neural_nets_intro.ipynb

      "350/350 [==============================] - 0s 103us/step - loss: 0.6937 - acc: 0.4971 - val_loss: 0.6821 - val_acc: 0.5133\n",
      "Epoch 17/100\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.6862 - acc: 0.5229 - val_loss: 0.6750 - val_acc: 0.5467\n",
      "Epoch 18/100\n",
      "350/350 [==============================] - 0s 146us/step - loss: 0.6786 - acc: 0.5371 - val_loss: 0.6680 - val_acc: 0.5733\n",
      "Epoch 19/100\n",
      "350/350 [==============================] - 0s 149us/step - loss: 0.6711 - acc: 0.5514 - val_loss: 0.6611 - val_acc: 0.5933\n",
      "Epoch 20/100\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.6639 - acc: 0.5800 - val_loss: 0.6546 - val_acc: 0.6133\n",
      "Epoch 21/100\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.6569 - acc: 0.5886 - val_loss: 0.6482 - val_acc: 0.6333\n",
      "Epoch 22/100\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.6503 - acc: 0.6114 - val_loss: 0.6422 - val_acc: 0.6467\n",
      "Epoch 23/100\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.6436 - acc: 0.6257 - val_loss: 0.6360 - val_acc: 0.6533\n",
      "Epoch 24/100\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.6371 - acc: 0.6343 - val_loss: 0.6303 - val_acc: 0.6667\n",
      "Epoch 25/100\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.6308 - acc: 0.6486 - val_loss: 0.6244 - val_acc: 0.7000\n",
      "Epoch 26/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.6246 - acc: 0.6600 - val_loss: 0.6189 - val_acc: 0.7000\n",
      "Epoch 27/100\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.6185 - acc: 0.6771 - val_loss: 0.6135 - val_acc: 0.7133\n",
      "Epoch 28/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.6126 - acc: 0.6914 - val_loss: 0.6083 - val_acc: 0.7267\n",
      "Epoch 29/100\n",
      "350/350 [==============================] - 0s 115us/step - loss: 0.6069 - acc: 0.7114 - val_loss: 0.6032 - val_acc: 0.7333\n",
      "Epoch 30/100\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.6013 - acc: 0.7314 - val_loss: 0.5981 - val_acc: 0.7267\n",
      "Epoch 31/100\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.5960 - acc: 0.7400 - val_loss: 0.5933 - val_acc: 0.7333\n",
      "Epoch 32/100\n",
      "350/350 [==============================] - 0s 133us/step - loss: 0.5907 - acc: 0.7486 - val_loss: 0.5885 - val_acc: 0.7533\n",
      "Epoch 33/100\n",
      "350/350 [==============================] - 0s 104us/step - loss: 0.5854 - acc: 0.7571 - val_loss: 0.5839 - val_acc: 0.7733\n",
      "Epoch 34/100\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.5802 - acc: 0.7686 - val_loss: 0.5791 - val_acc: 0.7667\n",
      "Epoch 35/100\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.5753 - acc: 0.7743 - val_loss: 0.5747 - val_acc: 0.7667\n",
      "Epoch 36/100\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.5704 - acc: 0.7829 - val_loss: 0.5703 - val_acc: 0.7733\n",
      "Epoch 37/100\n",
      "350/350 [==============================] - 0s 154us/step - loss: 0.5658 - acc: 0.7857 - val_loss: 0.5661 - val_acc: 0.7733\n",
      "Epoch 38/100\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.5613 - acc: 0.7829 - val_loss: 0.5620 - val_acc: 0.7933\n",
      "Epoch 39/100\n",
      "350/350 [==============================] - 0s 141us/step - loss: 0.5570 - acc: 0.7800 - val_loss: 0.5581 - val_acc: 0.7933\n",
      "Epoch 40/100\n",
      "350/350 [==============================] - 0s 64us/step - loss: 0.5528 - acc: 0.7886 - val_loss: 0.5545 - val_acc: 0.8000\n",
      "Epoch 41/100\n",
      "350/350 [==============================] - 0s 124us/step - loss: 0.5489 - acc: 0.7914 - val_loss: 0.5511 - val_acc: 0.7933\n",
      "Epoch 42/100\n",
      "350/350 [==============================] - 0s 128us/step - loss: 0.5449 - acc: 0.7971 - val_loss: 0.5477 - val_acc: 0.7933\n",
      "Epoch 43/100\n",
      "350/350 [==============================] - 0s 140us/step - loss: 0.5411 - acc: 0.7971 - val_loss: 0.5444 - val_acc: 0.7867\n",
      "Epoch 44/100\n",
      "350/350 [==============================] - 0s 120us/step - loss: 0.5372 - acc: 0.8029 - val_loss: 0.5410 - val_acc: 0.7867\n",
      "Epoch 45/100\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.5335 - acc: 0.8057 - val_loss: 0.5379 - val_acc: 0.7867\n",
      "Epoch 46/100\n",
      "350/350 [==============================] - 0s 119us/step - loss: 0.5298 - acc: 0.8029 - val_loss: 0.5346 - val_acc: 0.7800\n",
      "Epoch 47/100\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.5261 - acc: 0.8057 - val_loss: 0.5315 - val_acc: 0.7800\n",
      "Epoch 48/100\n",
      "350/350 [==============================] - 0s 142us/step - loss: 0.5225 - acc: 0.8057 - val_loss: 0.5283 - val_acc: 0.7800\n",
      "Epoch 49/100\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.5189 - acc: 0.8114 - val_loss: 0.5251 - val_acc: 0.7800\n",
      "Epoch 50/100\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.5152 - acc: 0.8086 - val_loss: 0.5220 - val_acc: 0.7800\n",
      "Epoch 51/100\n",
      "350/350 [==============================] - 0s 114us/step - loss: 0.5116 - acc: 0.8143 - val_loss: 0.5187 - val_acc: 0.7800\n",
      "Epoch 52/100\n",
      "350/350 [==============================] - 0s 121us/step - loss: 0.5079 - acc: 0.8286 - val_loss: 0.5153 - val_acc: 0.7800\n",
      "Epoch 53/100\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.5043 - acc: 0.8286 - val_loss: 0.5120 - val_acc: 0.7867\n",
      "Epoch 54/100\n",
      "350/350 [==============================] - 0s 138us/step - loss: 0.5007 - acc: 0.8257 - val_loss: 0.5089 - val_acc: 0.7867\n",
      "Epoch 55/100\n",
      "350/350 [==============================] - 0s 135us/step - loss: 0.4974 - acc: 0.8314 - val_loss: 0.5060 - val_acc: 0.7933\n",
      "Epoch 56/100\n",
      "350/350 [==============================] - 0s 102us/step - loss: 0.4941 - acc: 0.8314 - val_loss: 0.5031 - val_acc: 0.7933\n",
      "Epoch 57/100\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.4906 - acc: 0.8371 - val_loss: 0.5000 - val_acc: 0.7933\n",
