1.Tensor_Basics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tensor Crash.ipynb",
      "provenance": [],
      "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/github/avinregmi/PyTorch-Lessons/blob/master/1.Tensor_Basics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjwiT9LfjCAS",
        "colab_type": "code",
        "outputId": "e3595092-d03e-4347-bb03-a2d09986cf7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "!pip3 install torch"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.3.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch) (1.17.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6feQnODjGus",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0F9TiRujL5G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t_one = torch.tensor([1,2,3])\n",
        "t_two = torch.tensor([1,2,3])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2eLZw5jtPFS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = torch.linspace(0,10,100)\n",
        "y = torch.exp(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4oalEh9tRUf",
        "colab_type": "code",
        "outputId": "3b16ae88-fc9e-42c4-b57f-7d7cd1248b76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(x.numpy(),y.numpy())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7efc27459278>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iF+nFA42caOniP66cE3YpY0ZhICJykb6/vZbS4gLeuyjzTxz3UxiIiFyEE2fP8cKBRu5a\nUUFe7vj5Ch0/eyIikgIb//0IAHePoyEiUBiIiAxbR3eU72+v5fbFs6mcNjHscsaUwkBEZJj+b9Ux\n2rqirFs1L+xSxpzCQERkGPpizoZfH2H5nBKWZ+AP3l+IwkBEZBh+uf8ktc2dfHLVZWGXkhQKAxGR\nYfjOvx2mYmoht42jy0kTKQxERC5g97Gz7DjSzJ/eMJfIOLqcNNH43CsRkTH0yLaDTCnM44/fOT6e\nQzQYhYGIyNvYW9fCL/c3su6meUyakBd2OUmjMBAReRuPbKth0oQIa2+YG3YpSaUwEBEZQnVDK8+/\n3sC9N85jSuH4PSoAhYGIyJD+97YaigsifOLGuWGXknQKAxGRQRw82camPfWsveFSSiZm9o/dD4fC\nQERkEF/dfICi/AjrbhqfN5kNpDAQERmg6kgzv9h3kk/dfBnTisb/UQEoDEREzuPu/O2m/cyYVMAn\nbhp/D6QbisJARCTB5tdP8mrtWf7r+65gYn4k7HJSRmEgIhKI9sX4h83VzJ9RzEeurQi7nJRSGIiI\nBJ7aUcuhpg7+cvXCcfsMoqFk196KiAzhdHs3X918gBsun857r5oRdjkppzAQEQH+/vlqOnv6+OKa\nqzGzsMtJOYWBiGS9V46e4Zmq46xbNY/5MyaFXU4oFAYiktX6Ys4X/mUvsyZP4NO3LAi7nNAoDEQk\nqz3x70fYV9/KFz6wiKKC7LmUdKBRhYGZHTGzPWa228yqgrZpZrbFzA4G71ODdjOzh82sxsxeM7Pl\nCetZG/Q/aGZrR7dLIiLDc/R0B//w/AFuvqKMO66ZFXY5oRqLI4P3uPtSd18RzD8IbHX3BcDWYB7g\ndmBB8FoPPArx8AAeAq4DVgIP9QeIiEiyxGLO5374GpEc4ysfviYrTxonSsYw0RpgYzC9Ebgzof0J\nj3sZKDGz2cD7gS3u3uzuZ4AtwOok1CUi8qbvbT/K9sPNfOEDi5g9pTDsckI32jBw4Bdm9oqZrQ/a\nZrp7fTDdAMwMpsuBYwmfPR60DdX+e8xsvZlVmVlVU1PTKEsXkWxVe7qTv9tUzbuuKOOjK7LrTuOh\njPZsyU3uXmdmM4AtZladuNDd3cx8lNtIXN9jwGMAK1asGLP1ikj2iPbF+PNndseHhz6k4aF+ozoy\ncPe64L0R+AnxMf+TwfAPwXtj0L0OqEz4eEXQNlS7iMiY+8Yv3+CVo2f