notebook.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time 159.917896986\n"
     ]
    }
   ],
   "source": [
    "import Network as net\n",
    "import timeit \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import mminst_loader\n",
    "\n",
    "net = net.Network([784,30, 10], cost=net.Network.CrossEntropy, debug=False)\n",
    "\n",
    "training_data, validation_data, test_data = mminst_loader.load_data_wrapper()\n",
    "\n",
    "print \"Starting\"\n",
    "start_time = timeit.default_timer()\n",
    "\n",
    "net.SGD(training_data, 30, 10, 0.5, decay=5.0, test_data=test_data )\n",
    "\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print \"Elapsed time \" + str(elapsed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-63ffe283db5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch_test_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mx_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch_train_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mx_min\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mx_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch_test_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "x_test = np.arange(0., len(net.epoch_test_accuracy), 1)\n",
    "x_train = np.arange(0., len(net.epoch_train_accuracy), 1)\n",
    "\n",
    "x_min = 0\n",
    "x_max = len(net.epoch_test_accuracy)\n",
    "\n",
    "plt.plot(x_train, net.epoch_train_accuracy, 'r-')\n",
    "plt.title(\"Train Accuracy\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(x_train, net.epoch_train_cost, 'r-')\n",
    "plt.title(\"Train Cost\")\n",
    "plt.ylabel(\"Cost\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(x_test, net.epoch_test_accuracy, 'r-')\n",
    "plt.title(\"Test Accuracy\")\n",
    "plt.ylabel(\"Percentage\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.plot(x_test , net.epoch_test_cost , 'r-')\n",
    "plt.title(\"Test Cost\")\n",
    "plt.ylabel(\"Cost\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
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