diff --git a/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb b/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
index e9142f874..c184e566a 100644
--- a/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
+++ b/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
@@ -596,11 +596,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -1519,11 +1558,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n",
+      "labels = (n_inputs) = (1797,)\n",
+      "X = (n_inputs, n_features) = (1797, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# import necessary packages\n",
     "import numpy as np\n",
@@ -1588,11 +1645,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of training images: 1437\n",
+      "Number of test images: 360\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
@@ -1713,9 +1777,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# building our neural network\n",
@@ -1792,10 +1854,25 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "probabilities = (n_inputs, n_categories) = (1437, 10)\n",
+      "probability that image 0 is in category 0,1,2,...,9 = \n",
+      "[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03\n",
+      " 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03\n",
+      " 9.84443254e-01 3.11507992e-04]\n",
+      "probabilities sum up to: 1.0\n",
+      "\n",
+      "predictions = (n_inputs) = (1437,)\n",
+      "prediction for image 0: 8\n",
+      "correct label for image 0: 6\n"
+     ]
+    }
+   ],
    "source": [
     "# setup the feed-forward pass, subscript h = hidden layer\n",
     "\n",
@@ -1957,10 +2034,31 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Old accuracy on training data: 0.1440501043841336\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New accuracy on training data: 0.09672929714683368\n"
+     ]
+    }
+   ],
    "source": [
     "# to categorical turns our integer vector into a onehot representation\n",
     "#from keras.utils import to_categorical\n",
@@ -2061,9 +2159,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class NeuralNetwork:\n",
@@ -2186,10 +2282,16 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score on test set:  0.9416666666666667\n"
+     ]
+    }
+   ],
    "source": [
     "epochs = 100\n",
     "batch_size = 100\n",
@@ -2222,10 +2324,239 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.11666666666666667\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.20833333333333334\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.12222222222222222\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.14722222222222223\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.17777777777777778\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.16111111111111112\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.20277777777777778\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.5305555555555556\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.5944444444444444\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.5888888888888889\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.6111111111111112\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.5222222222222223\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.5555555555555556\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.8055555555555556\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.85\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.85\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.875\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8666666666666667\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8638888888888889\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9555555555555556\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.925\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9333333333333333\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9472222222222222\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9416666666666667\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9194444444444444\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9416666666666667\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8222222222222222\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.23333333333333334\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.125\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.125\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.09166666666666666\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.09166666666666666\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:44: RuntimeWarning: overflow encountered in exp\n",
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:45: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "eta_vals = np.logspace(-5, 1, 7)\n",
     "lmbd_vals = np.logspace(-5, 1, 7)\n",
@@ -2259,10 +2590,37 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# visual representation of grid search\n",
     "# uses seaborn heatmap, you can also do this with matplotlib imshow\n",
@@ -2321,10 +2679,513 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.18333333333333332\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.18611111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.13055555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.24444444444444444\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.23333333333333334\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.12777777777777777\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.1527777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9111111111111111\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8305555555555556\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.0001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.8944444444444445\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.975\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9777777777777777\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9777777777777777\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9444444444444444\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9722222222222222\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.9527777777777777\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.925\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9194444444444444\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8472222222222222\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8666666666666667\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9138888888888889\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.775\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.16944444444444445\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.16111111111111112\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.18611111111111112\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.18333333333333332\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.1111111111111111\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.09166666666666666\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.1361111111111111\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.058333333333333334\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "# store models for later use\n",
@@ -2354,10 +3215,29 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# optional\n",
     "# visual representation of grid search\n",
@@ -2439,9 +3319,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pip3 install tensorflow"
@@ -2457,9 +3335,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda install tensorflow"
@@ -2475,9 +3351,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# import necessary packages\n",
@@ -2527,9 +3401,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from keras.utils import to_categorical\n",
@@ -2559,9 +3431,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
@@ -2707,9 +3577,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "epochs = 100\n",
@@ -2724,9 +3592,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DNN_tf = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -2749,9 +3615,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -2790,9 +3654,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -2816,9 +3678,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "conda install keras"
@@ -2834,9 +3694,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pip3 install keras"
@@ -2852,9 +3710,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from keras.models import Sequential\n",
@@ -2877,9 +3733,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -2902,9 +3756,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# optional\n",
@@ -3102,7 +3954,25 @@
    ]
   }
  ],
- "metadata": {},
+ "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": "3.7.0"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }