intro_to_single_layer_nn_py.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Joycechidi/Secure-and-Private-AI/blob/master/intro_to_single_layer_nn_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#First import PyTorch\n",
        "#!pip install torch"
      ],
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      "outputs": [
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          "text": [
            "Requirement already satisfied: torch in /Users/joycechidiadi/anaconda3/lib/python3.6/site-packages (1.1.0)\n",
            "Requirement already satisfied: numpy in /Users/joycechidiadi/anaconda3/lib/python3.6/site-packages (from torch) (1.14.3)\n",
            "\u001b[33mYou are using pip version 10.0.1, however version 19.1.1 is available.\n",
            "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
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      "source": [
        "import torch"
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      "source": [
        "def activation(x):\n",
        "    \"\"\"Sigmoid activation function\n",
        "    \n",
        "    Arguments\n",
        "    ----------\n",
        "    x: torch.Tensor\n",
        "    \n",
        "    \"\"\"\n",
        "    \n",
        "    return 1/(1+torch.exp(-x))"
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      "source": [
        "#Generate some data \n",
        "torch.manual_seed(7) #Set the random seed so things are predictable\n",
        "\n",
        "#Features are 5 random normal variabes\n",
        "features = torch.randn((1, 5))\n",
        "#True weights for our data, random normal variables again\n",
        "weights = torch.randn_like(features)\n",
        "\n",
        "#and a true bias term\n",
        "bias = torch.randn((1, 1))\n",
        "weights.reshape(5, 1)\n",
        "weights.view(5, 1)"
      ],
      "execution_count": 0,
      "outputs": [
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          "data": {
            "text/plain": [
              "tensor([[-0.8948],\n",
              "        [-0.3556],\n",
              "        [ 1.2324],\n",
              "        [ 0.1382],\n",
              "        [-1.6822]])"
            ]
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      "source": [
        "#Calculate the output of this single layer network using torch.sum()\n",
        "y = activation(torch.sum(features * weights) + bias)\n",
        "print(y)\n",
        "\n",
        "y = activation((features * weights).sum() + bias)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[0.1595]])\n"
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        "#Calculate the output of this single layer network using matrix multiplication \n",
        "#The matrix multiplication is more efficient than the torch.sum\n",
        "\n",
        "y = activation(torch.mm(features, weights.view(5, 1)) + bias)\n",
        "print(y)"
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            "tensor([[0.1595]])\n"
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