{
 "metadata": {
  "name": "TEST_theanoml_formula"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import theanoml\n",
      "reload(theanoml)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<module 'theanoml' from 'theanoml/__init__.pyc'>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Load MNIST data\n",
      "reload(theanoml.formula)\n",
      "import cPickle\n",
      "X, y = cPickle.load(open('data/digits.pkl', 'rb'))\n",
      "print X.shape, y.shape\n",
      "from sklearn.cross_validation import train_test_split\n",
      "train_X, validation_X, train_y, validation_y = train_test_split(X, y, test_size = 0.2)\n",
      "v_train_X, v_validation_X = theanoml.formula.share_data(train_X), theanoml.formula.share_data(validation_X)\n",
      "v_train_y, v_validation_y = theanoml.formula.share_data(train_y, dtype = 'int32'), theanoml.formula.share_data(validation_y, dtype='int32')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(42000, 784) (42000,)\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## TEST theanoml LogisticRegression MLP\n",
      "reload(theanoml.formula)\n",
      "mlp = theanoml.formula.MLPClassifierFormula(n_in = 28 * 28, n_hidden = 500, n_out = 10)\n",
      "theanoml.formula.sgd(v_train_X, v_train_y, v_validation_X, v_validation_y, mlp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "epoch 1, minibatch 1680 / 1680, validation error 10.511905 %\n",
        "epoch 2, minibatch 1680 / 1680, validation error 10.238095 %"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 3, minibatch 1680 / 1680, validation error 9.928571 %"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 4, minibatch 1680 / 1680, validation error 9.261905 %"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 5, minibatch 1680 / 1680, validation error 9.511905 %"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 6, minibatch 1680 / 1680, validation error 9.226190 %"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "epoch 7, minibatch 1680 / 1680, validation error 9.202381 %"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "optimization complete with best valiation error 9.202381 %"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}