5_Prediction_MilkProdction/2_RNN_Many_to_Many_Keras.ipynb

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   "source": [
    "## The Data\n",
    "\n",
    "** Source: https://datamarket.com/data/set/22ox/monthly-milk-production-pounds-per-cow-jan-62-dec-75#!ds=22ox&display=line **\n",
    "\n",
    "**Monthly milk production: pounds per cow. Jan 62 - Dec 75**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5WTUlbh4JDXN"
   },
   "source": [
    "** Import numpy pandas and matplotlib **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xg2peEzGJDXO"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hBtEtgN6JDXS"
   },
   "source": [
    "** Use pandas to read the csv of the monthly-milk-production.csv file and set index_col='Month' **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "FPJdTW0TJcMA",
    "outputId": "eee86a04-6b5d-4bd3-816a-72aba1b4210c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive \n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nArYXHMPJDXT"
   },
   "outputs": [],
   "source": [
    "milk = pd.read_csv(\"gdrive/My Drive/dataML/monthly-milk-production.csv\",index_col='Month')\n",
    "\n",
    "#milk = pd.read_csv(\"monthly-milk-production.csv\",index_col='Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "RYvb00fEJDXW",
    "outputId": "80e1b876-e4d5-4df1-9b93-c3ae28c4501f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01 01:00:00</th>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01 01:00:00</th>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01 01:00:00</th>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01 01:00:00</th>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01 01:00:00</th>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production\n",
       "Month                               \n",
       "1962-01-01 01:00:00            589.0\n",
       "1962-02-01 01:00:00            561.0\n",
       "1962-03-01 01:00:00            640.0\n",
       "1962-04-01 01:00:00            656.0\n",
       "1962-05-01 01:00:00            727.0"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qd6qU_zOJDXb"
   },
   "source": [
    "** Check out the head of the dataframe**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z0aPxAkWJDXg"
   },
   "source": [
    "** Make the index a time series by using: **\n",
    "\n",
    "    milk.index = pd.to_datetime(milk.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ej2PnSz7JDXh"
   },
   "outputs": [],
   "source": [
    "milk.index = pd.to_datetime(milk.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "id": "TuNcJhMoJDXj",
    "outputId": "99ca28d0-fc1b-4261-8a40-c854063f6098"
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01 01:00:00</th>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01 01:00:00</th>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01 01:00:00</th>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01 01:00:00</th>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01 01:00:00</th>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production\n",
       "Month                               \n",
       "1962-01-01 01:00:00            589.0\n",
       "1962-02-01 01:00:00            561.0\n",
       "1962-03-01 01:00:00            640.0\n",
       "1962-04-01 01:00:00            656.0\n",
       "1962-05-01 01:00:00            727.0"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-rgkKTs6JDXn",
    "outputId": "18e3be75-7cc8-4bb5-93e7-783feac40177"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Milk Production'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z8Hk38WeJDXq"
   },
   "source": [
    "** Plot out the time series data. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "id": "qXNiSsZUJDXq",
    "outputId": "7bec9175-b49a-4f69-85c7-58c254ecb3bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb5df8a0240>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
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PoVyh+MAVtcPLmYXV6cnULgxtxlJKv0spvZxSei2AFQBHAcwzS0b5uqA8fRpyxM8YU+6r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\ncZwG5131vrncbqgAeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "milk.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fxbb9fhP1rsU"
   },
   "source": [
    "### Train Test Split\n",
    "\n",
    "** Let's attempt to predict a year's worth of data. (12 months or 12 steps into the future) **\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "TSYpC-nu1rsW",
    "outputId": "3f9a1daa-e308-4cc5-d3e9-001f1d26454f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 168 entries, 1962-01-01 01:00:00 to 1975-12-01 01:00:00\n",
      "Data columns (total 1 columns):\n",
      "Milk Production    168 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.6 KB\n"
     ]
    }
   ],
   "source": [
    "milk.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "00vc2Jms1rsc",
    "outputId": "6c4d44a8-7c69-41d9-f6b8-69246921cfa9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(milk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F_PjB7rq1rsf"
   },
   "outputs": [],
   "source": [
    "train_set= milk.head((1976-1962-1)*12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C6632CCp1rsi"
   },
   "outputs": [],
   "source": [
    "test_set = milk.tail(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_9PtX6Um1rsl"
   },
   "source": [
    "### Scale the Data\n",
    "\n",
    "** Use sklearn.preprocessing to scale the data using the MinMaxScaler. Remember to only fit_transform on the training data, then transform the test data. You shouldn't fit on the test data as well, otherwise you are assuming you would know about future behavior!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "czuAVyRj1rsl"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UFn7xXzj1rsp"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hu0-OYDX1rsq"
   },
   "outputs": [],
   "source": [
    "train_scaled = scaler.fit_transform(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O2uDgWsI1rsu"
   },
   "outputs": [],
   "source": [
    "test_scaled = scaler.transform(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EiI-RuSPKC7v"
   },
   "source": [
    "## Build Training Data(many to many)\n",
    "\n",
    "* X_train: Past 12 monthes productions, shape: (-1,12)\n",
    "* Y_train: Future 12 month productions, shape:(-1, 12) \n",
    "* Shift the window to get more trainning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6YaF3wjKKB4Y"
   },
   "outputs": [],
   "source": [
    "def build_train_data(data, past_monthes = 12, future_monthes = 12):\n",
    "  X_train, Y_train = [],[]\n",
    "  \n",
    "  for i in range(data.shape[0] + 1 - past_monthes - future_monthes):\n",
    "    X_train.append(np.array(data[i:i + past_monthes]))\n",
    "    Y_train.append(np.array(data[i + past_monthes:i + past_monthes + future_monthes]))\n",
