backward pytorch

backward-pytorch.ipynb

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      "name": "backward pytorch",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/f0nzie/2b3edad1c6ea92147852f0248ee0b13e/backward-pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFuj7UjM65J6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import numpy as np "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpbLnkLsEpHl",
        "colab_type": "text"
      },
      "source": [
        "## Example 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToFuUaMr6-3T",
        "colab_type": "code",
        "outputId": "b4a8a9c0-6341-482b-cbce-765a036438da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "x = np.arange(1,3,1)\n",
        "x = torch.from_numpy(x).reshape(len(x), 1)\n",
        "x = x.float()\n",
        "x.requires_grad = True\n",
        "print(x)\n",
        "w = torch.randn((2,2), requires_grad = True)\n",
        "print(w)\n",
        "y = w @ x\n",
        "print(y)\n",
        "\n",
        "# Compute gradients of w1 . x\n",
        "y[0].backward(retain_graph=True)\n",
        "print(w.grad)\n",
        "# tensor([[1., 2.],\n",
        "#        [0., 0.]])\n",
        "y[1].backward(retain_graph=True)\n",
        "print(w.grad)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[1.],\n",
            "        [2.]], requires_grad=True)\n",
            "tensor([[ 0.6973, -1.5348],\n",
            "        [-2.6904, -0.4845]], requires_grad=True)\n",
            "tensor([[-2.3724],\n",
            "        [-3.6594]], grad_fn=<MmBackward>)\n",
            "tensor([[1., 2.],\n",
            "        [0., 0.]])\n",
            "tensor([[1., 2.],\n",
            "        [1., 2.]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wywcl4g7EX6g",
        "colab_type": "text"
      },
      "source": [
        "## Example 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyKAHyp46_Hk",
        "colab_type": "code",
        "outputId": "604fd646-12cd-49f8-83c2-6eab0b4c8175",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# From an example on linear regression\n",
        "x = np.arange(1,16,1)\n",
        "x = torch.from_numpy(x)\n",
        "x = x.float()\n",
        "x = x.view(-1, 3)\n",
        "x.requires_grad = True # torch.Size([5, 3])\n",
        "print(x)\n",
        "# w represents the weights and y the model. very, very simple example\n",
        "w = torch.randn((5, 5), requires_grad = True)\n",
        "y = w @ x\n",
        "print(y)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[ 1.,  2.,  3.],\n",
            "        [ 4.,  5.,  6.],\n",
            "        [ 7.,  8.,  9.],\n",
            "        [10., 11., 12.],\n",
            "        [13., 14., 15.]], requires_grad=True)\n",
            "tensor([[-1.9718, -1.9902, -2.0085],\n",
            "        [ 0.4344,  0.8532,  1.2720],\n",
            "        [11.7566, 12.0687, 12.3809],\n",
            "        [39.5736, 45.3092, 51.0447],\n",
            "        [-6.0419, -6.4095, -6.7770]], grad_fn=<MmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNnt1Z4nF9Do",
        "colab_type": "code",
        "outputId": "3dc53689-1f8b-4d2f-afeb-f81442c7e9ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "y[0,0].backward(retain_graph=True) # this computes the derivative\n",
        "w.grad"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 1.,  4.,  7., 10., 13.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0uthKUaHJZG",
        "colab_type": "code",
        "outputId": "bf0b3f74-edf1-4852-8149-eec4d032d71d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "y[0,1].backward(retain_graph=True)  # this computes\n",
        "w.grad"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 3.,  9., 15., 21., 27.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWUqFCMCIyYg",
        "colab_type": "code",
        "outputId": "e948e4a0-509a-411b-f3a8-ece497b90d6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "\n",
        "y[1,1].backward(retain_graph=True) # this doesn't\n",
        "w.grad.data"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 3.,  9., 15., 21., 27.],\n",
              "        [ 8., 20., 32., 44., 56.],\n",
              "        [ 3.,  6.,  9., 12., 15.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekJdNRXNI-h0",
        "colab_type": "code",
        "outputId": "ad0dfe31-4058-4b72-ba01-cb9ed5995f8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "# this doesn't compute any derivative\n",
        "y[2,2].backward(retain_graph=True)\n",
        "w.grad"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 3.,  9., 15., 21., 27.],\n",
              "        [ 2.,  5.,  8., 11., 14.],\n",
              "        [ 3.,  6.,  9., 12., 15.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0.,  0.,  0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    }
  ]
}