TCN_TimeSeries_Approach.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishpatel26/tcn-keras-Examples/blob/master/TCN_TimeSeries_Approach.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNmNWqlKa_8E",
        "colab_type": "text"
      },
      "source": [
        "### Load Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEq6d7AUa9Wp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "d490ea40-c639-45ef-9ce9-6071e4247065"
      },
      "source": [
        "!pip install wget\n",
        "!pip install keras-tcn\n",
        "import wget\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tensorflow.keras import Input, Model\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "from tcn import TCN"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wget in /usr/local/lib/python3.6/dist-packages (3.2)\n",
            "Requirement already satisfied: keras-tcn in /usr/local/lib/python3.6/dist-packages (3.1.1)\n",
            "Requirement already satisfied: keras==2.3.1 in /usr/local/lib/python3.6/dist-packages (from keras-tcn) (2.3.1)\n",
            "Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.6/dist-packages (from keras-tcn) (1.18.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (1.15.0)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (1.4.1)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (3.13)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (1.1.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (2.10.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1->keras-tcn) (1.0.8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHIVSlGTbWgU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "48066cd3-9776-4ec0-92b4-6ca0c4e956a5"
      },
      "source": [
        "wget.download(\"https://github.com/philipperemy/keras-tcn/raw/master/tasks/monthly-milk-production-pounds-p.csv\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'monthly-milk-production-pounds-p (1).csv'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMr5G8xhcCxj",
        "colab_type": "text"
      },
      "source": [
        "### Read the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rf8_Eyn9ba2y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "milk = pd.read_csv('monthly-milk-production-pounds-p.csv', index_col=0, parse_dates=True)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUK__zQQcJhk",
        "colab_type": "text"
      },
      "source": [
        "### Display top5 Record"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJmbRdavcFJb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "6f47e2e8-b5be-4b8f-c16a-3ed800c21462"
      },
      "source": [
        "print(milk.shape)\n",
        "milk.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(168, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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_pounds</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</th>\n",
              "      <td>589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-02-01</th>\n",
              "      <td>561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-03-01</th>\n",
              "      <td>640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-04-01</th>\n",
              "      <td>656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-05-01</th>\n",
              "      <td>727</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            milk_production_pounds\n",
              "month                             \n",
              "1962-01-01                     589\n",
              "1962-02-01                     561\n",
              "1962-03-01                     640\n",
              "1962-04-01                     656\n",
              "1962-05-01                     727"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxtoyYB_cPJR",
        "colab_type": "text"
      },
      "source": [
        "### Lookback 12 month windows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8TY8aTQcIR4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lookback_window = 12 "
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zDy7WxEtcV96",
        "colab_type": "text"
      },
      "source": [
        "### Convert Milk Data into Numpy Array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOV5KuwJcR3Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "milk = milk.values  "
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTd4_RAhcf_G",
        "colab_type": "text"
      },
      "source": [
        "### Convert in to X, y format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRHxD8-vcZua",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
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        "outputId": "fe102cc0-2b28-4e6e-efb1-c701ec8f0964"
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      "source": [
        "x = []\n",
        "y = []\n",
        "for i in tqdm(range(lookback_window, len(milk))):\n",
        "    x.append(milk[i - lookback_window:i])\n",
        "    y.append(milk[i])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2df124fb46a42d9a8011c589d478eee",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=156.0), HTML(value='')))"
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          "metadata": {
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          "output_type": "stream",
          "text": [
            "\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6NKNRpUdU0e",
        "colab_type": "text"
      },
      "source": [
        "### Generate Array of list x and y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3xGy8bec9Q5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1fd1af26-ee70-40ef-d3c6-3b060253f1c0"
      },
      "source": [
        "x = np.array(x)\n",
        "y = np.array(y)\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(156, 12, 1)\n",
            "(156, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dqsLFT4dk7s",
        "colab_type": "text"
      },
      "source": [
        "### Model Design"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y783wDIxdeke",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "26fb6222-a92a-498c-dda1-2421ad9ea5c8"
      },
      "source": [
        "i = Input(shape=(lookback_window, 1))\n",
        "m = TCN()(i)\n",
        "m = Dense(1, activation='linear')(m)\n",
        "\n",
        "model = Model(inputs=[i], outputs=[m])\n",
        "model.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 12, 1)]           0         \n",
            "_________________________________________________________________\n",
            "tcn (TCN)                    (None, 64)                91136     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 91,201\n",
            "Trainable params: 91,201\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fsv10DsLdtKy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile('adam','mae')"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjY_u3rid3m0",
        "colab_type": "text"
      },
      "source": [
        "### Time for Model training..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfU_56-zd0eC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "956257c6-9c51-4c3d-b007-752b10849ac9"
      },
      "source": [
        "print('Train...')\n",
        "model.fit(x, y, epochs=100)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train...\n",
            "Epoch 1/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 271.5721\n",
            "Epoch 2/100\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 202.3601\n",
            "Epoch 3/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 129.1283\n",
            "Epoch 4/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 119.5586\n",
            "Epoch 5/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 82.7962\n",
            "Epoch 6/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 29.5778\n",
            "Epoch 7/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 26.9950\n",
            "Epoch 8/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 23.8581\n",
            "Epoch 9/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 27.1797\n",
            "Epoch 10/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 37.1005\n",
            "Epoch 11/100\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 38.2245\n",
            "Epoch 12/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 27.9166\n",
            "Epoch 13/100\n",
            "5/5 [==============================] - 0s 27ms/step - loss: 49.9146\n",
            "Epoch 14/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 31.0408\n",
            "Epoch 15/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 23.8059\n",
