PyTorch_41_Maxpooling.ipynb

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      "name": "PyTorch_41_Maxpooling.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMvfSBC5G9vGgG/zAowN5IV",
      "include_colab_link": true
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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/github/codeplinth/pytorchbootcamp/blob/master/PyTorch_41_Maxpooling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvQmF4t1p1h0"
      },
      "source": [
        "import torch\n",
        "from torch import nn"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-HbqRR8tCaW",
        "outputId": "f15404a2-69c4-4678-9ddc-fe1e9cdfabb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "image = torch.zeros(1,1,5,5)\n",
        "image[0,0,:,2] = 1\n",
        "image "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[0., 0., 1., 0., 0.],\n",
              "          [0., 0., 1., 0., 0.],\n",
              "          [0., 0., 1., 0., 0.],\n",
              "          [0., 0., 1., 0., 0.],\n",
              "          [0., 0., 1., 0., 0.]]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYyu6lfLtP4S",
        "outputId": "a8fef2fd-f8c2-4297-c427-3d081ec9f27a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "conv = nn.Conv2d(in_channels=1,out_channels=1,kernel_size=3)\n",
        "conv.state_dict()['weight'][0][0] = torch.tensor([[1.0,0.0,-1.0],[2.0,0.0,-2.0],[1.0,0.0,-1.0]])\n",
        "conv.state_dict()['bias'][0] = 0.0\n",
        "conv.state_dict()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('weight', tensor([[[[ 1.,  0., -1.],\n",
              "                        [ 2.,  0., -2.],\n",
              "                        [ 1.,  0., -1.]]]])), ('bias', tensor([0.]))])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luex1Hqit2N3",
        "outputId": "8ebc7ba5-e1b0-4edc-c431-72b701bc4c5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "Z = conv(image)\n",
        "Z"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[-4.,  0.,  4.],\n",
              "          [-4.,  0.,  4.],\n",
              "          [-4.,  0.,  4.]]]], grad_fn=<MkldnnConvolutionBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4s0i1mzLt9kb",
        "outputId": "6d91d361-f822-4f85-94ea-90c2dd6e04f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "relu = nn.ReLU()\n",
        "A = relu(Z)\n",
        "A"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[0., 0., 4.],\n",
              "          [0., 0., 4.],\n",
              "          [0., 0., 4.]]]], grad_fn=<ReluBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srQbS1SGuH0a",
        "outputId": "59aa77be-067c-496f-c633-31ae014152c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "image1 = torch.zeros(1,1,4,4)\n",
        "image1[0,0,0,:] = torch.tensor([1.0,2.0,3.0,-4.0])\n",
        "image1[0,0,1,:] = torch.tensor([0.0,2.0,-3.0,0.0])\n",
        "image1[0,0,2,:] = torch.tensor([0.0,2.0,3.0,1.0])\n",
        "image1"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[ 1.,  2.,  3., -4.],\n",
              "          [ 0.,  2., -3.,  0.],\n",
              "          [ 0.,  2.,  3.,  1.],\n",
              "          [ 0.,  0.,  0.,  0.]]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZuKxq2nu6gz",
        "outputId": "8be00088-72f0-4135-d0fa-9d35974ce697",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "max1 = nn.MaxPool2d(2,stride=1)\n",
        "max1(image1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[2., 3., 3.],\n",
              "          [2., 3., 3.],\n",
              "          [2., 3., 3.]]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zFefheSvJbg",
        "outputId": "40e08acc-cc62-45ae-9e4a-d85388dc4f79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "max2 = torch.nn.MaxPool2d(2)\n",
        "max2(image1)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[[2., 3.],\n",
              "          [2., 3.]]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRXJfrbqvUtJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
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}