https://www.youtube.com/watch?v=wY0dyFgNCgY&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_2_lstm.ipynb)\n",
    "* Part 10.3: Text Generation with Keras and TensorFlow [[Video]](https://www.youtube.com/watch?v=6ORnRAz3gnA&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_3_text_generation.ipynb)\n",
    "* Part 10.4: Image Captioning with Keras and TensorFlow [[Video]](https://www.youtube.com/watch?v=NmoW_AYWkb4&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_4_captioning.ipynb)\n",
    "* **Part 10.5: Temporal CNN in Keras and TensorFlow** [[Video]](https://www.youtube.com/watch?v=i390g8acZwk&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_5_temporal_cnn.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 10.5: Temporal CNN in Keras and TensorFlow\n",
    "\n",
    "Traditionally, Convolutional Neural Networks (CNNs) are used for image classification problems and Long Short Term Memory (LSTM) Networks have been used for time series.  However, recent research has shown CNN's to be very effective at time series problems. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]\n",
      " [0. 1. 0. 0.]]\n",
      "Build model...\n",
      "Train...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0820 09:12:45.687239 140736216040320 deprecation.py:323] From /Users/jheaton/miniconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6 samples\n",
      "Epoch 1/200\n",
      "6/6 [==============================] - 0s 20ms/sample - loss: 0.7007 - accuracy: 0.4583\n",
      "Epoch 2/200\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 82/200\n",
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      "6/6 [==============================] - 0s 277us/sample - loss: 0.4815 - accuracy: 0.7500\n",
      "Epoch 149/200\n",
      "6/6 [==============================] - 0s 271us/sample - loss: 0.4814 - accuracy: 0.7500\n",
      "Epoch 150/200\n",
      "6/6 [==============================] - 0s 272us/sample - loss: 0.4814 - accuracy: 0.7500\n",
      "Epoch 151/200\n",
      "6/6 [==============================] - 0s 435us/sample - loss: 0.4813 - accuracy: 0.7500\n",
      "Epoch 152/200\n",
      "6/6 [==============================] - 0s 316us/sample - loss: 0.4813 - accuracy: 0.7500\n",
      "Epoch 153/200\n",
      "6/6 [==============================] - 0s 269us/sample - loss: 0.4813 - accuracy: 0.7500\n",
      "Epoch 154/200\n",
      "6/6 [==============================] - 0s 365us/sample - loss: 0.4812 - accuracy: 0.7500\n",
      "Epoch 155/200\n",
      "6/6 [==============================] - 0s 331us/sample - loss: 0.4812 - accuracy: 0.7500\n",
      "Epoch 156/200\n",
      "6/6 [==============================] - 0s 379us/sample - loss: 0.4811 - accuracy: 0.7500\n",
      "Epoch 157/200\n",
      "6/6 [==============================] - 0s 350us/sample - loss: 0.4811 - accuracy: 0.7500\n",
      "Epoch 158/200\n",
      "6/6 [==============================] - 0s 323us/sample - loss: 0.4811 - accuracy: 0.7500\n",
      "Epoch 159/200\n",
      "6/6 [==============================] - 0s 382us/sample - loss: 0.4810 - accuracy: 0.7500\n",
      "Epoch 160/200\n",
      "6/6 [==============================] - 0s 329us/sample - loss: 0.4810 - accuracy: 0.7500\n",
      "Epoch 161/200\n",
      "6/6 [==============================] - 0s 278us/sample - loss: 0.4809 - accuracy: 0.7500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 162/200\n",
      "6/6 [==============================] - 0s 317us/sample - loss: 0.4809 - accuracy: 0.7500\n",
      "Epoch 163/200\n",
      "6/6 [==============================] - 0s 388us/sample - loss: 0.4809 - accuracy: 0.7500\n",
      "Epoch 164/200\n",
      "6/6 [==============================] - 0s 308us/sample - loss: 0.4808 - accuracy: 0.7500\n",
