swarajmr_ENPM690_Homework2.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pi9xptL0610e"
      },
      "source": [
        "# **Python Tutorial and Homework 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLA0tK0D6Jiu"
      },
      "source": [
        "\n",
        "This Colab Notebook contains 2 sections. The first section is a Python Tutorial intended to make you familiar with Numpy and Colab functionalities. **This section will not be graded**.\n",
        "\n",
        "The second section is a coding assignment (Homework 2). **This section will be graded**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nna9fw7nQV9n"
      },
      "source": [
        "# 1.0 **Python Tutorial**\n",
        "\n",
        "This assignment section won't be graded but is intended as a tutorial to refresh the basics of python and its dependencies. It also allows one to get familiarized with Google Colab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8pTiC3H9kJ3"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NeZCBbzQ99yz"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNUg9w0GVZPJ"
      },
      "source": [
        "## 1.0.0 Array manipulation using numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwFjdjtq42v7"
      },
      "source": [
        "#### Q1 - Matrix multiplication"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6Lrkst6QS38",
        "outputId": "159d8b41-499e-428f-d0e0-b251092d1156"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 2  4]\n",
            " [ 6  8]\n",
            " [10 12]] [[ 7 10 13]\n",
            " [16 19 22]]\n",
            "[[ 78  96 114]\n",
            " [170 212 254]\n",
            " [262 328 394]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "### Create two numpy arrays with the dimensions 3x2 and 2x3 respectively using  np.arange().\n",
        "### The elements of the vector are\n",
        "### Vector 1 elements = [ 2,  4,  6,  8, 10, 12];\n",
        "### Vector 2 elements = [ 7, 10, 13, 16, 19, 22]\n",
        "\n",
        "### Starting at 2, stepping by 2\n",
        "vector1 = np.arange(2,13,2).reshape(3,2)\n",
        "### Starting at 7, stepping by 3\n",
        "vector2 = np.arange(7,23,3).reshape(2,3)\n",
        "\n",
        "### Print vec\n",
        "print(vector1, vector2)\n",
        "\n",
        "# ### Take product of the two matricies (Matrix product)\n",
        "prod =np.dot(vector1, vector2)\n",
        "\n",
        "# ### Print\n",
        "print(prod)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vasaB_7a4_23"
      },
      "source": [
        "#### Q2 - Diagonals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvOzhJZTdbb7",
        "outputId": "10d1566f-b59f-4144-8582-f9f2d4fb2262"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[  2   5   8  11  14  17  20  23  26  29]\n",
            " [ 32  35  38  41  44  47  50  53  56  59]\n",
            " [ 62  65  68  71  74  77  80  83  86  89]\n",
            " [ 92  95  98 101 104 107 110 113 116 119]\n",
            " [122 125 128 131 134 137 140 143 146 149]\n",
            " [152 155 158 161 164 167 170 173 176 179]\n",
            " [182 185 188 191 194 197 200 203 206 209]\n",
            " [212 215 218 221 224 227 230 233 236 239]\n",
            " [242 245 248 251 254 257 260 263 266 269]\n",
            " [272 275 278 281 284 287 290 293 296 299]] \n",
            "\n",
            "\n",
            " [[ 35  44  53  62  71  80  89  98 107 116]\n",
            " [125 134 143 152 161 170 179 188 197 206]\n",
            " [215 224 233 242 251 260 269 278 287 296]\n",
            " [305 314 323 332 341 350 359 368 377 386]\n",
            " [395 404 413 422 431 440 449 458 467 476]\n",
            " [485 494 503 512 521 530 539 548 557 566]\n",
            " [575 584 593 602 611 620 629 638 647 656]\n",
            " [665 674 683 692 701 710 719 728 737 746]\n",
            " [755 764 773 782 791 800 809 818 827 836]\n",
            " [845 854 863 872 881 890 899 908 917 926]]\n",
            "[[ 92   0   0   0   0   0   0]\n",
            " [  0 125   0   0   0   0   0]\n",
            " [  0   0 158   0   0   0   0]\n",
            " [  0   0   0 191   0   0   0]\n",
            " [  0   0   0   0 224   0   0]\n",
            " [  0   0   0   0   0 257   0]\n",
            " [  0   0   0   0   0   0 290]] \n",
            "\n",
            "\n",
            " [[ 62   0   0   0   0   0   0]\n",
            " [  0 161   0   0   0   0   0]\n",
            " [  0   0 260   0   0   0   0]\n",
            " [  0   0   0 359   0   0   0]\n",
            " [  0   0   0   0 458   0   0]\n",
            " [  0   0   0   0   0 557   0]\n",
            " [  0   0   0   0   0   0 656]]\n",
            "[[  5704      0      0      0      0      0      0]\n",
            " [     0  20125      0      0      0      0      0]\n",
            " [     0      0  41080      0      0      0      0]\n",
            " [     0      0      0  68569      0      0      0]\n",
            " [     0      0      0      0 102592      0      0]\n",
            " [     0      0      0      0      0 143149      0]\n",
            " [     0      0      0      0      0      0 190240]]\n"
          ]
        }
      ],
      "source": [
        "### Create two numpy arrays with the dimensions 10x10 using the function np.arange().\n",
        "### Starting at 2, stepping by 3\n",
        "vector1 =np.arange(2,(3*10*10)+2,3).reshape(10,10)\n",
        "### Starting at 35, stepping by 9\n",
        "vector2 =np.arange(35,(9*10*10)+35,9).reshape(10,10)\n",
        "\n",
        "### Print vec\n",
        "print(vector1,'\\n\\n\\n',vector2)\n",
        "\n",
        "### Obtain the diagonal matrix of each vector1 such that the start of the diagonal is from (3,0) and the end is (9,6)\n",
        "### Reshape the the matrix such that it form a diagonal maritix of shape(7,7)\n",
        "vector1_offset_diagonal =np.diag(np.diag(vector1[3:10,0:7]))\n",
        "\n",
        "\n",
        "\n",
