SRU_timeseries_rnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "incorporated-upset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEA GAS</th>\n",
       "      <th>AIR MEA1</th>\n",
       "      <th>AIR MEA 2</th>\n",
       "      <th>AIR SWS</th>\n",
       "      <th>SWS GAS</th>\n",
       "      <th>H2S</th>\n",
       "      <th>SO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.6311319e-001</td>\n",
       "      <td>7.7427568e-001</td>\n",
       "      <td>4.7097005e-001</td>\n",
       "      <td>9.6043797e-002</td>\n",
       "      <td>1.9865240e-002</td>\n",
       "      <td>8.3362383e-002</td>\n",
       "      <td>1.9147575e-001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.5996003e-001</td>\n",
       "      <td>7.6602112e-001</td>\n",
       "      <td>4.7339843e-001</td>\n",
       "      <td>1.1102285e-001</td>\n",
       "      <td>2.7956697e-002</td>\n",
       "      <td>8.1264999e-002</td>\n",
       "      <td>1.7419153e-001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.5467015e-001</td>\n",
       "      <td>7.6751878e-001</td>\n",
       "      <td>4.7419789e-001</td>\n",
       "      <td>1.0540988e-001</td>\n",
       "      <td>2.8906770e-002</td>\n",
       "      <td>7.9167644e-002</td>\n",
       "      <td>1.6554378e-001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.5332529e-001</td>\n",
       "      <td>7.6992209e-001</td>\n",
       "      <td>4.7596348e-001</td>\n",
       "      <td>6.6221392e-002</td>\n",
       "      <td>2.9410771e-002</td>\n",
       "      <td>7.7070260e-002</td>\n",
       "      <td>1.6818991e-001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.5472610e-001</td>\n",
       "      <td>7.6044864e-001</td>\n",
       "      <td>4.7883328e-001</td>\n",
       "      <td>3.3745797e-002</td>\n",
       "      <td>3.1592118e-002</td>\n",
       "      <td>7.4972877e-002</td>\n",
       "      <td>1.7083602e-001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MEA GAS        AIR MEA1       AIR MEA 2         AIR SWS  \\\n",
       "0  6.6311319e-001  7.7427568e-001  4.7097005e-001  9.6043797e-002   \n",
       "1  6.5996003e-001  7.6602112e-001  4.7339843e-001  1.1102285e-001   \n",
       "2  6.5467015e-001  7.6751878e-001  4.7419789e-001  1.0540988e-001   \n",
       "3  6.5332529e-001  7.6992209e-001  4.7596348e-001  6.6221392e-002   \n",
       "4  6.5472610e-001  7.6044864e-001  4.7883328e-001  3.3745797e-002   \n",
       "\n",
       "          SWS GAS             H2S             SO2  \n",
       "0  1.9865240e-002  8.3362383e-002  1.9147575e-001  \n",
       "1  2.7956697e-002  8.1264999e-002  1.7419153e-001  \n",
       "2  2.8906770e-002  7.9167644e-002  1.6554378e-001  \n",
       "3  2.9410771e-002  7.7070260e-002  1.6818991e-001  \n",
       "4  3.1592118e-002  7.4972877e-002  1.7083602e-001  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import urllib.request \n",
    "url = 'https://raw.githubusercontent.com/onecoinbuybus/Database_chemoinformatics/master/SRU_data.txt'\n",
    "urllib.request.urlretrieve(url, 'SRU_data.txt') \n",
    "\n",
    "sru_data = pd.read_csv('SRU_data.txt',header=None, skiprows=1).iloc[:,0].apply(lambda x: pd.Series(x.split()))\n",
    "sru_data.columns = ['MEA GAS',\n",
    "                    'AIR MEA1',\n",
    "                    'AIR MEA 2',\n",
    "                    'AIR SWS',\n",
    "                    'SWS GAS',\n",
    "                    'H2S',\n",
    "                    'SO2']\n",
    "sru_data_array=np.array(sru_data)\n",
    "sru_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "beautiful-barbados",
   "metadata": {},
   "outputs": [],
   "source": [
    "sru_data_array=np.array(sru_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "speaking-album",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='H2S'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(30, 10))\n",