    "    \n",
    "  return np.array(X_train).reshape([-1,12]), np.array(Y_train).reshape([-1,12])\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t_z8xNdaQjvH"
   },
   "outputs": [],
   "source": [
    "x, y = build_train_data(train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "DiLmBOy4Qq3j",
    "outputId": "69125263-1978-4c14-a3f1-7118b65c16eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x shape; (132, 12) y shape:  (132, 12)\n"
     ]
    }
   ],
   "source": [
    "print('x shape;', x.shape, 'y shape: ', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JxbPdOLds9JD"
   },
   "source": [
    "Take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "3_tRprRSjkYY",
    "outputId": "ee22ea10-9710-4863-c371-ed641cfceb53"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb5cf69dc18>]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uC8szjfpC32fM1nw7v3BsgKJKkSzhKAW55j/YlrdBNes3nk48mcPQ99cze/1R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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[12])\n",
    "plt.plot(y[12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P_rsY5NQQ3op"
   },
   "source": [
    "## Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1GiFVMqlQ3or"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers, Sequential,models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AsBnLuapQ3ot"
   },
   "outputs": [],
   "source": [
    "RNN_CELLSIZE = 10\n",
    "SEQLEN = 12\n",
    "BATCHSIZE = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j96G7Ef0k5Z-"
   },
   "source": [
    "### Build and Train the Model\n",
    "Many to Many"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "vwLHV-p5k5Z0",
    "outputId": "7645031b-4c9b-480d-c2b7-a6c3cda668f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "reshape_2 (Reshape)          (None, 12, 1)             0         \n",
      "_________________________________________________________________\n",
      "gru_2 (GRU)                  (None, 12, 10)            360       \n",
      "_________________________________________________________________\n",
      "gru_3 (GRU)                  (None, 12, 10)            630       \n",
      "_________________________________________________________________\n",
      "time_distributed_2 (TimeDist (None, 12, 1)             11        \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 12)                0         \n",
      "=================================================================\n",
      "Total params: 1,001\n",
      "Trainable params: 1,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_layers = [\n",
    "    layers.Reshape((SEQLEN,1),input_shape=(SEQLEN,)),\n",
    "    layers.GRU(RNN_CELLSIZE, return_sequences=True),\n",
    "    layers.GRU(RNN_CELLSIZE, return_sequences=True),\n",
    "    layers.TimeDistributed(layers.Dense(1)),\n",
    "    layers.Flatten()\n",
    "    \n",
    "]\n",
    "model = Sequential(model_layers)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qU4zPwkzk5Zt"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "       loss = 'mean_squared_error',\n",
    "       optimizer = 'adam'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34017
    },
    "colab_type": "code",
    "id": "6AW4Vl5-k5ZP",
    "outputId": "f81f04d6-cb39-4c1d-fc85-900e74e4ac0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0017\n",
      "Epoch 2/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0016\n",
      "Epoch 3/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0016\n",
      "Epoch 4/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0016\n",
      "Epoch 5/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 6/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 7/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 8/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 9/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 10/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0018\n",
      "Epoch 11/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 12/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 13/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 14/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0018\n",
      "Epoch 15/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 16/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 17/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 18/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 19/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 20/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 21/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 22/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 23/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 24/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 25/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 26/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 27/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 28/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 29/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 30/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 31/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 32/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 33/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0017\n",
      "Epoch 34/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0015\n",
      "Epoch 35/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 36/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 37/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 38/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 39/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 40/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 41/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 42/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0016\n",
      "Epoch 43/1000\n",
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      "Epoch 800/1000\n",
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      "Epoch 892/1000\n",
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      "Epoch 893/1000\n",
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      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
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      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
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      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0013\n",
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      "Epoch 899/1000\n",
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      "Epoch 900/1000\n",
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      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0014\n",
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      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
