univariate_time_series_multi_step.ipynb

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    "colab": {
      "name": "univariate-time-series-multi-step.ipynb",
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      "include_colab_link": true
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mehrotrasan16/Keras-Deep-Learning/blob/05-RNN-LSTM-Seq2Seq-Networks/univariate_time_series_multi_step.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9_6QfmqUffG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d398d322-8946-43e9-8ece-1034b5f69e6e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o20AertRUURA"
      },
      "source": [
        "import sys\n",
        "import sklearn\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "from numpy import load\n",
        "import os\n",
        "\n",
        "# to make this notebook's output stable across runs\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)\n",
        "\n",
        "# Where to save the figures\n",
        "PROJECT_ROOT_DIR = \".\"\n",
        "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\")\n",
        "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
        "    print(\"Saving figure\", fig_id)\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tAPzzGWRUURJ"
      },
      "source": [
        "### Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXsJCzfxUURK"
      },
      "source": [
        "def plot_learning_curves(loss, val_loss):\n",
        "    plt.plot(np.arange(len(loss)) + 0.5, loss, \"b.-\", label=\"Training loss\")\n",
        "    plt.plot(np.arange(len(val_loss)) + 1, val_loss, \"r.-\", label=\"Validation loss\")\n",
        "    plt.gca().xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
        "    plt.axis([1, 20, 0, 0.05])\n",
        "    plt.legend(fontsize=14)\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.grid(True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jt2Y7nm7UURO"
      },
      "source": [
        "def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\"):\n",
        "    plt.plot(series, \".-\")\n",
        "    if y is not None:\n",
        "        plt.plot(n_steps, y, \"bx\", markersize=10)\n",
        "    if y_pred is not None:\n",
        "        plt.plot(n_steps, y_pred, \"ro\")\n",
        "    plt.grid(True)\n",
        "    if x_label:\n",
        "        plt.xlabel(x_label, fontsize=16)\n",
        "    if y_label:\n",
        "        plt.ylabel(y_label, fontsize=16, rotation=0)\n",
        "    plt.hlines(0, 0, 100, linewidth=1)\n",
        "    plt.axis([0, n_steps + 1, -1, 1])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEcz-_lyUURR"
      },
      "source": [
        "def plot_multiple_forecasts(X, Y, Y_pred):\n",
        "    n_steps = X.shape[1]\n",
        "    ahead = Y.shape[1]\n",
        "    plot_series(X[0, :, 0])\n",
        "    plt.plot(np.arange(n_steps, n_steps + ahead), Y[0, :, 0], \"ro-\", label=\"Actual\")\n",
        "    plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"bx-\", label=\"Forecast\", markersize=10)\n",
        "    plt.axis([0, n_steps + ahead, -1, 1])\n",
        "    plt.legend(fontsize=14)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CvxVat0UURU"
      },
      "source": [
        "## Forecasting Several Steps Ahead\n",
        "\n",
        "If we want to predict the next 10 values, instead of just 1, we can modify our previous model to predict the next 10 values. We first need to load the dataset that has sequences with 9 more time steps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Mv8yoOnUURV"
      },
      "source": [
        "n_steps = 50\n",
        "series = load('/content/drive/MyDrive/Colab Notebooks/CS581-TestResults/assignment5/HW5-supp-files/univariate-time-series-multi-step.npy')\n",
        "\n",
        "X_train, Y_train = series[:7000, :n_steps], series[:7000, -10:, 0]\n",
        "X_valid, Y_valid = series[7000:9000, :n_steps], series[7000:9000, -10:, 0]\n",
        "X_test, Y_test = series[9000:, :n_steps], series[9000:, -10:, 0]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2j3wIC5GUURY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbf4e787-49d2-4836-9150-2e0697b5ac3f"
      },
      "source": [
        "#Improve upon this model with your own model\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[50, 1]),\n",
        "    keras.layers.Dense(10)\n",
        "])\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
        "history = model.fit(X_train, Y_train, epochs=20,\n",
        "                    validation_data=(X_valid, Y_valid))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.1389 - val_loss: 0.0753\n",
            "Epoch 2/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0624 - val_loss: 0.0551\n",
            "Epoch 3/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0505 - val_loss: 0.0470\n",
            "Epoch 4/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0447 - val_loss: 0.0422\n",
            "Epoch 5/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0413 - val_loss: 0.0396\n",
            "Epoch 6/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0390 - val_loss: 0.0376\n",
            "Epoch 7/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0374 - val_loss: 0.0360\n",
            "Epoch 8/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0362 - val_loss: 0.0350\n",
            "Epoch 9/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0354 - val_loss: 0.0342\n",
            "Epoch 10/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0347 - val_loss: 0.0336\n",
            "Epoch 11/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0342 - val_loss: 0.0333\n",
            "Epoch 12/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0337 - val_loss: 0.0327\n",
            "Epoch 13/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0334 - val_loss: 0.0324\n",
            "Epoch 14/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0331 - val_loss: 0.0323\n",
            "Epoch 15/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0329 - val_loss: 0.0319\n",
            "Epoch 16/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0326 - val_loss: 0.0318\n",
            "Epoch 17/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0324 - val_loss: 0.0317\n",
            "Epoch 18/20\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.0321 - val_loss: 0.0316\n",
            "Epoch 19/20\n",
            "219/219 [==============================] - 1s 2ms/step - loss: 0.0320 - val_loss: 0.0313\n",
            "Epoch 20/20\n",