        "### Obtain a 7x7 matrix from the vector 2\n",
        "### starting from (left top element) = (0,3)\n",
        "### ending at (right bottom element) = (6,9)\n",
        "vector2_offset_diagonal =(np.diag(np.diag(vector2[0:7,3:10])))\n",
        "\n",
        "### Print diagonal matrix\n",
        "print(vector1_offset_diagonal,'\\n\\n\\n', vector2_offset_diagonal)\n",
        "\n",
        "\n",
        "### Take product of the two diagonal matricies (Matrix product)\n",
        "prod =np.dot(vector1_offset_diagonal,vector2_offset_diagonal)\n",
        "\n",
        "### Print\n",
        "print(prod)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9nEhxof5SG8"
      },
      "source": [
        "#### Q3 - Sin wave"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQpyqNI88_xX"
      },
      "source": [
        "Sample outputs,\n",
        "![download.png](data:image/png;base64,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) 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)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jU5-fwkyuZ35",
        "outputId": "88af5870-5a31-4e64-a3a6-03892f32c33d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "### Create a time matrix that evenly samples a sine wave at a frequency of 1Hz\n",
        "### Starting at time step T = 0\n",
        "### End at time step T = 500\n",
        "time =np.arange(0,501,1)\n",
        "\n",
        "### Given wavelength of\n",
        "wavelength = 500\n",
        "\n",
        "### Construct a sin wave using the formula sin(2*pi*(time/wavelength))\n",
        "y =np.sin(2*np.pi*(time/wavelength))\n",
        "\n",
        "#### Plot the wave\n",
        "plt.plot(time, y)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "bJn8tudh5jL8",
        "outputId": "164fcae9-fe26-44f0-c629-111d2cf56662"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#### Given a 2D mesh grid\n",
        "X, Y = np.meshgrid(time, time)\n",
        "#### wavelength and angle of rotation(phi) of the sin wave in 2d. Imagine a 2D sine wave is being rotating about the Z axes\n",
        "wavelength = 200\n",
        "phi = np.pi / 3\n",
        "\n",
        "#### Calculate the sin wave in 2d space using the formula sin(2*pi*(x'/wavelength)) where x'=Xcos(phi) + Ysin(phi)\n",
        "xprime=(X*np.cos(phi)) + (Y*np.sin(phi))\n",
        "grating = np.sin(2*np.pi*(xprime/wavelength))\n",
        "\n",
        "#### Plot the wave\n",
        "#### Intuition, think of the white area as hills and the black areas as valleys\n",
        "plt.set_cmap(\"gray\")\n",
        "plt.imshow(grating)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laDacTVv96_z"
      },
      "source": [
        "#### Q4 Car Brands"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YUg3IbAOKKFK"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pBauPLI9fRn",
        "outputId": "07b8c9f1-7819-4134-eb60-e1c84ae5d5b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Top Layer \n",
            "  [['Civic' 'Insight']\n",
            " ['Fit' 'Accord']\n",
            " ['Ridgeline' 'Avancier']] \n",
            "Bottom Layer\n",
            " [['Pilot' 'Legend']\n",
            " ['Beat' 'FR-V']\n",
            " ['HR-V' 'Shuttle']]\n",
            "\n",
            "Top Flattened :  ['Civic' 'Insight' 'Fit' 'Accord' 'Ridgeline' 'Avancier'] \n",
            "Bottom Flattened :  ['Pilot' 'Legend' 'Beat' 'FR-V' 'HR-V' 'Shuttle']\n",
            "\n",
            "\n",
            "Interwoven -  ['Civic' 'Pilot' 'Fit' 'Beat' 'Ridgeline' 'HR-V' 'Insight' 'Legend'\n",
            " 'Accord' 'FR-V' 'Avancier' 'Shuttle'] \n",
            "Concatenate and flatten -  ['Civic' 'Insight' 'Pilot' 'Legend' 'Fit' 'Accord' 'Beat' 'FR-V'\n",
            " 'Ridgeline' 'Avancier' 'HR-V' 'Shuttle']\n"
          ]
        }
      ],
      "source": [
        "cars = ['Civic', 'Insight', 'Fit', 'Accord', 'Ridgeline', 'Avancier','Pilot', 'Legend', 'Beat', 'FR-V', 'HR-V', 'Shuttle']\n",
        "\n",
        "#### Create a 3D array of cars of shape 2,3,2\n",
        "cars_3d =np.array(cars).reshape(2,3,2)\n",
        "\n",
        "#### Extract the top layer of the matrix. Top layer of a matrix A of shape(2,3,2) will have the following structue A_top = [[A[0,0,0], A[0,0,1]],[A[0,1,0],A[0,1,1]],[A[0,2,0],A[0,2,1]]]\n",
        "#### HINT - Array slicing or splitting\n",
        "cars_top_layer =cars_3d[0,:,:]\n",
        "\n",
        "#### Similarly extract the bottom layer\n",
        "#### HINT - Array slicing or splitting\n",
        "cars_bottom_layer =cars_3d[-1,:,:]\n",
        "\n",
        "#### Print layers\n",
        "print(\"\\nTop Layer \\n \",cars_top_layer,\"\\nBottom Layer\\n\", cars_bottom_layer)\n",
        "\n",
        "# #### Flatten the top layer\n",
        "cars_top_flat =cars_top_layer.flatten()\n",
        "# #### Flatten the bottom layer\n",
        "cars_bottom_flat =cars_bottom_layer.flatten()\n",
        "\n",
        "# #### Print layers\n",
        "print(\"\\nTop Flattened : \",cars_top_flat,\"\\nBottom Flattened : \",cars_bottom_flat)\n",
        "\n",
        "\n",
        "new_car_list = np.empty((cars_top_layer.size + cars_bottom_layer.size,), dtype=object)\n",
        "#### Interweave the to flattened lists and insert into new_car_list such that new_car_list=['Civic' 'Pilot' 'Fit' 'Beat' 'Ridgeline' 'HR-V' 'Insight' 'Legend' 'Accord' 'FR-V' 'Avancier' 'Shuttle']\n",
        "#### Using only array slicing\n",
        "new_car_list[0::2] = np.hstack((cars_top_flat[0::2],cars_top_flat[1::2]))\n",
        "new_car_list[1::2] = np.hstack((cars_bottom_flat[0::2],cars_bottom_flat[1::2]))\n",
        "\n",
        "#### Concatenate and flatten the top and bottom layer such that the final list is of the form cat_flat = ['Civic' 'Insight' 'Pilot' 'Legend' 'Fit' 'Accord' 'Beat' 'FR-V' 'Ridgeline' 'Avancier' 'HR-V' 'Shuttle']\n",
        "cat_flat = np.hstack((cars_top_layer,cars_bottom_layer)).flatten()\n",
        "\n",
        "#### Print layers\n",