      "Epoch 165/200\n",
      "6/6 [==============================] - 0s 343us/sample - loss: 0.4808 - accuracy: 0.7500\n",
      "Epoch 166/200\n",
      "6/6 [==============================] - 0s 334us/sample - loss: 0.4808 - accuracy: 0.7500\n",
      "Epoch 167/200\n",
      "6/6 [==============================] - 0s 302us/sample - loss: 0.4807 - accuracy: 0.7500\n",
      "Epoch 168/200\n",
      "6/6 [==============================] - 0s 326us/sample - loss: 0.4807 - accuracy: 0.7500\n",
      "Epoch 169/200\n",
      "6/6 [==============================] - 0s 283us/sample - loss: 0.4807 - accuracy: 0.7500\n",
      "Epoch 170/200\n",
      "6/6 [==============================] - 0s 285us/sample - loss: 0.4806 - accuracy: 0.7500\n",
      "Epoch 171/200\n",
      "6/6 [==============================] - 0s 353us/sample - loss: 0.4806 - accuracy: 0.7500\n",
      "Epoch 172/200\n",
      "6/6 [==============================] - 0s 330us/sample - loss: 0.4806 - accuracy: 0.7500\n",
      "Epoch 173/200\n",
      "6/6 [==============================] - 0s 291us/sample - loss: 0.4805 - accuracy: 0.7500\n",
      "Epoch 174/200\n",
      "6/6 [==============================] - 0s 314us/sample - loss: 0.4805 - accuracy: 0.7500\n",
      "Epoch 175/200\n",
      "6/6 [==============================] - 0s 372us/sample - loss: 0.4805 - accuracy: 0.7500\n",
      "Epoch 176/200\n",
      "6/6 [==============================] - 0s 388us/sample - loss: 0.4804 - accuracy: 0.7500\n",
      "Epoch 177/200\n",
      "6/6 [==============================] - 0s 282us/sample - loss: 0.4804 - accuracy: 0.7500\n",
      "Epoch 178/200\n",
      "6/6 [==============================] - 0s 338us/sample - loss: 0.4804 - accuracy: 0.7500\n",
      "Epoch 179/200\n",
      "6/6 [==============================] - 0s 389us/sample - loss: 0.4803 - accuracy: 0.7500\n",
      "Epoch 180/200\n",
      "6/6 [==============================] - 0s 307us/sample - loss: 0.4803 - accuracy: 0.7500\n",
      "Epoch 181/200\n",
      "6/6 [==============================] - 0s 359us/sample - loss: 0.4803 - accuracy: 0.7500\n",
      "Epoch 182/200\n",
      "6/6 [==============================] - 0s 304us/sample - loss: 0.4802 - accuracy: 0.7500\n",
      "Epoch 183/200\n",
      "6/6 [==============================] - 0s 329us/sample - loss: 0.4802 - accuracy: 0.7500\n",
      "Epoch 184/200\n",
      "6/6 [==============================] - 0s 383us/sample - loss: 0.4802 - accuracy: 0.7500\n",
      "Epoch 185/200\n",
      "6/6 [==============================] - 0s 349us/sample - loss: 0.4802 - accuracy: 0.7500\n",
      "Epoch 186/200\n",
      "6/6 [==============================] - 0s 345us/sample - loss: 0.4801 - accuracy: 0.7500\n",
      "Epoch 187/200\n",
      "6/6 [==============================] - 0s 381us/sample - loss: 0.4801 - accuracy: 0.7500\n",
      "Epoch 188/200\n",
      "6/6 [==============================] - 0s 311us/sample - loss: 0.4801 - accuracy: 0.7500\n",
      "Epoch 189/200\n",
      "6/6 [==============================] - 0s 348us/sample - loss: 0.4800 - accuracy: 0.7500\n",
      "Epoch 190/200\n",
      "6/6 [==============================] - 0s 313us/sample - loss: 0.4800 - accuracy: 0.7500\n",
      "Epoch 191/200\n",
      "6/6 [==============================] - 0s 376us/sample - loss: 0.4800 - accuracy: 0.7500\n",
      "Epoch 192/200\n",
      "6/6 [==============================] - 0s 274us/sample - loss: 0.4800 - accuracy: 0.7500\n",
      "Epoch 193/200\n",
      "6/6 [==============================] - 0s 367us/sample - loss: 0.4799 - accuracy: 0.7500\n",
      "Epoch 194/200\n",
      "6/6 [==============================] - 0s 317us/sample - loss: 0.4799 - accuracy: 0.7500\n",
      "Epoch 195/200\n",