      "Epoch 58/100\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.4871 - acc: 0.8400 - val_loss: 0.4969 - val_acc: 0.7867\n",
      "Epoch 59/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.4838 - acc: 0.8400 - val_loss: 0.4939 - val_acc: 0.7867\n",
      "Epoch 60/100\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.4803 - acc: 0.8400 - val_loss: 0.4906 - val_acc: 0.8000\n",
      "Epoch 61/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 123us/step - loss: 0.4767 - acc: 0.8400 - val_loss: 0.4876 - val_acc: 0.8000\n",
      "Epoch 62/100\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.4733 - acc: 0.8343 - val_loss: 0.4846 - val_acc: 0.7933\n",
      "Epoch 63/100\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.4699 - acc: 0.8400 - val_loss: 0.4816 - val_acc: 0.7933\n",
      "Epoch 64/100\n",
      "350/350 [==============================] - 0s 142us/step - loss: 0.4667 - acc: 0.8400 - val_loss: 0.4786 - val_acc: 0.8000\n",
      "Epoch 65/100\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.4636 - acc: 0.8371 - val_loss: 0.4758 - val_acc: 0.8000\n",
      "Epoch 66/100\n",
      "350/350 [==============================] - 0s 103us/step - loss: 0.4604 - acc: 0.8371 - val_loss: 0.4730 - val_acc: 0.8000\n",
      "Epoch 67/100\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4574 - acc: 0.8429 - val_loss: 0.4701 - val_acc: 0.8000\n",
      "Epoch 68/100\n",
      "350/350 [==============================] - 0s 134us/step - loss: 0.4545 - acc: 0.8457 - val_loss: 0.4677 - val_acc: 0.8000\n",
      "Epoch 69/100\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.4516 - acc: 0.8457 - val_loss: 0.4652 - val_acc: 0.8000\n",
      "Epoch 70/100\n",
      "350/350 [==============================] - 0s 123us/step - loss: 0.4486 - acc: 0.8457 - val_loss: 0.4625 - val_acc: 0.8000\n",
      "Epoch 71/100\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.4457 - acc: 0.8486 - val_loss: 0.4600 - val_acc: 0.8000\n",
      "Epoch 72/100\n",
      "350/350 [==============================] - 0s 106us/step - loss: 0.4426 - acc: 0.8457 - val_loss: 0.4574 - val_acc: 0.8067\n",
      "Epoch 73/100\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.4399 - acc: 0.8457 - val_loss: 0.4549 - val_acc: 0.8067\n",
      "Epoch 74/100\n",
      "350/350 [==============================] - 0s 107us/step - loss: 0.4369 - acc: 0.8486 - val_loss: 0.4523 - val_acc: 0.8067\n",
      "Epoch 75/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.4340 - acc: 0.8514 - val_loss: 0.4498 - val_acc: 0.8067\n",
      "Epoch 76/100\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.4311 - acc: 0.8514 - val_loss: 0.4472 - val_acc: 0.8200\n",
      "Epoch 77/100\n",
      "350/350 [==============================] - 0s 129us/step - loss: 0.4282 - acc: 0.8543 - val_loss: 0.4449 - val_acc: 0.8200\n",
      "Epoch 78/100\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.4255 - acc: 0.8571 - val_loss: 0.4425 - val_acc: 0.8267\n",
      "Epoch 79/100\n",
      "350/350 [==============================] - 0s 132us/step - loss: 0.4228 - acc: 0.8571 - val_loss: 0.4401 - val_acc: 0.8267\n",
      "Epoch 80/100\n",
      "350/350 [==============================] - 0s 157us/step - loss: 0.4201 - acc: 0.8571 - val_loss: 0.4377 - val_acc: 0.8200\n",
      "Epoch 81/100\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.4173 - acc: 0.8629 - val_loss: 0.4352 - val_acc: 0.8200\n",
      "Epoch 82/100\n",
      "350/350 [==============================] - 0s 109us/step - loss: 0.4146 - acc: 0.8600 - val_loss: 0.4328 - val_acc: 0.8200\n",
      "Epoch 83/100\n",
      "350/350 [==============================] - 0s 108us/step - loss: 0.4120 - acc: 0.8600 - val_loss: 0.4306 - val_acc: 0.8200\n",
      "Epoch 84/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.4095 - acc: 0.8629 - val_loss: 0.4284 - val_acc: 0.8200\n",
      "Epoch 85/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.4069 - acc: 0.8629 - val_loss: 0.4261 - val_acc: 0.8200\n",
      "Epoch 86/100\n",
      "350/350 [==============================] - 0s 131us/step - loss: 0.4043 - acc: 0.8657 - val_loss: 0.4238 - val_acc: 0.8200\n",
      "Epoch 87/100\n",
      "350/350 [==============================] - 0s 125us/step - loss: 0.4018 - acc: 0.8686 - val_loss: 0.4216 - val_acc: 0.8200\n",
      "Epoch 88/100\n",
      "350/350 [==============================] - 0s 117us/step - loss: 0.3993 - acc: 0.8686 - val_loss: 0.4193 - val_acc: 0.8200\n",
      "Epoch 89/100\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.3969 - acc: 0.8714 - val_loss: 0.4173 - val_acc: 0.8200\n",
      "Epoch 90/100\n",
      "350/350 [==============================] - 0s 137us/step - loss: 0.3945 - acc: 0.8771 - val_loss: 0.4151 - val_acc: 0.8200\n",
      "Epoch 91/100\n",
      "350/350 [==============================] - 0s 144us/step - loss: 0.3921 - acc: 0.8771 - val_loss: 0.4130 - val_acc: 0.8200\n",
      "Epoch 92/100\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.3899 - acc: 0.8743 - val_loss: 0.4109 - val_acc: 0.8200\n",
      "Epoch 93/100\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.3875 - acc: 0.8771 - val_loss: 0.4088 - val_acc: 0.8200\n",
      "Epoch 94/100\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.3854 - acc: 0.8800 - val_loss: 0.4068 - val_acc: 0.8200\n",
      "Epoch 95/100\n",
      "350/350 [==============================] - 0s 126us/step - loss: 0.3832 - acc: 0.8771 - val_loss: 0.4050 - val_acc: 0.8200\n",
      "Epoch 96/100\n",
      "350/350 [==============================] - 0s 95us/step - loss: 0.3811 - acc: 0.8771 - val_loss: 0.4030 - val_acc: 0.8200\n",
      "Epoch 97/100\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.3790 - acc: 0.8771 - val_loss: 0.4010 - val_acc: 0.8200\n",
      "Epoch 98/100\n",
      "350/350 [==============================] - 0s 150us/step - loss: 0.3768 - acc: 0.8743 - val_loss: 0.3990 - val_acc: 0.8200\n",
      "Epoch 99/100\n",
      "350/350 [==============================] - 0s 136us/step - loss: 0.3746 - acc: 0.8743 - val_loss: 0.3972 - val_acc: 0.8267\n",