        "print(\"\\n\\nInterwoven - \", new_car_list,\"\\nConcatenate and flatten - \", cat_flat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNwe-NoOI--l"
      },
      "source": [
        "## 1.0.1 Basics tensorflow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDPS9BkZNLwL"
      },
      "source": [
        "#### Helper functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29xMFUWtIaIT"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M95vh2HNNNOv"
      },
      "outputs": [],
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  true_label, img = true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  true_label = true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDPVHyakJVi0"
      },
      "source": [
        "#### Q1 MNIST Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42zICs6NJIQy",
        "outputId": "5ee56299-5920-4b27-be0d-5a507ecc2fa9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "## Import the MNIST dataset from keras\n",
        "mnist = tf.keras.datasets.mnist\n",
        "### Load the data\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "### Normalize the 8bit images with values in the range [0,255]\n",
        "x_train = tf.keras.utils.normalize(x_train, axis=1)\n",
        "x_test  = tf.keras.utils.normalize(x_test, axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W-PL5IHsJ4u6"
      },
      "outputs": [],
      "source": [
        "## Create a model with the following architecture Flatten -> Dense(128, relu) -> Dense(64,relu) -> outpuLayer(size=10)\n",
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
        "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
        "\n",
        "\n",
        "\n",
        "### Complie the model with the adam optimizer and crossentropy loss\n",
        "### HINT - No One hot encoding\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OGSLuqfKdHY",
        "outputId": "96118c07-eb9e-4992-dd6b-b84c8458a34f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 20s 9ms/step - loss: 0.2764 - accuracy: 0.9196\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1133 - accuracy: 0.9647\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0766 - accuracy: 0.9758\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0576 - accuracy: 0.9815\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0433 - accuracy: 0.9855\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e1465c6d3f0>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "### Train the model on the train data for 5 epochs\n",
        "model.fit(x_train, y_train, epochs=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SggUHdYkMFa7",
        "outputId": "8baaefb8-7eda-4249-b3df-fa07d23d8b33"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 - 1s - loss: 0.1051 - accuracy: 0.9697 - 655ms/epoch - 2ms/step\n",
            "\n",
            "Test accuracy: 0.9696999788284302\n"
          ]
        }
      ],
      "source": [
        "### Check the accuracy of the trained model\n",
        "test_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)\n",
        "print('\\nTest accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "Kitvyb3bMh4O",
        "outputId": "04320e05-3838-4ff9-9e4e-b9799efafe6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "### Convert the above model to a probabilistic model with a softmax as the output layer\n",
        "probability_model = tf.keras.Sequential([model,tf.keras.layers.Softmax()])\n",
        "\n",
        "### Run the test data through the new model and get predictions\n",
        "predictions = probability_model.predict(x_test)\n",
        "\n",
        "### Plot a test output\n",
        "i = 65 ### <---- Change this to some random number to see different predictions\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], y_test, x_test)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  y_test)\n",
        "plt.show()\n",
        "### Blue bars mean correct guess red bar means wrong guess!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhNXjiQtnNx3"
      },
      "source": [
        "## 1.0.2 Basic Pytorch Tutorial\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lwv29fkWnNi2",
        "outputId": "81c22d9f-1611-4982-c075-dffef73541a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1, Loss: 0.7497771851766084\n",
            "Epoch 2, Loss: 0.3669367114395729\n",
            "Epoch 3, Loss: 0.3210167052077332\n",
            "Epoch 4, Loss: 0.29383885016096933\n",
            "Epoch 5, Loss: 0.2732162083000707\n",
            "Epoch 6, Loss: 0.2538067396285374\n",
            "Epoch 7, Loss: 0.23667215858139337\n",
            "Epoch 8, Loss: 0.22128436360945072\n",
            "Epoch 9, Loss: 0.20731170845629054\n",
            "Epoch 10, Loss: 0.1948725388272167\n",
            "Finished Training\n",
            "Accuracy on test set: 94.49%\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "# Set the random seed for reproducibility\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Define a simple feedforward neural network\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(784, 128)  # 28x28 input size (MNIST images are 28x28 pixels)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.fc2 = nn.Linear(128, 10)   # 10 output classes (digits 0-9)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 784)  # Flatten the input image\n",
        "        x = self.fc1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "# Load the MNIST dataset and apply transformations\n",
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
        "\n",
        "trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
        "\n",
        "testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n",
        "\n",
        "# Initialize the neural network and optimizer\n",
        "net = Net()\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(net.parameters(), lr=0.01)\n",
        "\n",
        "# Training loop\n",
        "num_epochs = 10\n",