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      "Epoch 910/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 911/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 912/1000\n",
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      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 914/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 915/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
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      "Epoch 919/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 920/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 921/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 922/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 923/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 924/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 925/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 926/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 927/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0015\n",
      "Epoch 928/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 929/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 930/1000\n",
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      "Epoch 931/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 932/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 933/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 934/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 935/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0015\n",
      "Epoch 936/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 937/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0015\n",
      "Epoch 938/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 939/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 940/1000\n",
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      "Epoch 942/1000\n",
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      "Epoch 943/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 944/1000\n",
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      "Epoch 946/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 947/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 948/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 949/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 950/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 951/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 952/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 953/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 954/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 955/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 956/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 957/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 958/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 959/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 960/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 961/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 962/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 963/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 964/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 965/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 966/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 967/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 968/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0013\n",
      "Epoch 969/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 970/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 971/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 972/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 973/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 974/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 975/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 976/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 977/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 978/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 979/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 980/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 981/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 982/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 983/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 984/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 985/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 986/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0014\n",
      "Epoch 987/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 988/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 989/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 990/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 991/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 992/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 993/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0014\n",
      "Epoch 994/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 995/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0014\n",
      "Epoch 996/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0015\n",
      "Epoch 997/1000\n",
      "132/132 [==============================] - 1s 5ms/sample - loss: 0.0013\n",
      "Epoch 998/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 999/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0015\n",
      "Epoch 1000/1000\n",
      "132/132 [==============================] - 1s 6ms/sample - loss: 0.0013\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(x,y, batch_size= BATCHSIZE,\n",
    "              epochs = 1000)\n",
    "\n",
    "model.save('gdrive/My Drive/dataML/rnn_many_to_many_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "bMwy2IIozaRt",
    "outputId": "f2147b83-8dbf-4ea3-8545-663175f5653c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb580993828>]"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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75XZ8+aEPMPWDtb7CZNnmGnywKjwthbvWR/p1yhEAZqYnXRRShZ2Ecn+9mX/L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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h.history['loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r714-jBM1Yo-"
   },
   "source": [
    "### Predict Future \n",
    "* Use the last 12 monthes data of the train data set to predict the future 12 monthes production \n",
    "* Compare the predicted data with the real data in test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BnGC-2ohfLwz"
   },
   "outputs": [],
   "source": [
    "model = models.load_model('gdrive/My Drive/dataML/rnn_many_to_many_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QkYcxMMgqGN0"
   },
   "source": [
    "Get the last 12 monthes data of the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ncuo3aL1z3K_"