    "sns.lineplot(data=sru_data['H2S'][:60].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "perceived-western",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='H2S'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "sns.lineplot(data=sru_data['H2S'][60:120].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "realistic-final",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='H2S'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "sns.lineplot(data=sru_data['H2S'][120:180].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "painful-formation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='H2S'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "sns.lineplot(data=sru_data['H2S'][240:300].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "prepared-runner",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_generator():\n",
    "    yield 1\n",
    "    yield 2\n",
    "    yield 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "familiar-sapphire",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object my_generator at 0x7fe2527e2dd0>\n"
     ]
    }
   ],
   "source": [
    "print(list(my_generator()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "atlantic-cabinet",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator(data, lookback, delay, min_index, max_index,\n",
    "              shuffle=False, batch_size=128, step=6):\n",
    "    if max_index is None:\n",
    "        max_index = len(data) - delay - 1\n",
    "    i = min_index + lookback\n",
    "    while 1:\n",
    "        if shuffle:\n",
    "            #挑选任意\n",
    "            rows = np.random.randint(\n",
    "                min_index + lookback, max_index, size=batch_size)\n",
    "        else:\n",
    "            if i + batch_size >= max_index:\n",
    "                i = min_index + lookback\n",
    "            rows = np.arange(i, min(i + batch_size, max_index))\n",
    "            i += len(rows)\n",
    "\n",
    "        samples = np.zeros((len(rows),\n",
    "                           lookback // step,\n",
    "                           data.shape[-1]))\n",
    "        targets = np.zeros((len(rows),))\n",
    "        for j, row in enumerate(rows):\n",
    "            indices = range(rows[j] - lookback, rows[j], step)\n",
    "            samples[j] = data[indices]\n",
    "            targets[j] = data[rows[j] + delay][-1]\n",
    "        yield samples, targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "frequent-attribute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "effective-geology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10081, 7)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sru_data_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "grave-trace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 180, 7)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = np.zeros((len(rows),\n",
    "                           lookback // step,\n",
    "                           sru_data_array.shape[-1]))\n",
    "samples.shape # (128, 180, 7) #180个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "subjective-sustainability",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 7)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "based-argentina",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.33092424\n",
      "0.49895507\n",
      "0.66681183\n",
      "0.5904153\n",
      "0.85608797\n",
      "0.58957943\n",
      "0.4328503\n",
      "0.89603653\n",
      "0.43103919\n",
      "0.58017557\n",
      "0.44248047\n",
      "0.67045138\n",
      "0.77133261\n",
      "0.14673306\n",
      "0.86749456\n",
      "0.42165288\n",
      "0.22802315\n",
      "0.15371617\n",
      "0.67623304\n",
      "0.44209744\n",
      "0.335487\n",
      "0.1862288\n",
      "0.59299247\n",
      "0.68976376\n",
      "0.89708136\n",
      "0.77035737\n",
      "0.85129911\n",
      "0.49827593\n",
      "0.5995055\n",
      "0.90685091\n",
      "0.86425539\n",
      "0.85920532\n",
      "0.59752012\n",
      "0.89682015\n",
      "0.91151774\n",
      "0.51250356\n",
      "0.34595283\n",
      "0.31920462\n",
      "0.8538069\n",
      "0.51807604\n",
      "0.335487\n",
      "0.43046454\n",
      "0.5238577\n",
      "0.51487176\n",
      "0.82773762\n",
      "0.86362849\n",
      "0.58585274\n",
      "0.69551073\n",
      "0.90284546\n",
      "0.68450473\n",
      "0.89791722\n",
      "0.68307687\n",
      "0.52350935\n",
      "0.52354425\n",
