tp_12_Neural_network.ipynb

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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import np_utils\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataframe = pd.read_csv(\"irisdata.csv\", header=None)\n",
    "dataset = dataframe.values\n",
    "X = dataset[:,0:4].astype(float)\n",
    "Y = dataset[:,4] #150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode class values as integers\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(Y)\n",
    "encoded_Y = encoder.transform(Y) \n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "dummy_y = np_utils.to_categorical(encoded_Y) #shape: 150 x 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define baseline model\n",
    "def baseline_model():\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(8, input_dim=4, activation='relu'))\n",
    "    model.add(Dense(3, activation='softmax'))\n",
    "    # Compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.488264271225732, 0.06430408395937146, 2, array([[14.5,  8.5,  7. ],\n",
       "        [14.5,  8.5,  7. ]]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "m = np.array([14,12,4, 15, 5, 10]).reshape(2,3)\n",
    "chi2_contingency(m) #chi2, p, dof, expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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