   },
   "outputs": [],
   "source": [
    "last_12_month_data = train_scaled[-12:].reshape([1,12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5_sSDDnY0P1t"
   },
   "outputs": [],
   "source": [
    "predicts = model.predict(last_12_month_data).reshape([12,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y4DPuuxq0YmI"
   },
   "outputs": [],
   "source": [
    "results = scaler.inverse_transform(np.array(predicts).reshape(12,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "h1-x_pW71iM_",
    "outputId": "c9e1ff14-d4d3-4d26-efe6-cb5868dfdae7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test_set['Generated'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "xR3TovJi1iNF",
    "outputId": "87a590ac-2d94-4121-88dd-99e986a25a70"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "      <th>Generated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01 01:00:00</th>\n",
       "      <td>834.0</td>\n",
       "      <td>849.133240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-02-01 01:00:00</th>\n",
       "      <td>782.0</td>\n",
       "      <td>799.488281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-03-01 01:00:00</th>\n",
       "      <td>892.0</td>\n",
       "      <td>901.998291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01 01:00:00</th>\n",
       "      <td>903.0</td>\n",
       "      <td>912.084534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01 01:00:00</th>\n",
       "      <td>966.0</td>\n",
       "      <td>967.772278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-06-01 01:00:00</th>\n",
       "      <td>937.0</td>\n",
       "      <td>939.251404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01 01:00:00</th>\n",
       "      <td>896.0</td>\n",
       "      <td>898.477539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-08-01 01:00:00</th>\n",
       "      <td>858.0</td>\n",
       "      <td>854.013855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-09-01 01:00:00</th>\n",
       "      <td>817.0</td>\n",
       "      <td>802.374695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-10-01 01:00:00</th>\n",
       "      <td>827.0</td>\n",
       "      <td>797.896912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-11-01 01:00:00</th>\n",
       "      <td>797.0</td>\n",
       "      <td>760.601990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-12-01 01:00:00</th>\n",
       "      <td>843.0</td>\n",
       "      <td>801.358643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production   Generated\n",
       "Month                                           \n",
       "1975-01-01 01:00:00            834.0  849.133240\n",
       "1975-02-01 01:00:00            782.0  799.488281\n",
       "1975-03-01 01:00:00            892.0  901.998291\n",
       "1975-04-01 01:00:00            903.0  912.084534\n",
       "1975-05-01 01:00:00            966.0  967.772278\n",
       "1975-06-01 01:00:00            937.0  939.251404\n",
       "1975-07-01 01:00:00            896.0  898.477539\n",
       "1975-08-01 01:00:00            858.0  854.013855\n",
       "1975-09-01 01:00:00            817.0  802.374695\n",
       "1975-10-01 01:00:00            827.0  797.896912\n",
       "1975-11-01 01:00:00            797.0  760.601990\n",
       "1975-12-01 01:00:00            843.0  801.358643"
      ]
     },
     "execution_count": 96,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "id": "6y5o6kV21iNN",
    "outputId": "73228984-1656-4e8f-b99a-7a94e3802221"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb580b70ef0>"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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2jE2bNhEQEECTJk0YOnQoH3zwAcOHDycoKIhGjRoRHBzMyJEj7zpixsbGhkWLFjFp0iQa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\nhgbtxlVRKwEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_set.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WkrgRIqzkXgq"
   },
   "source": [
    "### Predict more \n",
    "Use the first months data to predict the future 13 years' milk productions and compare with the real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sbRNA4ERklYA"
   },
   "outputs": [],
   "source": [
    "train_seed = list(train_scaled[:12].flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ew_-Rpnu3OLx"
   },
   "outputs": [],
   "source": [
    "x_train = np.array(train_seed[-12:]).reshape(1,12)\n",
    "one_predict = model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F91e0IzvklYE"
   },
   "outputs": [],
   "source": [
    "def get_prediction(data_list):\n",
    "  train_seed = data_list\n",
    "  for i in range(13):\n",
    "    x_train = np.array(train_seed[-12:]).reshape(1,12)\n",
    "    one_predict = model.predict(x_train)\n",
    "    train_seed.extend(one_predict.flatten().tolist())\n",
    "   \n",
    "  return train_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BNW_8HJ2klYG"
   },
   "outputs": [],
   "source": [
    "train_seed = get_prediction(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "TvgurkwHklYI",
    "outputId": "e50c6770-f1ff-4737-b1c3-f19f3f6f7d68"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb56f1975f8>]"
      ]
     },
     "execution_count": 112,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L5yvKhkN4Y738tSNRbZ/Wq1UCPg8mB8NqwDes6ZTdqQi8HoZzS3lX1hXdtsap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\nyroSyjddN4ybJu3bXYjSzJ1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fK5Xh/9I/voDHTixIN0ilY0F4WLNUspXplSJ2DYQR8Hks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\ngtghUMAnCILYIVDAJwiC2CH8H7QyNhgNrCs8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pcNqRCbiklYN"
   },
   "outputs": [],
   "source": [
    "results = scaler.inverse_transform(np.array(train_seed).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K-8x-sOFmBav"
   },
   "outputs": [],
   "source": [
    "milk_predict = milk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "387zn4SBmKsl"
   },
   "outputs": [],
   "source": [
    "milk_predict['Generated'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "id": "PDvQXKtjklYX",
    "outputId": "eba33cdd-4d05-4596-b83a-bcfec9a841eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb56f14c668>"
      ]
     },
     "execution_count": 116,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U7NhwYwtX0hudy0fJuq+XHhFtqGjKUU6TXS7fHVU0NSFDiHo+5x+9Dbb/rCr0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\nMBgMHY5x9AaDwdDhGEdvMBgMHc7/BhNhgIIqnxq8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "milk_predict.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zIAwIfd6JDa8"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "2_RNN_Many_to_Many_Keras.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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}