      "6/6 [==============================] - 0s 392us/sample - loss: 0.4799 - accuracy: 0.7500\n",
      "Epoch 196/200\n",
      "6/6 [==============================] - 0s 323us/sample - loss: 0.4799 - accuracy: 0.7500\n",
      "Epoch 197/200\n",
      "6/6 [==============================] - 0s 350us/sample - loss: 0.4798 - accuracy: 0.7500\n",
      "Epoch 198/200\n",
      "6/6 [==============================] - 0s 422us/sample - loss: 0.4798 - accuracy: 0.7500\n",
      "Epoch 199/200\n",
      "6/6 [==============================] - 0s 367us/sample - loss: 0.4798 - accuracy: 0.7500\n",
      "Epoch 200/200\n",
      "6/6 [==============================] - 0s 380us/sample - loss: 0.4798 - accuracy: 0.7500\n",
      "Predicted classes: {} [[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n",
      "Expected classes: {} [[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D\n",
    "import numpy as np\n",
    "\n",
    "max_features = 4 # 0,1,2,3 (total of 4)\n",
    "x = [\n",
    "    [[0],[1],[1],[0],[0],[0]],\n",
    "    [[0],[0],[0],[2],[2],[0]],\n",
    "    [[0],[0],[0],[0],[3],[3]],\n",
    "    [[0],[2],[2],[0],[0],[0]],\n",
    "    [[0],[0],[3],[3],[0],[0]],\n",
    "    [[0],[0],[0],[0],[1],[1]]\n",
    "]\n",
    "x = np.array(x,dtype=np.float32)\n",
    "y = np.array([1,2,3,2,3,1],dtype=np.int32)\n",
    "\n",
    "# Convert y2 to dummy variables\n",
    "y2 = np.zeros((y.shape[0], max_features),dtype=np.float32)\n",
    "y2[np.arange(y.shape[0]), y] = 1.0\n",
    "print(y2)\n",
    "\n",
    "print('Build model...')\n",
    "model = Sequential()\n",
    "model.add(Conv1D(128, kernel_size=x.shape[1], input_shape=(None, 1)))\n",
    "model.add(Dense(4, activation='sigmoid'))\n",
    "\n",
    "# try using different optimizers and different optimizer configs\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print('Train...')\n",
    "model.fit(x,y2,epochs=200)\n",
    "pred = model.predict(x)\n",
    "predict_classes = np.argmax(pred,axis=1)\n",
    "print(\"Predicted classes: {}\",predict_classes)\n",
    "print(\"Expected classes: {}\",predict_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "def runit(model, inp):\n",
    "    inp = np.array(inp,dtype=np.float32)\n",
    "    pred = model.predict(inp)\n",
    "    return np.argmax(pred[0])\n",
    "\n",
    "print( runit( model, [[[0],[1],[1],[0],[0],[0]]] ))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sun Spots Example - CNN\n",
    "\n",
    "An example of RNN regression to predict sunspots.  The data files needed for this example can be found at the following location.\n",
    "\n",
    "* [Sunspot Data Files](http://www.sidc.be/silso/datafiles#total)\n",
    "* [Download Daily Sunspots](http://www.sidc.be/silso/INFO/sndtotcsv.php) - 1/1/1818 to now.\n",
    "\n",
    "The following code is used to load the sunspot file:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting file:\n",
      "   year  month  day  dec_year  sn_value  sn_error  obs_num\n",
      "0  1818      1    1  1818.001        -1       NaN        0\n",
      "1  1818      1    2  1818.004        -1       NaN        0\n",
      "2  1818      1    3  1818.007        -1       NaN        0\n",
      "3  1818      1    4  1818.010        -1       NaN        0\n",
      "4  1818      1    5  1818.012        -1       NaN        0\n",
      "5  1818      1    6  1818.015        -1       NaN        0\n",
      "6  1818      1    7  1818.018        -1       NaN        0\n",
      "7  1818      1    8  1818.021        65      10.2        1\n",
      "8  1818      1    9  1818.023        -1       NaN        0\n",
      "9  1818      1   10  1818.026        -1       NaN        0\n",
      "Ending file:\n",
      "       year  month  day  dec_year  sn_value  sn_error  obs_num\n",
