rmarch.ipynb

rmarch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rmarch.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/f0nzie/e2fc9846731f7d732494f3c36ce67b84/rmarch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCnh_ab8JCW8",
        "colab_type": "code",
        "outputId": "19e96904-1513-465c-c418-401df4cfb867",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "torch.manual_seed(123)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f203dd48c30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z585IKG3JYJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# my crazy function\n",
        "def g(x):\n",
        "  eq = x.clone()\n",
        "  eq[0] = 2*x[0] # this will produce error without cloning \"x\" first\n",
        "  # x = eq\n",
        "  return eq"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5lHEMeYJYXS",
        "colab_type": "code",
        "outputId": "97c5c53b-f800-4096-e429-5f4d76c30a53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "\n",
        "x0 = np.array([1, 2, 3, 4], dtype=\"float\")\n",
        "x0 = torch.tensor(x0)\n",
        "x0 = x0.view(2,2)\n",
        "x0.requires_grad = True\n",
        "\n",
        "print(x0)\n",
        "print(x0.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1., 2.],\n",
            "        [3., 4.]], dtype=torch.float64, requires_grad=True)\n",
            "torch.Size([2, 2])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVSO8P3GhdVT",
        "colab_type": "code",
        "outputId": "1a22f4c0-d28a-4e63-ece5-02c162b06a10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "y = g(x0)\n",
        "print(y)\n",
        "y.backward(x0, retain_graph=True)\n",
        "x0.grad"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[2., 4.],\n",
            "        [3., 4.]], dtype=torch.float64, grad_fn=<CopySlices>)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2., 4.],\n",
              "        [3., 4.]], dtype=torch.float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxAu1_fYhjaC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khlzZp_Yhj0Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PoL8PbeLOE_8",
        "colab_type": "code",
        "outputId": "a09c6078-e7c5-48e9-e0a3-a7bfecda8670",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# another crazy function\n",
        "w = torch.tensor([1, 2])\n",
        "w = w.double()\n",
        "print(w)\n",
        "\n",
        "def h(x):\n",
        "  return x @ w"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([1., 2.], dtype=torch.float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6clIvVZOFLf",
        "colab_type": "code",
        "outputId": "dcd0f475-06fc-414e-d58e-d640a40e5f07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y = h(x0)\n",
        "print(y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([ 5., 11.], dtype=torch.float64, grad_fn=<MvBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZnpRxCaPSrg",
        "colab_type": "code",
        "outputId": "6eee76aa-6a64-4baa-940b-d599fb260ba6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "y[1].backward(retain_graph=True)\n",
        "x0.grad"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2., 4.],\n",
              "        [4., 6.]], dtype=torch.float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvRq0QrMPS8h",
        "colab_type": "code",
        "outputId": "9261b1ae-c080-4ef3-c114-7d434d6b6845",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "y[0].backward(retain_graph=True)\n",
        "x0.grad"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[3., 6.],\n",
              "        [4., 6.]], dtype=torch.float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    }
  ]
}