arghyadeep99/pytorch-tutorial.ipynb

## pytorch-tutorial.ipynb
{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PyTorch Tutorial.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
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        "aqmgWMJvnxYV",
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        "lvNX2f4ksUBB",
        "Jo8Kv8bF-fXX"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/arghyadeep99/5f80ed548c3abfb2b207da8297d666f7/pytorch-tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ov1iAF2VS5ks",
        "colab_type": "text"
      },
      "source": [
        "#PyTorch Tutorial\n",
        "<ol>\n",
        "<li><a href=\"https://pytorch.org/\">What is PyTorch?</a></li>\n",
        "<li><a href=\"https://pytorch.org/tutorials/\">What can we do with it?</a></li>\n",
        "<li><a href=\"https://www.google.com/url?sa=i&source=imgres&cd=&cad=rja&uact=8&ved=2ahUKEwjBsLzk6LTkAhXFfn0KHeZRApgQjRx6BAgBEAQ&url=https%3A%2F%2Fblog.algorithmia.com%2Fexploring-the-deep-learning-framework-pytorch%2F&psig=AOvVaw3eLYpoGyGHRVB5KUBSwjIN&ust=1567605789915136\">What is a Computation Graph?</a></li>\n",
        "<li>Looking at a detailed example of PyTorch Graph.</li>  \n",
        "</ol>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8B7ImskqdjGJ",
        "colab_type": "text"
      },
      "source": [
        "##Installing PyTorch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kavThgwUZHXN",
        "colab_type": "code",
        "outputId": "afbcad81-f6b1-4667-8983-4064d0a936e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip3 install torch torchvision"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.1.0)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.3.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch) (1.16.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.12.0)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (4.3.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.1.1->torchvision) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzvrnoaRdvgp",
        "colab_type": "text"
      },
      "source": [
        "##Importing all Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXfCVDrogpYq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.autograd import Variable\n",
        "from math import pi\n",
        "from copy import deepcopy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EW_u9hYHkA7C",
        "colab_type": "text"
      },
      "source": [
        "##Making Data\n",
        "##Formula: mx + b + A1sin(w1x+c1) + A2sin(w2x+c2)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9UDNCd_jo80",
        "colab_type": "code",
        "outputId": "3a1189a4-7343-4dd8-f414-4a78b863954b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "x = np.array([_/100 for _ in range(1000)])\n",
        "y = 4.5*x + 3 + 2.8 * np.sin( 2.5 * x + pi/3) - 5.2 * np.sin(5 * x + pi/5) + np.random.normal(size=1000)\n",
        "\n",
        "\n",
        "plt.plot(x,y)\n",
        "x = Variable(torch.tensor(x,dtype=torch.double))\n",
        "y= Variable(torch.tensor(y,dtype=torch.double))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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8Zf2QGh+NxNhIpMYHrvPQYJMm9p4LewTsvUTNBZtcyK0oh4B+z4U98MiEXgCA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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqmgWMJvnxYV",
        "colab_type": "text"
      },
      "source": [
        "##How to compute a function in PyTorch\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QG2JAGkUm5z9",
        "colab_type": "code",
        "outputId": "6ee5979a-6de4-4193-bf2f-4bb28fb6c5bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m = Variable(torch.tensor(1.2,dtype=torch.double),requires_grad=True)\n",
        "b = Variable(torch.tensor(2.8,dtype=torch.double),requires_grad=True)\n",
        "A1 = Variable(torch.tensor(0.6,dtype=torch.double),requires_grad=True)\n",
        "w1 = Variable(torch.tensor(2.2,dtype=torch.double),requires_grad=True)\n",