48gcXc0mJhof6jTgMzKzI\nzCb1TwO3AXuB54D+K4LWAs8G088B9wRXFV0PtATDSZuB28xsanDi+LagTURkTL108BT/9OLv+OMV\nlaxZOuhodNYazTDRTOAnwSFWBPi+uz9vZjuBZ8xsHXAUuCvovwm4A6gBOoF7Ady92cy+BOwM+n3R\n3ZtHUZeIyO9pauvmsz/YzeVlxfz1H10ddjlpZ8Rh4O6HgCWDtJ8Gbh2k3YH7h1jXBmDDSGsREXk7\n0b4Yn3l6F21dvTz5yesozM8Nu6S0k72324lI1vjy/9vPb353mq99dAlXzsrOZw9diB5HISLj2jM7\nj/H4b47wyZvm8eEs+8Gai6EwEJFx65Wjzfz3f9nDqgWlPHj7wrDLSWsKAxEZlw41tXPfE69QXlLI\nI3cvz7pfLrtY+qcjIuNOY2sX92zYgQHfvXclUybmhV1S2tMJZBEZV1q7eln73Z00d/Tw1H3XM6+0\nKOySMoKODERk3OjsiXLfxioOnmzj0Y9fy5LKkrBLyhgKAxEZF8719LHu8Sp2Hmnma3ct4eYrysIu\nKaMoDEQk453r6eMTj+9k++HTfO2uJXrUxAjonIGIZLS2rl7WP/EKLx8+zdfvWsIHl+legpFQGIhI\nxmpq6+bex3ewv76Nb9y1lDuX6YhgpBQGIpKRjp7u4J4NO2hs7ebba1fwnitnhF1SRlMYiEjGqTrS\nzKe+9wrRmPPkfdexfI5+Nn20dAJZRDLKD3bWcvc/v8ykCXn86D/foCAYIzoyEJGM0BON8beb9vP4\nb46wakEpj9y9XHcWjyGFgYikvWPNnTzw/Vf57fEW1t00j8/fvlDPGhpjCgMRSWub9tTzlz96DYD/\n8/HlrF48O+SKxieFgYikpbOdPfzPn+7jJ7vqWFJZwiN3L6Ny2sSwyxq3FAYikna27j/J53+8h+aO\nHj596wIeeM988iMaFkomhYGIpI0TZ8/xpZ/t4+d7G1g4axIb/vSdLC6fEnZZWUFhICKh64728d1f\nH+HhrQeJufPfbruC9e+6XEcDKaQwEJHQxGLOT187wVc3H+D4mXO8b9FM/scHFuncQAgUBiKScu7O\nv77RxNe3vMFrx1u4avZknvjENbxLj50OjcJARFLG3XnhQCPf3FrDb4+dpbykkK99dAl3LisnN8fC\nLi+rKQxEJOm6o308t/sE33npMNUNbVRMLeTvPnQNH15eofMCaUJhICJJU99yjqd3HOP7O2ppautm\n4axJfPUj7+DOZeXk6Q7itKIwEJEx1RON8eKBRp6pOsa26kYcuPmKMtbdNI+b5pdipuGgdKQwEJFR\ni8WcXcfO8OzuE/z0tyc409lLaXEBn7r5cu5eOUdXB2UAhYGIjEi0L0bV0TM8v7eB5/c20NDaRX4k\nh9sWzeRDy8tZtaBMQ0EZRGEgIsPW1NbNr2tOsa26kRcPNNLaFSU/ksPNV5Tx4DULueWqGUyeoMdK\nZyKFgYgM6UxHDzuPNLP9cDO/rjlFdUMbANOL8rnt6lncunAGq64oo7hAXyWZTv8GRQSAvpjzu6Z2\ndtee5dXaM+yqPcuBk/Ev//xIDtfOmcrnVl/JqvllXH3JZHJ0X8C4ojAQyUKdPVHeONlOdX0r++tb\n2XuilX0nWjnX2wfAlMI8llaW8IdLZrNy3nSWVE6hIJIbctWSTAoDkXHK3Wls6+bwqQ6OnOrg0KkO\nahrbOdjYxvEz53CP9yvKz2XRJZP52MpKFl8yhaVzSpg3vUh/+WcZhYFIhor2xWhs66a+pYuGli7q\nznZSd+YcdWfPUdvcSW1zJ129sTf750dyuKy0iCUVJXx4eQULZ03mqtmTqJw6UV/8ojAQSRfuzrne\nPs529tLc0UNzRw9nOns41d7DqfZuTrd309TWTWPwOt3eTczPX8ekCRHKSwq5dHoRqxaUMWfaROaV\nFjGvtIhLSgr1/B8ZUtqEgZmtBr4J5ALfdvevhFySyLD0xeJf4p09Uc719NHZE5/u6I6/t3f30dEd\npT14tXX10tYVpa0rSsu5XlrO9dJ6rpez53rpicYG3UYkx5henE9pcQEzJhVwTfkUZkwqYHZJIbOm\nTGDW5AmUTy3UZZ0yYmkRBmaWC3wLeB9wHNhpZs+5+75wK5PRcHdi/tZ7LBikjrnjwXzM43evxgb0\n7XM/r70vmO6L+ZvT0Vi8T1/M6QuWRWNOX1/wHnOisRjRvvh7b1+8rbcvRjTm9EZj9AbzvdEYvX0x\nevpi9EQ9eO+jJxqjOxqjJxqjK9pHd2/8vas3RldvfL6nb/Av8MFEcoxJEyJMLsyLv0/IY8GMYqYU\n5jFlYh4lhflMnZhHycR8phfnM3ViPtOL8plSmKehHEmqtAgDYCVQ4+6HAMzsaWANMOZh8MmNOzly\nuvOC/dz9wn0uckFic+L6z29P7H/+is5bNtQ2/K1PDVxX//z5y+PtnlCTB8veXFfC8jeXJXyOYD7m\n5/fJFHm5Rn5uDnmRHPJyc8jPzaEgkkN+8CqI5FCQl8OUwjwK8nIoiOQy4c33XArzcinMzwneIxTl\n51KYn0txQYSJ+RGKCuLTRQURCiI5ejaPpKV0CYNy4FjC/HHguoGdzGw9sB5gzpw5I9rQpdOLhn+J\n3DD+nx2qy1D/w9t5fYZqt0HbBzZYwszAdfXPD+zzVj97q+3Nd0v43Ft1JC57s68Fax6wLMcSpnPs\nzWX9Y9Vm8T45wedycuLTOfbWtGHk5vT3M3Jz3lpHbtAv14zc3OA9J/6K5MSXRd6czyGSG5+P5OaQ\n19+em0NerpGXm0Mkx/TlLEL6hMGwuPtjwGMAK1asGNHfnl/4wKIxrUlEZDxIl6dI1QGVCfMVQZuI\niKRAuoTBTmCBmc0zs3zgY8BzIdckIpI10mKYyN2jZvYAsJn4paUb3P31kMsSEckaaREGAO6+CdgU\ndh0iItkoXYaJREQkRAoDERFRGIiIiMJAREQAG85jF9KRmTUBR0f48VLg1BiWkwm0z9kh2/Y52/YX\nRr/Pl7p72cDGjA2D0TCzKndfEXYdqaR9zg7Zts/Ztr+QvH3WMJGIiCgMREQke8PgsbALCIH2OTtk\n2z5n2/5CkvY5K88ZiIjI+bL1yEBERBIoDEREJLvCwMxWm9kBM6sxswfDrifZzKzSzF4ws31m9rqZ\nfSbsmlLFzHLNbJeZ/SzsWlLBzErM7IdmVm1m+83sD8KuKdnM7M+D/673mtlTZjYh7JrGmpltMLNG\nM9ub0DbNzLaY2cHgfepYbCtrwsDMcoFvAbcDi4C7zWy8/+xZFPgLd18EXA/cnwX73O8zwP6wi0ih\nbwLPu/tCYAnjfN/NrBz4NLDC3RcTf/T9x8KtKikeB1YPaHsQ2OruC4CtwfyoZU0YACuBGnc/5O49\nwNPAmpBrSip3r3f3V4PpNuJfEOXhVpV8ZlYB/Afg22HXkgpmNgV4F/AdAHfvcfez4VaVEhGg0Mwi\nwETgRMj1jDl3/xXQPKB5DbAxmN4I3DkW28qmMCgHjiXMHycLvhj7mdlcYBmwPdxKUuIfgc8BsbAL\nSZF5QBPw3WBo7NtmVhR2Ucnk7nXA/wJqgXqgxd1/EW5VKTPT3euD6QZg5lisNJvCIGuZWTHwI+Cz\n7t4adj3JZGYfABrd/ZWwa0mhCLAceNTdlwEdjNHQQboKxsnXEA/CS4AiM/t4uFWlnsfvDRiT+wOy\nKQzqgMqE+YqgbVwzszziQfCku/847HpS4Ebgj8zsCPGhwFvM7HvhlpR0x4Hj7t5/1PdD4uEwnr0X\nOOzuTe7eC/wYuCHkmlLlpJnNBgjeG8dipdkUBjuBBWY2z8zyiZ9sei7kmpLKzIz4OPJ+d/962PWk\ngrt/3t0r3H0u8X/H29x9XP/F6O4NwDEzuzJouhXYF2JJqVALXG9mE4P/zm9lnJ80T/AcsDaYXgs8\nOxYrTZvfQE42d4+a2QPAZuJXHmxw99dDLivZbgT+BNhjZruDtr8Kfm9axpf/AjwZ/KFzCLg35HqS\nyt23m9kPgVeJXzW3i3H4aAozewp4N1BqZseBh4CvAM+Y2Trij/G/a0y2pcdRiIhINg0TiYjIEBQG\nIiKiMBAREYWBiIigMBARERQGIiKCwkBERID/D2eRh0K6BEEgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klJkmc20uAZc",
        "colab_type": "code",
        "outputId": "0c0c68b6-e762-4cbb-989c-7adb581a02d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "one_d = torch.arange(0,9)\n",
        "one_d"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-UHBGzvbuUDr",
        "colab_type": "code",
        "outputId": "6ee2cb1d-67b2-4546-a93e-c109b9092573",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "two_d = one_d.view(3,3)\n",
        "two_d"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 1, 2],\n",
              "        [3, 4, 5],\n",
              "        [6, 7, 8]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lb1WI0MwuUnU",
        "colab_type": "code",
        "outputId": "786a7fe8-da5a-40dd-abb4-92127c202e87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "two_d.dim()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVGY6p_gvZsf",
        "colab_type": "code",
        "outputId": "ba564f62-232a-4eda-d4dc-e12f2b6baf0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "mat_a = torch.tensor([0,3,5,5,5,2]).view(2,3)\n",
        "mat_a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0, 3, 5],\n",
              "        [5, 5, 2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJjGzRzl4Jia",
        "colab_type": "code",
        "outputId": "0fb7e18a-15db-4ff5-e42f-aef5f7bec421",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "mat_b = torch.tensor([3,4,4,-2,4,-2]).view(3,2)\n",
        "mat_b"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 3,  4],\n",
              "        [ 4, -2],\n",
              "        [ 4, -2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNUKo2bM4yVB",
        "colab_type": "code",
        "outputId": "77a509a7-ace3-4e1e-9b45-3be5d32ed8f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "torch.matmul(mat_a,mat_b)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 32, -16],\n",
              "        [ 43,   6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiWuNGcN42IY",
        "colab_type": "code",
        "outputId": "997f0c64-e0b0-47ad-a6e1-5dc6703fee0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#d/dx(9x^4+2x^3+3x^2+6x+1) = 36x^3+6x^2+6x+6 @ 2 = 330\n",
        "\n",
        "x = torch.tensor(2.0, requires_grad=True)\n",
        "y = 9*x**4 + 2*x**3 + 3*x**2 + 6*x +1\n",
        "y.backward()\n",
        "x.grad\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(330.)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CkihXXI6DZz",
        "colab_type": "code",
        "outputId": "ed48014f-0ffd-4478-e101-aac708936026",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x1 = torch.tensor(1.0,requires_grad=True)\n",
        "z1 = torch.tensor(2.0, requires_grad=True)\n",
        "y = x1**2 + z1**3\n",
        "y.backward()\n",
        "x1.grad, z1.grad"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(2.), tensor(12.))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVXC3Rl5Bxbi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w = torch.tensor(3.0,requires_grad=True)\n",
        "b = torch.tensor(1.0, requires_grad=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dN7obAhxEQtb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def forward(x):\n",
        "  y = w*x + b\n",
        "  return y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwu3w0tzEYQN",
        "colab_type": "code",
        "outputId": "7686fba8-7026-4d87-98c1-855b3a5b0ebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x = torch.tensor(2)\n",
        "forward(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(7., grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lX94cULZErni",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}