      "Epoch 100/100\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.3726 - acc: 0.8743 - val_loss: 0.3953 - val_acc: 0.8333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6dcd8fef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we split the dataset into training (80%) and validation sets (20%) \n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3)\n",
    "\n",
    "num_epochs = 100\n",
    "\n",
    "model_run = model.fit(X_train, y_train, epochs=num_epochs, validation_data = (X_test,y_test))\n",
    "\n",
    "history_model = model_run.history\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1)[5:], history_model[\"acc\"][5:], \"--\") ;\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1)[5:], history_model[\"val_acc\"][5:]) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using SciKit learn functions on Keras models\n",
    "\n",
    "As we have seen from the previous chapters, SciKit learn offers very handy functions for evaluating and tuning the machine learning models.\n",
    "\n",
    "So the question is: Can we somehow use those functions with the models we build in Keras?\n",
    "\n",
    "The Answer is **YES !**\n",
    "\n",
    "Keras offers wrappers which allow its Sequential models to be used with SciKit learn. There 2 such wrappers: **KerasClassifier** and **KerasRegressor**.\n",
    "\n",
    "For more information:\n",
    "https://keras.io/scikit-learn-api/\n",
    "\n",
    "**Now lets see how this works!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.61428571 0.6        0.88571429 0.7        0.67142857]\n",
      "0.6942857147966113\n"
     ]
    }
   ],
   "source": [
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier \n",
    "from sklearn.model_selection import cross_val_score\n",
    "model_scikit = KerasClassifier(build_fn=a_simple_NN, **{\"epochs\":num_epochs, \"verbose\":0})\n",
    "cross_validation = cross_val_score(model_scikit, X_train, y_train, cv=5, verbose=0)\n",
    "print(cross_validation)\n",
    "print(np.mean(cross_validation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_flatten(list_of_list):\n",
    "    flattened_list = [i for j in list_of_list for i in j]\n",
    "    return flattened_list\n",
    "\n",
    "def train_and_plot_decision_surface(\n",
    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
    "):\n",
    "\n",
    "    features_2d = np.array(features_2d)\n",
    "    xmin, ymin = features_2d.min(axis=0)\n",
    "    xmax, ymax = features_2d.max(axis=0)\n",
    "\n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N)\n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "\n",
    "    if preproc is not None:\n",
    "        points_for_classifier = preproc.fit_transform(points)\n",
    "        features_2d = preproc.fit_transform(features_2d)\n",
    "    else:\n",
    "        points_for_classifier = points\n",
    "\n",
    "    classifier.fit(features_2d, labels, verbose=0)\n",
    "    predicted = classifier.predict(features_2d)\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        predicted = list_flatten(predicted)\n",
    "    \n",
    "    \n",
    "    if preproc is not None:\n",
    "        name += \" (w/ preprocessing)\"\n",
    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
    "    else:\n",
    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
    "    plt.plot(\n",
    "        points[~classes][:, 0],\n",
    "        points[~classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"black\",\n",
    "        markersize=1,\n",
    "        alpha=0.1,\n",
    "    )\n",
    "    plt.plot(\n",
    "        points[classes][:, 0],\n",
    "        points[classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"blue\",\n",
    "        markersize=1,\n",
    "        alpha=0.1,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "def a_simple_NN():\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(8, input_shape = (2,), activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(2, activation = \"relu\"))\n",
    "\n",
    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = a_simple_NN()\n",
    "\n",
    "num_epochs = 400\n",
    "model_scikit = KerasClassifier(build_fn=a_simple_NN, epochs=num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net:\t 487 / 500 correct\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6ff946320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#color=\"steelblue\",color=\"chocolate\" marker=marker,\n",
    "\n",
    "\n",
    "def plot_points(plt=plt, marker='o'):\n",
    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
    "\n",
    "_, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "xor = pd.read_csv(\"xor.csv\")\n",
    "features = xor.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = xor.iloc[:, -1]\n",
    "\n",
    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
    "plot_points(plt=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise: Create a neural network to classify the 2d points example from chapter 2 and **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"2d_points.csv\")\n",
    "features = df.iloc[:, :-1]\n",
    "labels = df.iloc[:, -1]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Might Remove the following: This beer example is not good for neural networks. Basically the dataset is far too small**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisiting the beer example\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Loading the beer data\n",
    "beer = pd.read_csv(\"beers.csv\")\n",
    "\n",
    "# Extracting the features and labels\n",
    "#beer_data.describe()\n",
    "features = beer.iloc[:, :-1]\n",
    "labels = beer.iloc[:, -1]\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Revisiting the beer example\n",
    "\n",
    "# Loading and preparing the data\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Loading the beer data\n",
    "beer = pd.read_csv(\"beers.csv\")\n",
    "\n",
    "# Extracting the features and labels\n",