        "c1 = Variable(torch.tensor(pi/4,dtype=torch.double),requires_grad=True)\n",
        "A2 = Variable(torch.tensor(-1.0,dtype=torch.double),requires_grad=True)\n",
        "w2 = Variable(torch.tensor(4.0,dtype=torch.double),requires_grad=True)\n",
        "c2 = Variable(torch.tensor(pi/3,dtype=torch.double),requires_grad=True)\n",
        "print(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(1.2000, dtype=torch.float64, requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEQ75Ph0o8Ml",
        "colab_type": "code",
        "outputId": "0a53a7f1-8bcf-4d41-fecb-6a951ac54bb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "Y = m*x+b+A1*torch.sin(w1*x+c1)+A2*torch.sin(w2*x+c2)\n",
        "plt.plot(x.detach().numpy(),Y.detach().numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1ceb700e10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3No83V+fw5KJsAMJCDGN7t+ep64dpLxSRFsajQDfGxAOzgcGABW611q5wojBp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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BpG5ftm4q2K6",
        "colab_type": "text"
      },
      "source": [
        "##Optimization in PyTorch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecp9byoBsPkN",
        "colab_type": "text"
      },
      "source": [
        "###Initialization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwWRtShPqOfj",
        "colab_type": "code",
        "outputId": "c9557e6f-2de5-482f-c19e-b56f39be3a78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "params = [0]*8\n",
        "params[0] = m = Variable(torch.tensor(1.2,dtype=torch.double),requires_grad=True)\n",
        "params[1] = b = Variable(torch.tensor(2.8,dtype=torch.double),requires_grad=True)\n",
        "params[2] = A1 = Variable(torch.tensor(0.6,dtype=torch.double),requires_grad=True)\n",
        "params[3] = w1 = Variable(torch.tensor(2.2,dtype=torch.double),requires_grad=True)\n",
        "params[4] = c1 = Variable(torch.tensor(pi/4,dtype=torch.double),requires_grad=True)\n",
        "params[5] = A2 = Variable(torch.tensor(-1.0,dtype=torch.double),requires_grad=True)\n",
        "params[6] = w2 = Variable(torch.tensor(4.0,dtype=torch.double),requires_grad=True)\n",
        "params[7] = c2 = Variable(torch.tensor(pi/3,dtype=torch.double),requires_grad=True)\n",
        "print(params[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(1.2000, dtype=torch.float64, requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwHY-8Q2qX6w",
        "colab_type": "code",
        "outputId": "69bbb7e5-8ba1-4e84-ea1d-0ae8a5d3a574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "Y = params[0]*x+params[1]+params[2]*torch.sin(params[3]*x+params[4])+params[5]*torch.sin(params[6]*x+params[7])\n",
        "plt.plot(x.detach().numpy(),Y.detach().numpy())\n",
        "plt.plot(x.detach().numpy(),y.detach().numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1cee40e5f8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RtNDbKnem2jcthgX2Qxs9sXU+fDYLMod7d70r8V2Nvv0BF3p3fUNt82MRsb7V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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lvNX2f4ksUBB",
        "colab_type": "text"
      },
      "source": [
        "###Computing the Loss and Gradients!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrascydGKaMn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialize\n",
        "\n",
        "params = [0]*8\n",
        "params[0] = m = Variable(torch.tensor(1.2,dtype=torch.double),requires_grad=True)\n",
        "params[1] = b = Variable(torch.tensor(2.8,dtype=torch.double),requires_grad=True)\n",
        "params[2] = A1 = Variable(torch.tensor(0.6,dtype=torch.double),requires_grad=True)\n",
        "params[3] = w1 = Variable(torch.tensor(2.2,dtype=torch.double),requires_grad=True)\n",
        "params[4] = c1 = Variable(torch.tensor(pi/4,dtype=torch.double),requires_grad=True)\n",
        "params[5] = A2 = Variable(torch.tensor(-1.0,dtype=torch.double),requires_grad=True)\n",