        "for epoch in range(num_epochs):\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        inputs, labels = data\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    print(f'Epoch {epoch+1}, Loss: {running_loss / len(trainloader)}')\n",
        "\n",
        "print('Finished Training')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "correct = 0\n",
        "total = 0\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy on test set: {100 * correct / total}%')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1PfCLS12a_C"
      },
      "source": [
        "# 2.0 **Homework 2**\n",
        "90 points\n",
        "\n",
        "*Note : This section will be graded and must be attemped using Pytorch only*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "siIdfSJFnosi"
      },
      "source": [
        "## Graded Section : Deep Learning Approach"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7s0xWLTGpFv7"
      },
      "source": [
        "Time-Series Prediction\n",
        "Time series and sequence prediction could be a really amazing to predict/estimate a robot's trajectory which requires temporal data at hand. In this assignemnt we will see how this could be done using Deep Learning.\n",
        "\n",
        "Given a dataset [link](https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv) for airline passengers prediction problem. Predict the number of international airline passengers in units of 1,000 given a year and a month.\n",
        "Here is how the data looks like."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fjiv8yIqIavt",
        "outputId": "33ae8ba3-2e6a-4e7f-b68f-27de25da0d09"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.utils.data as data\n",
        "import math\n",
        "\n",
        "#Importing the dataset\n",
        "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv\"\n",
        "#reading the dataset using pandas\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Extracting time series data\n",
        "timeseries = df[\"Passengers\"].values.astype('float32')\n",
        "monthseries = df[\"Month\"].values.astype('str')\n",
        "\n",
        "\n",
        "# Plotting the dataset\n",
        "plt.plot(timeseries)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "xdv7mZAfQCot",
        "outputId": "41c5c9f7-cc71-4548-a6d9-8533916054b3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcj0tuuUpQg0"
      },
      "source": [
        "1. Write the dataloader code to pre-process the data for pytorch tensors using any library of your choice. Here is a good resource for the dataloader [Video link](https://www.youtube.com/watch?v=PXOzkkB5eH0)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here for the dataloader in Pytorch\n",
        "# Using the timeseries data extracted above, separate data for training and testing\n",
        "train_size = math.ceil(int(len(timeseries) * 0.90))\n",
        "test_size = len(timeseries) - train_size\n",
        "train, test = timeseries[:train_size], timeseries[train_size:]\n",
        "\n",
        "#Class to split create data for the LSTM Model\n",
        "class DatasetCreator:\n",
        "    def __init__(self, lookback):\n",
        "        self.lookback = lookback\n",
        "\n",
        "    def __call__(self, dataset):\n",
        "        X, y = [], []\n",
        "        for i in range(len(dataset) - self.lookback):\n",
        "            feature = dataset[i:i + self.lookback]\n",
        "            target = dataset[i + self.lookback]\n",
        "            X.append(feature)\n",
        "            y.append(target)\n",
        "\n",
        "        X = np.array(X).astype(np.float32)\n",
        "        y = np.array(y).astype(np.float32)\n",
        "        return torch.tensor(X), torch.tensor(y)\n",
        "\n",
        "#Defining the Lookback value\n",
        "lookback = 4\n",
        "\n",
        "#Object of DatasetCreator Class\n",
        "dataset_creator = DatasetCreator(lookback)\n",
        "#Extracting training datasets\n",
        "X_train, y_train = dataset_creator.__call__(train)\n",
        "#Extracting testing datasets\n",
        "X_test, y_test = dataset_creator.__call__(test)\n",
        "\n",
        "#Dataloading the training and testing datasets\n",
        "trainloader = data.DataLoader(data.TensorDataset(X_train, y_train), shuffle=False, batch_size=12)\n",
        "testloader = data.DataLoader(data.TensorDataset(X_test, y_test), shuffle=False, batch_size=12)\n",
        "\n"
      ],
      "metadata": {
        "id": "qYyRMkLqWahy"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDyX4KRnpSv8"
      },
      "source": [
        "2. Create the model in pytorch here uinsg 1. Long-Short Term Memory (LSTM) and 2. Recurrent Neural Network (RNN). Here is a good resource for Custom model generation.\n",
        "\n",
        "  Train using the two models. Here is the resource for the same [Video link](https://www.youtube.com/watch?v=VVDHU_TWwUg)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# write your code here for the custom model creating for both the methods\n",
        "\n",
        "class LstmModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.lstm = nn.LSTM(input_size=1, hidden_size=50, num_layers=1, batch_first=True)\n",
        "        self.linear = nn.Linear(50, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x, _ = self.lstm(x)\n",
        "        x = self.linear(x[:, -1, :])\n",
        "        return x\n",
        "\n",
        "\n",
        "class RnnModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.rnn = nn.RNN(input_size=1, hidden_size=50, num_layers=1, batch_first=True)\n",
        "        self.linear = nn.Linear(50, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x, _ = self.rnn(x)\n",
        "        x = self.linear(x[:, -1, :])\n",
        "        return x\n",
        "\n"
      ],
      "metadata": {
        "id": "xHH8eKnpVLsG"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lv8qXvNpetv"
      },
      "source": [
        "3. Evaluate and Compare the result using proper metric. Justify the metrics used.\n",
        "In the model RMSE and Visual inspection is carried out to evaluate and compare the result of the model\n",
        " * Root Mean Squared Error(RMSE): RMSE measures the average magnitude of the error between predicted and actual values. Lower the RMSE, the better is the models performance and hence RMSE is used to evaluate the model.\n",
        " * The results have been compared by plotting the time series graph of the actual input values to the training and testing inputs to the LSTM and RNN model. This allows us to quantitatively visualise how well our model is capturing and analysing trends in the data.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Runnning the LSTM Model for the train and test dataset and calcualtion of\n",
        "lstmmodel = LstmModel()\n",
        "#Defining optimiser and Loss function\n",
        "optimizer = optim.Adam(lstmmodel.parameters(),lr=0.001)\n",
        "loss_fn = nn.MSELoss(reduction='mean')\n",
        "lossplot=[]\n",
        "n_epochs = 6000\n",
        "\n",
        "# Looping through epochs\n",
        "for epoch in range(n_epochs):\n",
        "    lstmmodel.train()\n",
        "    for X_batch, y_batch in trainloader:\n",
        "        y_pred = lstmmodel(X_batch.unsqueeze(-1))  #Forward pass: Getting predictions from the model\n",
        "        loss = loss_fn(y_pred, y_batch.unsqueeze(-1))  #Calculating the Loss\n",
        "        lossplot.append(loss)\n",
        "        optimizer.zero_grad()  # Zeroing the gradients\n",
        "        loss.backward() # Backpropagation\n",
        "        optimizer.step()\n",
        "    # Validation\n",
        "    if epoch % 100 == 0:\n",
        "        lstmmodel.eval() # Evaludating the model\n",
        "        with torch.no_grad():\n",
        "          y_pred_train = lstmmodel(X_train.unsqueeze(-1)).squeeze(-1)\n",
        "          y_pred_test = lstmmodel(X_test.unsqueeze(-1)).squeeze(-1)\n",
        "\n",
        "          # Calculating RMSE for train and test data\n",
        "          test_rmse = np.sqrt(loss_fn(y_pred_test, y_test))\n",
        "          train_rmse = np.sqrt(loss_fn(y_pred_train, y_train))\n",
        "\n",
        "          threshold = 0.1  # Define a threshold\n",
        "          correct_train = ((torch.abs(y_pred_train - y_train) / y_train) <= threshold).sum().item()\n",
        "          accuracy_train = correct_train / len(y_train) * 100\n",
        "          correct_test = ((torch.abs(y_pred_test - y_test) / y_test) <= threshold).sum().item()\n",
        "          accuracy_test = correct_test / len(y_test) * 100\n",
        "        print(\"Epoch %d: train RMSE: %.4f, test RMSE: %.4f, Loss: %.4f\" % (epoch, train_rmse, test_rmse,loss))\n",
        "\n",
        "with torch.no_grad():\n",
        "    # shifting the train predictions for plot\n",
        "    train_plot = np.ones_like(timeseries) * np.nan\n",
        "    y_pred = lstmmodel(X_train.unsqueeze(-1)).squeeze(-1)\n",
        "    train_plot[lookback:train_size] = y_pred\n",
        "    # shifting the test predictions for plot\n",
        "    test_plot = np.ones_like(timeseries) * np.nan\n",
        "    y_pred = lstmmodel(X_test.unsqueeze(-1)).squeeze(-1)\n",
        "    test_plot[train_size+lookback:len(timeseries)] = y_pred\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lM3eP4-5XRsC",
        "outputId": "91e77c15-c9cb-46bb-b10a-4c59d1b545a0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: train RMSE: 283.6658, test RMSE: 487.3471, Loss: 245203.3281\n",
            "Epoch 100: train RMSE: 243.2734, test RMSE: 443.9193, Loss: 203828.5469\n",
            "Epoch 200: train RMSE: 211.2160, test RMSE: 408.3787, Loss: 172748.3125\n",
            "Epoch 300: train RMSE: 182.1785, test RMSE: 374.7237, Loss: 145645.5625\n",
            "Epoch 400: train RMSE: 157.4483, test RMSE: 344.0069, Loss: 122882.5469\n",
            "Epoch 500: train RMSE: 133.1683, test RMSE: 311.6363, Loss: 100940.1172\n",
            "Epoch 600: train RMSE: 111.0234, test RMSE: 279.3086, Loss: 81103.7109\n",
            "Epoch 700: train RMSE: 94.0255, test RMSE: 251.9253, Loss: 65933.3906\n",
            "Epoch 800: train RMSE: 79.8559, test RMSE: 227.0689, Loss: 53455.9492\n",
            "Epoch 900: train RMSE: 68.1757, test RMSE: 204.3927, Loss: 43144.1797\n",
            "Epoch 1000: train RMSE: 58.7229, test RMSE: 183.8110, Loss: 34665.7773\n",
            "Epoch 1100: train RMSE: 51.3716, test RMSE: 166.3927, Loss: 27771.1836\n",
            "Epoch 1200: train RMSE: 45.6475, test RMSE: 150.1798, Loss: 22036.1035\n",
            "Epoch 1300: train RMSE: 41.4311, test RMSE: 135.8278, Loss: 17571.8750\n",
            "Epoch 1400: train RMSE: 37.6521, test RMSE: 121.8599, Loss: 13823.7324\n",
            "Epoch 1500: train RMSE: 34.5982, test RMSE: 110.9431, Loss: 10855.3105\n",
            "Epoch 1600: train RMSE: 32.2858, test RMSE: 101.0053, Loss: 8574.8154\n",
            "Epoch 1700: train RMSE: 30.2896, test RMSE: 93.8418, Loss: 6748.5601\n",
            "Epoch 1800: train RMSE: 28.3176, test RMSE: 88.3102, Loss: 5283.7876\n",
            "Epoch 1900: train RMSE: 27.2468, test RMSE: 83.7472, Loss: 4184.1978\n",
            "Epoch 2000: train RMSE: 26.2569, test RMSE: 80.1486, Loss: 3383.1245\n",
            "Epoch 2100: train RMSE: 25.5569, test RMSE: 79.3338, Loss: 2859.8625\n",
            "Epoch 2200: train RMSE: 23.8867, test RMSE: 79.3112, Loss: 2174.4580\n",
            "Epoch 2300: train RMSE: 23.3016, test RMSE: 79.3631, Loss: 1735.2740\n",
            "Epoch 2400: train RMSE: 22.4271, test RMSE: 84.6986, Loss: 1453.5647\n",
            "Epoch 2500: train RMSE: 21.8876, test RMSE: 82.3410, Loss: 1181.8951\n",
            "Epoch 2600: train RMSE: 21.6589, test RMSE: 81.8441, Loss: 857.1945\n",
            "Epoch 2700: train RMSE: 20.7263, test RMSE: 84.6497, Loss: 773.8430\n",
            "Epoch 2800: train RMSE: 20.0742, test RMSE: 86.0414, Loss: 652.7208\n",
            "Epoch 2900: train RMSE: 20.1812, test RMSE: 87.5726, Loss: 637.6691\n",