      "0.22467949\n",
      "0.33346672\n",
      "0.89534005\n",
      "0.4370645\n",
      "0.49303424\n",
      "0.098791364\n",
      "0.19079125\n",
      "0.51941697\n",
      "0.77394468\n",
      "0.59224354\n",
      "0.59880881\n",
      "0.49582059\n",
      "0.50430145\n",
      "0.59071119\n",
      "0.502142\n",
      "0.21426591\n",
      "0.89708136\n",
      "0.85145583\n",
      "0.52241228\n",
      "0.88825236\n",
      "0.5189468\n",
      "0.58322312\n",
      "0.76124961\n",
      "0.75811512\n",
      "0.89399912\n",
      "0.61063313\n",
      "0.43481812\n",
      "0.52960425\n",
      "0.91623723\n",
      "0.19739132\n",
      "0.51034411\n",
      "0.57359295\n",
      "0.60641892\n",
      "0.89185701\n",
      "0.52173314\n",
      "0.60880468\n",
      "0.60096826\n",
      "0.89845698\n",
      "0.21926374\n",
      "0.90068622\n",
      "0.58477281\n",
      "0.6049735\n",
      "0.41871002\n",
      "0.3552869\n",
      "0.074150153\n",
      "0.20087414\n",
      "0.61251382\n",
      "0.61580523\n",
      "0.34818186\n",
      "0.23551122\n",
      "0.60298833\n",
      "0.80660536\n",
      "0.90949788\n",
      "0.17969831\n",
      "0.77371837\n",
      "0.60679046\n",
      "0.90620646\n",
      "0.77371837\n",
      "0.51105814\n",
      "0.83872586\n",
      "0.5961445\n",
      "0.35796876\n",
      "0.68823142\n",
      "0.50471917\n",
      "0.9007907\n",
      "0.67177476\n",
      "0.83513876\n",
      "0.52800211\n",
      "0.89788253\n",
      "0.83907442\n",
      "0.36183483\n",
      "0.84731121\n",
      "0.41085605\n",
      "0.52956956\n"
     ]
    }
   ],
   "source": [
    "for j, row in enumerate(rows):\n",
    "            indices = range(rows[j] - lookback, rows[j], step)\n",
    "            samples[j] = sru_data_array[indices]\n",
    "            targets[j] = sru_data_array[rows[j] + delay][1]  \n",
    "            print(targets[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "spiritual-greece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['3.7181487e-001', '3.3092445e-001', '3.1897257e-001',\n",
       "       '7.1913719e-001', '7.0275008e-001', '1.0415586e-001',\n",
       "       '1.1129496e-001'], dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sru_data_array[7425]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "jewish-brass",
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = np.zeros((len(rows),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "chinese-silly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7425, 6543, 3338, 1404, 5168, 1291, 1725, 5490, 1970, 1234, 1913,\n",
       "       3179,  221, 1569,  575, 3641, 7899, 1529, 3379, 1684, 2569, 7648,\n",
       "       6820, 3095, 5732, 3952,  569, 4179, 1094, 6318,  400,  528, 1380,\n",
       "       5561, 6184, 2394, 2531, 2294,  649, 6521, 2569, 1920, 2414, 7137,\n",
       "       4870,  363,  941, 3016, 5255, 3378, 5877, 3266, 4446, 7213, 7965,\n",
       "       7324, 6118, 3639, 4087, 7587, 7719, 4187, 3993, 6842, 3829, 4215,\n",
       "       4135,  939, 7283, 7977, 5732,  529, 4222, 5731, 4493, 2344, 3967,\n",
       "        201, 6376, 6795, 1770, 4445, 5309, 7781, 2452, 6733, 3821, 6270,\n",
       "       2388, 6863, 1183, 5890, 7932, 5948, 6763, 7055, 2140, 2785, 7584,\n",
       "       7790,  923, 1098, 2260, 7964, 4576, 4671, 5259, 1602,  260, 7031,\n",
       "       5381,  293, 2450, 4951, 4539, 2510, 2955, 2384, 6236, 2936, 4905,\n",
       "       4354, 4632, 4984, 7394, 2826, 1999, 4312])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = np.random.randint(\n",
    "                0 + lookback, 8001, size=batch_size)\n",
    "rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "overall-advocacy",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "broad-mississippi",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookback = 180\n",
    "step = 1\n",
    "delay = 60\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "dirty-silver",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator(sru_data_array,\n",
    "                      lookback=lookback,\n",
    "                      delay=delay,\n",
    "                      min_index=0,\n",