        "params[6] = w2 = Variable(torch.tensor(4.0,dtype=torch.double),requires_grad=True)\n",
        "params[7] = c2 = Variable(torch.tensor(pi/3,dtype=torch.double),requires_grad=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKCEQTGOqmZB",
        "colab_type": "code",
        "outputId": "dbdb3cd8-4067-4e8e-982f-b772e242927f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "# Compute the function\n",
        "Y = params[0]*x+params[1]+params[2]*torch.sin(params[3]*x+params[4])+params[5]*torch.sin(params[6]*x+params[7])\n",
        "\n",
        "# Compute the loss\n",
        "loss = ((Y-y)*(Y-y))/2\n",
        "loss = loss.sum()\n",
        "print(loss)\n",
        "\n",
        "# Zero out the gradients\n",
        "for i in params:\n",
        "  if i.grad!=None:\n",
        "    i.grad.data=torch.tensor(0.0,dtype=torch.double)\n",
        "\n",
        "# Compute Gradients\n",
        "loss.backward()\n",
        "for i in params:\n",
        "  print(i.grad)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(195247.8528, dtype=torch.float64, grad_fn=<SumBackward0>)\n",
            "tensor(-111146.0445, dtype=torch.float64)\n",
            "tensor(-16694.4921, dtype=torch.float64)\n",
            "tensor(-408.6225, dtype=torch.float64)\n",
            "tensor(5220.8800, dtype=torch.float64)\n",
            "tensor(278.4904, dtype=torch.float64)\n",
            "tensor(-1326.2558, dtype=torch.float64)\n",
            "tensor(-5679.0080, dtype=torch.float64)\n",
            "tensor(-883.0339, dtype=torch.float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jo8Kv8bF-fXX",
        "colab_type": "text"
      },
      "source": [
        "###Updating the parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZC_QPP0D-ebU",
        "colab_type": "code",
        "outputId": "b4c291e9-0ff7-4164-f94c-34c7f6270449",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "# Update Paramters\n",
        "lr = 1e-5\n",
        "for i in range(len(params)):\n",
        "  params[i] = Variable(torch.tensor(params[i] - lr*params[i].grad.data,dtype=torch.double),requires_grad=True)\n",
        "\n",
        "for i in params:\n",
        "  print(i)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor(2.3115, dtype=torch.float64, requires_grad=True)\n",
            "tensor(2.9669, dtype=torch.float64, requires_grad=True)\n",
            "tensor(0.6041, dtype=torch.float64, requires_grad=True)\n",
            "tensor(2.1478, dtype=torch.float64, requires_grad=True)\n",
            "tensor(0.7826, dtype=torch.float64, requires_grad=True)\n",
            "tensor(-0.9867, dtype=torch.float64, requires_grad=True)\n",
            "tensor(4.0568, dtype=torch.float64, requires_grad=True)\n",
            "tensor(1.0560, dtype=torch.float64, requires_grad=True)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSkTXLFJLNDW",
        "colab_type": "text"
      },
      "source": [
        "##Complete Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEIGtSH0LMT3",
        "colab_type": "code",
        "outputId": "820a3644-5eaf-4088-bc61-4db281015af7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "losses = []\n",
        "\n",
        "x = np.array([_/100 for _ in range(1000)])\n",
        "y = 4.5*x + 3 + 2.8 * np.sin( 0.5 * pi * x + pi/3) - 5.2 * np.sin(0.25 * pi * x + pi/5) + np.random.normal(size=1000)\n",
        "\n",
        "\n",
        "plt.plot(x,y)\n",
        "x = Variable(torch.tensor(x,dtype=torch.double))\n",
        "y= Variable(torch.tensor(y,dtype=torch.double))\n",
        "\n",
        "params = [0]*8\n",
        "params[0] = m = Variable(torch.tensor(2,dtype=torch.double),requires_grad=True)\n",
        "params[1] = b = Variable(torch.tensor(0,dtype=torch.double),requires_grad=True)\n",
        "params[2] = A1 = Variable(torch.tensor(2.7,dtype=torch.double),requires_grad=True)\n",
        "params[3] = w1 = Variable(torch.tensor(0.45*pi,dtype=torch.double),requires_grad=True)\n",