            "219/219 [==============================] - 1s 2ms/step - loss: 0.0318 - val_loss: 0.0312\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jECM5QCNUURc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "95e908b9-ff08-4e35-f22d-db5397dabe22"
      },
      "source": [
        "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAd6GMrxUURg"
      },
      "source": [
        "Now let's predict the next 10 values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5B2AvKrUURh"
      },
      "source": [
        "series = load('/content/drive/MyDrive/Colab Notebooks/CS581-TestResults/assignment5/HW5-supp-files/univariate-time-series-multi-step-test.npy')\n",
        "X_new, Y_new = series[:, :50, :], series[:, -10:, :]\n",
        "Y_pred = model.predict(X_new)[..., np.newaxis]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jw5pVbZQUURk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "cda75d28-1879-4795-862b-0d3a620ac703"
      },
      "source": [
        "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PT3BdOdNUURn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecbcc9d7-281b-45ae-c47a-35c16c4dd3d6"
      },
      "source": [
        "model.evaluate(X_test, Y_test)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32/32 [==============================] - 0s 1ms/step - loss: 0.0325\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.032544732093811035"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GROEEmotWqif"
      },
      "source": [
        "## RNN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzTES3EIXi6_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bfa0d47-6c59-4208-efda-f2105ea2b392"
      },
      "source": [
        "X_train.shape,X_test.shape,Y_train.shape,Y_test.shape,X_new.shape,Y_new.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7000, 50, 1), (1000, 50, 1), (7000, 10), (1000, 10), (1, 50, 1), (1, 10, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjTMMPL2WS6k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b32feb49-165d-42fc-c43d-6e0f0c94908c"
      },
      "source": [
        "rnnmodel = keras.models.Sequential([\n",
        "    keras.layers.LSTM(256,input_shape=[50,1]),\n",
        "    keras.layers.Dense(100),\n",
        "    keras.layers.Dense(10)\n",
        "])\n",
        "rnnmodel.compile(loss=\"mse\", optimizer=\"adam\")\n",
        "print(rnnmodel.summary())\n",
        "rnnhistory = rnnmodel.fit(X_train,Y_train,validation_data=(X_valid,Y_valid),epochs=20, batch_size=128)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 256)               264192    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               25700     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 290,902\n",
            "Trainable params: 290,902\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/20\n",
            "55/55 [==============================] - 1s 18ms/step - loss: 0.0810 - val_loss: 0.0573\n",
            "Epoch 2/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0501 - val_loss: 0.0440\n",
            "Epoch 3/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0400 - val_loss: 0.0361\n",
            "Epoch 4/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0354 - val_loss: 0.0315\n",
            "Epoch 5/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0307 - val_loss: 0.0260\n",
            "Epoch 6/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0258 - val_loss: 0.0257\n",
            "Epoch 7/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0237 - val_loss: 0.0210\n",
            "Epoch 8/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0226 - val_loss: 0.0221\n",
            "Epoch 9/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0213 - val_loss: 0.0209\n",
            "Epoch 10/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0202 - val_loss: 0.0181\n",
            "Epoch 11/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0183 - val_loss: 0.0194\n",
            "Epoch 12/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0179 - val_loss: 0.0164\n",
            "Epoch 13/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0170 - val_loss: 0.0161\n",
            "Epoch 14/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0153 - val_loss: 0.0135\n",
            "Epoch 15/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0135 - val_loss: 0.0142\n",
            "Epoch 16/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0125 - val_loss: 0.0124\n",
            "Epoch 17/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0124 - val_loss: 0.0120\n",
            "Epoch 18/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0126 - val_loss: 0.0119\n",
            "Epoch 19/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0114 - val_loss: 0.0129\n",
            "Epoch 20/20\n",
            "55/55 [==============================] - 0s 9ms/step - loss: 0.0112 - val_loss: 0.0105\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGccr1VGnLDt"
      },
      "source": [
        "Y1 = rnnmodel(X_valid)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MafIlFksnQ5w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96461442-ee36-4f2c-acb9-fa771b73dcea"
      },
      "source": [
        "Y1.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([2000, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "P3scvDJvFHj0",
        "outputId": "3bcf174a-7a9d-4273-9761-19207d937fdc"
      },
      "source": [
        "plot_learning_curves(rnnhistory.history[\"loss\"], rnnhistory.history[\"val_loss\"])\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUWslX8NCIjK"
      },
      "source": [
        "series = load('/content/drive/MyDrive/Colab Notebooks/CS581-TestResults/assignment5/HW5-supp-files/univariate-time-series-multi-step-test.npy')\n",
        "X_new, Y_new = series[:, :50, :], series[:, -10:, :]\n",
        "Y_pred = rnnmodel.predict(X_new)[..., np.newaxis]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "tsxYDveiCIjL",
        "outputId": "eac044c4-8c62-423a-b8a9-f2504e3c0f34"
      },
      "source": [
        "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubSd_L2xXQmV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a183955-9675-42fa-b631-c630d0ff3460"
      },
      "source": [
        "scores = rnnmodel.evaluate(X_test,Y_test)\n",
        "print(f\"RMSE: {scores}\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32/32 [==============================] - 0s 3ms/step - loss: 0.0112\n",
            "RMSE: 0.011172199621796608\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}