Single_layer_network_Pytorch.ipynb

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    "colab": {
      "name": "Single-layer network Pytorch.ipynb",
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    "kernelspec": {
      "name": "python3",
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/paulgureghian/Google_Colab_Notebooks/blob/master/Single_layer_network_Pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "wKZAFxEwphlp",
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      "source": [
        "** Created by Paul A. Gureghian on 11/19/18.**  \n",
        "\n",
        "** This notebook contains the code for a single-layer neural network in Pytorch.**\n"
      ]
    },
    {
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "cell_type": "code",
      "source": [
        "### install pytorch and torchvision\n",
        "!pip3 install -U -q http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl\n",
        "!pip3 install -U -q torchvision "
      ],
      "execution_count": 0,
      "outputs": [
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          "text": [
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        "id": "g-boidbfD56H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### import packages \n",
        "import torch \n",
        "import numpy as np\n",
        "import torchvision"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rQFamqq9fRJA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### define activation function\n",
        "def activation(x):\n",
        "    return 1 /  (1 + torch.exp(-x)) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dWVS8j7Y0CoB",
        "colab_type": "code",
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      "cell_type": "code",
      "source": [
        "### generate some data \n",
        "torch.manual_seed(7)\n",
        "features = torch.randn((1, 5))\n",
        "weights = torch.randn_like(features)\n",
        "bias = torch.randn((1, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "id": "LJrW-qpV3YMD",
        "colab_type": "code",
        "outputId": "c4c1bcdc-f951-4f80-997a-608b5f8e502f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "cell_type": "code",
      "source": [
        "### make labels for the data and weights \n",
        "y = activation(torch.sum(features * weights) + bias)\n",
        "print(y) \n",
        "### or \n",
        "### y = activation((weights * features).sum() +  bias) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.1595]])\n"
          ],
          "name": "stdout"
        }
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "cell_type": "code",
      "source": [
        "### calculate the output of the network with matrix multiplication\n",
        "y = activation(torch.mm(features, weights.view(5,1)) + bias)  \n",
        "print(y) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.1595]])\n"
          ],
          "name": "stdout"
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