        "params[4] = c1 = Variable(torch.tensor(1.1*pi/3,dtype=torch.double),requires_grad=True)\n",
        "params[5] = A2 = Variable(torch.tensor(-5.0,dtype=torch.double),requires_grad=True)\n",
        "params[6] = w2 = Variable(torch.tensor(0.23*pi,dtype=torch.double),requires_grad=True)\n",
        "params[7] = c2 = Variable(torch.tensor(0.9*pi/5,dtype=torch.double),requires_grad=True)\n",
        "\n",
        "lr = 1e-6\n",
        "\n",
        "for epoch in range(10001):\n",
        "  \n",
        "  # Learning Rate Scheduling\n",
        "  #lr = lr*0.99\n",
        "  \n",
        "  # Compute the function\n",
        "  Y = params[0]*x+params[1]+params[2]*torch.sin(params[3]*x+params[4])+params[5]*torch.sin(params[6]*x+params[7])\n",
        "\n",
        "  # Compute the loss\n",
        "  loss = ((Y-y)*(Y-y))/2\n",
        "  loss = loss.sum()\n",
        "\n",
        "  # Zero out the gradients\n",
        "  for i in params:\n",
        "    if i.grad!=None:\n",
        "      i.grad.data=torch.tensor(0.0,dtype=torch.double)\n",
        "\n",
        "  # Compute Gradients\n",
        "  loss.backward()\n",
        "  # for i in params:\n",
        "    # print(i.grad)\n",
        "  \n",
        "  losses.append(loss.detach().numpy())\n",
        "    \n",
        "  # Update Parameters\n",
        "  for i in range(len(params)):\n",
        "    params[i] = Variable(torch.tensor(params[i] - lr*params[i].grad.data,dtype=torch.double),requires_grad=True)\n",
        "  \n",
        "  if epoch%1000==0:\n",
        "    print('Epoch: ',epoch)\n",
        "    print(\"Loss: \",loss)\n",
        "    plt.plot(x.detach().numpy(),Y.detach().numpy())\n",
        "    plt.plot(x.detach().numpy(),y.detach().numpy())\n",
        "    plt.show()\n",
        "    \n",
        "for i in params:\n",
        "  print(i)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch:  0\n",
            "Loss:  tensor(133580.2945, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:49: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Dawt/6jEKgMf73sLgJo/ywQ1PA7j37dTOkjdLFqEnCV1UKw7MolD5jnycTifp2ScBsFA0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\nJKELIUSEkIQuhBAR4v8B4JFnv0daRM4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  1000\n",
            "Loss:  tensor(962.6994, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0eGj61h8iX5NfmLTiiEjqxVNDfwoIHjZ4EzDdWtsTmO45lrpo7/bI59odkrpy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\n2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  2000\n",
            "Loss:  tensor(811.3658, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yQDAP06hhoyQXSkRSqdqAbox53hiz1hgzOyithTFmgjFmoW+7d3KLKdV6qDs8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\n0EVEsoQCuohIllBAFxHJEgroIiJZ4v8BLFud98xGRaUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  3000\n",
            "Loss:  tensor(720.1560, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ujFlhjPkyrKytMWacMebb0FaZIdtuagPPnBJbvm5R3I80atQkjQGJSKYl00J/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\n0KmrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  4000\n",
            "Loss:  tensor(663.7037, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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el7SxuBOnVv2VS0cPzlChRCSZFNDzhdfvfMf6fUlf72nOkOOOo1ubZhkqlIgk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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  5000\n",
            "Loss:  tensor(625.9262, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b5KhQIpJOCujFYOV82LDS7T87YlOyKW3BmUP75KZMIpJ2eilaDO7sH5jcvaod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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  6000\n",
            "Loss:  tensor(599.5695, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4I3fDGJOjSolIOqnLpRjMnwYrv3HHr9+wJbtDu3YM7Nc5R5USkXRTQC901sJD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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  7000\n",
