pytorch_and.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.fc1 = nn.Linear(2, 3, True)\n",
    "        self.fc2 = nn.Linear(3, 1, True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.sigmoid(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialising pytorch variables with features and targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "AND_X = [\n",
    "    [0, 0],\n",
    "    [0, 1],\n",
    "    [1, 0],\n",
    "    [1, 1]\n",
    "]\n",
    "\n",
    "AND_Y = [\n",
    "    [0],\n",
    "    [0],\n",
    "    [0],\n",
    "    [1]\n",
    "]\n",
    "\n",
    "inputs = list(Variable(torch.Tensor([s])) for s in AND_X)\n",
    "targets = list(Variable(torch.Tensor([s])) for s in AND_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an object of pytorch neuralnet Net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net = Net()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criterion and loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating an object of pytorch neural net and training the model with given dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "print(\"Training loop:\")\n",
    "for idx in range(0, 50000):\n",
    "    for input, target in zip(inputs, targets):\n",
    "        optimizer.zero_grad()   \n",
    "        output = net(input)\n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()    \n",
    "    if idx % 5000 == 0:\n",
    "        print(\"Epoch {: >8} Loss: {}\".format(idx, loss.data.numpy()[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"\")\n",
    "print(\"Final results:\")\n",
    "for input, target in zip(inputs, targets):\n",
    "    output = net(input)\n",
    "    print(\"Input:[{},{}] Target:[{}] Predicted:[{}] Error:[{}]\".format(\n",
    "        int(input.data.numpy()[0][0]),\n",
    "        int(input.data.numpy()[0][1]),\n",
    "        int(target.data.numpy()[0]),\n",
    "        round(float(output.data.numpy()[0]), 4),\n",
    "        round(float(abs(target.data.numpy()[0] - output.data.numpy()[0])), 4)\n",
    "    ))"
   ]
  }
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