            "Epoch 3000: train RMSE: 20.0577, test RMSE: 85.9973, Loss: 686.0533\n",
            "Epoch 3100: train RMSE: 20.2364, test RMSE: 66.9181, Loss: 682.4213\n",
            "Epoch 3200: train RMSE: 19.0405, test RMSE: 66.3239, Loss: 469.5577\n",
            "Epoch 3300: train RMSE: 18.4801, test RMSE: 66.0961, Loss: 324.0569\n",
            "Epoch 3400: train RMSE: 18.4539, test RMSE: 67.1485, Loss: 425.8816\n",
            "Epoch 3500: train RMSE: 18.2011, test RMSE: 63.0015, Loss: 118.3030\n",
            "Epoch 3600: train RMSE: 17.9727, test RMSE: 59.3432, Loss: 214.6743\n",
            "Epoch 3700: train RMSE: 17.6532, test RMSE: 63.2923, Loss: 214.1452\n",
            "Epoch 3800: train RMSE: 16.8878, test RMSE: 64.0543, Loss: 130.3487\n",
            "Epoch 3900: train RMSE: 16.5961, test RMSE: 63.4125, Loss: 75.2223\n",
            "Epoch 4000: train RMSE: 18.3780, test RMSE: 59.6778, Loss: 647.7572\n",
            "Epoch 4100: train RMSE: 15.9803, test RMSE: 64.0311, Loss: 44.8738\n",
            "Epoch 4200: train RMSE: 15.9507, test RMSE: 65.3455, Loss: 48.1899\n",
            "Epoch 4300: train RMSE: 15.5754, test RMSE: 66.3013, Loss: 43.8142\n",
            "Epoch 4400: train RMSE: 17.7673, test RMSE: 63.8307, Loss: 461.3052\n",
            "Epoch 4500: train RMSE: 15.6008, test RMSE: 63.9796, Loss: 90.2026\n",
            "Epoch 4600: train RMSE: 17.9432, test RMSE: 62.9175, Loss: 398.2756\n",
            "Epoch 4700: train RMSE: 15.0865, test RMSE: 65.7506, Loss: 151.6618\n",
            "Epoch 4800: train RMSE: 15.3566, test RMSE: 62.6022, Loss: 95.0289\n",
            "Epoch 4900: train RMSE: 14.4834, test RMSE: 63.6913, Loss: 53.6872\n",
            "Epoch 5000: train RMSE: 13.8981, test RMSE: 64.1724, Loss: 56.1613\n",
            "Epoch 5100: train RMSE: 14.2670, test RMSE: 67.6250, Loss: 199.8257\n",
            "Epoch 5200: train RMSE: 13.9442, test RMSE: 62.3501, Loss: 59.9440\n",
            "Epoch 5300: train RMSE: 14.1799, test RMSE: 65.3634, Loss: 111.1066\n",
            "Epoch 5400: train RMSE: 13.5518, test RMSE: 65.6400, Loss: 61.4863\n",
            "Epoch 5500: train RMSE: 18.3785, test RMSE: 63.7021, Loss: 442.2151\n",
            "Epoch 5600: train RMSE: 15.3458, test RMSE: 64.2083, Loss: 273.9984\n",
            "Epoch 5700: train RMSE: 13.0065, test RMSE: 64.9778, Loss: 29.9530\n",
            "Epoch 5800: train RMSE: 12.2167, test RMSE: 66.1986, Loss: 3.3790\n",
            "Epoch 5900: train RMSE: 12.0523, test RMSE: 65.4371, Loss: 2.2215\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_array = [loss.detach().numpy() for loss in lossplot]\n",
        "plt.plot(loss_array)\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training Loss for LSTM Model')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "8XGTwViUrYKe",
        "outputId": "9a701404-5906-463e-b4dd-48847767ec08"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualising the model training and testing plots to check the model accuracy\n",
        "plt.plot(timeseries, label='Original Input Data')\n",
        "plt.plot(train_plot, label='LSTM Training data Predictions')\n",
        "plt.plot(test_plot, label='LSTM Testing data Predictions')\n",
        "plt.title('LSTM Model Prediction')\n",
        "plt.xlabel(\"Number of Months\")\n",
        "plt.ylabel(\"Passengers in 1000's\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7fVNVdSUXUgW",
        "outputId": "24fbcb3d-23cb-4f62-c981-a1799e30c49d"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Instantiating the optimizer and Loss Function\n",
        "\n",
        "rnnmodel = RnnModel()\n",
        "# Defining optimizer and Loss function\n",
        "optimizer = optim.Adam(rnnmodel.parameters(), lr=0.001)\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "n_epochs = 6000\n",
        "# Looping through epochs\n",
        "for epoch in range(n_epochs):\n",
        "    rnnmodel.train()\n",
        "    for X_batch, y_batch in trainloader:\n",
        "        rnny_pred = rnnmodel(X_batch.unsqueeze(-1))  # Adding an extra dimension for LSTM input\n",
        "        loss = loss_fn(rnny_pred, y_batch.unsqueeze(-1))  # Adding an extra dimension for target\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    # Validation\n",
        "    if epoch % 100 == 0:\n",
        "        rnnmodel.eval()\n",
        "        with torch.no_grad():\n",
        "            rnny_pred_train = rnnmodel(X_train.unsqueeze(-1)).squeeze(-1)\n",
        "            train_rmse = np.sqrt(loss_fn(rnny_pred_train, y_train))\n",
        "            rnny_pred_test = rnnmodel(X_test.unsqueeze(-1)).squeeze(-1)\n",
        "            test_rmse = np.sqrt(loss_fn(rnny_pred_test, y_test))\n",
        "\n",
        "            threshold = 0.1  # Define a threshold\n",
        "            rnncorrect_train = ((torch.abs(rnny_pred_train - y_train) / y_train) <= threshold).sum().item()\n",
        "            rnnaccuracy_train = rnncorrect_train / len(y_train) * 100\n",
        "            rnncorrect_test = ((torch.abs(rnny_pred_test - y_test) / y_test) <= threshold).sum().item()\n",
        "            rnnaccuracy_test = rnncorrect_test / len(y_test) * 100\n",
        "        print(\"Epoch %d: train RMSE: %.4f, test RMSE: %.4f, Loss: %.4f\" % (epoch, train_rmse, test_rmse,loss))\n",
        "\n",
        "with torch.no_grad():\n",
        "    # shift train predictions for plotting\n",
        "    rnntrain_plot = np.ones_like(timeseries) * np.nan\n",
        "    rnny_pred = rnnmodel(X_train.unsqueeze(-1)).squeeze(-1)\n",
        "    rnntrain_plot[lookback:train_size] = rnny_pred\n",
        "    # shift test predictions for plotting\n",
        "    rnntest_plot = np.ones_like(timeseries) * np.nan\n",
        "    rnny_pred = rnnmodel(X_test.unsqueeze(-1)).squeeze(-1)\n",
        "    rnntest_plot[train_size+lookback:len(timeseries)] = rnny_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wzn_f-MMXm4n",