      "73616  2019      7   22  2019.555         8       0.8       25\n",
      "73617  2019      7   23  2019.558         0       0.0       39\n",
      "73618  2019      7   24  2019.560         0       0.0       46\n",
      "73619  2019      7   25  2019.563         0       0.0       45\n",
      "73620  2019      7   26  2019.566         0       0.0       44\n",
      "73621  2019      7   27  2019.568         0       0.0       30\n",
      "73622  2019      7   28  2019.571         0       0.0       28\n",
      "73623  2019      7   29  2019.574         0       0.0       39\n",
      "73624  2019      7   30  2019.577         0       0.0       40\n",
      "73625  2019      7   31  2019.579         0       0.0       37\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# Replacce the following path with your own file. It can be downloaded from:\n",
    "# http://www.sidc.be/silso/INFO/sndtotcsv.php\n",
    "path = \"./data/\"\n",
    "    \n",
    "filename = os.path.join(path,\"SN_d_tot_V2.0.csv\")    \n",
    "names = ['year', 'month', 'day', 'dec_year', 'sn_value' , 'sn_error', 'obs_num']\n",
    "df = pd.read_csv(filename,sep=';',header=None,names=names,na_values=['-1'], index_col=False)\n",
    "\n",
    "print(\"Starting file:\")\n",
    "print(df[0:10])\n",
    "\n",
    "print(\"Ending file:\")\n",
    "print(df[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there is quite a bit of missing data near the end of the file.  We would like to find the starting index where the missing data no longer occurs.  This is somewhat sloppy, it would be better to find a use for the data between missing values.  However, the point of this example is show how to use LSTM with a somewhat simple time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11314\n"
     ]
    }
   ],
   "source": [
    "start_id = max(df[df['obs_num'] == 0].index.tolist())+1  # Find the last zero and move one beyond\n",
    "print(start_id)\n",
    "df = df[start_id:] # Trim the rows that have missing observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 55160 observations.\n",
      "Test set has 7152 observations.\n"
     ]
    }
   ],
   "source": [
    "df_train = df[df['year']<2000]\n",
    "df_test = df[df['year']>=2000]\n",
    "\n",
    "spots_train = df_train['sn_value'].tolist()\n",
    "spots_test = df_test['sn_value'].tolist()\n",
    "\n",
    "print(\"Training set has {} observations.\".format(len(spots_train)))\n",
    "print(\"Test set has {} observations.\".format(len(spots_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set: (55134, 25, 1)\n",
      "Shape of test set: (7126, 25, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def to_sequences(seq_size, obs):\n",
    "    x = []\n",
    "    y = []\n",
    "\n",
    "    for i in range(len(obs)-SEQUENCE_SIZE-1):\n",
    "        #print(i)\n",
    "        window = obs[i:(i+SEQUENCE_SIZE)]\n",
    "        after_window = obs[i+SEQUENCE_SIZE]\n",
    "        window = [[x] for x in window]\n",
    "        #print(\"{} - {}\".format(window,after_window))\n",
    "        x.append(window)\n",
    "        y.append(after_window)\n",
    "        \n",
    "    return np.array(x),np.array(y)\n",
    "    \n",
    "    \n",
    "SEQUENCE_SIZE = 25\n",
    "x_train,y_train = to_sequences(SEQUENCE_SIZE,spots_train)\n",
    "x_test,y_test = to_sequences(SEQUENCE_SIZE,spots_test)\n",
    "\n",
    "print(\"Shape of training set: {}\".format(x_train.shape))\n",
    "print(\"Shape of test set: {}\".format(x_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[353],\n",
       "        [240],\n",
       "        [275],\n",
       "        ...,\n",
       "        [270],\n",
       "        [289],\n",
       "        [308]],\n",