    "                      max_index=8000,\n",
    "                      shuffle=True,\n",
    "                      step=step, \n",
    "                      batch_size=batch_size)\n",
    "val_gen = generator(sru_data_array,\n",
    "                    lookback=lookback,\n",
    "                    delay=delay,\n",
    "                    min_index=8001,\n",
    "                    max_index=9000,\n",
    "                    step=step,\n",
    "                    batch_size=batch_size)\n",
    "test_gen = generator(sru_data_array,\n",
    "                     lookback=lookback,\n",
    "                     delay=delay,\n",
    "                     min_index=9001,\n",
    "                     max_index=None,\n",
    "                     step=step,\n",
    "                     batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "continent-finnish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stpes=8000//batch_size\n",
    "val_steps = (9000 - 8001 - lookback) // batch_size\n",
    "test_steps = (len(sru_data_array) - 9001 - lookback) // batch_size\n",
    "lookback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "increasing-innocent",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from keras.models import Sequential\n",
    "# from keras import layers\n",
    "# from keras.optimizers import RMSprop\n",
    "\n",
    "# model = Sequential()\n",
    "# model.add(layers.Flatten(input_shape=(lookback // step, sru_data_array.shape[-1])))\n",
    "# model.add(layers.Dense(32, activation='relu'))\n",
    "# model.add(layers.Dense(1))\n",
    "\n",
    "# model.compile(optimizer=RMSprop(), loss='mae')\n",
    "# history = model.fit(train_gen,\n",
    "#                               steps_per_epoch=500,\n",
    "#                               epochs=20,\n",
    "#                               validation_data=val_gen,\n",
    "#                               validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "eligible-gibraltar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "62/62 [==============================] - 31s 433ms/step - loss: 0.0827 - val_loss: 0.0513\n",
      "Epoch 2/40\n",
      "62/62 [==============================] - 38s 605ms/step - loss: 0.0398 - val_loss: 0.0459\n",
      "Epoch 3/40\n",
      "62/62 [==============================] - 28s 451ms/step - loss: 0.0381 - val_loss: 0.0474\n",
      "Epoch 4/40\n",
      "62/62 [==============================] - 27s 428ms/step - loss: 0.0369 - val_loss: 0.0444\n",
      "Epoch 5/40\n",
      "62/62 [==============================] - 26s 428ms/step - loss: 0.0369 - val_loss: 0.0448\n",
      "Epoch 6/40\n",
      "62/62 [==============================] - 32s 513ms/step - loss: 0.0368 - val_loss: 0.0450\n",
      "Epoch 7/40\n",
      "62/62 [==============================] - 23s 376ms/step - loss: 0.0364 - val_loss: 0.0457\n",
      "Epoch 8/40\n",
      "62/62 [==============================] - 20s 324ms/step - loss: 0.0363 - val_loss: 0.0450\n",
      "Epoch 9/40\n",
      "62/62 [==============================] - 20s 330ms/step - loss: 0.0348 - val_loss: 0.0446\n",
      "Epoch 10/40\n",
      "62/62 [==============================] - 22s 348ms/step - loss: 0.0354 - val_loss: 0.0439\n",
      "Epoch 11/40\n",
      "62/62 [==============================] - 21s 337ms/step - loss: 0.0358 - val_loss: 0.0439\n",
      "Epoch 12/40\n",
      "62/62 [==============================] - 18s 296ms/step - loss: 0.0355 - val_loss: 0.0435\n",
      "Epoch 13/40\n",
      "62/62 [==============================] - 20s 320ms/step - loss: 0.0363 - val_loss: 0.0449\n",
      "Epoch 14/40\n",
      "62/62 [==============================] - 20s 321ms/step - loss: 0.0356 - val_loss: 0.0436\n",
      "Epoch 15/40\n",
      "62/62 [==============================] - 19s 301ms/step - loss: 0.0359 - val_loss: 0.0431\n",
      "Epoch 16/40\n",
      "62/62 [==============================] - 18s 293ms/step - loss: 0.0351 - val_loss: 0.0443\n",
      "Epoch 17/40\n",
      "62/62 [==============================] - 20s 326ms/step - loss: 0.0352 - val_loss: 0.0438\n",
      "Epoch 18/40\n",