    "#beer_data.describe()\n",
    "features = beer.iloc[:, :-1]\n",
    "labels = beer.iloc[:, -1]\n",
    "\n",
    "# Here we split the dataset into training (70%) and validation sets (30%) \n",
    "#X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.5, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3)\n",
    "\n",
    "# Scaling the data\n",
    "# NOTE: The features should be normalized before being fed into the neural network\n",
    "scaling = MinMaxScaler()\n",
    "scaling.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaling.transform(X_train)\n",
    "X_test_scaled = scaling.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 157 samples, validate on 68 samples\n",
      "Epoch 1/1000\n",
      "157/157 [==============================] - 1s 6ms/step - loss: 0.6730 - acc: 0.5350 - val_loss: 0.6769 - val_acc: 0.5147\n",
      "Epoch 2/1000\n",
      "157/157 [==============================] - 0s 406us/step - loss: 0.6704 - acc: 0.5350 - val_loss: 0.6754 - val_acc: 0.5147\n",
      "Epoch 3/1000\n",
      "157/157 [==============================] - 0s 256us/step - loss: 0.6693 - acc: 0.5350 - val_loss: 0.6740 - val_acc: 0.5147\n",
      "Epoch 4/1000\n",
      "157/157 [==============================] - 0s 215us/step - loss: 0.6679 - acc: 0.5350 - val_loss: 0.6728 - val_acc: 0.5147\n",
      "Epoch 5/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.6668 - acc: 0.5350 - val_loss: 0.6716 - val_acc: 0.5147\n",
      "Epoch 6/1000\n",
      "157/157 [==============================] - 0s 107us/step - loss: 0.6658 - acc: 0.5350 - val_loss: 0.6704 - val_acc: 0.5147\n",
      "Epoch 7/1000\n",
      "157/157 [==============================] - 0s 303us/step - loss: 0.6652 - acc: 0.5350 - val_loss: 0.6693 - val_acc: 0.5147\n",
      "Epoch 8/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.6637 - acc: 0.5350 - val_loss: 0.6682 - val_acc: 0.5147\n",
      "Epoch 9/1000\n",
      "157/157 [==============================] - 0s 92us/step - loss: 0.6626 - acc: 0.5350 - val_loss: 0.6670 - val_acc: 0.5147\n",
      "Epoch 10/1000\n",
      "157/157 [==============================] - 0s 90us/step - loss: 0.6616 - acc: 0.5350 - val_loss: 0.6657 - val_acc: 0.5147\n",
      "Epoch 11/1000\n",
      "157/157 [==============================] - 0s 92us/step - loss: 0.6605 - acc: 0.5350 - val_loss: 0.6644 - val_acc: 0.5147\n",
      "Epoch 12/1000\n",
      "157/157 [==============================] - 0s 305us/step - loss: 0.6596 - acc: 0.5350 - val_loss: 0.6633 - val_acc: 0.5147\n",
      "Epoch 13/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.6587 - acc: 0.5350 - val_loss: 0.6622 - val_acc: 0.5147\n",
      "Epoch 14/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.6578 - acc: 0.5350 - val_loss: 0.6612 - val_acc: 0.5147\n",
      "Epoch 15/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.6567 - acc: 0.5350 - val_loss: 0.6601 - val_acc: 0.5147\n",
      "Epoch 16/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.6558 - acc: 0.5350 - val_loss: 0.6591 - val_acc: 0.5147\n",
      "Epoch 17/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.6551 - acc: 0.5350 - val_loss: 0.6580 - val_acc: 0.5147\n",
      "Epoch 18/1000\n",
      "157/157 [==============================] - 0s 106us/step - loss: 0.6540 - acc: 0.5350 - val_loss: 0.6570 - val_acc: 0.5147\n",
      "Epoch 19/1000\n",
      "157/157 [==============================] - 0s 97us/step - loss: 0.6531 - acc: 0.5350 - val_loss: 0.6559 - val_acc: 0.5147\n",
      "Epoch 20/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.6523 - acc: 0.5350 - val_loss: 0.6549 - val_acc: 0.5147\n",
      "Epoch 21/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.6512 - acc: 0.5350 - val_loss: 0.6537 - val_acc: 0.5147\n",
      "Epoch 22/1000\n",
      "157/157 [==============================] - 0s 288us/step - loss: 0.6506 - acc: 0.5350 - val_loss: 0.6527 - val_acc: 0.5147\n",
      "Epoch 23/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.6496 - acc: 0.5414 - val_loss: 0.6517 - val_acc: 0.5147\n",
      "Epoch 24/1000\n",
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      "Epoch 28/1000\n",
      "157/157 [==============================] - 0s 257us/step - loss: 0.6447 - acc: 0.5541 - val_loss: 0.6461 - val_acc: 0.5147\n",
      "Epoch 29/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.6437 - acc: 0.5541 - val_loss: 0.6449 - val_acc: 0.5147\n",
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      "Epoch 31/1000\n",
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      "Epoch 32/1000\n",
      "157/157 [==============================] - 0s 103us/step - loss: 0.6407 - acc: 0.5669 - val_loss: 0.6414 - val_acc: 0.5147\n",
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      "Epoch 36/1000\n",
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      "Epoch 38/1000\n",
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      "Epoch 39/1000\n",
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      "Epoch 45/1000\n",
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      "Epoch 50/1000\n",
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      "Epoch 51/1000\n",
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      "Epoch 52/1000\n",
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      "Epoch 55/1000\n",
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      "Epoch 56/1000\n",
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      "Epoch 57/1000\n",
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      "Epoch 58/1000\n",
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      "Epoch 59/1000\n",
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      "Epoch 60/1000\n",
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      "Epoch 61/1000\n",
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      "Epoch 62/1000\n",
      "157/157 [==============================] - 0s 409us/step - loss: 0.6006 - acc: 0.6306 - val_loss: 0.5943 - val_acc: 0.6176\n",