       "\n",
       "       [[240],\n",
       "        [275],\n",
       "        [352],\n",
       "        ...,\n",
       "        [289],\n",
       "        [308],\n",
       "        [327]],\n",
       "\n",
       "       [[275],\n",
       "        [352],\n",
       "        [268],\n",
       "        ...,\n",
       "        [308],\n",
       "        [327],\n",
       "        [323]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 97],\n",
       "        [ 79],\n",
       "        [ 70],\n",
       "        ...,\n",
       "        [128],\n",
       "        [116],\n",
       "        [ 95]],\n",
       "\n",
       "       [[ 79],\n",
       "        [ 70],\n",
       "        [ 87],\n",
       "        ...,\n",
       "        [116],\n",
       "        [ 95],\n",
       "        [ 85]],\n",
       "\n",
       "       [[ 70],\n",
       "        [ 87],\n",
       "        [ 81],\n",
       "        ...,\n",
       "        [ 95],\n",
       "        [ 85],\n",
       "        [103]]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build model...\n",
      "Train...\n",
      "Train on 55134 samples, validate on 7126 samples\n",
      "Epoch 1/1000\n",
      "55134/55134 - 4s - loss: 1066.3207 - val_loss: 662.3279\n",
      "Epoch 2/1000\n",
      "55134/55134 - 4s - loss: 919.5086 - val_loss: 758.8296\n",
      "Epoch 3/1000\n",
      "55134/55134 - 4s - loss: 903.9341 - val_loss: 675.9912\n",
      "Epoch 4/1000\n",
      "55134/55134 - 4s - loss: 901.2349 - val_loss: 664.1927\n",
      "Epoch 5/1000\n",
      "55134/55134 - 4s - loss: 884.1174 - val_loss: 537.8667\n",
      "Epoch 6/1000\n",
      "55134/55134 - 4s - loss: 885.5698 - val_loss: 658.6137\n",
      "Epoch 7/1000\n",
      "55134/55134 - 4s - loss: 882.8827 - val_loss: 667.2707\n",
      "Epoch 8/1000\n",
      "55134/55134 - 5s - loss: 874.7731 - val_loss: 480.2243\n",
      "Epoch 9/1000\n",
      "55134/55134 - 5s - loss: 874.8989 - val_loss: 599.1845\n",
      "Epoch 10/1000\n",
      "55134/55134 - 4s - loss: 865.9352 - val_loss: 605.0286\n",
      "Epoch 11/1000\n",
      "55134/55134 - 5s - loss: 864.1161 - val_loss: 614.2303\n",
      "Epoch 12/1000\n",
      "55134/55134 - 4s - loss: 858.3618 - val_loss: 671.9098\n",
      "Epoch 13/1000\n",
      "55134/55134 - 4s - loss: 857.7733 - val_loss: 483.9761\n",
      "Epoch 14/1000\n",
      "55134/55134 - 4s - loss: 849.7772 - val_loss: 558.1835\n",
      "Epoch 15/1000\n",
      "55134/55134 - 4s - loss: 850.1291 - val_loss: 589.8786\n",
      "Epoch 16/1000\n",
      "55134/55134 - 4s - loss: 845.1786 - val_loss: 567.5958\n",
      "Epoch 17/1000\n",
      "55134/55134 - 4s - loss: 843.5014 - val_loss: 475.8242\n",
      "Epoch 18/1000\n",
      "55134/55134 - 4s - loss: 849.6218 - val_loss: 490.3745\n",
      "Epoch 19/1000\n",
      "55134/55134 - 4s - loss: 840.2739 - val_loss: 497.9359\n",
      "Epoch 20/1000\n",
      "55134/55134 - 4s - loss: 839.5976 - val_loss: 565.5624\n",
      "Epoch 21/1000\n",
      "55134/55134 - 4s - loss: 837.5939 - val_loss: 530.9252\n",
      "Epoch 22/1000\n",
      "55134/55134 - 4s - loss: 841.0287 - val_loss: 603.3853\n",
      "Epoch 23/1000\n",
      "55134/55134 - 4s - loss: 839.8064 - val_loss: 516.4423\n",
      "Epoch 24/1000\n",
      "55134/55134 - 5s - loss: 832.1009 - val_loss: 513.4699\n",
      "Epoch 25/1000\n",
      "55134/55134 - 5s - loss: 833.1351 - val_loss: 483.7453\n",
      "Epoch 26/1000\n",
      "55134/55134 - 5s - loss: 829.6962 - val_loss: 499.6495\n",
      "Epoch 27/1000\n",
      "55134/55134 - 5s - loss: 832.6751 - val_loss: 717.8602\n",
      "Epoch 28/1000\n",
      "55134/55134 - 5s - loss: 828.2242 - val_loss: 595.2160\n",
      "Epoch 29/1000\n",
      "55134/55134 - 5s - loss: 822.6834 - val_loss: 486.4044\n",
