Baisc_Tensors_actions.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Baisc Tensors actions.ipynb",
      "version": "0.3.2",
      "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/Vishwaak/Time-for-Machine/blob/master/Baisc_Tensors_actions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8DdvdEuujug",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtlhzf9ivzC9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def activation(x):\n",
        "  \n",
        "  return 1/(1+torch.exp(-x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsaQnN3x1b-T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "6a0854a3-b498-4215-b7b3-43fea7a106e2"
      },
      "source": [
        "activation(features)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-391e7ddf4e1d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mactivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-3-a81809a83cb5>\u001b[0m in \u001b[0;36mactivation\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'int' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbtJm1X-wDca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.manual_seed(7)\n",
        "\n",
        "features  = torch.randn((1,5))\n",
        "weights = torch.rand_like(features)\n",
        "\n",
        "bias = torch.randn((1,1))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBI24TU3w9qI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mine = torch.mul(features,weights)\n",
        "#mul function helps in mutiply tensors of different sizes "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJU2ZCH2yto8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine =  mine.sum() + bias"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JH_tPnZUzF3s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "198fab71-9b60-489d-be2c-e53d93d90eed"
      },
      "source": [
        "y =(activation(wine))\n",
        "print(y)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.6140]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XaRQ5aLB085l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#simple algebric solution\n",
        "y = activation((features * weights).sum() + bias)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCk5Al_53aTo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d123c10-0343-4a34-94cb-c921dacc5bc9"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.6140]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hz35Plrf3b4z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "99744020-90c7-4476-e9d8-8ca298fe3010"
      },
      "source": [
        "y= activation(torch.mm(features, weights.view(5,1))+  bias)\n",
        "print(y)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.6140]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efp6xmJc4naG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "62cdddb4-c3c6-4ef5-e35c-849c59f0b338"
      },
      "source": [
        "#sum of multiple layer network\n",
        "torch.manual_seed(7)\n",
        "features =torch.randn((1,3))\n",
        "n_input = features.shape[1]\n",
        "n_hidden = 2\n",
        "n_output = 1  \n",
        "W1 = torch.randn(n_input , n_hidden)\n",
        "W2 = torch.randn(n_hidden , n_output)\n",
        "\n",
        "B1 = torch.randn((1, n_hidden))\n",
        "B2 = torch.randn((1 ,n_output))\n",
        "\n",
        "h = activation(torch.mm(features , W1) + B1)\n",
        "output  = activation(torch.mm(h , W2) + B2)\n",
        "\n",
        "print(output)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.3171]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aa0suitK6bG-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}