        "outputId": "9512f4c6-d933-470a-c84b-f6c9ffe7a8bb"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0: train RMSE: 282.1872, test RMSE: 485.7993, Loss: 243724.5000\n",
            "Epoch 100: train RMSE: 236.3437, test RMSE: 436.3376, Loss: 196995.6250\n",
            "Epoch 200: train RMSE: 197.1136, test RMSE: 392.2644, Loss: 159496.3750\n",
            "Epoch 300: train RMSE: 164.2597, test RMSE: 352.7437, Loss: 129171.1406\n",
            "Epoch 400: train RMSE: 137.5921, test RMSE: 317.7661, Loss: 104934.3906\n",
            "Epoch 500: train RMSE: 115.1113, test RMSE: 285.5721, Loss: 84784.3906\n",
            "Epoch 600: train RMSE: 96.5154, test RMSE: 256.2759, Loss: 68244.6719\n",
            "Epoch 700: train RMSE: 80.9765, test RMSE: 229.4886, Loss: 54617.3828\n",
            "Epoch 800: train RMSE: 68.3021, test RMSE: 205.2157, Loss: 43501.2305\n",
            "Epoch 900: train RMSE: 58.1824, test RMSE: 183.3255, Loss: 34477.8086\n",
            "Epoch 1000: train RMSE: 50.3498, test RMSE: 163.8373, Loss: 27240.0820\n",
            "Epoch 1100: train RMSE: 44.1780, test RMSE: 146.6948, Loss: 21481.7988\n",
            "Epoch 1200: train RMSE: 39.1259, test RMSE: 131.8990, Loss: 16885.3027\n",
            "Epoch 1300: train RMSE: 35.3696, test RMSE: 119.1354, Loss: 13210.6064\n",
            "Epoch 1400: train RMSE: 32.0399, test RMSE: 108.1314, Loss: 10263.2100\n",
            "Epoch 1500: train RMSE: 30.2227, test RMSE: 98.6982, Loss: 8064.3032\n",
            "Epoch 1600: train RMSE: 27.6884, test RMSE: 91.5418, Loss: 6214.1416\n",
            "Epoch 1700: train RMSE: 27.1464, test RMSE: 93.3818, Loss: 6557.6436\n",
            "Epoch 1800: train RMSE: 25.8435, test RMSE: 87.3162, Loss: 5056.8647\n",
            "Epoch 1900: train RMSE: 25.4259, test RMSE: 85.4993, Loss: 4355.1030\n",
            "Epoch 2000: train RMSE: 23.1621, test RMSE: 81.0218, Loss: 3540.7878\n",
            "Epoch 2100: train RMSE: 22.1724, test RMSE: 79.7689, Loss: 3038.2781\n",
            "Epoch 2200: train RMSE: 21.2177, test RMSE: 75.7229, Loss: 2474.8464\n",
            "Epoch 2300: train RMSE: 20.4017, test RMSE: 75.5479, Loss: 2105.8530\n",
            "Epoch 2400: train RMSE: 19.8006, test RMSE: 72.0948, Loss: 2055.2297\n",
            "Epoch 2500: train RMSE: 18.8869, test RMSE: 71.6884, Loss: 1565.4844\n",
            "Epoch 2600: train RMSE: 19.1389, test RMSE: 70.9878, Loss: 1268.1133\n",
            "Epoch 2700: train RMSE: 19.1260, test RMSE: 71.6259, Loss: 1320.2576\n",
            "Epoch 2800: train RMSE: 17.9423, test RMSE: 72.3820, Loss: 969.9998\n",
            "Epoch 2900: train RMSE: 17.5874, test RMSE: 67.1378, Loss: 986.1608\n",
            "Epoch 3000: train RMSE: 17.0555, test RMSE: 67.5810, Loss: 761.6896\n",
            "Epoch 3100: train RMSE: 22.9555, test RMSE: 58.5589, Loss: 1135.4772\n",
            "Epoch 3200: train RMSE: 20.5413, test RMSE: 66.0816, Loss: 1710.0006\n",
            "Epoch 3300: train RMSE: 19.6002, test RMSE: 64.9503, Loss: 1287.5342\n",
            "Epoch 3400: train RMSE: 21.1239, test RMSE: 63.4499, Loss: 1699.7233\n",
            "Epoch 3500: train RMSE: 19.0506, test RMSE: 64.7407, Loss: 1265.1819\n",
            "Epoch 3600: train RMSE: 19.2403, test RMSE: 65.4385, Loss: 1154.6608\n",
            "Epoch 3700: train RMSE: 20.4830, test RMSE: 71.2157, Loss: 2008.4141\n",
            "Epoch 3800: train RMSE: 18.2935, test RMSE: 67.2945, Loss: 1615.1718\n",
            "Epoch 3900: train RMSE: 17.9690, test RMSE: 71.2326, Loss: 1551.6117\n",
            "Epoch 4000: train RMSE: 20.9183, test RMSE: 64.9591, Loss: 1926.8652\n",
            "Epoch 4100: train RMSE: 16.2384, test RMSE: 67.6016, Loss: 933.8312\n",
            "Epoch 4200: train RMSE: 16.5064, test RMSE: 71.5516, Loss: 941.5156\n",
            "Epoch 4300: train RMSE: 15.8152, test RMSE: 60.9481, Loss: 963.2586\n",
            "Epoch 4400: train RMSE: 14.7469, test RMSE: 60.8394, Loss: 290.3502\n",
            "Epoch 4500: train RMSE: 14.0826, test RMSE: 63.5884, Loss: 416.2492\n",
            "Epoch 4600: train RMSE: 15.4595, test RMSE: 62.2370, Loss: 521.0163\n",
            "Epoch 4700: train RMSE: 13.6059, test RMSE: 63.7560, Loss: 289.2528\n",
            "Epoch 4800: train RMSE: 13.3668, test RMSE: 63.1970, Loss: 212.2537\n",
            "Epoch 4900: train RMSE: 13.3835, test RMSE: 61.5233, Loss: 160.1872\n",
            "Epoch 5000: train RMSE: 13.7180, test RMSE: 65.0818, Loss: 526.5771\n",
            "Epoch 5100: train RMSE: 22.2873, test RMSE: 59.9410, Loss: 1317.8331\n",
            "Epoch 5200: train RMSE: 17.9827, test RMSE: 64.7171, Loss: 297.7516\n",
            "Epoch 5300: train RMSE: 17.3548, test RMSE: 61.1212, Loss: 274.8957\n",
            "Epoch 5400: train RMSE: 16.1121, test RMSE: 68.5883, Loss: 101.1858\n",
            "Epoch 5500: train RMSE: 14.6710, test RMSE: 68.1638, Loss: 87.0218\n",
            "Epoch 5600: train RMSE: 14.4215, test RMSE: 68.4792, Loss: 73.2922\n",
            "Epoch 5700: train RMSE: 14.0843, test RMSE: 71.6664, Loss: 45.6507\n",
            "Epoch 5800: train RMSE: 14.0319, test RMSE: 68.9918, Loss: 41.6092\n",
            "Epoch 5900: train RMSE: 13.6696, test RMSE: 73.1099, Loss: 34.1758\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualising the model training and testing plots to check the model accuracy\n",
        "plt.plot(timeseries, label='Original Input Data')\n",
        "plt.plot(rnntrain_plot, label='RNN Training data Predictions')\n",
        "plt.plot(rnntest_plot, label='RNN Testing data Predictions')\n",
        "plt.title('RNN Model Prediction')\n",
        "plt.xlabel(\"Number of Months\")\n",
        "plt.ylabel(\"Passengers in 1000's\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ihav0eEEZBaP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "faf3745c-3145-4e02-b404-4f6038af59d0"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Input the Year and month in YYYY-MM format to obtain the prediction for the number of passengers(in 1000's) from the LSTM and RNN Model for the particular year and month combination***\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "n8fP87fuFD5W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "\n",
