Lab2_ANN_r.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOkpOdAB5BevQHUM3wuojRQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ridhikapila27/Deep_learning/blob/main/Lab2_ANN_r.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch #creates deep neural networks\n",
        "import torch.nn as nn #nn used for defining modules, graph recording of data\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the model\n",
        "class ANN(nn.Module):\n",
        "    def __init__(self): #__init__() initializes attributes of class\n",
        "        super(ANN, self).__init__() #using super() we can use objects of parent class\n",
        "        self.fc1 = nn.Linear(1, 10) #single layer feed forward network with(n,m) n=inputs,m=outputs\n",
        "        self.fc2 = nn.Linear(10, 1)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = torch.sigmoid(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "# Create an instance of the model\n",
        "model = ANN()\n",
        "\n",
        "# Define the loss function and optimizer\n",
        "criterion = nn.MSELoss() #mean square error\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.1) #stochastic gradient descent\n",
        "\n",
        "# Generate some fake data for training\n",
        "x_train = torch.linspace(-10, 10, 100).reshape(-1, 1)\n",
        "y_train = x_train + torch.randn(x_train.shape) * 2\n",
        "\n",
        "# Train the model\n",
        "for epoch in range(100):\n",
        "    optimizer.zero_grad()\n",
        "    y_pred = model(x_train)\n",
        "    loss = criterion(y_pred, y_train)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    if (epoch+1) % 1 == 0:\n",
        "        print(f\"Epoch {epoch+1}, Loss: {loss.item()}\")\n",
        "\n",
        "# Generate some fake data for testing\n",
        "x_test = torch.linspace(-10, 10, 50).reshape(-1, 1)\n",
        "y_test = x_test + torch.randn(x_test.shape) * 2\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred = model(x_test)\n",
        "\n",
        "# Plot the results\n",
        "plt.scatter(x_train.numpy(), y_train.numpy(), label=\"Training Data\")\n",
        "plt.scatter(x_test.numpy(), y_test.numpy(), label=\"Testing Data\")\n",
        "plt.plot(x_test.numpy(), y_pred.detach().numpy(), label=\"Predictions\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-Pp7M8crVun3",
        "outputId": "ab178083-34f5-4fe8-8ea0-964464f4a655"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Loss: 41.030513763427734\n",
            "Epoch 2, Loss: 28.741317749023438\n",
            "Epoch 3, Loss: 19.809307098388672\n",
            "Epoch 4, Loss: 13.457535743713379\n",
            "Epoch 5, Loss: 9.961636543273926\n",
            "Epoch 6, Loss: 8.208098411560059\n",
            "Epoch 7, Loss: 7.3197479248046875\n",
            "Epoch 8, Loss: 6.841848850250244\n",
            "Epoch 9, Loss: 6.543554306030273\n",
            "Epoch 10, Loss: 6.317986965179443\n",
            "Epoch 11, Loss: 6.123845100402832\n",
            "Epoch 12, Loss: 5.949304103851318\n",
            "Epoch 13, Loss: 5.793208122253418\n",
            "Epoch 14, Loss: 5.6561126708984375\n",
            "Epoch 15, Loss: 5.536906719207764\n",
            "Epoch 16, Loss: 5.432898998260498\n",
            "Epoch 17, Loss: 5.341246128082275\n",
            "Epoch 18, Loss: 5.259770393371582\n",
            "Epoch 19, Loss: 5.187002182006836\n",
            "Epoch 20, Loss: 5.121965408325195\n",
            "Epoch 21, Loss: 5.063957691192627\n",
            "Epoch 22, Loss: 5.012368679046631\n",
            "Epoch 23, Loss: 4.9665608406066895\n",
            "Epoch 24, Loss: 4.925867080688477\n",
            "Epoch 25, Loss: 4.889645576477051\n",
            "Epoch 26, Loss: 4.857330322265625\n",
