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    "# Machine Learning Practical: Coursework 1\n",
    "\n",
    "**Release date: Monday 10th October 2016**  \n",
    "**Due date: 16:00 Thursday 27th October 2016**\n",
    "\n",
    "Instructions for the coursework are [available as a PDF here](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/coursework_1.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Learning rate schedules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The below code will set up the data providers, random number\n",
    "# generator and logger objects needed for training runs. As\n",
    "# loading the data from file take a little while you generally\n",
    "# will probably not want to reload the data providers on\n",
    "# every training run. If you wish to reset their state you\n",
    "# should instead use the .reset() method of the data providers.\n",
    "import numpy as np\n",
    "import logging\n",
    "from mlp.data_providers import MNISTDataProvider\n",
    "\n",
    "# Seed a random number generator\n",
    "seed = 10102016 \n",
    "rng = np.random.RandomState(seed)\n",
    "\n",
    "# Set up a logger object to print info about the training run to stdout\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "logger.handlers = [logging.StreamHandler()]\n",
    "\n",
    "# Create data provider objects for the MNIST data set\n",
    "train_data = MNISTDataProvider('train', batch_size=50, rng=rng)\n",
    "valid_data = MNISTDataProvider('valid', batch_size=50, rng=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# The model set up code below is provided as a starting point.\n",
    "# You will probably want to add further code cells for the\n",
    "# different experiments you run.\n",
    "\n",
    "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer\n",
    "from mlp.errors import CrossEntropySoftmaxError\n",
    "from mlp.models import MultipleLayerModel\n",
    "from mlp.initialisers import ConstantInit, GlorotUniformInit\n",
    "\n",
    "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
    "\n",
    "weights_init = GlorotUniformInit(rng=rng)\n",
    "biases_init = ConstantInit(0.)\n",
    "\n",
    "model = MultipleLayerModel([\n",
    "    AffineLayer(input_dim, hidden_dim, weights_init, biases_init), \n",
    "    SigmoidLayer(),\n",
    "    AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init), \n",
    "    SigmoidLayer(),\n",
    "    AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
    "])\n",
    "\n",
    "error = CrossEntropySoftmaxError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Momentum learning rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The below code will set up the data providers, random number\n",
    "# generator and logger objects needed for training runs. As\n",
    "# loading the data from file take a little while you generally\n",
    "# will probably not want to reload the data providers on\n",
    "# every training run. If you wish to reset their state you\n",
    "# should instead use the .reset() method of the data providers.\n",
    "import numpy as np\n",
    "import logging\n",
    "from mlp.data_providers import MNISTDataProvider\n",
    "\n",
    "# Seed a random number generator\n",
    "seed = 10102016 \n",
    "rng = np.random.RandomState(seed)\n",
    "\n",
    "# Set up a logger object to print info about the training run to stdout\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "logger.handlers = [logging.StreamHandler()]\n",
    "\n",
    "# Create data provider objects for the MNIST data set\n",
    "train_data = MNISTDataProvider('train', batch_size=50, rng=rng)\n",
    "valid_data = MNISTDataProvider('valid', batch_size=50, rng=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The model set up code below is provided as a starting point.\n",
    "# You will probably want to add further code cells for the\n",
    "# different experiments you run.\n",
    "\n",
    "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer\n",
    "from mlp.errors import CrossEntropySoftmaxError\n",
    "from mlp.models import MultipleLayerModel\n",
    "from mlp.initialisers import ConstantInit, GlorotUniformInit\n",
    "\n",
    "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
    "\n",
    "weights_init = GlorotUniformInit(rng=rng)\n",
    "biases_init = ConstantInit(0.)\n",
    "\n",
    "model = MultipleLayerModel([\n",
    "    AffineLayer(input_dim, hidden_dim, weights_init, biases_init), \n",
    "    SigmoidLayer(),\n",
    "    AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init), \n",
    "    SigmoidLayer(),\n",
    "    AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
    "])\n",
    "\n",
    "error = CrossEntropySoftmaxError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Adaptive learning rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The below code will set up the data providers, random number\n",
    "# generator and logger objects needed for training runs. As\n",
    "# loading the data from file take a little while you generally\n",
    "# will probably not want to reload the data providers on\n",
    "# every training run. If you wish to reset their state you\n",
    "# should instead use the .reset() method of the data providers.\n",
    "import numpy as np\n",
    "import logging\n",
    "from mlp.data_providers import MNISTDataProvider\n",
    "\n",
    "# Seed a random number generator\n",
    "seed = 10102016 \n",
    "rng = np.random.RandomState(seed)\n",
    "\n",
    "# Set up a logger object to print info about the training run to stdout\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "logger.handlers = [logging.StreamHandler()]\n",
    "\n",
    "# Create data provider objects for the MNIST data set\n",
    "train_data = MNISTDataProvider('train', batch_size=50, rng=rng)\n",
    "valid_data = MNISTDataProvider('valid', batch_size=50, rng=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The model set up code below is provided as a starting point.\n",
    "# You will probably want to add further code cells for the\n",
    "# different experiments you run.\n",
    "\n",
    "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer\n",
    "from mlp.errors import CrossEntropySoftmaxError\n",
    "from mlp.models import MultipleLayerModel\n",
    "from mlp.initialisers import ConstantInit, GlorotUniformInit\n",
    "\n",
    "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
    "\n",
    "weights_init = GlorotUniformInit(rng=rng)\n",
    "biases_init = ConstantInit(0.)\n",
    "\n",
    "model = MultipleLayerModel([\n",
    "    AffineLayer(input_dim, hidden_dim, weights_init, biases_init), \n",
    "    SigmoidLayer(),\n",
    "    AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init), \n",
    "    SigmoidLayer(),\n",
    "    AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
    "])\n",
    "\n",
    "error = CrossEntropySoftmaxError()"
   ]
  }
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