      "Epoch 63/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.5991 - acc: 0.6306 - val_loss: 0.5922 - val_acc: 0.6324\n",
      "Epoch 64/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.5981 - acc: 0.6369 - val_loss: 0.5906 - val_acc: 0.6324\n",
      "Epoch 65/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.5958 - acc: 0.6433 - val_loss: 0.5889 - val_acc: 0.6471\n",
      "Epoch 66/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.5945 - acc: 0.6433 - val_loss: 0.5871 - val_acc: 0.6471\n",
      "Epoch 67/1000\n",
      "157/157 [==============================] - 0s 378us/step - loss: 0.5929 - acc: 0.6433 - val_loss: 0.5852 - val_acc: 0.6471\n",
      "Epoch 68/1000\n",
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      "Epoch 69/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.5901 - acc: 0.6497 - val_loss: 0.5816 - val_acc: 0.6471\n",
      "Epoch 70/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.5885 - acc: 0.6497 - val_loss: 0.5797 - val_acc: 0.6765\n",
      "Epoch 71/1000\n",
      "157/157 [==============================] - 0s 208us/step - loss: 0.5867 - acc: 0.6561 - val_loss: 0.5778 - val_acc: 0.6765\n",
      "Epoch 72/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.5850 - acc: 0.6561 - val_loss: 0.5755 - val_acc: 0.6765\n",
      "Epoch 73/1000\n",
      "157/157 [==============================] - 0s 279us/step - loss: 0.5831 - acc: 0.6624 - val_loss: 0.5733 - val_acc: 0.6765\n",
      "Epoch 74/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.5812 - acc: 0.6688 - val_loss: 0.5712 - val_acc: 0.6912\n",
      "Epoch 75/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.5791 - acc: 0.6752 - val_loss: 0.5688 - val_acc: 0.7059\n",
      "Epoch 76/1000\n",
      "157/157 [==============================] - 0s 223us/step - loss: 0.5771 - acc: 0.6752 - val_loss: 0.5665 - val_acc: 0.7059\n",
      "Epoch 77/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.5750 - acc: 0.6879 - val_loss: 0.5643 - val_acc: 0.7059\n",
      "Epoch 78/1000\n",
      "157/157 [==============================] - 0s 217us/step - loss: 0.5728 - acc: 0.6879 - val_loss: 0.5619 - val_acc: 0.7059\n",
      "Epoch 79/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.5708 - acc: 0.6943 - val_loss: 0.5596 - val_acc: 0.7059\n",
      "Epoch 80/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.5687 - acc: 0.7006 - val_loss: 0.5570 - val_acc: 0.7206\n",
      "Epoch 81/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.5666 - acc: 0.7070 - val_loss: 0.5545 - val_acc: 0.7206\n",
      "Epoch 82/1000\n",
      "157/157 [==============================] - 0s 109us/step - loss: 0.5643 - acc: 0.7006 - val_loss: 0.5519 - val_acc: 0.7206\n",
      "Epoch 83/1000\n",
      "157/157 [==============================] - 0s 258us/step - loss: 0.5623 - acc: 0.7134 - val_loss: 0.5495 - val_acc: 0.7206\n",
      "Epoch 84/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.5600 - acc: 0.7197 - val_loss: 0.5469 - val_acc: 0.7206\n",
      "Epoch 85/1000\n",
      "157/157 [==============================] - 0s 120us/step - loss: 0.5577 - acc: 0.7197 - val_loss: 0.5443 - val_acc: 0.7206\n",
      "Epoch 86/1000\n",
      "157/157 [==============================] - 0s 166us/step - loss: 0.5550 - acc: 0.7197 - val_loss: 0.5411 - val_acc: 0.7353\n",
      "Epoch 87/1000\n",
      "157/157 [==============================] - 0s 134us/step - loss: 0.5529 - acc: 0.7325 - val_loss: 0.5383 - val_acc: 0.7353\n",
      "Epoch 88/1000\n",
      "157/157 [==============================] - 0s 185us/step - loss: 0.5498 - acc: 0.7325 - val_loss: 0.5347 - val_acc: 0.7353\n",
      "Epoch 89/1000\n",
      "157/157 [==============================] - 0s 194us/step - loss: 0.5471 - acc: 0.7516 - val_loss: 0.5314 - val_acc: 0.7647\n",
      "Epoch 90/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.5451 - acc: 0.7452 - val_loss: 0.5283 - val_acc: 0.7941\n",
      "Epoch 91/1000\n",
      "157/157 [==============================] - 0s 292us/step - loss: 0.5430 - acc: 0.7580 - val_loss: 0.5258 - val_acc: 0.8088\n",
      "Epoch 92/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.5399 - acc: 0.7580 - val_loss: 0.5234 - val_acc: 0.8088\n",
      "Epoch 93/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.5383 - acc: 0.7643 - val_loss: 0.5210 - val_acc: 0.8088\n",
      "Epoch 94/1000\n",
      "157/157 [==============================] - 0s 231us/step - loss: 0.5356 - acc: 0.7643 - val_loss: 0.5184 - val_acc: 0.8088\n",
      "Epoch 95/1000\n",
      "157/157 [==============================] - 0s 96us/step - loss: 0.5334 - acc: 0.7643 - val_loss: 0.5158 - val_acc: 0.8235\n",
      "Epoch 96/1000\n",
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      "Epoch 97/1000\n",
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      "Epoch 98/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.5263 - acc: 0.7898 - val_loss: 0.5074 - val_acc: 0.8235\n",
      "Epoch 99/1000\n",
      "157/157 [==============================] - 0s 182us/step - loss: 0.5243 - acc: 0.7962 - val_loss: 0.5044 - val_acc: 0.8088\n",
      "Epoch 100/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.5234 - acc: 0.7834 - val_loss: 0.5024 - val_acc: 0.8088\n",
      "Epoch 101/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.5198 - acc: 0.8025 - val_loss: 0.5002 - val_acc: 0.8088\n",
      "Epoch 102/1000\n",
      "157/157 [==============================] - 0s 320us/step - loss: 0.5184 - acc: 0.7962 - val_loss: 0.4978 - val_acc: 0.8088\n",
      "Epoch 103/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.5162 - acc: 0.8025 - val_loss: 0.4956 - val_acc: 0.8088\n",
      "Epoch 104/1000\n",
      "157/157 [==============================] - 0s 131us/step - loss: 0.5136 - acc: 0.8025 - val_loss: 0.4932 - val_acc: 0.8088\n",
      "Epoch 105/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.5115 - acc: 0.7962 - val_loss: 0.4903 - val_acc: 0.8235\n",
      "Epoch 106/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.5091 - acc: 0.8025 - val_loss: 0.4877 - val_acc: 0.8382\n",
      "Epoch 107/1000\n",
