P02-C06-regularization-gluon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overfitting and regularization (with ``gluon``)\n",
    "\n",
    "Now that we've built a [logistic regression model from scratch](./P02-C03-softmax-regression-scratch.ipynb), let's make this more efficient with ``gluon``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import mxnet as mx\n",
    "from mxnet import nd, autograd\n",
    "from mxnet import gluon\n",
    "import numpy as np\n",
    "ctx = mx.gpu(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mnist = mx.test_utils.get_mnist()\n",
    "num_examples = 1000\n",
    "batch_size = 64\n",
    "train_data = mx.io.NDArrayIter(\n",
    "    mnist[\"train_data\"][:num_examples], \n",
    "    mnist[\"train_label\"][:num_examples], \n",
    "    batch_size, shuffle=True)\n",
    "test_data = mx.io.NDArrayIter(\n",
    "    mnist[\"test_data\"][:num_examples], \n",
    "    mnist[\"test_label\"][:num_examples], \n",
    "    batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiclass Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net = gluon.nn.Sequential()\n",
    "with net.name_scope():\n",
    "    net.add(gluon.nn.Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter initialization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Softmax Cross Entropy Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss = gluon.loss.SoftmaxCrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01, 'wd': 0.00})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evaluate_accuracy(data_iterator, net):\n",
    "    numerator = 0.\n",
    "    denominator = 0.\n",
    "    data_iterator.reset()\n",
    "    for i, batch in enumerate(data_iterator):\n",
    "        data = batch.data[0].as_in_context(ctx).reshape((-1,784))\n",
    "        label = batch.label[0].as_in_context(ctx)\n",
    "        label_one_hot = nd.one_hot(label, 10)\n",
    "        output = net(data)        \n",
    "        predictions = nd.argmax(output, axis=1)\n",
    "        numerator += nd.sum(predictions == label)\n",
    "        denominator += data.shape[0]\n",
    "    return (numerator / denominator).asscalar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 1000\n",
    "moving_loss = 0.\n",
    "\n",
    "for e in range(epochs):\n",
    "    train_data.reset()\n",
    "    for i, batch in enumerate(train_data):\n",
    "        data = batch.data[0].as_in_context(ctx).reshape((-1,784))\n",
    "        label = batch.label[0].as_in_context(ctx)\n",
    "        with autograd.record():\n",
    "            output = net(data)\n",
    "            cross_entropy = loss(output, label)\n",
    "        cross_entropy.backward()\n",
    "        trainer.step(data.shape[0])\n",
    "        \n",
    "        ##########################\n",
    "        #  Keep a moving average of the losses\n",
    "        ##########################\n",
    "        if i == 0:\n",
    "            moving_loss = nd.mean(cross_entropy).asscalar()\n",
    "        else:\n",
    "            moving_loss = .99 * moving_loss + .01 * nd.mean(cross_entropy).asscalar()\n",
    "            \n",
    "    test_accuracy = evaluate_accuracy(test_data, net)\n",
    "    train_accuracy = evaluate_accuracy(train_data, net)\n",
    "    if e % 100 == 99:\n",
    "        print(\"Completed epoch %s. Loss: %s, Train_acc %s, Test_acc %s\" % \n",
    "              (e+1, moving_loss, train_accuracy, test_accuracy))           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Now let's take a look at how to implement modern neural networks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "For whinges or inquiries, [open an issue on  GitHub.](https://github.com/zackchase/mxnet-the-straight-dope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = net.collect_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params['sequential0_dense0_weight'].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}