            "Epoch 27, Loss: 4.828434944152832\n",
            "Epoch 28, Loss: 4.802524566650391\n",
            "Epoch 29, Loss: 4.779193878173828\n",
            "Epoch 30, Loss: 4.758068084716797\n",
            "Epoch 31, Loss: 4.738816738128662\n",
            "Epoch 32, Loss: 4.721159934997559\n",
            "Epoch 33, Loss: 4.7048726081848145\n",
            "Epoch 34, Loss: 4.689774036407471\n",
            "Epoch 35, Loss: 4.675718784332275\n",
            "Epoch 36, Loss: 4.662590503692627\n",
            "Epoch 37, Loss: 4.650291919708252\n",
            "Epoch 38, Loss: 4.638742446899414\n",
            "Epoch 39, Loss: 4.627871990203857\n",
            "Epoch 40, Loss: 4.61761999130249\n",
            "Epoch 41, Loss: 4.607934474945068\n",
            "Epoch 42, Loss: 4.598780632019043\n",
            "Epoch 43, Loss: 4.590176582336426\n",
            "Epoch 44, Loss: 4.582531929016113\n",
            "Epoch 45, Loss: 4.578914642333984\n",
            "Epoch 46, Loss: 4.602357864379883\n",
            "Epoch 47, Loss: 4.833463668823242\n",
            "Epoch 48, Loss: 6.305437088012695\n",
            "Epoch 49, Loss: 13.093415260314941\n",
            "Epoch 50, Loss: 5.686138153076172\n",
            "Epoch 51, Loss: 5.337251663208008\n",
            "Epoch 52, Loss: 5.435696601867676\n",
            "Epoch 53, Loss: 5.140023231506348\n",
            "Epoch 54, Loss: 5.307809829711914\n",
            "Epoch 55, Loss: 5.079836368560791\n",
            "Epoch 56, Loss: 5.314149856567383\n",
            "Epoch 57, Loss: 5.064046859741211\n",
            "Epoch 58, Loss: 5.398778915405273\n",
            "Epoch 59, Loss: 5.096757411956787\n",
            "Epoch 60, Loss: 5.569488525390625\n",
            "Epoch 61, Loss: 5.108020782470703\n",
            "Epoch 62, Loss: 5.594837665557861\n",
            "Epoch 63, Loss: 5.0879106521606445\n",
            "Epoch 64, Loss: 5.517621994018555\n",
            "Epoch 65, Loss: 5.076772689819336\n",
            "Epoch 66, Loss: 5.490432262420654\n",
            "Epoch 67, Loss: 5.072646617889404\n",
            "Epoch 68, Loss: 5.484560966491699\n",
            "Epoch 69, Loss: 5.069900989532471\n",
            "Epoch 70, Loss: 5.479818344116211\n",
            "Epoch 71, Loss: 5.06680154800415\n",
            "Epoch 72, Loss: 5.473487377166748\n",
            "Epoch 73, Loss: 5.0637617111206055\n",
            "Epoch 74, Loss: 5.467749118804932\n",
            "Epoch 75, Loss: 5.060981273651123\n",
            "Epoch 76, Loss: 5.462948799133301\n",
            "Epoch 77, Loss: 5.058351039886475\n",
            "Epoch 78, Loss: 5.458554744720459\n",
            "Epoch 79, Loss: 5.055765628814697\n",
            "Epoch 80, Loss: 5.454259872436523\n",
            "Epoch 81, Loss: 5.053199291229248\n",
            "Epoch 82, Loss: 5.450019359588623\n",
            "Epoch 83, Loss: 5.0506486892700195\n",
            "Epoch 84, Loss: 5.445827484130859\n",
            "Epoch 85, Loss: 5.048105239868164\n",
            "Epoch 86, Loss: 5.4416632652282715\n",
            "Epoch 87, Loss: 5.045565605163574\n",
            "Epoch 88, Loss: 5.437506198883057\n",
            "Epoch 89, Loss: 5.043024063110352\n",
            "Epoch 90, Loss: 5.433345794677734\n",
            "Epoch 91, Loss: 5.040478229522705\n",
            "Epoch 92, Loss: 5.429178237915039\n",
            "Epoch 93, Loss: 5.037927627563477\n",
            "Epoch 94, Loss: 5.425000190734863\n",
            "Epoch 95, Loss: 5.035369396209717\n",
            "Epoch 96, Loss: 5.420808792114258\n",
            "Epoch 97, Loss: 5.032805919647217\n",
            "Epoch 98, Loss: 5.4166059494018555\n",
            "Epoch 99, Loss: 5.030235290527344\n",
            "Epoch 100, Loss: 5.412389755249023\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}