      "157/157 [==============================] - 0s 351us/step - loss: 0.5065 - acc: 0.8089 - val_loss: 0.4851 - val_acc: 0.8382\n",
      "Epoch 108/1000\n",
      "157/157 [==============================] - 0s 370us/step - loss: 0.5041 - acc: 0.8025 - val_loss: 0.4822 - val_acc: 0.8529\n",
      "Epoch 109/1000\n",
      "157/157 [==============================] - 0s 345us/step - loss: 0.5016 - acc: 0.8089 - val_loss: 0.4795 - val_acc: 0.8529\n",
      "Epoch 110/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.4996 - acc: 0.8025 - val_loss: 0.4765 - val_acc: 0.8529\n",
      "Epoch 111/1000\n",
      "157/157 [==============================] - 0s 135us/step - loss: 0.4972 - acc: 0.8089 - val_loss: 0.4739 - val_acc: 0.8529\n",
      "Epoch 112/1000\n",
      "157/157 [==============================] - 0s 266us/step - loss: 0.4944 - acc: 0.8280 - val_loss: 0.4716 - val_acc: 0.8529\n",
      "Epoch 113/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.4918 - acc: 0.8153 - val_loss: 0.4686 - val_acc: 0.8529\n",
      "Epoch 114/1000\n",
      "157/157 [==============================] - 0s 174us/step - loss: 0.4894 - acc: 0.8471 - val_loss: 0.4656 - val_acc: 0.8529\n",
      "Epoch 115/1000\n",
      "157/157 [==============================] - 0s 157us/step - loss: 0.4869 - acc: 0.8408 - val_loss: 0.4624 - val_acc: 0.8676\n",
      "Epoch 116/1000\n",
      "157/157 [==============================] - 0s 276us/step - loss: 0.4846 - acc: 0.8089 - val_loss: 0.4592 - val_acc: 0.8676\n",
      "Epoch 117/1000\n",
      "157/157 [==============================] - 0s 146us/step - loss: 0.4818 - acc: 0.8408 - val_loss: 0.4565 - val_acc: 0.8676\n",
      "Epoch 118/1000\n",
      "157/157 [==============================] - 0s 246us/step - loss: 0.4792 - acc: 0.8535 - val_loss: 0.4539 - val_acc: 0.8676\n",
      "Epoch 119/1000\n",
      "157/157 [==============================] - 0s 116us/step - loss: 0.4768 - acc: 0.8408 - val_loss: 0.4506 - val_acc: 0.8676\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.4756 - acc: 0.8471 - val_loss: 0.4482 - val_acc: 0.8676\n",
      "Epoch 121/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.4726 - acc: 0.8599 - val_loss: 0.4463 - val_acc: 0.8676\n",
      "Epoch 122/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.4708 - acc: 0.8471 - val_loss: 0.4438 - val_acc: 0.8676\n",
      "Epoch 123/1000\n",
      "157/157 [==============================] - 0s 245us/step - loss: 0.4682 - acc: 0.8599 - val_loss: 0.4415 - val_acc: 0.8676\n",
      "Epoch 124/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.4658 - acc: 0.8535 - val_loss: 0.4390 - val_acc: 0.8676\n",
      "Epoch 125/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.4635 - acc: 0.8599 - val_loss: 0.4361 - val_acc: 0.8824\n",
      "Epoch 126/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.4614 - acc: 0.8535 - val_loss: 0.4332 - val_acc: 0.8824\n",
      "Epoch 127/1000\n",
      "157/157 [==============================] - 0s 327us/step - loss: 0.4584 - acc: 0.8726 - val_loss: 0.4307 - val_acc: 0.8824\n",
      "Epoch 128/1000\n",
      "157/157 [==============================] - 0s 181us/step - loss: 0.4571 - acc: 0.8535 - val_loss: 0.4279 - val_acc: 0.8824\n",
      "Epoch 129/1000\n",
      "157/157 [==============================] - 0s 268us/step - loss: 0.4550 - acc: 0.8726 - val_loss: 0.4258 - val_acc: 0.8824\n",
      "Epoch 130/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.4517 - acc: 0.8599 - val_loss: 0.4230 - val_acc: 0.8824\n",
      "Epoch 131/1000\n",
      "157/157 [==============================] - 0s 281us/step - loss: 0.4497 - acc: 0.8726 - val_loss: 0.4204 - val_acc: 0.8824\n",
      "Epoch 132/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.4476 - acc: 0.8662 - val_loss: 0.4178 - val_acc: 0.8824\n",
      "Epoch 133/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.4456 - acc: 0.8726 - val_loss: 0.4153 - val_acc: 0.8824\n",
      "Epoch 134/1000\n",
      "157/157 [==============================] - 0s 137us/step - loss: 0.4433 - acc: 0.8790 - val_loss: 0.4131 - val_acc: 0.8824\n",
      "Epoch 135/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.4409 - acc: 0.8854 - val_loss: 0.4108 - val_acc: 0.8824\n",
      "Epoch 136/1000\n",
      "157/157 [==============================] - 0s 167us/step - loss: 0.4381 - acc: 0.8726 - val_loss: 0.4082 - val_acc: 0.8824\n",
      "Epoch 137/1000\n",
      "157/157 [==============================] - 0s 272us/step - loss: 0.4357 - acc: 0.8854 - val_loss: 0.4053 - val_acc: 0.8824\n",
      "Epoch 138/1000\n",
      "157/157 [==============================] - 0s 286us/step - loss: 0.4338 - acc: 0.8726 - val_loss: 0.4025 - val_acc: 0.8824\n",
      "Epoch 139/1000\n",
      "157/157 [==============================] - 0s 164us/step - loss: 0.4308 - acc: 0.8726 - val_loss: 0.3994 - val_acc: 0.8824\n",
      "Epoch 140/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.4286 - acc: 0.8790 - val_loss: 0.3968 - val_acc: 0.8824\n",
      "Epoch 141/1000\n",
      "157/157 [==============================] - 0s 196us/step - loss: 0.4266 - acc: 0.8726 - val_loss: 0.3944 - val_acc: 0.8824\n",
      "Epoch 142/1000\n",
      "157/157 [==============================] - 0s 285us/step - loss: 0.4241 - acc: 0.8790 - val_loss: 0.3924 - val_acc: 0.8824\n",
      "Epoch 143/1000\n",
      "157/157 [==============================] - 0s 136us/step - loss: 0.4224 - acc: 0.8726 - val_loss: 0.3902 - val_acc: 0.8824\n",
      "Epoch 144/1000\n",
      "157/157 [==============================] - 0s 243us/step - loss: 0.4204 - acc: 0.8726 - val_loss: 0.3882 - val_acc: 0.8824\n",
      "Epoch 145/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.4177 - acc: 0.8726 - val_loss: 0.3860 - val_acc: 0.8824\n",
      "Epoch 146/1000\n",
      "157/157 [==============================] - 0s 210us/step - loss: 0.4167 - acc: 0.8854 - val_loss: 0.3840 - val_acc: 0.8824\n",
      "Epoch 147/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.4133 - acc: 0.8726 - val_loss: 0.3815 - val_acc: 0.8824\n",