            "Loss:  tensor(580.8741, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  8000\n",
            "Loss:  tensor(567.4760, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KVfNgy5ro9LqamJeUlVdksEAikm0JA7ox5h5jzApjzCchaV2MMS8aY74MbDtn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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  9000\n",
            "Loss:  tensor(557.7858, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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okuOCiEg2qYZeLFZ8BRtXxqY31MW9pKxctXORYpI0oBtjJhpjlhljPglL62GM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\nbMkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch:  10000\n",
            "Loss:  tensor(550.7140, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a0I0xDxpjVhpjvghL62SMedUY84237ZjZYkpSfz8I7j4oNn3dwvjXlGm+c5FC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\nFxEpEAroIiIFQgFdRKRAKKCLiBSI/wfW6/F6STPBmgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "tensor(4.5977, dtype=torch.float64, requires_grad=True)\n",
            "tensor(2.4771, dtype=torch.float64, requires_grad=True)\n",
            "tensor(2.8006, dtype=torch.float64, requires_grad=True)\n",
            "tensor(1.6128, dtype=torch.float64, requires_grad=True)\n",
            "tensor(0.8402, dtype=torch.float64, requires_grad=True)\n",
            "tensor(-5.1978, dtype=torch.float64, requires_grad=True)\n",
            "tensor(0.7993, dtype=torch.float64, requires_grad=True)\n",
            "tensor(0.6075, dtype=torch.float64, requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUpls0GpSv3L",
        "colab_type": "code",
        "outputId": "b1977e18-38b4-426a-ee64-bd15882c204c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "losses = np.array(losses)\n",
        "plt.plot(losses)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f62f3286710>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljZ0fP4bz7xS",
        "colab_type": "code",
        "outputId": "3572ee10-0bfb-416d-e4fa-23cde8928d4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torchviz import make_dot, make_dot_from_trace\n",
        "\n",
        "x = np.array([_/100 for _ in range(1000)])\n",
        "y = 4.5*x + 3 + 2.8 * np.sin( 0.5 * pi * x + pi/3) - 5.2 * np.sin(0.25 * pi * x + pi/5) + np.random.normal(size=1000)\n",
        "\n",
        "\n",
        "plt.plot(x,y)\n",
        "x = Variable(torch.tensor(x,dtype=torch.double))\n",
        "y= Variable(torch.tensor(y,dtype=torch.double))\n",
        "\n",
        "params = [0]*8\n",
        "params[0] = m = Variable(torch.tensor(1.2,dtype=torch.double),requires_grad=True)\n",
        "params[1] = b = Variable(torch.tensor(0,dtype=torch.double),requires_grad=True)\n",
        "params[2] = A1 = Variable(torch.tensor(2.7,dtype=torch.double),requires_grad=True)\n",
        "params[3] = w1 = Variable(torch.tensor(0.45*pi,dtype=torch.double),requires_grad=True)\n",
        "params[4] = c1 = Variable(torch.tensor(1.1*pi/3,dtype=torch.double),requires_grad=True)\n",
        "params[5] = A2 = Variable(torch.tensor(-5.0,dtype=torch.double),requires_grad=True)\n",
        "params[6] = w2 = Variable(torch.tensor(0.23*pi,dtype=torch.double),requires_grad=True)\n",
        "params[7] = c2 = Variable(torch.tensor(0.9*pi/5,dtype=torch.double),requires_grad=True)\n",
        "\n",
        "# Compute the function\n",
        "Y = params[0]*x+params[1]+params[2]*torch.sin(params[3]*x+params[4])+params[5]*torch.sin(params[6]*x+params[7])\n",
        "\n",
        "# Compute the loss\n",
        "loss = ((Y-y)*(Y-y))/2\n",
        "loss = loss.sum()\n",
        "\n",
        "make_dot(loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ],
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7zl24FZe89COUUhZ93ZomHoq+t+4YAGD3qSL8a+sppM7/DvklVabjxpazsWvFevKQPaeL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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}