        "# Entered combination\n",
        "entered_year = 1960\n",
        "entered_month = 12\n",
        "\n",
        "# Year and month entered by the user\n",
        "year, month = map(int, input(\"Enter a year and month after 1960-12 for obtaining the prediction using LSTM Model and RNN Model  (in format 'YYYY-MM'): \").split('-'))\n",
        "\n",
        "# Convert entered year and month to datetime object\n",
        "entered_date = datetime(entered_year, entered_month, 1)\n",
        "user_date = datetime(year, month, 1)\n",
        "\n",
        "# Calculate the number of months between the entered combination and the user input\n",
        "num_months = (user_date.year - entered_date.year) * 12 + (user_date.month - entered_date.month)\n",
        "\n",
        "print(\"Number of months between '1960-12' and the entered combination:\", num_months)"
      ],
      "metadata": {
        "id": "PjDrI3fGZf-D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84ec3b79-e488-48cf-f20c-240c77f0ea9a"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a year and month after 1960-12 for obtaining the prediction using LSTM Model and RNN Model  (in format 'YYYY-MM'): 1961-12\n",
            "Number of months between '1960-12' and the entered combination: 12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction using LSTM Model\n",
        "timeseriesX = np.array(timeseries).astype(np.float32)\n",
        "timeseriesX = torch.tensor(timeseriesX)\n",
        "\n",
        "num_forecasted_steps=num_months\n",
        "sequence_to_plot=timeseriesX.squeeze().numpy()\n",
        "historical_data=sequence_to_plot[-6:]\n",
        "\n",
        "forecasted_values=[]\n",
        "\n",
        "with torch.no_grad():\n",
        "  for _ in range(num_forecasted_steps):\n",
        "    historical_data_tensor=torch.as_tensor(historical_data).view(1, -1, 1).float()\n",
        "    predicted_value = lstmmodel(historical_data_tensor).squeeze(-1)\n",
        "    forecasted_values.append(predicted_value[0])\n",
        "    historical_data = np.roll(historical_data, shift=-1)\n",
        "    historical_data[-1] = predicted_value\n",
        "predicted_value = predicted_value.item()\n",
        "print(f\"The predicted passengers for the given input {year:04d}-{month:02d} using LSTM predcition is: \",predicted_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zv8ik7s-hRq0",
        "outputId": "f1714de1-a560-4ae8-9ea5-0b68a306e78b"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The predicted passengers for the given input 1961-12 using LSTM predcition is:  538.1292114257812\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction using RNN Model\n",
        "timeseriesX = np.array(timeseries).astype(np.float32)\n",
        "timeseriesX = torch.tensor(timeseriesX)\n",
        "\n",
        "rnnnum_forecasted_steps=num_months\n",
        "rnnsequence_to_plot=timeseriesX.squeeze().numpy()\n",
        "rnnhistorical_data=rnnsequence_to_plot[-6:]\n",
        "rnnforecasted_values=[]\n",
        "\n",
        "with torch.no_grad():\n",
        "  for _ in range(rnnnum_forecasted_steps):\n",
        "    rnnhistorical_data_tensor=torch.as_tensor(rnnhistorical_data).view(1, -1, 1).float()\n",
        "    rnnpredicted_value = rnnmodel(rnnhistorical_data_tensor).squeeze(-1)\n",
        "    rnnforecasted_values.append(rnnpredicted_value.item())\n",
        "    rnnhistorical_data = np.roll(rnnhistorical_data, shift=-1)\n",
        "    rnnhistorical_data[-1] = rnnpredicted_value\n",
        "\n",
        "rnnpredicted_value = rnnpredicted_value.item()\n",
        "print(f\"The predicted passengers for the given input {year:04d}-{month:02d} using LSTM predcition is: \",rnnpredicted_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f28YWH0Ohji3",
        "outputId": "35c21d16-c05f-4205-ed28-40d02d085515"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The predicted passengers for the given input 1961-12 using LSTM predcition is:  346.64129638671875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCy6QBvLqHgr"
      },
      "source": [
        "[Bonus 5 points] Suggest some things that could be done to improve the results.\n",
        "\n",
        "Solution: To imporove the results:\n",
        "1. A bigger dataset is required : LSTM networks usually require lot of data so perhaps the airline data is not big enough.\n",
        "2. Tuning the Hyper Parameters: Neural networks in general have a huge number of hyperparameters to deal with and small changes can produce noticeable differences.\n",
        "3. The model can be over fitted and some sort of dropout or regularization can be implemented to help improve the results\n",
        "4. Experimenting with different model architectures such as adding more layers and more number of units in each layer or using Bi-Directional models that capture information form both future and past time steps\n",
        "5. Preprocessing of Data= Normalising or scaling the data can help the model converge faster and can avoid numerical instabilities\n",
        "\n",
        "\n",
        "[Bonus 5 points] Suggest where this could be used in Robotics other than the example given in the beginning.\n",
        "\n",
        "\n",
        "Solution: the RNN and LSTM models can be implemented to carry out segmentation of data, this can be used by medical robots in classification tasks like tumor detection and disease diagnosis.\n",
        "\n",
        "Furthermore, the robot can predict its future path and navigate through a place where data is not available.\n",
        "\n",
        "Also, the robots can learn from demonstration and can replicate complex manipulation and motion tasks.\n",
        "\n",
        "LSTM and RNN models can also be used to predict future behaviours during interaction with humans and adapt their behavior accordingly."
      ]
    }
  ]
}