      "Epoch 148/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.4112 - acc: 0.8790 - val_loss: 0.3791 - val_acc: 0.8824\n",
      "Epoch 149/1000\n",
      "157/157 [==============================] - 0s 274us/step - loss: 0.4098 - acc: 0.8854 - val_loss: 0.3771 - val_acc: 0.8824\n",
      "Epoch 150/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.4075 - acc: 0.8726 - val_loss: 0.3743 - val_acc: 0.8824\n",
      "Epoch 151/1000\n",
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      "Epoch 152/1000\n",
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      "Epoch 153/1000\n",
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      "Epoch 154/1000\n",
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      "Epoch 155/1000\n",
      "157/157 [==============================] - 0s 150us/step - loss: 0.3970 - acc: 0.8981 - val_loss: 0.3635 - val_acc: 0.8824\n",
      "Epoch 156/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.3944 - acc: 0.8981 - val_loss: 0.3613 - val_acc: 0.8824\n",
      "Epoch 157/1000\n",
      "157/157 [==============================] - 0s 124us/step - loss: 0.3928 - acc: 0.8981 - val_loss: 0.3594 - val_acc: 0.8824\n",
      "Epoch 158/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.3903 - acc: 0.8917 - val_loss: 0.3567 - val_acc: 0.8824\n",
      "Epoch 159/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.3881 - acc: 0.8981 - val_loss: 0.3543 - val_acc: 0.8824\n",
      "Epoch 160/1000\n",
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      "Epoch 161/1000\n",
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      "Epoch 162/1000\n",
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      "Epoch 163/1000\n",
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      "Epoch 164/1000\n",
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      "Epoch 165/1000\n",
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      "Epoch 166/1000\n",
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      "Epoch 167/1000\n",
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      "Epoch 168/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.3711 - acc: 0.9172 - val_loss: 0.3368 - val_acc: 0.8971\n",
      "Epoch 169/1000\n",
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      "Epoch 170/1000\n",
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      "Epoch 171/1000\n",
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      "Epoch 172/1000\n",
      "157/157 [==============================] - 0s 189us/step - loss: 0.3638 - acc: 0.9108 - val_loss: 0.3291 - val_acc: 0.8971\n",
      "Epoch 173/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.3617 - acc: 0.9236 - val_loss: 0.3275 - val_acc: 0.8971\n",
      "Epoch 174/1000\n",
      "157/157 [==============================] - 0s 136us/step - loss: 0.3595 - acc: 0.9236 - val_loss: 0.3257 - val_acc: 0.8971\n",
      "Epoch 175/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.3579 - acc: 0.9236 - val_loss: 0.3240 - val_acc: 0.8971\n",
      "Epoch 176/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.3565 - acc: 0.9172 - val_loss: 0.3219 - val_acc: 0.8971\n",
      "Epoch 177/1000\n",
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      "Epoch 178/1000\n",
      "157/157 [==============================] - 0s 374us/step - loss: 0.3529 - acc: 0.9108 - val_loss: 0.3181 - val_acc: 0.8971\n",
      "Epoch 179/1000\n",
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      "Epoch 180/1000\n",
      "157/157 [==============================] - 0s 319us/step - loss: 0.3485 - acc: 0.9236 - val_loss: 0.3136 - val_acc: 0.8971\n",
      "Epoch 181/1000\n",
      "157/157 [==============================] - 0s 166us/step - loss: 0.3467 - acc: 0.9236 - val_loss: 0.3116 - val_acc: 0.8971\n",
      "Epoch 182/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.3450 - acc: 0.9236 - val_loss: 0.3103 - val_acc: 0.8971\n",
      "Epoch 183/1000\n",
      "157/157 [==============================] - 0s 282us/step - loss: 0.3439 - acc: 0.9172 - val_loss: 0.3084 - val_acc: 0.8971\n",
      "Epoch 184/1000\n",
      "157/157 [==============================] - 0s 287us/step - loss: 0.3413 - acc: 0.9172 - val_loss: 0.3064 - val_acc: 0.8971\n",
      "Epoch 185/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.3405 - acc: 0.9108 - val_loss: 0.3047 - val_acc: 0.9118\n",
      "Epoch 186/1000\n",
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      "Epoch 187/1000\n",
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      "Epoch 188/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.3347 - acc: 0.9236 - val_loss: 0.2989 - val_acc: 0.9118\n",
      "Epoch 189/1000\n",
      "157/157 [==============================] - 0s 231us/step - loss: 0.3334 - acc: 0.9299 - val_loss: 0.2972 - val_acc: 0.9118\n",
      "Epoch 190/1000\n",
      "157/157 [==============================] - 0s 208us/step - loss: 0.3302 - acc: 0.9299 - val_loss: 0.2961 - val_acc: 0.8971\n",
      "Epoch 191/1000\n",
      "157/157 [==============================] - 0s 213us/step - loss: 0.3284 - acc: 0.9299 - val_loss: 0.2943 - val_acc: 0.8971\n",
      "Epoch 192/1000\n",
      "157/157 [==============================] - 0s 184us/step - loss: 0.3265 - acc: 0.9299 - val_loss: 0.2917 - val_acc: 0.9118\n",
      "Epoch 193/1000\n",
      "157/157 [==============================] - 0s 369us/step - loss: 0.3259 - acc: 0.9299 - val_loss: 0.2908 - val_acc: 0.8971\n",
      "Epoch 194/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.3226 - acc: 0.9299 - val_loss: 0.2889 - val_acc: 0.8971\n",
      "Epoch 195/1000\n",
      "157/157 [==============================] - 0s 203us/step - loss: 0.3237 - acc: 0.9236 - val_loss: 0.2873 - val_acc: 0.8971\n",
      "Epoch 196/1000\n",
      "157/157 [==============================] - 0s 207us/step - loss: 0.3194 - acc: 0.9236 - val_loss: 0.2857 - val_acc: 0.8971\n",
      "Epoch 197/1000\n",
      "157/157 [==============================] - 0s 291us/step - loss: 0.3173 - acc: 0.9236 - val_loss: 0.2830 - val_acc: 0.9118\n",