      "Epoch 30/1000\n",
      "55134/55134 - 4s - loss: 826.2379 - val_loss: 526.5464\n",
      "Epoch 31/1000\n",
      "55134/55134 - 5s - loss: 819.7617 - val_loss: 582.9074\n",
      "Epoch 32/1000\n",
      "55134/55134 - 4s - loss: 824.6743 - val_loss: 654.4880\n",
      "Epoch 33/1000\n",
      "55134/55134 - 4s - loss: 819.9012 - val_loss: 508.9884\n",
      "Epoch 34/1000\n",
      "55134/55134 - 4s - loss: 817.2283 - val_loss: 637.8522\n",
      "Epoch 35/1000\n",
      "55134/55134 - 4s - loss: 814.3524 - val_loss: 493.3017\n",
      "Epoch 36/1000\n",
      "55134/55134 - 4s - loss: 812.2793 - val_loss: 670.3544\n",
      "Epoch 37/1000\n",
      "55134/55134 - 4s - loss: 811.5134 - val_loss: 613.1647\n",
      "Epoch 38/1000\n",
      "55134/55134 - 5s - loss: 808.7720 - val_loss: 579.2622\n",
      "Epoch 39/1000\n",
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      "Epoch 40/1000\n",
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      "Epoch 41/1000\n",
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      "Epoch 42/1000\n",
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      "Epoch 46/1000\n",
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      "Epoch 47/1000\n",
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      "Epoch 48/1000\n",
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      "Epoch 49/1000\n",
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      "Epoch 50/1000\n",
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      "Epoch 51/1000\n",
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      "Epoch 53/1000\n",
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      "Epoch 54/1000\n",
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      "Epoch 55/1000\n",
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      "Epoch 59/1000\n",
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      "Epoch 60/1000\n",
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      "Epoch 61/1000\n",
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      "Epoch 62/1000\n",
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      "Epoch 63/1000\n",
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      "Epoch 64/1000\n",
      "55134/55134 - 6s - loss: 778.5236 - val_loss: 535.1437\n",
      "Epoch 65/1000\n",
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      "Epoch 66/1000\n",
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      "Epoch 67/1000\n",
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      "Epoch 68/1000\n",
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      "Epoch 69/1000\n",
      "55134/55134 - 5s - loss: 768.0628 - val_loss: 458.7038\n",
      "Epoch 70/1000\n",
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      "Epoch 71/1000\n",
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      "Epoch 72/1000\n",
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      "Epoch 73/1000\n",
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      "Epoch 74/1000\n",
      "55134/55134 - 5s - loss: 763.3812 - val_loss: 534.9288\n",
      "Epoch 75/1000\n",
      "55134/55134 - 6s - loss: 768.7040 - val_loss: 542.1573\n",
      "Epoch 76/1000\n",
      "55134/55134 - 6s - loss: 765.6276 - val_loss: 596.7121\n",
      "Epoch 77/1000\n",
      "55134/55134 - 5s - loss: 758.8731 - val_loss: 598.4492\n",
      "Epoch 78/1000\n",
      "55134/55134 - 5s - loss: 756.4415 - val_loss: 474.0171\n",
      "Epoch 79/1000\n",
      "55134/55134 - 5s - loss: 759.9539 - val_loss: 540.0702\n",
      "Epoch 80/1000\n",
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      "Epoch 81/1000\n",
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      "Epoch 82/1000\n",
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      "Epoch 83/1000\n",
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      "Epoch 84/1000\n",
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      "Epoch 85/1000\n",
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      "Epoch 86/1000\n",
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      "Epoch 87/1000\n",
      "55134/55134 - 5s - loss: 754.4745 - val_loss: 496.6894\n",
      "Epoch 88/1000\n",
      "55134/55134 - 5s - loss: 751.4658 - val_loss: 476.5977\n",
      "Epoch 89/1000\n",
      "55134/55134 - 5s - loss: 753.3145 - val_loss: 525.3587\n",
      "Epoch 90/1000\n",
      "55134/55134 - 5s - loss: 752.7930 - val_loss: 535.6166\n",
      "Epoch 91/1000\n",
      "55134/55134 - 5s - loss: 749.0368 - val_loss: 457.2584\n",
      "Epoch 92/1000\n",
      "55134/55134 - 5s - loss: 747.9747 - val_loss: 571.9077\n",
      "Epoch 93/1000\n",
      "55134/55134 - 5s - loss: 746.9112 - val_loss: 575.6821\n",
      "Epoch 94/1000\n",
      "55134/55134 - 5s - loss: 745.4786 - val_loss: 541.4511\n",
      "Epoch 95/1000\n",
      "55134/55134 - 4s - loss: 745.5626 - val_loss: 497.0084\n",
      "Epoch 96/1000\n",
      "55134/55134 - 5s - loss: 745.5665 - val_loss: 570.2581\n",
      "Epoch 97/1000\n",
      "55134/55134 - 5s - loss: 747.2369 - val_loss: 528.7269\n",
      "Epoch 98/1000\n",
      "55134/55134 - 5s - loss: 746.4165 - val_loss: 597.8555\n",
      "Epoch 99/1000\n",
      "55134/55134 - 5s - loss: 740.2500 - val_loss: 676.2606\n",
      "Epoch 100/1000\n",
      "55134/55134 - 5s - loss: 740.6110 - val_loss: 607.0649\n",
      "Epoch 101/1000\n",
      "55134/55134 - 5s - loss: 742.3115 - val_loss: 451.9269\n",
      "Epoch 102/1000\n",
      "55134/55134 - 5s - loss: 745.2654 - val_loss: 599.7291\n",
      "Epoch 103/1000\n",
      "55134/55134 - 5s - loss: 742.0000 - val_loss: 555.3617\n",
      "Epoch 104/1000\n",
      "55134/55134 - 5s - loss: 740.8315 - val_loss: 574.1045\n",
      "Epoch 105/1000\n",
      "55134/55134 - 5s - loss: 743.8572 - val_loss: 544.4294\n",
      "Epoch 106/1000\n",
      "55134/55134 - 5s - loss: 740.0955 - val_loss: 506.5172\n",
      "Epoch 107/1000\n",
      "55134/55134 - 5s - loss: 741.2172 - val_loss: 520.4397\n",
      "Epoch 108/1000\n",
      "55134/55134 - 5s - loss: 737.4039 - val_loss: 541.2157\n",
      "Epoch 109/1000\n",
      "55134/55134 - 5s - loss: 731.2534 - val_loss: 587.6735\n",
      "Epoch 110/1000\n",
      "55134/55134 - 5s - loss: 738.4458 - val_loss: 533.3410\n",
      "Epoch 111/1000\n",
      "55134/55134 - 5s - loss: 733.3471 - val_loss: 491.6626\n",
      "Epoch 112/1000\n",
      "55134/55134 - 6s - loss: 739.5632 - val_loss: 598.2503\n",
      "Epoch 113/1000\n",
      "55134/55134 - 5s - loss: 735.2497 - val_loss: 539.9097\n",
      "Epoch 114/1000\n",
      "55134/55134 - 5s - loss: 729.8523 - val_loss: 612.4719\n",
      "Epoch 115/1000\n",
      "55134/55134 - 5s - loss: 735.9904 - val_loss: 614.0140\n",
      "Epoch 116/1000\n",
      "55134/55134 - 5s - loss: 737.2625 - val_loss: 506.1800\n",
      "Epoch 117/1000\n",
      "55134/55134 - 5s - loss: 729.4427 - val_loss: 495.1240\n",
      "Epoch 118/1000\n",
      "55134/55134 - 5s - loss: 733.9974 - val_loss: 498.5499\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 119/1000\n",
      "55134/55134 - 5s - loss: 726.5970 - val_loss: 528.7048\n",
      "Epoch 120/1000\n",
      "55134/55134 - 5s - loss: 724.4342 - val_loss: 566.6720\n",
      "Epoch 121/1000\n",
      "55134/55134 - 5s - loss: 729.4925 - val_loss: 509.8670\n",
      "Epoch 122/1000\n",
      "55134/55134 - 5s - loss: 730.2105 - val_loss: 617.2604\n",
      "Epoch 123/1000\n",
      "55134/55134 - 5s - loss: 730.8583 - val_loss: 577.1652\n",