            "Epoch 16/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 18.7888\n",
            "Epoch 17/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 25.1425\n",
            "Epoch 18/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 32.6869\n",
            "Epoch 19/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 31.6958\n",
            "Epoch 20/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 31.2011\n",
            "Epoch 21/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 22.9983\n",
            "Epoch 22/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 27.7421\n",
            "Epoch 23/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 29.9864\n",
            "Epoch 24/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 68.7743\n",
            "Epoch 25/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 41.1511\n",
            "Epoch 26/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 35.2367\n",
            "Epoch 27/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 66.6526\n",
            "Epoch 28/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 40.3432\n",
            "Epoch 29/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 50.4716\n",
            "Epoch 30/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 48.0320\n",
            "Epoch 31/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 34.7563\n",
            "Epoch 32/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 27.2386\n",
            "Epoch 33/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 32.2853\n",
            "Epoch 34/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 26.1964\n",
            "Epoch 35/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 22.9360\n",
            "Epoch 36/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 25.1416\n",
            "Epoch 37/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 30.5617\n",
            "Epoch 38/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 24.7003\n",
            "Epoch 39/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 14.7676\n",
            "Epoch 40/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 14.6580\n",
            "Epoch 41/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 12.4486\n",
            "Epoch 42/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 15.5033\n",
            "Epoch 43/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 20.4509\n",
            "Epoch 44/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 27.3433\n",
            "Epoch 45/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 25.6425\n",
            "Epoch 46/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 23.2722\n",
            "Epoch 47/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 19.9241\n",
            "Epoch 48/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 26.6964\n",
            "Epoch 49/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 51.5725\n",
            "Epoch 50/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 46.1796\n",
            "Epoch 51/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 43.5478\n",
            "Epoch 52/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 48.7085\n",
            "Epoch 53/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 46.6810\n",
            "Epoch 54/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 37.4476\n",
            "Epoch 55/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 40.0287\n",
            "Epoch 56/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 31.3757\n",
            "Epoch 57/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 23.3440\n",
            "Epoch 58/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 25.6717\n",
            "Epoch 59/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 22.8951\n",
            "Epoch 60/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 16.8260\n",
            "Epoch 61/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 19.1047\n",
            "Epoch 62/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 21.6529\n",
            "Epoch 63/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 16.5754\n",
            "Epoch 64/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 12.2943\n",
            "Epoch 65/100\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 14.4700\n",
            "Epoch 66/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 12.4727\n",
            "Epoch 67/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 12.2146\n",
            "Epoch 68/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 24.3314\n",
            "Epoch 69/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 23.2591\n",
            "Epoch 70/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 21.5991\n",
            "Epoch 71/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 34.6669\n",
            "Epoch 72/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 58.7299\n",
            "Epoch 73/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 30.8628\n",
            "Epoch 74/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 36.4043\n",
            "Epoch 75/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 45.6252\n",
            "Epoch 76/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 29.5786\n",
            "Epoch 77/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 16.1575\n",
            "Epoch 78/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 24.9460\n",
            "Epoch 79/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 17.0462\n",
            "Epoch 80/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 13.5069\n",
            "Epoch 81/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 14.0239\n",
            "Epoch 82/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 12.9607\n",
            "Epoch 83/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 13.6181\n",
            "Epoch 84/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 12.3904\n",
            "Epoch 85/100\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 13.5270\n",
            "Epoch 86/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 18.3469\n",
            "Epoch 87/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 20.0561\n",
            "Epoch 88/100\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 19.5092\n",
            "Epoch 89/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 22.7791\n",
            "Epoch 90/100\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 20.7960\n",
            "Epoch 91/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 26.3365\n",
            "Epoch 92/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 21.1719\n",
            "Epoch 93/100\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 19.5793\n",
            "Epoch 94/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 22.3828\n",
            "Epoch 95/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 20.0138\n",
            "Epoch 96/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 26.0298\n",
            "Epoch 97/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 15.8576\n",
            "Epoch 98/100\n",
            "5/5 [==============================] - 0s 23ms/step - loss: 26.3697\n",
            "Epoch 99/100\n",
            "5/5 [==============================] - 0s 24ms/step - loss: 14.8465\n",
            "Epoch 100/100\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 13.5129\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5d56dfbef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDtjBtPQf1pd",
        "colab_type": "text"
      },
      "source": [
        "### Prediction with TCN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMEjd-8Gd-VK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict = model.predict(x)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-7DoomPf5ak",
        "colab_type": "text"
      },
      "source": [
        "### Plot the Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9EpeosteOkK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "0df79df5-6d18-4fa6-ac7d-e78d57dd0381"
      },
      "source": [
        "plt.style.use(\"fivethirtyeight\")\n",
        "plt.figure(figsize = (15,7))\n",
        "plt.plot(predict)\n",
        "plt.plot(y)\n",
        "plt.title('Monthly Milk Production (in pounds)')\n",
        "plt.legend(['predicted', 'actual'])\n",
        "plt.xlabel(\"Moths Counts\")\n",
        "plt.ylabel(\"Milk Production in Pounds\")\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}