      "Epoch 198/1000\n",
      "157/157 [==============================] - 0s 235us/step - loss: 0.3165 - acc: 0.9299 - val_loss: 0.2819 - val_acc: 0.9118\n",
      "Epoch 199/1000\n",
      "157/157 [==============================] - 0s 160us/step - loss: 0.3166 - acc: 0.9236 - val_loss: 0.2805 - val_acc: 0.8971\n",
      "Epoch 200/1000\n",
      "157/157 [==============================] - 0s 308us/step - loss: 0.3128 - acc: 0.9236 - val_loss: 0.2790 - val_acc: 0.9118\n",
      "Epoch 201/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.3109 - acc: 0.9299 - val_loss: 0.2772 - val_acc: 0.9118\n",
      "Epoch 202/1000\n",
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      "Epoch 203/1000\n",
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      "Epoch 204/1000\n",
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      "Epoch 205/1000\n",
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      "Epoch 206/1000\n",
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      "Epoch 207/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.2998 - acc: 0.9427 - val_loss: 0.2670 - val_acc: 0.9118\n",
      "Epoch 208/1000\n",
      "157/157 [==============================] - 0s 128us/step - loss: 0.2988 - acc: 0.9299 - val_loss: 0.2651 - val_acc: 0.9118\n",
      "Epoch 209/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.2970 - acc: 0.9299 - val_loss: 0.2626 - val_acc: 0.9118\n",
      "Epoch 210/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.2945 - acc: 0.9427 - val_loss: 0.2626 - val_acc: 0.8971\n",
      "Epoch 211/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.2932 - acc: 0.9299 - val_loss: 0.2599 - val_acc: 0.9118\n",
      "Epoch 212/1000\n",
      "157/157 [==============================] - 0s 317us/step - loss: 0.2919 - acc: 0.9427 - val_loss: 0.2590 - val_acc: 0.8971\n",
      "Epoch 213/1000\n",
      "157/157 [==============================] - 0s 241us/step - loss: 0.2898 - acc: 0.9236 - val_loss: 0.2560 - val_acc: 0.9118\n",
      "Epoch 214/1000\n",
      "157/157 [==============================] - 0s 396us/step - loss: 0.2892 - acc: 0.9427 - val_loss: 0.2547 - val_acc: 0.9118\n",
      "Epoch 215/1000\n",
      "157/157 [==============================] - 0s 317us/step - loss: 0.2863 - acc: 0.9427 - val_loss: 0.2529 - val_acc: 0.9118\n",
      "Epoch 216/1000\n",
      "157/157 [==============================] - 0s 254us/step - loss: 0.2870 - acc: 0.9363 - val_loss: 0.2518 - val_acc: 0.9118\n",
      "Epoch 217/1000\n",
      "157/157 [==============================] - 0s 255us/step - loss: 0.2839 - acc: 0.9363 - val_loss: 0.2511 - val_acc: 0.9118\n",
      "Epoch 218/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.2816 - acc: 0.9363 - val_loss: 0.2490 - val_acc: 0.9118\n",
      "Epoch 219/1000\n",
      "157/157 [==============================] - 0s 228us/step - loss: 0.2807 - acc: 0.9427 - val_loss: 0.2484 - val_acc: 0.9118\n",
      "Epoch 220/1000\n",
      "157/157 [==============================] - 0s 140us/step - loss: 0.2789 - acc: 0.9427 - val_loss: 0.2471 - val_acc: 0.9118\n",
      "Epoch 221/1000\n",
      "157/157 [==============================] - 0s 267us/step - loss: 0.2770 - acc: 0.9363 - val_loss: 0.2438 - val_acc: 0.9118\n",
      "Epoch 222/1000\n",
      "157/157 [==============================] - 0s 251us/step - loss: 0.2760 - acc: 0.9427 - val_loss: 0.2423 - val_acc: 0.9118\n",
      "Epoch 223/1000\n",
      "157/157 [==============================] - 0s 298us/step - loss: 0.2745 - acc: 0.9299 - val_loss: 0.2407 - val_acc: 0.9118\n",
      "Epoch 224/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.2726 - acc: 0.9490 - val_loss: 0.2411 - val_acc: 0.9118\n",
      "Epoch 225/1000\n",
      "157/157 [==============================] - 0s 293us/step - loss: 0.2707 - acc: 0.9363 - val_loss: 0.2380 - val_acc: 0.9118\n",
      "Epoch 226/1000\n",
      "157/157 [==============================] - 0s 157us/step - loss: 0.2703 - acc: 0.9427 - val_loss: 0.2386 - val_acc: 0.9118\n",
      "Epoch 227/1000\n",
      "157/157 [==============================] - 0s 213us/step - loss: 0.2681 - acc: 0.9490 - val_loss: 0.2374 - val_acc: 0.9118\n",
      "Epoch 228/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.2680 - acc: 0.9363 - val_loss: 0.2365 - val_acc: 0.9118\n",
      "Epoch 229/1000\n",
      "157/157 [==============================] - 0s 156us/step - loss: 0.2668 - acc: 0.9236 - val_loss: 0.2342 - val_acc: 0.9118\n",
      "Epoch 230/1000\n",
      "157/157 [==============================] - 0s 213us/step - loss: 0.2652 - acc: 0.9363 - val_loss: 0.2324 - val_acc: 0.9118\n",
      "Epoch 231/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.2634 - acc: 0.9490 - val_loss: 0.2320 - val_acc: 0.9118\n",
      "Epoch 232/1000\n",
      "157/157 [==============================] - 0s 258us/step - loss: 0.2624 - acc: 0.9427 - val_loss: 0.2310 - val_acc: 0.9118\n",
      "Epoch 233/1000\n",
      "157/157 [==============================] - 0s 245us/step - loss: 0.2627 - acc: 0.9427 - val_loss: 0.2299 - val_acc: 0.9118\n",
      "Epoch 234/1000\n",
      "157/157 [==============================] - 0s 396us/step - loss: 0.2597 - acc: 0.9490 - val_loss: 0.2293 - val_acc: 0.9118\n",
      "Epoch 235/1000\n",
      "157/157 [==============================] - 0s 192us/step - loss: 0.2584 - acc: 0.9490 - val_loss: 0.2292 - val_acc: 0.9118\n",
      "Epoch 236/1000\n",
      "157/157 [==============================] - 0s 294us/step - loss: 0.2579 - acc: 0.9427 - val_loss: 0.2271 - val_acc: 0.9118\n",
      "Epoch 237/1000\n",
      "157/157 [==============================] - 0s 200us/step - loss: 0.2564 - acc: 0.9427 - val_loss: 0.2262 - val_acc: 0.9118\n",
      "Epoch 238/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 251us/step - loss: 0.2542 - acc: 0.9490 - val_loss: 0.2261 - val_acc: 0.9118\n",
      "Epoch 239/1000\n",
      "157/157 [==============================] - 0s 183us/step - loss: 0.2552 - acc: 0.9363 - val_loss: 0.2241 - val_acc: 0.9118\n",
      "Epoch 240/1000\n",