      "Epoch 124/1000\n",
      "55134/55134 - 5s - loss: 728.7460 - val_loss: 539.5504\n",
      "Epoch 125/1000\n",
      "55134/55134 - 5s - loss: 726.8272 - val_loss: 550.7886\n",
      "Epoch 126/1000\n",
      "55134/55134 - 5s - loss: 725.8271 - val_loss: 453.5865\n",
      "Epoch 127/1000\n",
      "55134/55134 - 5s - loss: 719.9203 - val_loss: 536.5451\n",
      "Epoch 128/1000\n",
      "55134/55134 - 5s - loss: 723.1689 - val_loss: 540.5680\n",
      "Epoch 129/1000\n",
      "55134/55134 - 5s - loss: 717.3914 - val_loss: 570.6946\n",
      "Epoch 130/1000\n",
      "55134/55134 - 5s - loss: 722.7900 - val_loss: 489.6232\n",
      "Epoch 131/1000\n",
      "55134/55134 - 5s - loss: 724.1328 - val_loss: 583.4160\n",
      "Epoch 132/1000\n",
      "55134/55134 - 5s - loss: 724.7606 - val_loss: 494.0735\n",
      "Epoch 133/1000\n",
      "55134/55134 - 5s - loss: 716.3300 - val_loss: 544.2348\n",
      "Epoch 134/1000\n",
      "55134/55134 - 5s - loss: 724.1335 - val_loss: 528.1422\n",
      "Epoch 135/1000\n",
      "55134/55134 - 5s - loss: 722.1686 - val_loss: 472.1933\n",
      "Epoch 136/1000\n",
      "55134/55134 - 5s - loss: 719.4449 - val_loss: 499.0100\n",
      "Epoch 137/1000\n",
      "55134/55134 - 5s - loss: 720.7552 - val_loss: 580.7640\n",
      "Epoch 138/1000\n",
      "55134/55134 - 5s - loss: 716.7628 - val_loss: 701.7534\n",
      "Epoch 139/1000\n",
      "55134/55134 - 5s - loss: 723.6438 - val_loss: 639.0301\n",
      "Epoch 140/1000\n",
      "55134/55134 - 4s - loss: 719.2243 - val_loss: 518.0730\n",
      "Epoch 141/1000\n",
      "55134/55134 - 4s - loss: 717.3335 - val_loss: 462.6637\n",
      "Epoch 142/1000\n",
      "55134/55134 - 5s - loss: 715.5079 - val_loss: 602.0342\n",
      "Epoch 143/1000\n",
      "55134/55134 - 5s - loss: 717.3937 - val_loss: 541.2527\n",
      "Epoch 144/1000\n",
      "55134/55134 - 4s - loss: 713.2536 - val_loss: 602.4165\n",
      "Epoch 145/1000\n",
      "55134/55134 - 5s - loss: 715.9182 - val_loss: 568.3298\n",
      "Epoch 146/1000\n",
      "55134/55134 - 4s - loss: 711.7964 - val_loss: 524.0739\n",
      "Epoch 147/1000\n",
      "55134/55134 - 4s - loss: 712.7393 - val_loss: 532.7556\n",
      "Epoch 148/1000\n",
      "55134/55134 - 4s - loss: 714.4069 - val_loss: 537.4490\n",
      "Epoch 149/1000\n",
      "55134/55134 - 5s - loss: 712.8823 - val_loss: 539.2185\n",
      "Epoch 150/1000\n",
      "55134/55134 - 4s - loss: 711.5519 - val_loss: 480.0818\n",
      "Epoch 151/1000\n",
      "Restoring model weights from the end of the best epoch.\n",
      "55134/55134 - 5s - loss: 714.3140 - val_loss: 553.0885\n",
      "Epoch 00151: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x63452e780>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D, Dropout, MaxPooling1D, Flatten\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import numpy as np\n",
    "\n",
    "print('Build model...')\n",
    "model = Sequential()\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(SEQUENCE_SIZE,1)))\n",
    "model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=50, verbose=1, mode='auto', restore_best_weights=True)\n",
    "print('Train...')\n",
    "\n",
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),callbacks=[monitor],verbose=2,epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score (RMSE): 21.25857311904512\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# from sklearn import metrics\n",
    "pred = model.predict(x_test)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred.flatten(),y_test))\n",
    "print(\"Score (RMSE): {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3.6 (tensorflow)",
   "language": "python",
   "name": "rga"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}