      "62/62 [==============================] - 28s 445ms/step - loss: 0.0351 - val_loss: 0.0426\n",
      "Epoch 19/40\n",
      "62/62 [==============================] - 28s 452ms/step - loss: 0.0362 - val_loss: 0.0446\n",
      "Epoch 20/40\n",
      "62/62 [==============================] - 27s 436ms/step - loss: 0.0354 - val_loss: 0.0432\n",
      "Epoch 21/40\n",
      "62/62 [==============================] - 26s 418ms/step - loss: 0.0342 - val_loss: 0.0438\n",
      "Epoch 22/40\n",
      "62/62 [==============================] - 25s 400ms/step - loss: 0.0354 - val_loss: 0.0434\n",
      "Epoch 23/40\n",
      "62/62 [==============================] - 20s 322ms/step - loss: 0.0358 - val_loss: 0.0438\n",
      "Epoch 24/40\n",
      "62/62 [==============================] - 22s 351ms/step - loss: 0.0352 - val_loss: 0.0440\n",
      "Epoch 25/40\n",
      "62/62 [==============================] - 22s 351ms/step - loss: 0.0348 - val_loss: 0.0432\n",
      "Epoch 26/40\n",
      "62/62 [==============================] - 24s 389ms/step - loss: 0.0348 - val_loss: 0.0443\n",
      "Epoch 27/40\n",
      "62/62 [==============================] - 22s 360ms/step - loss: 0.0349 - val_loss: 0.0442\n",
      "Epoch 28/40\n",
      "62/62 [==============================] - 24s 383ms/step - loss: 0.0353 - val_loss: 0.0449\n",
      "Epoch 29/40\n",
      "62/62 [==============================] - 23s 372ms/step - loss: 0.0333 - val_loss: 0.0444\n",
      "Epoch 30/40\n",
      "62/62 [==============================] - 22s 353ms/step - loss: 0.0350 - val_loss: 0.0442\n",
      "Epoch 31/40\n",
      "62/62 [==============================] - 22s 348ms/step - loss: 0.0358 - val_loss: 0.0441\n",
      "Epoch 32/40\n",
      "62/62 [==============================] - 23s 365ms/step - loss: 0.0360 - val_loss: 0.0449\n",
      "Epoch 33/40\n",
      "62/62 [==============================] - 20s 318ms/step - loss: 0.0348 - val_loss: 0.0470\n",
      "Epoch 34/40\n",
      "62/62 [==============================] - 20s 319ms/step - loss: 0.0336 - val_loss: 0.0440\n",
      "Epoch 35/40\n",
      "62/62 [==============================] - 20s 317ms/step - loss: 0.0344 - val_loss: 0.0445\n",
      "Epoch 36/40\n",
      "62/62 [==============================] - 21s 346ms/step - loss: 0.0341 - val_loss: 0.0439\n",
      "Epoch 37/40\n",
      "62/62 [==============================] - 21s 346ms/step - loss: 0.0350 - val_loss: 0.0450\n",
      "Epoch 38/40\n",
      "62/62 [==============================] - 21s 346ms/step - loss: 0.0365 - val_loss: 0.0455\n",
      "Epoch 39/40\n",
      "62/62 [==============================] - 23s 373ms/step - loss: 0.0336 - val_loss: 0.0447\n",
      "Epoch 40/40\n",
      "62/62 [==============================] - 24s 384ms/step - loss: 0.0348 - val_loss: 0.0446\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras import layers\n",
    "from keras.optimizers import RMSprop\n",
    "\n",
    "model = Sequential()\n",
    "model.add(layers.GRU(32,\n",
    "                     dropout=0.2,\n",
    "                     recurrent_dropout=0.5,\n",
    "                     return_sequences=True,\n",
    "                     input_shape=(None, sru_data_array.shape[-1])))\n",
    "model.add(layers.LSTM(64, activation='relu',\n",
    "                     dropout=0.1, \n",
    "                     recurrent_dropout=0.5))\n",
    "model.add(layers.Dense(1))\n",
    "\n",
    "model.compile(optimizer=RMSprop(learning_rate=0.001), loss='mae')\n",
    "history = model.fit(train_gen,\n",
    "                              steps_per_epoch=train_stpes,\n",
    "                              epochs=40,\n",
    "                              validation_data=val_gen,\n",
    "                              validation_steps=val_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "arbitrary-isaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(loss))\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "plt.plot(epochs, loss, label='Training loss')\n",
    "plt.plot(epochs, val_loss, label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "organized-squad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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